Capote/Krakauer Comparison :: essays papers

Capote/Krakauer Comparison Essay The most important thing any writer can do is to give their characters a feel of dimension to make them seem real. Although Capote and Krakauer do that in very different ways in In Cold Blood and Into Thin Air, they both reached the same end result: characters you believe. They give them thoughts, faces and personalities. They don’t portray everyone as flawless, they display the faults and the little quirks. They give them life through words, making these stories believable. Despite the fact both incidents happened years before each book was written, the use of detailed facts and personality profiles make each story seem incredibly realistic. But while Capote chooses to write an entirely objective piece, Krakauer relies heavily on personal opinion and experience, creating two very distinct frames of mind and causing the reader too see the characters in each book very differently. In 1959 the Clutter family was murdered in a tiny Kansas town called Holcomb. Six years later Truman Capote wrote a very detailed book about the whole case, from the day of the murder to the court case prosecuting the two murderers, Dick and Perry. Although he wasn’t there when the four murders happened, through word choice, description and characterization he creates an accurate portrait of the many intense events surrounding such a tragic story. In comparison, in 1996 esteemed climber Rob Hall led an expedition of moderately experienced climbers attempting to climb Mt. Everest, only to result in disaster and the loss of nine people’s lives. Jon Krakauer was a member of that expedition, and wrote a piece about the misadventure for Outside magazine. Feeling there was more to be said, soon after he wrote a book. Krakauer takes a similar approach as Capote, yet inserting more opinions and less of a feeling of objectiveness to his characters. This is most likely since Krakauer was living Everest first hand, as opposed to Capote who put himself into the environment years later, picking up details here and there instead of relying solely on memory and friends. One of Capote’s greatest strengths is to create thought for his characters, making it almost appear as if he knows what they are thinking. All summer Perry undulated between half-awake stupors and stickly, sweat-drenched sleep. Voices roared through his head; one voice persistently asked him, â€Å"Where is Jesus? Where?† And once he woke up shouting, â€Å"The bird is Jesus! The Bird is Jesus!† (381) This selection almost creates a feeling that Capote is talking about himself as opposed to a man he never met. Capote/Krakauer Comparison :: essays papers Capote/Krakauer Comparison Essay The most important thing any writer can do is to give their characters a feel of dimension to make them seem real. Although Capote and Krakauer do that in very different ways in In Cold Blood and Into Thin Air, they both reached the same end result: characters you believe. They give them thoughts, faces and personalities. They don’t portray everyone as flawless, they display the faults and the little quirks. They give them life through words, making these stories believable. Despite the fact both incidents happened years before each book was written, the use of detailed facts and personality profiles make each story seem incredibly realistic. But while Capote chooses to write an entirely objective piece, Krakauer relies heavily on personal opinion and experience, creating two very distinct frames of mind and causing the reader too see the characters in each book very differently. In 1959 the Clutter family was murdered in a tiny Kansas town called Holcomb. Six years later Truman Capote wrote a very detailed book about the whole case, from the day of the murder to the court case prosecuting the two murderers, Dick and Perry. Although he wasn’t there when the four murders happened, through word choice, description and characterization he creates an accurate portrait of the many intense events surrounding such a tragic story. In comparison, in 1996 esteemed climber Rob Hall led an expedition of moderately experienced climbers attempting to climb Mt. Everest, only to result in disaster and the loss of nine people’s lives. Jon Krakauer was a member of that expedition, and wrote a piece about the misadventure for Outside magazine. Feeling there was more to be said, soon after he wrote a book. Krakauer takes a similar approach as Capote, yet inserting more opinions and less of a feeling of objectiveness to his characters. This is most likely since Krakauer was living Everest first hand, as opposed to Capote who put himself into the environment years later, picking up details here and there instead of relying solely on memory and friends. One of Capote’s greatest strengths is to create thought for his characters, making it almost appear as if he knows what they are thinking. All summer Perry undulated between half-awake stupors and stickly, sweat-drenched sleep. Voices roared through his head; one voice persistently asked him, â€Å"Where is Jesus? Where?† And once he woke up shouting, â€Å"The bird is Jesus! The Bird is Jesus!† (381) This selection almost creates a feeling that Capote is talking about himself as opposed to a man he never met.

A Most Special Person in My Life Essay

Except my family, there is one person, Thu Cuu, who I will always remember and respect because of her personalities, she is kind and also she is the one I love. Nothing is impossible with her no matter what is it, where is it and why is it, that is what I learn from her. A girl is perfect for every situation; she does not need to be dazzling, but people still have looked at her. Basically, I think every man in this world wants this lady to become his wife, even me. Surely, her personalities can make people who do not like to talk will talk, and her kindness can make everybody respects her, even that is the guy who hates her the most. In my opinion, it is hard to find one like her in this century, whether there is at least one or not, I still always think of her. First of all, her personalities attract me when we first met each other, I really admire her. She is friendly and kind of cute with her voice and smile to keep people around her. She can be a center in a crowd easily. I like her at the first time we met; we talk about a lot of things just like we are best friends. That is also the first time I feel free with a girl, because I usually stuck when I try to talk to a girl. Read more: The person I admire the most is my mother  essay She is very sociable, that is the reason why she has a lot of friends. Moreover, she likes to play no matter what kind of games. You can image that when a girl play some games that only reserve for boys like bias, she can play and play even better than boys. The trick is she does with all her heart and forgets what people say about her, just be happy when she plays. She respects friendship and loves her family. I am very sure that she is going to do anything to protect them. For example, I remembered clearly one time that she was late for a party and I yelled on her, then we altercated. Soon, I found out that she was late because she had to pick up her mom from work; it was not her fault. I did not know that, but the first person said sorry was her. Later I knew that the first person said sorry, it did not mean that person was wrong, it meant that person respected relationship between them more than other. I felt like I was guilty; I apologized her, instead of still getting angry on me, then she smiled with me like nothing never happened Secondly, her kindness can make a most kindness person must be jealous. She is very kind for everyone, not only her friends, just because that is who she is. Imagine that when you go to the gas station and someone goes to ask you to give them some money, because their car is out of gas and they are out of money. Certainly, you will look at their car, what they wear and think is it a trick. In opposite way, while you are busy at exploration, she already gives them money, because one time she told me helping people is the happiest thing in the world. She does not care much about it is a trick or not, even if it is a trick, she is still happy. Moreover, she is a good listener. Usually, when I am sad, or any kind of feeling, I will share with her, she will be there to hear them. Not just listening, she also gives me a best advice. I believe that she never tell anyone else what I tell her, she extremely knows how to keep secret. That is why people believe her mostly. Sometimes, I get angry on her because of my stuffs, but she is still quite, listens and never complains a word. Say, for other example, I got 35/100 on a physical exam; I was very sad and disappointed. When she knew that, she made a plan to revive me. She knew what I like and I do not like. Something I like but she does not, but she accepted it and did it for me. We went to the movie theater, went to eat my favorite food and got my major drinks. We went to play skating and skiing, then heading to the beach. Beach was my favorite place; I usually went there when I was in feeling, but she did not like the beach at much. We went down to the beach and high to the mountain. Really, that was the funniest day with me, we talked a lot, I forgot why I was sad and headed to the future. Thu Cuu is the best Last but not least, Thu Cuu is the one I love, a hundred percent surely. One day, a girl appeared without many attractive characteristics who changed my life. She was not like any other girls I met before. When I was stuck in the dark and very disappointed, she stood there in front of me with her smile such as a brightest star and showed me which ways were right or wrong. She told me that I did not know what was waiting for me ahead; I got to learn how to fight it. For example, at the final time last semester, my family had some problems like my brother sold his car and went to Vietnam without asking my parents a word, my dad and my mom’s jobs were in trouble, and my study was more difficult. I was blind and disappointed. At that time, there was no one helping me except one, Thu Cuu. Side by side, she and I figured out every problem and solved it like she helped me to contact and persuade my brother to come back here. She helped me to study, kept me in calm and bought me drinks and food, so I could focus on the exams. She also had her exams, but she still spent her time to help me. Honestly, I knew this was the person I could live with forever. Another time, her friend from other state came here to visit her. I was jealous. Without her, I felt like I was in the hole. I was easy to get angry because of nothing, I just wanted that she was only for me, and I knew that I loved her. Beside her, I always feel happy and love life. One of the most beautiful things about Thu is her smile, if she just smiles with me, I am very sure that all of my stress will be gone, also it is the most reason why I love her. A girl without many attractions who I think of most of my time always stays with me when I am in trouble. Totally, she is a hundred percent perfect. After all, Thu Cuu is the only one I will treat with all my heart. She is now such as my family member that I never want to lose. No matter what people say about her, in my mind, she is always a girl I first met and loved. Because of her personalities, I know that nothing is impossible in this world. She helped me to be able to understand how to be a good man. Her kindness brings me much knowledge of treatment. That helps me how to get respected from people who hate me. Love is the most beautiful thing in the world that people do not use machine to know, and it will be prettier if you love a person who you never forget.

A Study Of The Indian Stock Market - Free Essay Example

Sample details Pages: 18 Words: 5476 Downloads: 7 Date added: 2017/06/26 Category Statistics Essay Did you like this example? 1.0 Introduction Seasonal variations in production and sales are a well known fact in business. Seasonality refers to regular and repetitive fluctuation in a time series which occurs periodically over a span of less than a year. The main cause of seasonal variations in time series data is the change in climate. Don’t waste time! Our writers will create an original "A Study Of The Indian Stock Market" essay for you Create order For example, sales of woolen clothes generally increase in winter season. Besides this, customs and tradition also affect economic variables for instance sales of gold increase during marriage seasons. Similarly, stock returns exhibits systematic patterns at certain times of the day, week or month. The most common of these are monthly patterns; certain months provide better returns as compared to others i.e. the month of the year effect. Similarly, some days of the week provides lower returns as compared to other trading days i.e. days of the week effect. The existence of seasonality in stock returns however violates an important hypothesis in finance that is efficient market hypothesis. The efficient market hypothesis is a central paradigm in finance. The EMH relates to how quickly and accurately the market reacts to new information. New data are constantly entering the market place via economic reports, company announcements, political statements, or public surveys. If the market is informationally efficient then security prices adjust rapidly and accurately to new information. According to this hypothesis, security prices reflect fully all the information that is available in the market. Since all the information is already incorporated in prices, a trader is not able to make any excess returns. Thus, EMH proposes that it is not possible to outperform the market through market timing or stock selection. However, in the context of financial markets and particularly in the case of equity market seasonal component have been recorded. They are called calendar anomalies (effects) in literature. The presence of seasonality in stock returns violates the weak form of market efficiency because equity prices are no longer random and can be predicted based on past pattern. This facilitates market participants to devise trading strategy which could fetch abnormal profits on the basis of past pattern. For instance, if there are evidences of à ¢Ã¢â€š ¬Ã‹Å"day of the week effectà ¢Ã¢â€š ¬Ã¢â€ž ¢, investors may devise a trading strategy of selling securities on Fridays and buying on Mondays in order to make excess profits. Aggarwal and Tandon (1994) and Mills and Coutts (1995) pointed out that mean stock returns were unusually high on Fridays and low on Mondays. One of the explanation put forward for the existence of seasonality in stock returns is the à ¢Ã¢â€š ¬Ã‹Å"tax-loss-selling hypothesis. In the USA, December is the tax month. Thus, the financial houses sell shares whose values have fallen to book losses to reduce their taxes. As of result of this selling, stock prices declin e. However, as soon as the December ends, people start acquiring shares and as a result stock prices bounce back. This lead to higher returns in the beginning of the year, that is, January month. This is called à ¢Ã¢â€š ¬Ã‹Å"January effectà ¢Ã¢â€š ¬Ã¢â€ž ¢. In India, March is the tax month, it would be interesting to find à ¢Ã¢â€š ¬Ã‹Å"April Effectà ¢Ã¢â€š ¬Ã¢â€ž ¢. 2.0 Theoretical Background The term à ¢Ã¢â€š ¬Ã‹Å"efficient marketà ¢Ã¢â€š ¬Ã¢â€ž ¢ refers to a market that adjusts rapidly to new information. Fama (1970) stated , à ¢Ã¢â€š ¬Ã‹Å" A market in which prices always fully reflect available information is called efficient.à ¢Ã¢â€š ¬Ã¢â€ž ¢ If capital markets are efficient, investors cannot expect to achieve superior profits by adopting a certain trading strategy. This is popularly called as the efficient market hypothesis. The origins of the EMH can be traced back to Bachelierà ¢Ã¢â€š ¬Ã¢â€ž ¢s doctoral thesis à ¢Ã¢â€š ¬Ã‹Å"Theory of Speculationà ¢Ã¢â€š ¬Ã¢â€ž ¢ in 1900 and seminal paper titled à ¢Ã¢â€š ¬Ã‹Å"Proof That Properly Anticipated Prices Fluctuate Randomlyà ¢Ã¢â€š ¬Ã¢â€ž ¢ by Nobel Laureate Paul Samuelson in 1965. But it was Eugane Famaà ¢Ã¢â€š ¬Ã¢â€ž ¢s work (1970) à ¢Ã¢â€š ¬Ã‹Å"Efficient Capital Marketsà ¢Ã¢â€š ¬Ã¢â€ž ¢ who coined the term EMH and advocated that in efficient market securities prices fully reflect all the information. It is important to note that efficiency here does not refer to the organisational or operational efficiency but informational efficiency of the market. Informational efficiency of the market takes three forms depending upon the information reflected by securities prices. First, EMH in its weak form states that all information impounded in the past price of a stock is fully reflected in current price of the stock. Therefore, information about recent or past trend in stock prices is of no use in forecasting future price. Clearly, it rules out the use of technical analysis in predicting future prices of securities. The semi-strong form takes the information set one step further and includes all publically available information. There is plethora of information of potential interest to investors. Besides past stock prices, such things as economic reports, brokerage firm recommendations, and investment advisory letters. However, the semi-strong form of the EMH states that current market p rices reflect all publically available information. So, analysing annual reports or other published data with a view to make profit in excess is not possible because market prices had already adjusted to any good or bad news contained in such reports as soon as they were revealed. The EMH in its strong form states that current market price reflect all à ¢Ã¢â€š ¬Ã¢â‚¬Å"both public and private information and even insiders would find it impossible to earn abnormal returns in the stock market. However, there is the notion that some stocks are priced more efficiently than others which is enshrined in the concept of semi-efficient market hypothesis. Thus, practitioners support the thesis that the market has several tiers or that a pecking order exist. The first tier contains well-known stocks such as Reliance Industries and Sail which are priced more efficiently than other lesser-known stocks such as UCO Bank. However, instead of considering stocks, we analyzed this phenomenon using Nif ty Junior index which is an index of next most liquid stocks after SP Nifty. 3.0 Review of Literature Seasonality or calendar anomalies such as month of the year and day of the week effects has remained a topic of interest for research since long time in developed as well as developing countries. Watchel (1942) reported seasonality in stock returns for the first time. Rozeff and Kinney (1976) documented the January effect in New York Exchange stocks for the period 1904 to 1974. They found that average return for the month of January was higher than other months implying pattern in stock returns. Keim (1983) along with seasonality also studied size effects in stock returns. He found that returns of small firms were significantly higher than large firms in January month and attributed this finding to tax-loss-selling and information hypothesis. A similar conclusion was found by Reinganum (1983), however, he was of the view that the entire seasonality in stock returns cannot be explained by tax-loss-selling hypothesis. Gultekin and Gultekin (1983) examined the presence of stock market seasonality in sixteen industrial countries. Their evidence shows strong seasonalities in the stock market due to January returns, which is exceptionally large in fifteen of sixteen countries. Brown et al. (1985) studied the Australian stock market seasonality and found the evidence of December-January and July-August seasonal effects, with the latter due to a June-July tax year. However, Raj and Thurston (1994) found that the January and April effects are not statistically significant in the NZ stock market. Mill and Coutts (1995) studied calendar effect in FTSE 100, Mid 250 and 350 indices for the period 1986 and 1992. They found calendar effect in FTSE 100. Ramcharan (1997), however, didnà ¢Ã¢â€š ¬Ã¢â€ž ¢t find seasonal effect in stock retruns of Jamaica. Choudhary (2001) reported January effect on the UK and US returns but not in German returns. Fountas and Segredakis (2002) studied 18 markets and reported seasonal patterns in returns. The reasons for the January effect in stock returns in most of the developed countries such as US, and UK attributed to the tax loss selling hypothesis, settlement procedures, insider trading information. Another effect is window dressing which is related to institutional trading. To avoid reporting to many losers in their portfolios at the end of year, institutional investors tend to sell losers in Decembers. They buy these stocks after the reporting date in January to hold their desired portfolio structure again. Researchers have also reported half- month effect in literature. Various studies have reported that daily stock returns in first half of month are relatively higher than last half of the month. Ariel (1987) conducted a study using US market indices from 1963 to 1981 to show this effect. Aggarwal and Tandon (1994) found in their study such effect in other international markets. Ziemba (1991) found that returns were consistently higher on first and last four days of the month. The holiday effect refers to higher returns around holidays, mainly in the pre-holiday period as compared to returns of the normal trading days. Lakonishok and Smidt (1988) studied Dow Jones Industrial Average and reported that half of the positive returns occur during the 10 pre-holiday trading days in each year. Ariel (1990) showed using US stock market that more than one-third positive returns each year registered in the 8 trading days prior to a market-closed holiday. Similar conclusion were brought by Cadsby and Ratner (1992) which documented significant pre-holiday effects for a number of stock markets. However, he didnà ¢Ã¢â€š ¬Ã¢â€ž ¢t find such effect in the European stock markets. Husain (1998) studied Ramadhan effect in Pakistan stock market. He found significant decline in stock returns volatility in this month although the mean return indicates no significant change. There are also evidences of day of the week effect in stock market returns. The Monday effect was identified as early as the 1920s. Kelly (1930) based on three years data of the US market found Monday to be the worse day to buy stocks. Hirsch (1968) reported negative returns in his study. Cross (1973) found the mean returns of the SP 500 for the period 1953 and 1970 on Friday was higher than mean return on Monday. Gibbons and Hess (1981) also studied the day of the week effect in US stock returns of SP 500 and CRSP indices using a sample from 1962 to 1978. Gibbons and Hess reported negative returns on Monday and higher returns on Friday. Smirlock and Starks (1986) reported similar results. Jaffe and Westerfield (1989) studied day of the week effect on four international stock markets viz. U.K., Japan, Canada and Australia. They found that lowest returns occurred on Monday in the UK and Canada. However, in Japanese and Australian market, they found lowest return occurred on Tuesday. B rooks and Persand (2001) studied the five southeast Asian stock markets namely Taiwan, South Korea, The Philippines, Malaysia and Thailand. The sample period was from 1989 to 1996. They found that neither South Korea nor the Philippines has significant calendar effects. However, Malaysia and Thailand showed significant positive return on Monday and significant negative return on Tuesday. Ajayi al. (2004) examined eleven major stock market indices on Eastern Europe using data from 1990 to 2002. They found negative return on Monday in six stock markets and positive return on Monday in rest of them. Pandey (2002) reported the existence of seasonal effect in monthly stock returns of BSE Sensex in India and confirmed the January effect. Bodla and Jindal (2006) studied Indian and US market and found evidence of seasonality. Kumari and Mahendra (2006) studied the day of the week effect using data from 1979 to 1998 on BSE and NSE. They reported negative returns on Tuesday in the Indian stoc k market. Moreover, they found returns on Monday were higher compared to the returns of other days in BSE and NSE. Choudhary and Choudhary (2008) studied 20 stock markets of the world using parametric as well as non-parametric tests. He reported that out of twenty, eighteen markets showed significant positive return on various day other than Monday. The scope of the study is restricted to days-of-the week effect, weekend effect and monthly effect in stock returns of SP CNX Nifty and select firms. The half month effect and holiday effect are not studied here. 4.0 Objective The objective of the study are as follows: To examine days of the week effect in the returns of SP CNX Nifty To examine weekend effect in SP CNX Nifty returns. To examine the seasonality in monthly returns of the BSE Sensex. 5.0 Hypotheses a) Our first hypothesis is that returns on all the days of weeks are equal. Symbolically, H 0 : ÃŽÂ ²1 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²2 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²3 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²4 H1 : at least one ÃŽÂ ²i is different b) Our second hypothesis is as follows: H 0 : ÃŽÂ ²1 à ¯Ã¢â€š ¬Ã‚ ½ 0 H1 : ÃŽÂ ²1 à ¢Ã¢â‚¬ °Ã‚  0 c) Our third hypothesis is: H 0 : ÃŽÂ ²1 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²2 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²3 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²4 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²5 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²6 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²7 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²8 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²9 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²10 à ¯Ã¢â€š ¬Ã‚ ½ ÃŽÂ ²11 H1 : at least one ÃŽÂ ² is different 6.0 Data and its Sources The monthly data on SP Nifty for the period April 1997 to March 2009 obtained from the Handbook of Statistics on Indian Economy published by the Reserve Bank of India. We also collected daily data on SP Nifty from 1st January 2005 to 31st December 2008 from www. nseindia.com for studying the above objectives. 7.0 Research Methodology To examine the stock market seasonality in India, first we measure stock return of Nifty as given below: Rt à ¯Ã¢â€š ¬Ã‚ ½ (ln Pt à ¢Ã‹â€ ln Pt à ¢Ã‹â€ 1 ) *100 (1) where Rt is the return in period t, Pt and Pt-1 are the monthly (daily) closing prices of the Nifty at time t and t-1 respectively. It is also important to test stationarity of a series lest OLS regression results will be spurious. Therefore, we will first test whether Nifty return is stationary by AR(1) model. We also use DF and ADF tests which are considered more formal tests of stationarity. For testing stationarity, let us consider an AR(1) model yt à ¯Ã¢â€š ¬Ã‚ ½ à ?1 yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ « et (2) The simple AR(1) model represented in equation (2) is called a random walk model. In this AR(1) model if | à ?1 |à ¯Ã¢â€š ¬Ã‚ ¼1, then the series is I(0) i.e. stationary in level, but if à ?1 à ¯Ã¢â€š ¬Ã‚ ½1 then there exist what is called unit root problem. In other words, series is non-stationary. Most economists think that differencing is warranted if estimated à ? à ¯Ã¢â€š ¬Ã‚ ¾ 0.9 ; some would difference when estimated à ? à ¯Ã¢â€š ¬Ã‚ ¾ 0.8 . Besides this, there are some formal ways of testing for stationarity of a series. . Dickey-Fuller test involve estimating regression equation and carrying out the hypothesis test The simplest approach to testing for a unit root is with an AR(1) model:. Let us consider an AR(1) process: yt à ¯Ã¢â€š ¬Ã‚ ½ c à ¯Ã¢â€š ¬Ã‚ « à ? yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ µt (3) where c and à ? are parameters and is assumed to be white noise. If à ¢Ã‹â€ 1 p à ? p1, then y is a stationary series while if à ? à ¯Ã¢â€š ¬Ã‚ ½1 , y is a non-stationary series. If the absolute value of à ? is greater than one, the series is explosive. Therefore, the hypothesis of a stationary series is involves whether the absolute value of à ? is strictly less than one. The test is carried out by estimating an equation with yt à ¢Ã‹â€ 1 subtracted from both sides of the equation: à ¢Ã‹â€ Ã¢â‚¬  yt à ¯Ã¢â€š ¬Ã‚ ½ c à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ³ yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ µt (4) where ÃŽÂ ³ à ¯Ã¢â€š ¬Ã‚ ½ à ? à ¢Ã‹â€ 1 , and the null and alternative hypotheses are H0 : ÃŽÂ ³ à ¯Ã¢â€š ¬Ã‚ ½ 0 H1 : ÃŽÂ ³ p0 The DF test is valid only if the series is an AR(1) process. If the series is correlated at higher order lags, the assumption of white noise disturbances is violated. The ADF controls for higher-order correlation by adding lagged difference terms of the dependent variable to the right-hand side of the regression: à ¢Ã‹â€ Ã¢â‚¬   yt à ¯Ã¢â€š ¬Ã‚ ½ c à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ³ yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ´ 1 à ¢Ã‹â€ Ã¢â‚¬   yt à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ´ 2 à ¢Ã‹â€ Ã¢â‚¬   yt à ¢Ã‹â€  2 à ¯Ã¢â€š ¬Ã‚ « . à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ´ p à ¢Ã‹â€ Ã¢â‚¬   yt à ¢Ã‹â€  p à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ µt (5) This augmented specification is then tested for H0 : ÃŽÂ ³ à ¯Ã¢â€š ¬Ã‚ ½ 0 H1 : ÃŽÂ ³ p0 in this regression. Next, to test the presence of seasonality in stock returns of Nifty, we have used one technique called dummy variable regression model. This technique is used to quantity qualitative aspects such as race, gender, religion and after that one can include as an another explanatory variable in the regression model. The variable which takes only two values is called dummy variable. They are also called categorical, indicator or binary variables in literature. While 1 indicates the presence of an attribute and 0 indicates absence of an attribute. There are mainly two types of model namely ANOVA and ANCOVA. This study uses ANOVA model. Analysis of variance (ANOVA) model is that model where the dependent variable is quantitative in nature and all the independent variables are categorical in nature. To examine the weekend effect and days of the week effect, the following dummy variable regression model is specified as follows: Nifty returns à ¯Ã¢â€š ¬Ã‚ ½ÃƒÅ½Ã‚ ± à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²1Monday à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²2Tuesday à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²3 wednesday à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²4thrusday à ¯Ã¢â€š ¬Ã‚ « à ¯Ã¢â‚¬Å¡Ã‚ µ (6) The variables Monday, Tuesday, Wednesday and Thursday are defined as: Monday = 1 if trading day is Monday; 0 otherwise Tuesday = 1 if trading day is Tuesday; 0 otherwise, Wednesday = 1 if the trading day is Wednesday; 0 otherwise Thursday = 1 if the trading day is Thursday; 0 otherwise ÃŽÂ ± represents the return of the benchmark category which is Friday in our study. Similarly, to find whether there are monthly effects in Nifty returns, we used ANOVA model specified below as: Nifty returns à ¯Ã¢â€š ¬Ã‚ ½ÃƒÅ½Ã‚ ± à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²1 DJune à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²2 DJuly à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²3 DAug à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²4 Dsep à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²5 DOct à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²6 DNov à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²7 DDec à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²8 DJan à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²9 DFeb à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²10 DMar à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²11 DApril à ¯Ã¢â€š ¬Ã‚ « à ¯Ã¢â‚¬Å¡Ã‚ µ (7) where Y = Monthly returns of Nifty D1= 1 if the month is June; 0 otherwise D2 = 1 if the month is July; 0 otherwise D3 = 1 if the month is August; 0 otherwise D4 = 1 if the month is September; 0 otherwise D5 = 1 if the month is October; 0 otherwise D6 = 1 if the month is November; 0 otherwise D7 = 1 if the month is December; 0 otherwise D8 = 1if the month is January; 0 otherwise D9 = 1 if the month is February; 0 otherwise D10 = 1 if the month is March; 0 otherwise D11 = 1 if the month is April; 0 otherwise ÃŽÂ ± represents the mean return on the May month where as ÃŽÂ ²1 to ÃŽÂ ²11 indicate the shift in mean returns across months. Statistically significant values of ÃŽÂ ²Ãƒ ¢Ã¢â€š ¬Ã¢â€ž ¢s imply significant shifts in mean monthly returns, thus confirming the existence of the month of the year effect. The problem with this approach is that disturbance error term may have autocorrelation. Besides this, residual may contain ARCH effect. Therefore, we will test autocorrelation and ARCH effect in residual and improve our (6) and (7) model accordingly. 8.0 Results At the outset, we plotted the trend of SP CNX Nifty in Fig.1 which shows the movement of index over the sample period. For a long time hovering between 1000 and 2000, Nifty crossed the 2000 mark November 2005. Since then the one can see rising trend in Nifty till September 2008. After September 2008, we witnessed a stock market crash in the backdrop of mortgage crisis in the US followed by economic slowdown round the world which is quite visible in the movement of Nifty also. Fig. 1 Next, we computed descriptive statistics of returns of Nifty and Junior Nifty. The results are reported in Table 1 which show the mean returns of Nifty and Junior Nifty for the period April 1997 and March 2009 are 0.93 and 1.38 percent respectively. Junior Nifty provided higher mean return than the Nifty over the sample period. As the Nifty and Junior Nifty returns are not normally distributed evident from coefficient of skewness and kurtosis, one can use median return instead of mean to represent returns of Nifty and Junior Nifty which are 1.58 and 2.38 percent respectively. Thus, it is clear that Junior Nifty yielded better returns over the sample period. Table 1: Descriptive Statistics (%) Summary Statistics Nifty Junior Nifty Mean 0.93 1.38 Median 1.58 2.38 Standard Deviation 6.71 9.75 Minimum -23.71 -27.66 Maximum 17.01 32.09 Skewness -0.6029 -0.44 Kurtosis 0.5049 0.97 The variability in returns as measured by standard deviation which is the square root of variance The standard deviation is a conventional measure of volatility. Volatility as measured by standard deviations of returns of the sample period for Nifty and Junior Nifty are 6.71 and 9.75 percent respectively. Thus, it is evident that Junior Nifty is more volatile than the Nifty implying investment in Junior Nifty is more riskier. Table 2: AR(1) Model Monthly Series Level Series Return Series Niftyt = 35.0224 + 0.989 Niftyt à ¢Ã‹â€ 1 Niftyt = 0.58 + 0.2686 Niftyt à ¢Ã‹â€ 1 (1.21) (83.725) (0.9) (3.29) NJuniort = 35.0224 + 0.989 NJuniort à ¢Ã‹â€ 1 NJuniort = 35.0224 + 0.989 NJuniort à ¢Ã‹â€ 1 (1.21) (83.725) (0.74) (4.11) Daily Series Level Series Return Series Niftyt = 11.87 + 0.9969 Niftyt à ¢Ã‹â€ 1 Niftyt = 0.79 + 0.07 Niftyt à ¢Ã‹â€ 1 (1.46) (466.11) (0.33) (2.25) NJuniort = 20.01 + 0.997 NJuniort à ¢Ã‹â€ 1 NJuniort = 0.0154 + 0.1624 NJuniort à ¢Ã‹â€ 1 (1.17) (409.28) (0.00) (5.18) In time series econometrics, it is now customary to check stationarity of a series before using it in regression analysis in order to avoid spurious regression. We tested the stationarity of Nifty, Junior Nifty by AR(1) model and augmented Dickey-Fuller Test; while the former is an informal test, the later is a formal test of stationarity. The results of AR(1) model and ADF are reported in Table 2 and Table 3. The results of AR(1) model show that monthly and daily Nifty and Nifty Junior series are not stationary in their level form. However, AR(1) model fitted to Nifty and Nifty Junior return series are stationary. Table 3: Results of ADF Test Series Original Series Return Series Monthly Nifty -1.1851 -4.59* Monthly Junior Nifty -1.564 -4.2 Daily Nifty -1.48 -15.15 Daily Junior Nifty -1.32 -15.46 * MacKinnon critical values for rejection of hypothesis of a unit root at 1%, 5% and 10% are -3.4786, 2.8824 and -2.5778 respectively. The results of augmented Dickey-Fuller test is very much in consistent with AR(1) model. Table 3 shows that both monthly and daily Nifty and Nifty Junior are non-stationary in their level form. However, return series of Nifty and Nifty Junior are stationary as the null of unit root can be rejected at conventional level of 1%, 5% and 10%. Thus, analysis of stock market seasonality is based on return series of Nifty and Nifty Junior as they are stationary. Next, we estimated model (6) to study days of the week effects in daily Nifty and Nifty Junior returns. The results for Nifty are reported in Table 4. The benchmark day in the model is Friday represented by the intercept which provided a return of 0.08 percent on an average of the sample period. Table 4. Results of Equation (6) for Nifty Variables Coefficients t-statistic P-Value Intercept 0.0836 0.624 0.53 Monday -0.0875 -0.46 0.64 Tuesday -0.0405 -0.21 0.83 Wednesday -0.0432 -0.22 0.82 Thursday -0.0784 -0.41 0.68 R2 =0.0002 F Statistic = 0.06( 0.99) Ljung-Box Q(2) = 0.7045 (0.40) D-W Statistic = 1.86 ARCH LM Test(1): F- stat = 54.31 (0.00) Note: Figures in () are p-values Returns of Monday, Tuesday, Wednesday and Thursday can be found out by deducting the coefficients of these days from the benchmark day, that is, Friday which were 0.1711, 0.1241, 0.1268 and 0.162 respectively. The coefficient of Monday is not significant at 5 percent level which indicates that there is no weekend effect in Nifty returns. Further, none of the coefficients are significant at conventional levels of significance indicating that there is no days of the week effects in the Nifty returns. R2 is 0.0002 which is very low, and F-statistic indicates that the overall fit of the model is poor. Further, Durban-Watson statistic of 1.86 indicates autocorrelation in the residuals. The Ljung-Box Q statistic for the hypothesis that there is no serial correlation upto order of 2 is 0.7045 with an insignificant p-value of 0.40 which indicates that we have autocorrelation problem of order one. However, return series exhibits autoregressive conditional heteroskedasticity (ARCH) effects. We corrected the results for autocorrelation of order one by including an AR(1) term on the right hand side of the dummy regression model and ARCH effect is taken care of by fitting a benchmark GARCH (1,1) model. Table 5: Results of Equation (6) for Nifty corrected for autocorrelation and ARCH Effect Mean Equation Variables Coefficients t-statistic P-Value Intercept 0.2368 2.53 0.01 Monday -0.0838 -0.72 0.46 Tuesday -0.1362 -1.018 0.30 Wednesday -0.0912 -0.70 0.47 Thursday -0.0164 -0.13 0.89 AR(1) 0.0767 2.03 0.04 Variance Equation C 0.09 4.94 0.00 ARCH(1) 0.1674 8.45 0.00 GARCH(1) 0.8086 40.53 0.00 Ljung à ¢Ã¢â€š ¬Ã¢â‚¬Å"Box Q (5) = 5.33 (0.25) ARCH LM Test(1): F- stat = 0.1645(0.68) Table 5 shows that after correcting for serial autocorrelation and ARCH effect, we found Friday effect in Nifty returns. However, our analysis do not find weekend effect. The Ljung-Box Q statistic shows that there is no pattern in residual. ARCH LM test also indicate that there is no ARCH effect in residual now. We also examined the presence of seasonality in Nifty Junior. The results are given in Table 6 which shows that there is neither weekend effect or days of the week effects in Nifty Junior. Table 6. Results of Equation (6) for Nifty Junior Variables Coefficients t-statistic P-Value Intercept 0.1824 1.20 0.22 Monday -0.2988 -1.40 0.16 Tuesday -0.0766 -0.35 0.72 Wednesday -0.2191 -1.024 0.30 Thursday -0.3149 -1.46 0.14 R2 =0.003 F Statistic = 0.84 (0.49) Ljung-Box Q(5) = 26.55 (0.00) D-W Statistic = 1.70 ARCH LM Test(1): F- stat = 145.54 (0.00) Note: Figures in () are p-values. The coefficient of Monday is not significant at 5 percent level which indicates that there is no weekend effect in Nifty Junior returns. None of the coefficients are significant at conventional levels of significance implying that there are no days of the week effects in the Nifty Junior returns. R2 is 0.003 which is very low, and F-statistic indicates that the overall fit of the model is poor. Further, Durban-Watson statistic of 1.7 indicates autocorrelation in the residuals. The Ljung-Box Q statistic for the hypothesis that there is no serial correlation upto order of 5 is 26.55 with a significant p-value of 0.00 which indicates that we have autocorrelation problem of higher order. Nifty Junior series also exhibits autoregressive conditional heteroskedasticity (ARCH) effects. We corrected the results for autocorrelation of order one by including an AR(1) term on the right hand side of the dummy regression model and ARCH effect is taken care of by fitting a benchmark GARCH (1,1) mod el. Autoregressive conditional heteroskedasticity (ARCH) model was first introduced by Engle (1982), which does not assume variance of error to be constant. In ARCHGARCH models, the conditional mean equation is specified, in the baseline scenario, by an AR(p) process i.e. is regressed on its own past values. Let the conditional mean under the ARCH model may be represented as: y à ¯Ã¢â€š ¬Ã‚ ½ÃƒÅ½Ã‚ ± à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ² x à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ² x à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ² x à ¯Ã¢â€š ¬Ã‚ « à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ² x à ¯Ã¢â€š ¬Ã‚ «Ãƒ ¯Ã¢â€š ¬Ã‚  Ãƒ ¯Ã¢â‚¬Å¡Ã‚ µ and à ¯Ã¢â‚¬Å¡Ã‚ µ ~ (N ,0, à Ã†â€™ 2 ) (8) t 1 1 2 2 3 3 n n t t t In equation (8), the dependent variable yt varies over time. Similarly, conditional variance of à ¯Ã¢â‚¬Å¡Ã‚ µt may be denoted as à Ã†â€™t2 , which can be represented as: à Ã†â€™t2 à ¯Ã¢â€š ¬Ã‚ ½ var(ut | ut à ¢Ã‹â€ 1 ,ut à ¢Ã‹â€ 2 ..) à ¯Ã¢â€š ¬Ã‚ ½ E[(ut à ¢Ã‹â€  E(ut )2 | ut à ¢Ã‹â€ 1 ,ut à ¢Ã‹â€ 2 .)] It is usually assumed that E(à ¯Ã¢â‚¬Å¡Ã‚ µt ) à ¯Ã¢â€š ¬Ã‚ ½ 0 , so: à Ã†â€™t2 à ¯Ã¢â€š ¬Ã‚ ½ var(ut | ut à ¢Ã‹â€ 1 ,ut à ¢Ã‹â€ 2 .) à ¯Ã¢â€š ¬Ã‚ ½ E(ut2 | ut à ¢Ã‹â€ 1 ,ut à ¢Ã‹â€ 2 ,.) (9) Equation (9) states that the conditional variance of a zero mean is normally distributed random variable ut is equal to the conditional expected value of the square of ut . In ARCH model, à ¢Ã¢â€š ¬Ã‹Å"autocorrelation in volatilityà ¢Ã¢â€š ¬Ã¢â€ž ¢ is modeled by allowing the conditional variance of the error term, à Ã†â€™t2 , to depend immediately previous value of the squared error. This may be represented as: à Ã†â€™t2 à ¯Ã¢â€š ¬Ã‚ ½ÃƒÅ½Ã‚ ±0 à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ ±1ut2à ¢Ã‹â€ 1 (10) The above model is ARCH (1) where, the conditional variance is regressed on constant and lagged values of the squared error term obtained from the mean equation. In equation (5.12), conditional variance must be strictly positive. To ensure that these always result in positive conditional variance, all coefficients in the conditional variance are usually required to be non- negative. In other words, this model make sense if ÃŽÂ ±0 à ¯Ã¢â€š ¬Ã‚ ¾ 0 and ÃŽÂ ±1 à ¢Ã¢â‚¬ °Ã‚ ¥ 0 . However, if ÃŽÂ ±1 à ¯Ã¢â€š ¬Ã‚ ½ 0 , there are no dynamics in the variance equation. An ARCH (p) can be specified as: ht à ¯Ã¢â€š ¬Ã‚ ½ à Ã¢â‚¬ ° à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ± 1ÃŽÂ µ t2à ¢Ã‹â€ 1 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ± 2 ÃŽÂ µ t2à ¢Ã‹â€  2 à ¯Ã¢â€š ¬Ã‚ « .. à ¯Ã¢â€š ¬Ã‚ «ÃƒÅ½Ã‚ ± p ÃŽÂ µt2à ¢Ã‹â€ p (11) This ARCH model might call for a long-lag structure to model the underlying volatility. A more parsimonious model was developed by Bollerslev (1986) leading to generalized ARCH class of models called GARCH in which, the conditional variance depends not only on the squared residuals of the mean equation but also on its own past values. The simplest GARCH (1, 1) is: à Ã†â€™ 2 à ¯Ã¢â€š ¬Ã‚ ½ à Ã¢â‚¬ ° à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ± ÃŽÂ µ 2 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²Ãƒ Ã†â€™ 2 (12) t 1 t à ¢Ã‹â€ 1 1 t à ¢Ã‹â€ 1 The conditional volatility as defined in the above equation is determined by three effects namely, the intercept term given by w , the ARCH term expressed by ÃŽÂ ± ÃŽÂ µ2 and the forecasted 1 t à ¢Ã‹â€ 1 volatility from the previous period called GARCH component expressed by ÃŽÂ ²Ãƒ Ã†â€™1 t2à ¢Ã‹â€ 1 . Parameters w and ÃŽÂ ± should be higher than 0 and ÃŽÂ ² should be positive in order to ensure conditional variance à Ã†â€™2 to be nonnegative. Besides this, it is necessary thatÃŽÂ ±1 à ¯Ã¢â€š ¬Ã‚ « ÃŽÂ ²1 p1 . This condition secures covariance stationarity of the conditional variance. A straightforward interpretation of the estimated coefficients in (12) is that the constant term à Ã¢â‚¬ ° is the long-term average volatility, i.e. conditional variance, whereas ÃŽÂ ± and ÃŽÂ ² represent how volatility is affected by current and past information, respectively. Table 7: Results of Equation (6) for Nifty Junior corrected for autocorrelation and ARCH Effect Mean Equation Variables Coefficients t-statistic P-Value Intercept 0.3572 3.74 0.001 Monday -0.2962 -2.47 0.01 Tuesday -0.2183 -1.53 0.12 Wednesday -0.2849 -2.1 0.03 Thursday -0.1672 -1.27 0.2 AR(1) 0.1667 4.74 0.00 Variance Equation C 0.1387 4.78 0.00 ARCH(1) 0.1833 9.41 0.00 GARCH(1) 0.789 41.99 0.00 F-stat = 2.28 (0.02) Ljung à ¢Ã¢â€š ¬Ã¢â‚¬Å"Box Q (5) =7.12(0.12) ARCH LM Test(1): F- stat = 1.37 (0.24) Table 7 shows that after correcting for serial autocorrelation and ARCH effect, we found weekend effect in Nifty Junior returns. Our study also found significant seasonality in Nifty Junior returns across the days. Returns of Monday, Wednesday and Friday are significantly different from each other. The F-statistic shows that at least one beta coefficient is different from zero. The Ljung-Box Q statistic shows that there is no pattern in residual. ARCH LM test also indicate that there is no ARCH effect in residual now. We also examined seasonality of Nifty and Nifty Junior return using monthly data. We estimated equation (7). The results for Nifty are reported in Table 8. The benchmark month in the model is May represented by the intercept which provided negative return of -0.7132 percent on an average over the sample period. None of the coefficients are significant except December month which indicate the presence of December effect in Nifty monthly returns. Table 8: Results of Equation (7) for Nifty Variables Coefficients t-statistic P-Value Intercept -0.7132 -0.35 0.71 June -0.8535 -0.30 0.76 July 3.1781 1.13 0.25 August 1.5309 0.54 0.58 September 2.1704 0.77 0.44 October -0.2136 -0.07 0.93 November 1.8055 0.64 0.52 December 5.047 1.79 0.07 January 3.4969 1.24 0.21 February 1.1607 0.41 0.67 March -0.2425 -0.08 0.93 April -0.2809 -0.09 0.92 R2 =0.06 F Statistic = 0.84( 0.59) Ljung-Box Q(5) = 11.85(0.03) D-W Statistic = 1.46 ARCH LM Test(1): F- stat = 0.8851 (0.34) Note: Figures in () are p-values R2 is 0.06 which is very low, and F-statistic indicates that the overall fit of the model is poor. Further, Durban-Watson statistic of 1.46 indicates autocorrelation in the residuals. The Ljung-Box Q statistic for the hypothesis that there is no serial correlation up to order of 5 is 11.85 with a significant p-value of 0.03 which indicates that we have autocorrelation problem of higher order. However, monthly Nifty returns do not exhibits autoregressive conditional heteroskedasticity (ARCH) effects. Therefore, we augmented the model specified in equation (7) with autoregressive order of 5 and moving average order of 1 and 5 on a trial and error basis. The results are reported in Table 9 which shows the presence of seasonality in monthly returns of Nifty. The coefficients of July, September and January are statistically significant at 5 percent level. The coefficient of December month is statistically highly significant at 1 percent level of significance. The augmented model has R-squ are of 0.22 which shows that 22 percent of the variations are explained by these months. F-statistic is 2.62 with significant p-value of 0.002 implying that the null of all slope coefficients is rejected at 1 percent level of significance. Table 9: Results of Equation (7) for Nifty Variables Coefficients t-statistic P-Value Intercept -1.6045 -1.03 0.30 June -0.13 -0.06 0.94 July 4.3899 1.97 0.05 August 2.2566 0.91 0.36 September 3.9858 1.86 0.06 October -0.0504 -0.02 0.98 November 3.1714 1.54 0.12 December 5.8317 2.52 0.01 January 4.8644 2.08 0.03 February 2.5038 1.07 0.28 March 0.1636 0.07 0.94 April 0.7953 0.39 0.69 AR(5) 0.6094 6.77 0.00 MA(1) 0.3559 453.72 0.00 MA(5) 0.689 -9.89 0.00 R2 =0.22 F Statistic = 2.62( 0.002) Ljung-Box Q(5) = 1.73 (0.42) D-W Statistic = 1.96 Note: Figures in () are p-values Ljung à ¢Ã¢â€š ¬Ã¢â‚¬Å"Box Q statistic of augmented model of order up to 5 is 1.73 with insignificant p value of 0.42 which implies that there is no pattern left in residual. This is also evident from D-W statistics of 1.96 which is very close to 2. Table 10: Results of Equation (7) for Nifty Junior Variables Coefficients t-statistic P-Value Intercept -0.0106 -0.0037 0.99 June -3.1408 -0.79 0.42 July 2.5269 0.63 0.52 August 2.78 0.70 0.48 September 1.6919 0.42 0.67 October -2.1813 -0.55 0.58 November 1.6522 0.41 0.67 December 7.2491 1.82 0.06 January 4.0079 1.01 0.31 February 0.131 0.03 0.97 March -3.3807 -0.85 0.39 April -0.3954 -0.09 0.92 R2 =0.09 F Statistic = 1.20( 0.28) Ljung-Box Q(5) = 19.31(0.00) D-W Statistic = 1.33 ARCH LM Test(1): F- stat = 12.36 (0.00) Note: Figures in () are p-values Finally, we examined the seasonality of monthly Nifty Junior returns. We estimated the model specified in equation (7) for Nifty Junior. The results are reported in Table 10 which shows that December effect is present in Nifty Junior returns. Besides this, the coefficient of June month is found to be statistically significant at 5 percent level indicating the presence of seasonality in the returns of Nifty Junior. In this regression model, R2 is 0.09 which is very low, and F-statistic indicates that the overall fit of the model is poor. Further, Durban-Watson statistic of 1.33 indicates autocorrelation in the residuals. The Ljung-Box Q statistic for the hypothesis that there is no serial correlation up to order of 5 is 19.31 with a significant p-value of 0.00 which indicates that we have autocorrelation problem of higher order. However, unlike Nifty monthly Nifty Junior returns exhibits autoregressive conditional heteroskedasticity (ARCH) effects. Table 11: Results of Equation (7) for Nifty Junior corrected for autocorrelation and ARCH Effect Mean Equation Variables Coefficients t-statistic P-Value Intercept 1.9045 0.85 0.39 June -4.67 -1.93 0.05 July 2.3638 0.48 0.62 August 0.6749 0.17 0.86 September 0.253 0.06 0.94 October -2.9230 -0.80 0.42 November 0.038 0.01 0.99 December 5.86 1.69 0.08 January 2.7228 0.70 0.47 February -1.2328 -0.33 0.74 March -2.7668 -1.01 0.31 April -0.7839 -0.29 0.76 AR(1) 0.364 4.08 0.00 Variance Equation C 8.13 0.11 ARCH(1) 0.1648 0.10 GARCH(1) 0.00 0.7393 F-stat = 1.73(0.04) Ljung à ¢Ã¢â€š ¬Ã¢â‚¬Å"Box Q (5) = 2.07 (0.72) ARCH LM Test(1): F- stat = 0.0142 (0.9051) Note: Figures in () are p-values Table 11 shows that after correcting for serial autocorrelation and ARCH effect, we found June and December effect in monthly Nifty Junior returns because the coefficient of these dummy variables are found statistically significant at 5 and 10 percent respectively. The F-statistic shows that at least one beta coefficient is different from zero. The Ljung-Box Q statistic shows that there is no pattern in residual. ARCH LM test also indicate that there is no ARCH effect in residual now. 9.0 Conclusion In this study, we tried to examine the seasonality of stock market in India. We considered the SP CNX Nifty as the representative of stock market in India and tested whether seasonality are present in Nifty and Nifty Junior returns using daily and monthly data sets. The study found that daily and monthly seasonality are present in Nifty and Nifty Junior returns. The analysis of stock market seasonality using daily data, we found Friday Effect in Nifty returns while Nifty Junior returns were statistically significant on Friday, Monday and Wednesday. In case of monthly analysis of returns, the study found that Nifty returns were statistically significant in July, September, December and January. In case of Nifty Junior, June and December months were statistically significant. The results established that the Indian stock market is not efficient and investors can improve their returns by timing their investment.

A Clockwork Orange By Anthony Burgess - 2443 Words

In A Clockwork Orange by Anthony Burgess, Alex, the protagonist is a fifteen-year-old boy who commits ultra-violent acts out of pure pleasure. The allegory present throughout the novel shows that Alex is ruthless and does not feel pain when experiencing the deaths of others. Throughout the journey of a small portion of Alex’s life, vivid representations of settings are used to portray the dark deeds done by Alex and his friends. Burgess also uses distinct dialect to individualize Alex and his friends from the rest of the community to represent their violent actions with their dialect. He uses a configuration of symbolism to represent the violent nature of Alex’s surroundings which cause him to commit these violent actions throughout the story. Through the use of these elements, Burgess proves how violence becomes a crucial element of surviving amongst society. To begin with, many events take place throughout the novel where Alex commits cruel crimes against several citizens of the dystopian society. Since Alex and his friends are often seen committing such acts, they must also be able to defend themselves in case one of their victim fights back. During these events allegory is used when the victims of the violent actions Alex and his friends commit, bleed out from the injuries they inflict upon them. The language used throughout the novel tends to eliminate some elements of allegory, however, if you translate â€Å"young men doing the ultra-violent on a young woman who wasShow MoreRelatedAnthony Burgess and A Clockwork Orange987 Words   |  4 Pagesnothing you can do about it. Anthony Burgess created this world through his novel, A Clockwork Orange. Anthony Burgess was born in 1917 and died in 1963. 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Through College Literature Journal’s article â€Å"O My Brothers†, the unnamed author draws interesting connections between the main character’s development and how pseudo-families and pseudo- self plays a part on this said developmentRead MoreA Clockwork Orange By Anthony Burgess1383 Words   |  6 PagesFree Will in Humans In the novel, A Clockwork Orange, Anthony Burgess argues how free will is empowered by society and the government. Through the character Alex, the author is able to explicate his ideas of how the government strips Alex’s freewill while being in presence of violence in order to force him to be good. But is Alex still considered human without choice? Is goodness considered good when it is not chosen? People have the right to choose right from wrong on their own, just like AlexRead MoreA Clockwork Orange, by Anthony Burgess1034 Words   |  5 PagesImagine having stolen, raped, and even murdered all at the age of 15. The new canon of dark literature and controversy has finally hit the stage. A Clockwork Orange by Anthony Burgess written in 1962 could only be described in the old cockney expression â€Å"queer as a clockwork orange†. Meaning it is bizarre internally, but appears natural on the surface. The story begins with the protagonist and narrator Alex a 15-year-old boy, who sets the bar for the most cold-blooded and callous characters of literatureRead MoreA Clockwork Orange By Anthony Burgess1473 Words   |  6 PagesLinking the fundamental conflict between individual identity and societal identity with musical imagery in the story â€Å"A Clockwork Orange† by Anthony Burgess, creates a lens through which one can recognize the tendency that violence can destroy an individual’s identity. The main protagonist and narrator of the story is Alex and although he associates violence with his own individual identity and sense of self, he consistently reveals the impossibility of remaining an individual in the face of group-orientedRead MoreA Clockwork Orange By Anthony Burgess2327 Words   |  10 Pagesat the last round the bearded lips of God, to attempt to impose, I say, laws and conditions appropriate to a mechanical creation, against this I raise my sword-pen,† Anthony Burgess in his novel ‘A Clockwork Orange’ , which happens to be a scathing critique of totalitarian government, through the character of F. Alexander. Burgess is attempting to criticize the type of governments that try to limit the freedom of an individual through science and technology. To be more specific, the use of ‘LudovicoRead MoreA Clockwork Orange By Anthony Burgess2415 Words   |  10 PagesA Clockwork Orange, by Anthony Burgess, a story of a young troublemaker who rebels in every way possible against his society’s norms. The main character, Alex progresses throughout the story learning how his actions affect his future. Along the way Alex conforms, or at least pretends to, whenever necessary to survive or to get his way. However, during his incarceration, he underwent a procedure that altered his ability to rebel. This made Alex realize there are other was to adapt and overcome besidesRead MoreEssay on Anthony Burgess A Clockwork Orange1497 Words   |  6 PagesAnthony Burgess A Clockwork Orange Choice and free will are necessary to maintain humanity, both individually and communally; without them, man is no longer human but a â€Å"clockwork orange†, a mechanical toy, as demonstrated in Anthony Burgess’ novel, â€Å"A Clockwork Orange†. The choice between good and evil is a decision every man must make throughout his life in order to guide his actions and control his future. Forcing someone to be good is not as important as the act of someone choosing to beRead MoreA Clockwork Orange By Anthony Burgess1410 Words   |  6 Pages Anthony Burgess’s A Clockwork Orange has long been regarded as one of the most difficult books to read, both due to its heavy use of made-up slang, and the overtly violent nature of the main character, Alex. When Stanley Kubrick’s version was produced in 1971, the movie earned an R or NC-17 rating, due to the sheer amount of violence. The subject matter of the movie was violence at it’s very nature. However, upon closer examination, there are many references to religion, Christianity in particularRead MoreAnalysis Of Anthony Burgess s A Clockwork Orange819 Words   |  4 Pageshumans from machines. Anthony Burgess, author of A Clockwork Orange, believes this trait is a person’s freedom to make conscious decisions. By taking away a person’s ability to choose between doing the right thing or the wrong thing, you also take away what makes them human. A Clockwork Orange creates a world documenting the decay of a person’s will to live and the lo ss of their humanity when their freedom of choice is taken away. Alex, the protagonist of A Clockwork Orange, is a textbook example

The Effect Of Classroom On Students Hours A Week On...

Discussion Starting with the obvious, it is evident that working 20+ hours a week impacts a student’s grades. Upon finding out those results, a comparison was made to find out if working 15 hours a week has an effect on academic performance, and the same results occurred. Although there isn’t a major change in global R-Score, the change is still there. The average R-Score is 27.285 while the working R-Score of 20+ hours a week is 26.875. Oddly enough, if we compare the male student R-Scores, they actually improved in performance and most of them also agreed that working during the semester would have a negative impact on their grades. A working R-Score of 27.46 compared to a non-working R-Score of 25.5. This can be attributed to the fact†¦show more content†¦There is also a higher percentage of women who work to provide for their living expenses (30%) compared to men. The second interviewee (who works to pay for his living expenses/help parents out) said that working long hours causes extreme fatigue which may lead to a lack of motivation. The combination of the extra work hours and the stress of providing for themselves corresponds to the decrease in academic performance. The majority of students surveyed (39%) said that they work to buy personal goods while the remaining said they work to provide for their living expenses (26%) or to have some extra cash (35%). For the â€Å"personal goods† category, 50% of the women chose this reason, which explains their poor saving habits. For the men, 46% of them chose the â€Å"extra cash† category which explains their higher savings percentage compared to the women. The average R-Score for men who work to provide for their living expenses is 25, as appose to an R-Score of 28.1 for the men who work to spend on personal goods or those who just want extra cash. The average R-Score for women who work to provide for their living expense is 26, as appose to an R-Score of 27.54 for the ones who spend on personal goods or those who just want extra cash. This is because those who work to provide for their living expenses, tend to work more hours a week as well, in addition to their stressful

My Boyfriend Miguel Essay Example For Students

My Boyfriend Miguel Essay I often think of Miguel often and at very odd times. I am always haunted by who he was and his memory. I think of him so much now as I dress and prepare to go to a party at the Wilshire Hotel in Los Angeles. Miguel was one of the most remarkable people I have ever met in my whole life. To me he still retains a lifelong ambivalent quality to him that no one will be able to take away from me. He comes back to me in my mind always in ever present illusory and recurring dreams. As I sit still, I remember him since it was so long ago I wait for a minute looking at myself in the mirror all those years later and wonder how I have weather the years so well if Miguel was still alive where we would be living today. I know very little about Miguel and what became of him. I have often always wondered if anyone today does know where he is. My very first meeting with him at a Theater hall in December of 1955 in Madrid. The place was called the Le Revue Villa right there in Downtown Madrid. It was a cool fine day. Christmas was fast approaching. Very few places are as beautiful at the Spanish Countryside where the Villa was located. I would always picture it in mind. The rugged green hills and the narrow winding road down Carmenita way through the street to the Theater. The usual people that hung out at the theater on those cold winter nights back then were an unusual bunch of people. You had German scientists, Spanish and Italian movie stars political refugees young expatriates, artists, French, American, English, Swedish, and Austrian adventurers. It was as one of the Grandest Annual Parties in all of Europe. The woman who threw the party was known Princess of Gibraltar because she was born there. She was there with her gigolo Raul. They were both talking and laughing. The Princess had two small children. My friend at the time Colette was the governess to the Princess children. Colette had invited her friend Miranda and three Pilots to the Party. As I looking around that night I thought to myself all Roads in Spain must lead through this place in one way or another with such an odd assortment of people. Here, There, and Everywhere I looked among the sea of images and faces at this party there were some clearly stood out. One face in particular was that of a man who was about forty years old with black slicked back hair. He was dressed very elegantly with a neatly trimmed beard and a black and white suit. He seemed to be a very affectionate man and was very well dressed compared to some of the other men there and from my perspective looked to be the best looking one at the party. This man also had another quality that also drew me to him. He seemed to have an ageless survivor of life quality about him that I hadn’t seen in other people before. I had seen that look in the faces of other men at those prisoner of war camps during the great war years earlier. His eyes were black and burned in me as we stared at each other. As I was standing by a pond in the back near the garden steps talking to Colette, Miranda and Raul, I was thinking. â€Å" This dinner party is better than any other I have ever attended and quite unlike any one I have been to before. † Miranda was an avid art lover and appreciated all kinds of great works that were all over Europe. Me and Collete and Miranda would frequent a lot of the galleries together in places like Barcelona and Seville. We would also exchange ideas about the latest and most artworks that were being completed by these painters. As the band began to play a song I really liked to listen to, Miguel walked over to me and bent down and kissed my hand. He said with with an soft voice. â€Å"What is your name? I answered it was Marcie â€Å"Oh he said. You have an English accent. â€Å"No it is an Australian accent I responded†. â€Å"I was in Australia once he said†. As I looked at him with intensity I good tell there was an immediate attraction between the two of us that I have never experienced with anyone else before. His thick hair was curled back from his ears and the rest of it was combed back. â€Å"I was in Australia he once replied. Sydney over fifteen years ago. I was stationed there during the early years of the great war in the Pacific fighting against the Japanese. He smiled and looked and said. You must have been just a child then Marcie. â€Å"Wow I said. I can’t believe you were in the war? † I asked. He looked away. Ya Miguel said. â€Å"I am glad I was. It was a great experience for me. I then replied. â€Å"It seemed everyone wanted to serve their country in the great war. † He then shrugged, pouting his lips again and again then smiling disarmingly to show his white well formed teeth. I always liked men with nice teeth, but then it also occurred to me that I was unable to draw my eyes away from his for what seemed like a long moment in time. I realized in that brief moment that Miguel fulfilled my ideal of what I wanted in a man. We walked around the side of the villa, past groups of different people which included some Spanish and Italian and Princes British royalty as well. They all glanced at us as we passed. Miguel was oblivious to them however and pulling on my arm really gently we eventually made under a tree in the yard alone where nobody else at the party could bother us. Where do you live† Miguel asked pointedly. Trying to sound more sophisticated than I really was I said, â€Å" Oh here and there I said but I live in Madrid now. I used to live in Rome a while back. † He then smiled, showing deep curved lines in his face around the corners of his mouth. This was incredibly attractive to me. I have a tendency to judge a man by his mouth. The eyes maybe a window to someone’s soul, but to me the mouth indicates one inner emotions, and ones inner depth of feeling. I never did end up giving him my address while at the party. I had had some bad experiences doing things that way but to Miguel I now replied inventively serious. Pulling out a piece of paper Miguel then wrote on it and split it in half and proceeded to hand one half to me. At that very moment some blond haired German Albino looking man turned the corner to where me and Miguel were talking and turned his back to us and suddenly flashed a light in our eyes. Miguel responded angrily to him. It was the paparazzi taking pictures. Miguel grabbed the man’s camera and saying as he rushed off. , â€Å"I must go now. Much to my dismay, after squeezing his hand firmly, he then ran off with another photographer yelling and chasing him as well. I guess he didn’t like the media. As I watched Miguel and German Albino man disappear, I felt totally frustrated. Miguel excited me, intrigued me and now he was gone. I felt completely deflated. As Raul and Colette approached I stuck the piece of paper in my purse after first glancing at it. It said simply, â€Å"Plaza Del Oro. He wanted to meet at a place called the Plaza Del Oro Restaurant in Madrid on Friday at 1:00 for lunch. Later that night at the party, I met a famous director named Rosarita Brazzi and her charming assistant Lisa Harrison. She was a famous film director at the time in Europe and was a protege of Alfred Hitchcock but I kept thinking about Miguel. Even later, when I met with my friends and some wine and appetizers I kept thinking about Miguel. The airline pilots at the party were very boyish and entertaining, but Miguel was all I could think about and the thoughts would not leave my mind. His image was like a fuzzy picture which at times grew clearer and more concise and at other times faded into just a blur of blankness. I thought about Miguel constantly before that Thursday I was supposed to meet him. I was mulling over my decision and whether I should go meet him, and when I knew and decided I ultimately would, and did, a sixth sense sort of told me that door had opened which I may very well never be able to close. I finally arrived at the Plaza Del Oro just shortly before 1:00. I sat down and waited for Miguel to arrive. I was feeling both apprehensive and excited. I looked like a Spanish girl in my Italian looking clothes I had purchased. I was young and pretty at the time and I also felt good about myself as I arrived at my destination. A little after 1:00 as I sat on the edge of the fountain waiting, and sitting at the table staring at the old church across the street from the restaurant I decided to order a drink. All of sudden to my amazement of the church stepped Miguel dressed as a priest. I was absolutely in a state of complete shock. I stared at him transfixed as he came across to the restaurant and walked directly over to me and without saying a word sat my table and ordered some spaghetti and wine for us for lunch. My curiosity finally got the better of me and I said to him. â€Å" Miguel I said, or should I should call you Father Miguel? † He proceeded to smile at me enigmatically,† Yes my child. â€Å"This is becoming to much for me, I continued, I am perhaps naive he said. † â€Å"I am sorry he said, â€Å"I am not Miguel, that is I am not the man you met at the party the other day. † I looked at him closely. His face looked like Miguel’s face. His voice sounded exactly like Miguel’s voice. If he was not Miguel then who was he? Then again. I started wondering who I was for a brief minute. My mind swirling strange thoughts. How in the world did he know me, who I was or where I would be at. Why would this man who is a priest arrange to meet me where Miguel was supposed to meet me. Now I wanted some answers from him. I stared into his eyes and asked him point blank. â€Å" I feel like I am in a maze and I can’t find my out†, I said, as if almost regretting I had come yet in a strange way excited by the whole episode of how the day was going and where it will lead to next. â€Å"I am Miguel’s twin brother and I know that he arranged to meet you here. I have his diary and whether or not you know or are aware of it or not, there is a picture of you taken at a party that he met you at in his diary with the name of this place and time he was supposed to meet you written on it. I was totally surprised by what Miguel’s supposed twin brother was saying. My mind started racing and then I took a couple of deep breaths and started to slow my mind down and think logically. Should I believe or not believe what he was saying. I was very confused. â€Å"Now, if I had not of come, would you have tried to find me I asked him. Why did you come instead of Miguel? Where is Miguel? Are you really Miguel and could it be that you are lying to me? † These were all questions that I was pondering at that very moment. He hesitated for a moment and did not immediately answer my questions. He stared around the restaurant and plaza while I stared back at him and eyes started lingering on his mouth. He finally opened his mouth and spoke. â€Å"My brother is dead he said. He died in a car crash. Don’t you read the paper young lady. It was on Monday when it happened. The day after the party. † I was completely shocked by what this man was saying to me. I don’t read the news much anymore, â€Å" I stammered. I am sorry, to hear that news but you are the exact of image of your brother Miguel the man I met at the party. â€Å"Si, Si,† he said, wiping his brow dramatically. â€Å"I to my dear am also very upset. I don’t know how well you knew my brother but he was a very good man and I felt you should know. That is why I have come to see you today. To give you this message. † He proceeded to sip his wine slowly from his glass, his long brown fingers circling the top it. I downed the wine in one gulp and asked him. â€Å"Father could I have another drink of wine. † He got me another and sat downs watching me closely as I drank. I was incredibly upset and obviously very saddened by what I just heard. How could I be sure however that this man who was priest and who looked exactly like Miguel was not really Miguel even though he looked like his spitting image. I then said to him. â€Å"Oh my god, I thought, I have heard of identical twins. † On one hand I felt like saying to him. Come on Miguel what kind of game are you playing with me. Off with the disguise. † On the other hand I looked into his dark eyes which were so cool and distant and I thought. â€Å"No. † This whole thing is to much for me. I just wanted to go home and take a reset and forget this man and his twin brother, forget the whole thing. I began to feel ill. â€Å"I asked him to see the photograph of me that Miguel took at the party I had originally met him at. † I asked him if he would give me the picture and let me keep it. I was kind of surprised by own feelings of ambivalence I was having at the moment of the whole matter. He then reached into his pockets and said. â€Å" Oh my dear, I don’t think I have it on me. His English for a Spanish Priest was impeccable just like his brother’s. The two of us then walked outside the restaurant into the daylight of the plaza where some small children were playing with a dog in the street. 12 Angry Men EssayOne face is a day face, it is kind of good and and sweet and the other is the one you have now which is a night face. But it can be a little naughty to, and it also be a little naughty to,† He said. He then began to hold me tight. â€Å"I like to have both faces. † I said. Sighing as he held me, he then said,† â€Å"Marcie, life can be unkind sometimes. † We then looked at each other. I wanted to imprint his face forever in the memory bank of my mind. In this dark hotel room, the flashes of fluorescent light of the cafe across the street from our room lit up his face. He flashed kaleidoscopically in front of me on and off, off and on. There were so many questions I wanted to ask him but just filed them away in the bank of my mind. I couldn’t think. He was to close to me. â€Å"Miguel,† I whispered, Oh, Miguel. We began to kiss. I had known love once before, but it was never like this. I felt complete bliss in his arms. I also felt myself drifting on waves, and different tides of emotions, some I have never ever felt before and until that then had never experienced before until that very moment with him in the hotel. At that very moment as we were kissing the door suddenly burst wide open and it this wall. Two men came in and were both holding revolvers. They both stood silently, pointing the guns at Miguel. They spoke in what I thought was a Russian type of accent and were telling Miguel to get out of bed. Miguel then kissed me and as I pulled the sheets over my body he began to dress hurriedly. I was in total shock and was also dazed, watching him come to his senses, I then jumped up and pulled on him not to go. â€Å"No Miguel,† I cried, â€Å" I will never find you again, I know it, don’t go, don’t go! † One of the men then pushed me roughly back to the bed and told me to stay there or I would be in trouble as well. Miguel angrily snapped back at him and said something to him. The man answered him in clipped and chopped up syllables. He then pushed Miguel out of the door and Miguel glanced back at me one final time. I could see the pain and uncertainty in his eyes as he was leaving. That expression I will always remember. After he left I cried in bed for several hours and I was shaking hysterically in pain. Then I sat down in the flickering lights of the room and finally dozed off. The telephone ring woke me up. It was Simon. â€Å"What the hell happened to you? † he asked. â€Å"I called the hotel, and the said you were not there. † I got a sick,† I said to him. I had to lie to Simon. I will make it up to you tonight. † I told him. I eventually made it up to Simon that next night but my heart just really wasn’t in it. Simon’s face kept turning into Miguel’s. The next day, Simon saw me off and took me to the train station. â€Å"See you in Washington D. C. in six months†, he yelled as he looked at me hi s face showing a concerned look on it. â€Å"Maybe I will see you† I thought, â€Å"maybe Simon. † When the train finally reached the German border, it stopped all of a sudden. Seven armed soldiers with rifles and a machine gun came on board, and going walking slowly around the train with rifles, they began to check passports. You have no exit visa,† they said to me and they dragged me off the train along with my suitcases as well. â€Å"You must go back to Berlin and get your exit Visa. † â€Å"But my boat leaves tomorrow from Hamburg. † They shrugged their shoulders. â€Å" Berlin exit visa,† they kept repeating over and over again. Out of my blurred memory I recalled one little man in one of the towns that I passed on the train in East Germany who kept saying to me: â€Å"You must get an exit visa in Berlin to leave† â€Å"Oh hell,† I said as the train to Hamburg took off without me. Two huge looking heavy set German woman were standing on the platform next to me. Berlin,† I said to them, â€Å"train, Berlin. † Just then a funny little box like train came along. The two women picked up my bags as if they were packages and threw them on the train to Berlin just in time for me get on it. In what seemed like an eternity I was back in Berlin. When I got off the train the first thing I did was to check my bags in with a porter and went off to find where I should get my exit visa. â€Å"God help me,† I said aloud. All of a sudden a young German man appeared. His name was Horst. He told me after where I should go to get my exit visa. Unfortunately,† He said, â€Å" I cannot got with you, the place you need to go is in East Berlin,† He wrote down and gave me directions on a piece of paper, and his address in Berlin in case for whatever reason I had to contact him and if I didn’t take the boat. â€Å"Perhaps we will meet again. † I caught the commuter train to East Berlin and after an hour or two of waling along across many streets I finally found the place that Horst had told me about. As I walked inside, I went in total shock. There were many hundreds of people inside the room. â€Å"I will never get that train now to Hamburg I thought to myself. While waiting in line I began to think about Miguel. I did not want to leave Berlin but I was running out of time and money and I did not want to be stranded in Germany in any more me sses than I was in now. Where was Miguel I wondered and who was he and what did he get into trouble for. These were all questions I pondered on as I was waiting. By now I began to have my suspicions but suspicions are not really facts. However I knew that Miguel was into something beyond my comprehension since I was just a naive young Australian girl. All of sudden I looked up in line and old man was right at my elbow. Are you from Australia? † he asked. â€Å" I heard you talking to yourself. Do you have a problem? Do you need an exit visa,† he asked me. I told him recognizing a disguised Australian accent, â€Å"I am a new Australian,† he told me, â€Å"just got back in Germany and I am visiting relatives here,† he said. â€Å"You are going to need a blue card like me,† he said. He took me over to a desk where an official sat, and he said something to him in German. The official asked him for our passports and then told us to wait for a few minutes till he came back. â€Å"How about all these people? † I asked looking around. They are Germans wanting to go from East Germany to the Western Side. They will most likely never make it† you and I will however,† he said. Before I left, after getting the blue card, which in those days was the same as a passport today, the old man gave me his name and number of where he lived in Perth, Western Australia. â€Å"It is a small world,† he said, â€Å"perhaps we will meet there someday. † On my long trek back to the train station, I passed an old building where outside of which there were several Russian guards in uniform. They all stared at me with their crooked looking faces as I walked by them. I went passed them as quickly as possible. A car then pulled up with some Russian officers in it who were apparently very high ranking soldiers. They were wearing light colored brown uniforms and had red braided gold symbols on their shoulders. The soldiers at the door of the building clicked their heels and saluted as the men walked in. â€Å"Oh my God,† I breathed as one of the men briefly glanced by me in my direction and I caught a part of his profile as he did pass me. â€Å"Miguel? † I thought to myself, walking back. The man then vanished through the doorway. â€Å"Now, I am seeing everywhere,† I thought to myself. I then ran into the doorway to see if I could get in the building but the guards there pointed their guns at me and wouldn’t let me through. â€Å"Power,† I thought, â€Å"the effects the paralyzing effects of power and fear. Fear, a million different kinds of fear that was running through me right now†. I then began to think of the incident with Miguel at the hotel room and what had happened with us there when he was apprehended by the officials. In the weirdest way. â€Å"Love can paralyze that fear even if just for the briefest of instances in life. † Power, fear, love all work in different ways to paralyze ones emotions. The next day I was in Hamburg and finally on my boat to Quebec Province in North America. A week later I reached Quebec City and took the train to Montreal. I then finally made it to New York and back to Washington D. C. where I stayed with some friends and later with my relatives. A few weeks after I arrived in Washington I was able to get a job with the British Embassy on Massachusetts Avenue for almost a year. I was still working there when the Suez Canal crisis erupted in July 1956, and a mail girl that was working down the street from the Embassy I worked for blew up and got killed. The incident attracted headlines all over the world. Somehow I got into one of the pictures and saw myself my horrified face staring back at me from the front page of the Washington Post. I left Washington shortly after the incident and came to Los Angeles. I got a job there in L. A. Working for the British consulate for a while located Pershing Square. One day I received a package in the mail while I was working there with no return address on it. It had been forwarded from the British Embassy in Washington D. C. and it had some French Stamps on it as well a Paris Postmark. I thought it must be from my old Colette back in Madrid. I opened it up to find a black colored ring with a large pearl in the middle of it. Inside was a note that said simply in Spanish. â€Å"To my dear. † I knew who had sent it. Things in my life after that began to happen quickly. A year later I married an American. I have lived many years now now since that time. I have been married over twenty six years. Time and the years have gone by. There have been good years and bad ones. I always feel that life should be judged by events that happen not by chronology. I often think of something a teacher once said to me years ago as a child: â€Å"One age of crowded life that is interesting is worth more than a whole life that is boring. † And then I think of Miguel and that one moment years ago in Berlin and what almost could have been. Tonight however being Christmas Eve 1981, I will dress very fashionably and go with my husband to a cosmopolitan business party at the Beverly Wilshire Hotel in Los Angels. At the party is husband is busy talking business deals with fellow confidants and partners who he works with while I hear the band start to play something to my liking. I look up and they are playing something I have not heard in years. It brings back memories that are very sentimental to me all these years later. Through the crowd of chattering people, I look across the room to catch a glimpse of a silver-haired elegant man leaning against the bar having a drink. I think of the word in Spanish and Italian both that means affectionate and I finally remember it. â€Å"Affascinante† I think of in Italian. The man begins to look at me as I am having a drink and I feel there is something deeply familiar about him. I begin to have a weird sense of de ja vu. As we walks closer and stands still for a moment his look becomes more piercing. Suddenly my husband returns from his conversation with his associates and asks me if I want to dance. He says â€Å"they are playing that old favorite of yours. â€Å"Anema e cuore† I say looking over his shoulder at the silver haired man. â€Å"There a lot of European political refugees here tonight† he says to me. â€Å"Some rather ones as well,† he also says. We begin to dance. â€Å"Really,† I say. â€Å"Some Russians who have political asylum to seems like an interesting bunch. My hand with the black ring that I got years ago rests on his shoulder. I twist around to see the silver-haired man. He still staring at me. He looks at the ring. My heart begins to jump very fast and a tingling starts to run up my body. I hurriedly excuse myself, â€Å"I have to go the bathroom for a minute,† I tell my husband who returns to his table and his friends. I am magnetically impelled towards the silver-haired man, and the room begins to shrink in size with everybody dancing and fade out as me and man draw closer. I see his lips pout. My heart begins to jump in my mouth and I whisper. â€Å"Miguel? Miguel? Miguel!

Management Information Technology

Abstract Malware is software, which performs malicious actions on a network once it gets access to it. Malwares are destructive hence; they are unacceptable in any organization network. The desire of every organization is to have a network as safe as possible. In today’s networks, various threats are eminent and their modes of attacks keep changing every day.Advertising We will write a custom essay sample on Management: Information Technology – Information Assurance specifically for you for only $16.05 $11/page Learn More This is a common challenge to networks; even the World Wide Web. In essence, the struggle of every network is to discover the most appropriate preventive, protective and corrective strategies against malware. Some networks apply malware-detecting systems. They do this by implementing software known as sandbox to monitor the behavior of programs in order to ascertain whether they are malwares or not. Sandbox is a vital tool for decision making regarding relevant treatments to malware threats upon detection. This paper dwells on the execution of various methods to protect networks against possible malware attacks, inclusive of sandbox. Introduction The rate of Computer usage has increased tremendously especially owing to the use of internet and multimedia applications. Another contributing factor is the declining computer prices as well as internet connection costs, accelerated by the higher speeds of connection. Internet encourages disguise of identity among users. Studies have shown that approximately 80% of subscribers to social sites use anonymous identity to communicate, a fact that promotes internet crime (Couture Massicotte, 2009). Similarly, malwares originate from unknown sources yet their impacts are devastating. Malware suppliers masquerade as noble solution providers on the internet to unsuspecting users. A malware code exists as a single auto run file with the destructive instructions. Im plementation of malware protection is necessary including disabling of auto run applications. A risk evaluation of every file is equally vital. This is where a sand box plays an essential role. Examples of Malware There are various types of malwares with various modes of attack on systems. Malware cause destruction to systems by deleting files, corrupting file contents, causing system failure and even stealing system access information. One type of malware is the Ransom ware Trojans. This is an executable code, which captures and encrypts files on the attacked system. What follow a ransom ware attack is that the malware creators issue the decryption keys to victims but demand payment (Couture Massicotte, 2009). The second type of malware is Password Reading Trojans. This type steals system access credentials such as login user names and passwords. They even steal passwords for emails, databases, games and secret financial information for banks.Advertising Looking for essay on business economics? Let's see if we can help you! Get your first paper with 15% OFF Learn More The third malware is the Key loggers. This is a malware, which keeps track of the keystrokes, records every typing and sends to a remote computer. This malware also aids in password stealing. The challenge in dealing with key loggers is that some vendors sell them as genuine software for legitimate purposes. The attacker can perform a variety of actions including installation of other malware, fraudulent financial transactions and installing other malware codes. Network Vulnerability The Internet is currently highly insecure. The need for internet protection is in the area of virus protection, data encryption and decryption, access privilege control and the use of firewalls to safeguard data in the computer. The large number of internet users exposes various networks to external attacks and breach of civic obligation. Aside from that, computer users connected to the Internet also have a civic responsibility (Khouzani et al, 2008). Failure to update antivirus protection or to enable an active firewall compromises the system and subjects it to attacks that are more serious. Another option, which is an alternative to virus protection, is to reduce the duration of internet connection. This implies that whenever internet is not in use, the user has to disconnect the internet. A Malware are ever ready programs on the internet, awaiting user actions so that they can auto run. Majority of today’s security control measures to malware defense are more passive than they are active. They concentrate on reinforcing defense on attacks without first soliciting vital information about the possible attacks. The condition worsens with time owing to the aggressive nature of malware attackers as they aim at maximizing on their financial gain. Malware attackers have fundamental advantages over the network administrators, because they quickly realize the security lapses, much before the system administrators. There is need for more intensive research, focusing on proactive malware defense mechanisms to identify existing potential but undiscovered vulnerabilities. This can be more effective if the defense mechanism detects and fixes the vulnerabilities before the malware attackers take advantages, and before they attack the entire system. Present Security Situation The rapid spread of information coupled with practical security challenges has enabled people to acquire Network security awareness. This has brought along new techniques to scrutinize network security situation. The rate of antimalware systems and data recovery applications is fast accelerating, indicating the index of security awareness (Khouzani et al, 2008). Nevertheless, the vulnerability of networks to malware attacks remains high due to increased use of social networks. The multimedia data usage also jeopardizes the security situation. Most of the games and entertainment programs co ntain malware codes.Advertising We will write a custom essay sample on Management: Information Technology – Information Assurance specifically for you for only $16.05 $11/page Learn More Malware coders target these considering that the probability of their usage is extremely high and proportional to the populations of youths. Approximately 69.8 % of social network subscribers have encountered malware attacks at least once in their process of socializing (Grain et al, 2001). Some of the malicious codes also spread through emails. Notwithstanding the availability of anti malware programs, the challenge is how to maintain regular updates. Possible Attacks Malwares exist in form of various programs codes such as worms, virus, Trojans and spyware. They move from one system to another through storage media and various communication channels. A virus attacks at least one file in a vulnerable computer system then it spreads to other files. When this file reaches other computers, it continues to infect the files in those computers as well. Worms are equally destructive as they destroy data on computers. They can clog and slow down network systems by performing unauthorized electronic communications. Worms can generate additional copies of files on a computer, initiate and send emails to other computers containing the infected file. Unlike viruses, worms do not necessarily require human actions to move to other computers. Trojan horses have the capacity to cause a computer or a network system to perform unanticipated actions, which are detrimental to the system itself as well as its user (Grain et al, 2001). This category of malware masks itself in emails. It can also hide in web sites, targeting unsuspecting users. Spyware is a system in the form of a website, which monitors the user’s actions while navigating a particular website. Spywares are not entirely disastrous because they provide vital information about a Web site, w hich can help in improving the design of the website. Regrettably, some hackers use spyware to install malware on unsuspecting computers. A war dialing dials a series of successive phone numbers in attempt to locate a modem at random. Once it accesses a modem, it takes control over the network and performs malicious actions. The table below gives a sample data of malware attacks on social networks in 2011. Type of Malware Virus Trojan Worm Spyware War Dialing Frequency of Attack in a Week 77 120 65 60 32 Table1: Malware Attacks on Social Networks in 2011 Advertising Looking for essay on business economics? Let's see if we can help you! Get your first paper with 15% OFF Learn More Figure 1: Malware Attacks on Social Networks in 2011 Sustainable Solutions Malware attacks in the modern world of technology have grown dynamically in the use of variety of methods to perpetrate crime. Perhaps the optimal approach for antimalware defense is the use of more proactive solutions (Goranin et al, 2001). The ideal approach is to make computer systems, networks, databases and websites as less vulnerable as possible. The system administrator stands a proactive chance to initiate the process of identifying the possible vulnerabilities of the system in order to eliminate them. Rather than make efforts to block 100% of possible attacks, it is safer to make the network immune to external attack (Patidar, 2011). Malware attackers use botnets; automated programs with the capability to identify vulnerability of a computer system. Botnets use only few parameters to assess vulnerability, and these parameters are limited to an automated knowledge base. This means they cannot detect v ulnerabilities, which do not match the descriptions in the knowledge base. To win the war against botnets, it is necessary to access and install missing patches, reconfiguring vulnerability parameters and restricting access to web server-based applications. System vulnerability status requires a periodic check because malware keep regenerating on a daily basis (Patidar, 2011). The second approaches include audits to server logs and firewall logs. These logs have to undergo periodic updates to be able to eliminate system vulnerability to malware attacks. Closely related to this is the third strategy in fighting malware, which is the periodic monitoring of new installations. New installations are intruders to the system until they pass the vulnerability confirmation test. The forth strategy is Monitoring of system performance. Any unique and suspicious system behavioral change is an indicator to vulnerability status, and needs serious attention. Figure 2: Firewall in a Network Finall y, antimalware demands periodic monitoring of anti-virus protection. Any system or network must have an antivirus, which again requires periodic update to be able to fight the newly generated virus, worms and Trojan horses. The validity of update definitions lasts for a standard period of between thirty to sixty days. Cited Instances of Security Threats In the recent past, viruses and other malware codes were nothing other than mere hoax to make life more interesting. However, today’s Internet security attacks relate to the intention to earn money. A research in 2005 from a University in Indiana revealed that seventy percent of phishing attacks on social websites actually succeed. The victims to these attacks ended up exposing their secret codes such as usernames and passwords, bank account details and other private matters. In the first quarter of this year, Twitter encountered uncountable phishing attacks. Malicious criminals designed a web page, which resembled Twitter pag e and used it successfully to play hoax on unsuspecting users. Social sites are the present targets of malware attacks, according to network security experts (Radmand, 2009). In 2004, Russia released a malware attack on USA military network and stole vital security information. Later on in 2008, USA retaliated by sending a malware which deactivated various private and public website in Russia (Valeriano Maness, 2010). Challenges to Antimalware protection In spite of the tireless efforts of security experts to fight malware crimes, some malware writers create immunity against antimalware software. This causes a serious threat to vulnerability detection and prevention. The other challenge is that malware designers produce malware and viruses that are more complicated each day. Some programs are not easy to detect especially when the antimalware system has not been undergone update. Malware programs often hide in the computer system in locations where no antimalware program can detect them. Finally, the challenge with malwares is that the rate at which the malware writers spread them on the internet (Radmand, 2009). They are more aggressive than the system users, since their principle aim is to earn monetary return. Consequently, the number of attacks at any one time on the internet becomes overwhelming to the system users. Conclusion Security is a very vital subject whose aspects everyone ought to understand. A user needs to know the levels of risk that are tolerable. For a successful fight against malware, systems need to comply with standard security policies. From a regular assessment of a system security situation, it is very crucial to find out the areas, which need improvement, to guarantee permanent security against malware. A great percentage of network administrators are ignorant of their security weaknesses. They prefer easier options of using default security configurations instead of customizing security features. Unknown to them is the fact that ma lware attackers, with the use of botnets, easily detect these gaps, this being their fundamental objective. System users often feel that security regulations are too restrictive and they formulate lenient options giving malware an upper hand. Security issue has always been solely the responsibility of system administrators. However, in the modern world, it has to be the responsibility for a successful fight against malware. References Couture, M Massicotte, F. (2009). Using Anticipative Malware Analysis to Support Decision Making. Ottawa, Ontario, Canada: Communications Research Centre Canada. Goranin, N., Cenys, A. Juknius, J. (2001). Extension of the Genetic Algorithm Based Malware Strategy Evolution Forecasting Model for Botnet Strategy Evolution Modeling. Vilnius, Lithuania: Information Security Laboratory, Department of Information System, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University. Khouzani, M.H, Sarkar, S. Altman, E.(2008). A Dynamic Game Solut ion to Malware Attack. Pennsylvania: University of Pennsylvania. Patidar, P. (2011). Network Security. Indore: Swati Jain Academy. Radmand, A. (2009). A ghost in software. Columbus, GA, USA: Columbus State University. Valeriano, B. Maness, R (2010). Persistent Enemies and Cyberwar: Rivalry Relations in an Age of Information Warfare. Chicago: University of Illinois at Chicago, Department of Political Science. This essay on Management: Information Technology – Information Assurance was written and submitted by user Sage to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. You can donate your paper here.