The proposed topic is “A study on impact of stock market volatility on individual investorsinvestment decisions”. This project would give the understanding of the reasons for the volatility in stock markets and its impact on investment decisions of the individual customers. The study also focuses on the various factors that play role in investment decisions of the individual investors in stock markets. 1. 1 Background Volatility in equity market has become a matter of mutual concern in recent years for investors, regulators and brokers.

Stock return volatility hinders economic performance through consumer spending(Kaur, 2002). Stock Return Volatility may also affect business investment spending. Further the extreme volatility could disrupt the smooth functioning of the financial system and lead to structural or regulatory changes. However, increase in volatility per se is not a problem but increased volatility reflects underlying problems in fundamental forces affecting economic activities and expectations about them.

In fact the more quickly and accurately prices reflect the available information; the more efficient would be pricing of securities and thereby allocation of resources. A market in which prices fully reflect available information is called “efficient” where share prices fluctuate randomly around their “intrinsic” values. In this project we will find the various parameters those are taken into consideration by individual investors while investing in stock markets and weather stock market volatility have an impact on investment decisions of the individual investors or not. . 1. 1Indian Stock Market The Indian stock market is represented by two most prominent stock indices, viz. , Bombay Stock Exchange’s (BSE) Sensitive Index (Sensex) and NSE’s S&P CNX Nifty (Nifty). The Sensex is generally considered to be the bellwether of the Indian stock market. It is the older and the more often quoted index. However, of late, with the growing popularity of the NSE, due to its moretransparent trading mechanism and lower trading cost, Nifty has come to be considered as an important and broader-based market index (Kaur, 002). As per SEBI’s Annual Report of 2002-2003 (available at www. sebi. gov. in), the BSE and NSE together account for more than 95 1 per cent of the total business transacted on all the stock exchanges of the country. Additionally, according to the data available on the respective exchange web sites (www. bseindia. com and www. nseindia. com), a major portion (around 75%) of the total market t urnover of the respective stock exchanges is accounted for by the index (Sensex and Nifty) stocks.

The Indian Equity market is divided in to two parts Primary market – where the share is first issued in the form of IPO (Initial Public Offering) and after issuing the share it is listed on exchange and share is traded on exchange where shares can be bought and sold this is secondary market. Before 2000 shares was held in Physical form but the main difficulty with Physical shares is method of transaction which is open outcry system and process is not transparent to investor also Physical shares were prone to duplication and fraud.

So in 2000 NSE introduced the electronic screen based trading system further the introduction of Dematerialization(Conversion of physical share in to electronic form) and depository(where the electronic form of share is kept) revolutionized the Indian Stock market. Currently there are mainly two Depository (DP) NSDL and CDSL and these DP are like bank of share. Individual/Firm can de al through Broker (who is registered and having membership in Exchanges and Depository) for buying and selling securities. Today NSE outpaced BSE in volume of trade.

So, Stock market serves the company by providing company the finance for long term needs a nd for investor an opportunity to park their savings in corporate world and in turn give their hand in Nation’s development so stock exchange have a very vital role in country’s economic development. So basically an investor invests in the market with a viewpoint of making profit out of their savings. Share market for many is gamble and they find the stock jargons to be alien. But the Basics of Stock Market Type of investment option Online trading Market Volatility Types of Volatility Objectives reality is something far from that.

Stocks are also a viable investment option that can give the investors huge returns if the investment is done meticulously. There are so many factors that make the share market a chosen area of investment for millions of investors. Stocks can give multiple returns on investment that no other asset class can match. If you can invest wisely in the potentially strong stocks, you are all set to gain hugely from your investment. 2 1. 1. 2 Investing Options There are so many options in stock market investment (Kaur, 2004).

You can choose to do intraday trading or delivery trading and you can trade in cash segment or in derivative segment. You also get a variety of choices in equity trading itself (Nagy, 1994). You can invest in growth stocks to gain rapidly, or you can invest in dividend stocks for long term and enjoy the dividends that keep coming to you. In short share market investments have plenty of options that you can choose according to your need and your budget. With the advent of online trading, investing in the stock market and trading in the stock market has become easier for the individual stock investors (Christie, 1982).

They can buy or sell stocks by themselves with just a click of the mouse and that too sitting at the comfort of their home or office. In case of online stock trading, there is no broker involved and there is no paper work either. The brokerage in online trading is also lower in comparison to the conventional stock trading. In short online share trading has made the share market investment a profitable proposition for the individual investors. In this age of internet and media boom, information has become easy accessible.

Anyone can get detailed information on any business quite easily through internet. Companies pub lish quarterly and annual reports that are very much helpful to judge the financial health and standing of the company. 1. 1. 3 Market Volatility Volatility is the most basic statistical risk measure (Shiller, 1989). It can be used to measure the market risk of a single instrument or an entire portfolio of instruments. While volatility can be expressed in different ways, statistically, volatility of a random variable is its standard deviation.

In day-to-day practice, volatility is calculated for all sorts of random financial variables such as stock returns, interest rates, the market value of a portfolio, etc . (Odean, 1989). Stock return volatility measures the random variability of the stock returns. Simply put, stock return volatility is the variation of the stock returns in time. More specifically, it is the standard deviation of daily stock returns around the mean value and the stock market volatility is the return volatility of the aggregate market portfolio (Kaur, 2002). Volatility of stock returns has been mainly studied in the developed economies. After the seminal work of Engle (1982) on the Autoregressive Conditional Heteroscedasticity (ARCH) model and its generalized form (GARCH) by Bollerslev (1986), much of the empirical work has used these models and their extensions (see, for example, French, Schwert and Stambaugh 1987; Ballie and DeGennaro, 1990; Corhay and Tourani, 1994; Geyer, 1994; Nicholls and Tonuri, 1995; de Lima, 1998; and Sakata and White, 1998).

There is relatively less empirical research on stock return volatility in the emerging markets. In the Indian context, Roy and Karmakar (1995) focused on the measurement of the average level of volatility as the sample standard deviation and examined whether volatility has increased in the early 1990s; Goyal (1995) used conditional volatility estimates as suggested by Schwert (1989) to study the nature and trend of stock return volatility and the impact of carry forward system on the level of volatility; Reddy (1997 -98) analysed the effects of market microstructure, e. g. establishment of the National Stock Exchange (NSE) and the introduction of Bombay Stock Exchange Online Trading (BOLT) system on the stock return volatility measured as the sample standard deviation of the closing prices; Kaur (2002) analysed the extent and pattern of stock return volatility during 1990 – 2000 and examined the effect of company size, day-of-the-week, and FII investments on volatility measured as the sample standard deviation. 1. 1. 4 Investor’s Behaviour Savings form an important part of economy of any nation (Bhaskar, 2002). With the savings invested in various options available to people, the money acts s the driver for growth of the country. Indian stock market too presents a plethora of avenues to the investors. Though certainly not the best or deepest of markets in the world, it has reasonable options for an ordinary man to invest hi savings. One needs to invest and earn return on their idle resources and generate a specified sum of money for a specific goal in life and make a provision for an uncertain future (Jasmeen, 2009). One of the important reasons why one needs to invest wisel y is to meet cost of inflation. Inflation is the rate at which cost of living rises. 4

Cost of living is simply what is cost to buy the goods and services you need to live. Inflation causes money to lose value because it will not buy the same amount of goods or services in future as it does now or did in past. The sooner one starts investing the better it is. Stock related public information has been one of the most important components in determining the behaviour of individuals. In case of their behaviour in stock market, it becomes even more critical to access and incorporate into their decision making updated information included in financial reports, periodical press releases, and media coverage and so on. Chandra Abhijeet, 2001) 1. 2 Need Of The Project Themain purpose of this project is to find and analyze the key factors that insist or motivate individual investors to invest in stock markets and how their decisions are affected by the volatility in stock prices. In the present day scenario investments in stock markets has became a part of the individual investor’s portfolio. Especially for those investors who are ready to take more risk but want higher returns stock market investments are the great so urces. The investment objectives of the investors are varying from one to other.

A careful analysis and close observation helps us to see how volatility plays a role in investor’s investment . 1. 3 Review Of Literature There are numerous studies done across the globe are done mainly in context of transmission of volatility across economies and the contagion effects of a financial crisis which include the work done by Forbes and Rigobon (2002), Bekaert, Harvey and Lumsdaine(2002a,b), Edwards (2000) and others. Rogobon (2003) focus his study on alternative measures of volatility in the equity and bond markets in the period adjacent the financial crises.

Bekaert and Harvey (2000) analyse equity returns in a group of emerging economies before and after financial reforms in a group of emerging markets and find mixed results. During 1985-95 in a study done by Aggarwal, Inclan and Leal (1999) analyze volatility in emerging stock markets. ICSS algorithm was used to identify the p oints of abrupt changes in the 5 variance of returns they examine the nature of events that cause large shifts in stock returnvolatility in these economies. Local events jumps were held responsible for stock market volatility of the emerging markets by a study done by Aggarwal et al.

They find no systematic effect of liberalisation after studying the behaviour of stock prices after the economy was opened to foreigners or large foreign inflows Kim and Singal (1997) & De Santis and Imorohoroglu (1994). The results of this study corroborate Bekaert’s findings that volatility in emerging markets is unrelated to his measure of market integration. Richards (1996) use two sets of data and three types of methodologies to estimate volatility of emerging markets and fin d no increase in volatility following the process of liberalisation.

Levine and Zervos (1995) find results contrary to Richards (1996) that there may increase volatility after liberalisation. No systematic evidence that foreign trading tends to increase market volatility more than trading by domestic groups was revealed by Hamao and Mei (2001) who examined the impact of foreign and domestic trading on market volatility for Japan. The period of time study mainly relates to the time period during which the foreign portfolio investment in Japa n was rather small.

The study done by Folkerts – Landau and Ito (1995) on volatility of emerging markets in periods that differ in their intensity of portfolio flows generated mixed results with Mexican stock prices being least volatile when flows are most volatile and vice versa for Hong Kong. Excess volatility was reported by Nilsson (2002) following the process of liberalisation using the Markov regime switching model. He finds evidence of higher expected return, higher volatility and stronger links with international stock markets characteristic of the deregulated period in all Nordic stock markets.

Studies analyzing the behaviour of stock prices over financial cycles have been undertaken in the recent years. Itwas confirmed in the study that owing to liberalisation the stock markets tend to become more stable. Time varying patterns of financial cycles before and after financial liberalization was examined by Kaminsky and Schumkler (2001, 2002) in 28 countries. The results indicate that more liberalisation causefinancial extremes in the short -run and also brings a change in the institutional set up of which will have a supporting and better functioning of financial markets.

A temporary rise in volatility was observed in a ll countries immediately after the liberalisation. In a study done by Edwards et al (2003) ,the stock price behaviour in six emerging economies is analyzed . The results they find exhibit that emerging economies like Korea are in the process of recuperating their stability where as more stable relatively. 6 1. 3. 1 Stock Market Volatility A study by MT Raju et al (2004) find that the volatility is less in mature markets and provid e very high returns. India and China exhibit high returns where as all other emer ging economies exhibit low returns.

A study is done on spot price indices of BSE Sensex and S&PCNX Nifty to explain the extent and patterns of stock return volatility. The results exhibits rise in volatility in the period 1990-2000, with a sharp fall in 1992 till 1995, after which therewas a sharp rise, however the volatility in the portfolio into India was very small in comparison to other emerging markets, (Gordan J and P Gupta (2003), HarivinderKaur’s (2004). Stock market volatility plays an imperative ro le in the financial development as well as growth.

Daily volatility is calculated as a standard deviation of the natural log of daily returns on the indices for the respective months. Volatility in stock prices in India, by and large has shown a flagging d rift except in few certain months. The market may have turned riskier, messy politics could resurface, and oil prices are at a new high (but at present low); but nothing seems to worry local investors, who feel the index can go up further. AnandBansal and J. S. Pasricha (2009) studied the impact of market opening to FIIs on Indian stock market behaviour.

They empirically analyze the change of market return and volatility after the entry of FIIs to Indian capital market and found that there is no significant change in the Indian stock market average returns; volatility is significantly reduced after India unlocked its stock market to foreign investors. Using a simple present value model, Shiller (1981) finds that the level of stock market volatility is too high relative to the variation in the underlying micro and macro fundamentals. Specifically, he finds that the changes in real dividends and real interest rates cannot explain the level of mark et volatility.

Studies that examine the variation in market volatility also conclude that standard macro factors and corporate characteristics cannot explain the time-varying nature of equity volatility. Specifically, Officer (1973), Black (1976), and Christie (1982) find that financial leverage only weakly explains the variation in market volatility. Schwert (1989) finds that standard macroeconomic variables, such as inflation, money growth, and industrial production, also do not sufficiently explain the variation in the market volatility. Therefore, nonfundamentally based volatility drivers likely exist and may have better explanatory powers.

Behavioral finance literature points to information herding (cascading), noise trading, and liquidity-driven transactions as potential reasons for the higher level of market volatility, relative 7 to the volatility in the underlying information flow. Theoretical work by Banerjee (1992) and Bikhchandani et al. (1992) suggests that information cascade can lead to price o vershooting, which would inject additional volatility, in excess of the contribution from the existing volatility drivers. Campbell and Kyle (1993) and DeLong et al. (1990) study the effect f non-informed trading (uninformed speculation by noise trader or portfolio trading driven by liquidity shocks to the investor). They suggest that these uninformed trading activities create a new source of shocks to prices. This additionally creates excess equity market volatility. The return predictability literature and the value premium literature offer rational pricing models as well as behavioral explanations for time-varying market volatility. Ferson and Harvey (1991) find that expected stock market return and volatility vary over time in a predictable way.

Lettau and Ludvigson (2001), Chordia and Shivakumar (2002), and Zhang (2005) offer models that relate variation in aggregate risk aversion to decline in aggregate wealth. Intuitively, a period of negative returns driven by shocks to fundamentals will lead to aggr egate wealth destruction; this can increase the aggregate risk aversion, which further decreases prices today and increases forward-looking return and increases volatility contemporaneously. Equilibrium models of cyclical volatility are often difficult to apply; in addition, they often do not match well to data or offer insufficient degrees of freedom for empirical calibration.

For this reason, statistical models are often relied upon for modeling stochastic volatility; these statistical models can be used with great flexibility for asset pricing or asset allocation exercises. Various statistical volatility models have been developed specifically to capture and measure time -varying volatilities. Engle (1982) and Bollerslev (1986) provide the basic framewor k for such modeling with the ARCH/GARCH process (autoregressive conditional heteroskedasticity/generalized autoregressive conditional heteroskedasticity). The technique has been applied widely to the estimation of the time-varying equity market volatility.

Recent researches have proposed new techniques that could improve forecasting power through the usage of high-frequency tick-by-tick data. Anderson et al. (2001, 2003, 2005) use 5-minute realized volatility with a vector autoregessive model of log standard deviation, which eliminates much of the serial dependence in the volatilities and appears to outperform the traditional ARCH/GARCH specifications. Ghysels et al. (2006) also use higherfrequency data but propose a regression model using a beta weighting function to estimate and 8 forecast volatility.

Their model appears to be easier to parameterize and provides better forecasts against traditional ARCH/GARCH models. Vasilellis and Meade (1996) show that the implied stock volatility from option prices is an efficient forecast for future volatility. Poon and Granger (2003, 2005) show that option-implied volatility provides the best forecast for future volatility; they used option-implied volatility data from the last 20 years and compare against volatility models such as time-weighted volatility, rolling volatility, ARCH/GARCH, and other stochastic volatility models. So why should we care about time-varying market volatility?

If we do not properly characterize the time-varying nature of volatility and covariance for the various capital markets we invest in, our asset pricing model would be flawed, our portfolio allocation would be suboptimal, and our ex ante risk assessment would be incorrect. Bentz (2003) and Bollerslev et al. (1988) show that using a time-varying covariance estimate (beta estimate) can improve the application of the capital asset pricing model for forecasting returns. Horasanh and Fidan (2007) s how that applying GARCH estimates for volatility can improve portfolio allocation efficiency.

Blake and Timmermann (2002) find evidence that some pension funds seem to vary asset allocation to take advantage of time-varying asset class volatilities and risk premia. Myers (1991) finds that using GARCH models can improve the effectiveness of hedging fixed-income exposure relative to traditional regression approach with constant variance. Baillie and Myers (1991) extend the study into the commodities market and find that GARCH-based hedging provides a substantial improvement in risk reduction effectiveness. 1. 3. 2 Investor Behavior

Kadiyala and Rau (2004) investigated investor reaction to corporate event announcements. They concluded that investors appear to under react to prior information as well as to information conveyed by the event, leading to the different patterns: return continuations and return reveals, both documented in long-horizon return. Merikas et. al, (2003) adopted a modified questionnaire to analyze factors influencing Greek investor behavior on the Athens Stock Exchange. The results indicate that individual’s base their stock purchase decisions on economic criteria combined with diverse other variables.

They do not rely on a single integrated approach, but rather on many categories of factors. The results also revealed that there is a certain degree of correlation between the factors that behavioral finance theory and previous empirical 9 evidence identify as the influencing factors for the average equity investor, and the individual behavior of active investors in the Athens Stock Exchange(ASE) influencing by the overall trends prevailing at the time of the survey in the ASE. Malmendier and Shanthiku mar (2003) tried to answer the question: Are small investor naive?

They found that large investors generate abnormal volumes of buyer -initiated trades after a positive recommendation only if the analyst is unaffiliated. Small traders exert abno rmal buy pressure after all positive recommendations, including those of affiliated analysts. Hodge (2003) analyzed investors’ perceptions of earnings quality, auditor independence, and the usefulness of audited financial information. He concluded that lower perceptions of earnings quality are associated with greater reliance on a firm’s audited financial statements and fundamental analysis of those statements when making investment decisions.

Krishnan and Booker (2002) analyzed the factors influencing the decisions of investor who use analysts’ recommendations to arrive at a short-term decision to hold or to sell a stock. The results indicate that a strong form of the analyst summary recommendation report, i. e. , one with additional information supporting the analysts’ position further, reduces the disposition error for gains and also reduces the disposition error for losses. Nagy and Obenberger (1994) examined factors influencing investor behavior.

Their findings suggested that classical wealth – maximization criteria are important to investors, even though investors employ diverse criteria when choosing stocks. Contemporary concerns such as local or international operations, environmental track record and the firm’s ethical posture appear to be given only cursory consideration. The recommendations of brokerage house, individual stock brokers, family members and co -workers go largely unheeded. Many individual investors discount the benefits of valuation models when evaluating stocks.

Epstein (1994) examined the demand for social information by individual investors. The results indicate the usefulness of annual reports to corporate shareholders. The results also indicate a strong demand for information aboutproduct safety and quality, and about the company’s environmental activities. Furthermore, a majority of the shareholders surveyed also want the company to report on corporate ethics, employee relations and community involvement. Sofia Jasmeen has analyzed that a little 50 percent of the re spondents have lower risk 10 nvestments. More than one third of the respondents have gone for high risk investments and the remaining has gone for medium risk. Age wise classification has shown the same trend. Gender wise it is observed that women have made moderate and high risk investments compared to men. Qualification wise classification indicated that more no. of graduates (professionals) has gone for high risk instruments compared to others. The trend of low risk, high risk and medium risk investments are there in almost all the categories.

The association between profile of the respondents, age, gender, religion, qualification, income and profession and risk taken while making investments is not significant. Investors do not follow economists’ advice to buy and hold the market portfolio. Individual investors typically fail to diversify, holding instead a single stock or a small number of stocks (Lewellen, Schlarbaum and Lease, 1974). They often pick stock through their own research. They may get their pseudo irrationally belief that these signals carry information. They are being termed as noise trader.

Noise traders falsely believe that they have special information about future price of the risky asset. (De Long, Shleifer, Summers, Waldman, 1990). Ratio nal traders will belief on fundamental analysis for choosing stocks. EMH will be applicable in any market if the investors are taking decision based on the rational decision based on the rational decision making model. The rigid assumptions of the rational model are often unrealistic (Steers and Black 1994). Roads (1998) proved that simple problem with the few alternative courses of action or complex problems with the costless and easy solvable alternatives are fit for rational model.

Decisions are categorized in two formsprogrammed (repetitive operational problems, well defined goals, clear information and alternatives, certainly about outcome) and non-programmed (novel strategic problems, ill defined goals, ambiguous information and alternatives, uncerta inly about outcomes) (Steers and Black 1994). Hilton (2001) mentioned that over confidence, confirmation bias, optimism and risk aversion will affect the investor’s financial decision. Second, trader that are over confident are said to hold under diversified portfolios (Odean 1998, Nofsinger 2002).

Investors usually keep the stocks whose price are below their costs, because they want to avoid loses instead of risk. So they are loss averse not risk averse. 11 These studies prove that random walk does not exist at Indian stock price. Therefore there will be two kinds of investors in Indian market and rational traders are not able to erase the noise trader’s effect from the price. Also, Abdul Aziz Ansari and Samiran Jana (2009) study showing that rational traders are using both functional analysis and technical analysis as stock selection tools, which does not support the view of finance theorist.

In the next section we are discussing the data sources and methodology of the study understanding volatility is therefore central to risk management in an economy. Asymmetry in stock market volatility has its own significance, which implies volatility rises after negative shock than positive shock of similar magnitude. If the stock market is efficient, then the volatility of stock returns should be related to the volatility of the variables that affect asset prices. Stock market volatility tends to be persistent; that is, periods of high volatility as well as low volatility tend to last for months.

In particular, periods of high volatility tend to occur when stock prices are falling and during recessions. The relationship of Stock market volatility with that of economic variables like inflation, Industrial production and debt levels in the Indian corporate sector also is positively related to volatility in economic variables, such as inflation, industrial production, and debt levels in the corporate sector (Schwert 1989). 12 2. Research Methodology Research is a collection and analysis of data gathered from a sample of individuals relating to their characteristics, behavior, attitudes or opinions (Market research society, 1998).

Research objectives can be obtained or answered by using both primary and secondary-research. Collins (1985) further defines research as, “syste matic investigation to establish facts or principles or to collect information on the subject” (p. 1690). This research is set out to measure the investor’s reaction towards stock market and to know what volatilities influences an investor investing in stock market. Investors are being evaluated on three parameters in this study: their investment made, motive of investment and their portfolio.

Consequently, further investigation in the form of primary research needs to be carried out to gain deep-insights and achieve the objectives. This chapter would include methods selected for data collection/analysis, rationale behind selecting those methods, its limitations and advantages for conducting the research. 2. 1 Conceptual model: Dependent Variable Independent Variable Global Scenario Investor’s Investment Behavior Government Policies Market Expectations Company Profile Dependent Variable Independent Variable High Returns Tax Benefits Investor’s Motive Regular Income Capital Appreciation

Investment Security 13 Dependent Variable Independent Variable Decrease in equity portion Investor’s Portfolio Investing in debt securities Complete disposal of equity Table 2. 1 2. 2 Research Design: The information about investor sentiments is obtained via a survey conducted at a sample of the general investor population. The survey questionnaire is designed and distributed to the respondents. Targeted respondents are from the general public who normally invest capital in stock market. In order for a research to produce a realistic outcome, the collection of data has to be distributed over a large population.

Thus, the survey questionnaires is designed to apply to a heterogeneous population, where targeted respondents come from the general open public (fr om difference genders, races, age groups, marital status, education backgrounds, designations and professions). Owing to the fact that different levels of the society have different expectations and needs, therefore, the idea of choosing respondents from different backgrounds will most certainly generate a more reliable outcome towards sentiments of investors investing in stock market.

While some responded promptly to the survey, others took a little bit time to comprehend the questions and enquiries. Nonetheless, overall, most of them were very helpful and kind enough to fill our questionnaire patiently . The survey questionnaires were conducted via face to face interviews plus through other avenues such as email, Facebook, so as to ensure that the survey encompasses a broader geographical area. 2. 2. 1 Objectives: 2. 2. 1. 1 The Main Objective: To study the effect of stock market volatility on investors investing in stock market. 2. 2. 1. 2. Specific Objectives: . To study influence of dimensions of market volatility on Investment by Investor . 2. To study relationship between market volatility ; investor’s motive. 3. To study relationship between market volatility ; investor’s portfolio. 14 2. 2. 2 Hypothesis: 2. 2. 2. 1 List of Hypothesis: H1: Significant influence of dimensions of market volatility on Investment by Investor. H1. a: Positive relationship between volatility due to global scenario on investor’s investment. H1. b: Positive relationship between volatility due to government policy on investor’s investment.

H1. c: Positive relationship between volatility due to market expectation on investor’s investment. H1. d: Positive relationship between volatility due to company profile on investor’s investment. Table 2. 2 (a) H2: Significant relationship between market volatility ; investor’s motive. H2. a: Positive relationship between volatility ; investor investing due to high return. H2. b: Positive relationship between volatility ; investor investing to avail tax benefit. H2. c: Positive relationship between volatility ; investor investing to have regular income. H2. : Positive relationship between volatility ; investor investing to gain capital appreciation. H2. e: Positive relationship between volatility ; investor investing to have investment security. Table 2. 2 (b) 15 H3: Significant relationship between market volatility ; investor’s portfolio. H3. a: Positive relationship between market volatility ; investor’s d ecrease in equity portion. H3. b: Positive relationship between market volatility ; investor’s i nvesting in debt securities. H3. c: Positive relationship between market volatility ; investor’s complete disposal of equity. Table 2. 2 (c) 2. 2. 2. Hypothesis Table: Dependent Investment By Investor Independent Global Scenario H1. a Government Policies H1. b Market Expectations H1. c Company Profile H1. d Table 2. 2 (d) Dependent Investor’s Motive Independent High Returns H2. a Tax Benefits H2. b Regular Income H2. c Capital Appreciation H2. d Investment Security H2. e Table 2. 2 (e) 16 Dependent Investor’s Portfolio Independent Decrease in equity portion H3. a Investing in debt securities H3. b Complete disposal of equity H3. c Table 2. 2 (f) 2. 2. 2. 2 Pictorial Representation of Project: Objectives Hypothesis Variables Independent Dependent

Questionnaire Scale 6, 12,13 H1. a To study influence of dimensions of market volatility on H1. b Investment by Investor H1. c H1. d Global Likert Scale Scenario Government Policies Market Expectations Company Profile Investor’s Investment 11 Likert Scale 24-33 Likert Scale 3,21,22,23 Likert Scale Behavior Table 2. 2 (g) 17 Objectives Hypothesis Variables Independent Dependent Questionnaire Scale 8 H2. a To High Returns H2. b Tax Benefits studyrelationship between market H2. c volatility ; investor’s motive. H2. d H2. e Likert Scale 16 Regular Investor’s Income Motive Capital Likert Scale 14 Likert Scale 5 Appreciation Investment 17 Securities Likert Scale Table 2. 2 (h) Objectives Hypothesis Variables Independent Dependent Questionnaire Scale 37-39 decrease in To study H3. a volatility ; Likert Scale portion relationship between market equity Investment investor’s portfolio. in Debt Investor’s Securities H3. b Portfolio 40 Likert Scale 36 Likert Scale Complete H3. c disposal of equity. Table 2. 2 (i) 18 Age demographic variables on the H4 hypothesis Ordinal 1 Gender To study the effect of Nominal 2 Marital Status Nominal 3 Annual Income Ordinal 4 Occupation Nominal 5 Table 2. 2 (j) 2. 3 Primary Research:

Primary-research consists of surveys, observations and experiment data that are collected to solve a particular problem under investigation, usually conducted by an individual interested in or studying a specific subject area (Gates et al. , 2007). Primary research would include quantitative research which will be based on the research-objectives. 2. 3. 1Scale Used: Likert Scale has been used to get the questionnaires filled. It is a psychometric scale. When responding to a Likert questionnaire item, respondents specify their level of agreement or disagreement on a symmetric agree-disagree scale for a series of statements.

The Likert scale is the sum of responses on several Likert items. The format of a typical five-level Likert item: 1. Strongly disagree 2. Disagree 3. Neither agree nor disagree 4. Agree 5. Strongly agree 2. 3. 2 Selecting Sample: The primary source of selecting data for research is Questionnaire to be filled by random people as random sampling method (In a simple random sample of a given size, all subsets of the frame are given an equal probability) was selected to choose customers. People residing in Chandigarh region are our population. 19 2. . 3 Collecting Sample: A sample of 84 respondents from different areas at sector 14 ; 15 in Chandigarh was randomly selected. 2. 3. 4 Analysis of Data: To arrive at pertinent analysis, the collected data was put to a planned statistical analysis using SPSS package. After scoring the questionnaires the data of all the people was pooled and tabulated. To arrive at certain conclusion regarding the hypothesis advanced in the process investigation, the description of the statistical tools which were applied for the analysis of data is as follows: ) Descriptive analysis: Measures of central tendency such as mean, standard deviation were worked out to study the nature and distribution of scores of various variables. b) Correlation Analysis: The relationship between dimensions of independent variables was analyzed by correlation matrix. c) Regression analysis: The regression analysis was done to examine the significant effect of independent variables on the dependent variable. 2. 3. 5 Limitations ; Problems: ? Generaliziability is a issue of data sample collected. The sample used for the study will not represent the whole population.

Analysis of about 84 people cannot determine the perspective of 1. 2 billion people. ? Respondents may provide invalid answers or may not want to be a part of the research. As Clarke and Critcher (1985) explain that there is often gap between what the respondents say and do. ? Time was very crucial as the primary research was to be conducted within a time constraint. 20 3 Data Analysis 3. 1 Exploratory Analysis 3. 1. 1 Age Age group: Cumulative Frequency Valid Percent Valid Percent Percent 18-30 14 16. 7 16. 7 16. 7 31-45 42 50. 0 50. 0 66. 7 45 and above 27 32. 1 32. 1 100. 0 Total 84 100. 100. 0 Table 3. 1 (a) Figure 1 Above graph shows that 16. 7% of the respondents are in age group 18 -30, 50% of the respondents are in age group 30-45, 32. 1% of the respondents are in age group 45 and above. 21 3. 1. 2 Gender Your Gender: Cumulative Frequency Valid Percent Valid Percent Percent Male 63 75. 0 75. 0 75. 0 Female 21 25. 0 25. 0 100. 0 Total 84 100. 0 100. 0 Table 3. 1 (b) Figure 1 Above graph shows that 75% of the respondents are males, 25 % of the respondents are females. 22 3. 1. 3 Marital Status Marital Status: Cumulative Frequency Valid Percent Valid Percent Percent

Married 70 83. 3 83. 3 83. 3 Single 14 16. 7 16. 7 100. 0 Total 84 100. 0 100. 0 Table 3. 2 (c) Figure 1 Above graph shows that 83. 3% of the respondents are married, 16. 7% of the respondents are single. 23 3. 1. 4 Annual Income Your annual household Income: Cumulative Frequency Valid below 3 lakhs Percent Valid Percent Percent 7 8. 3 8. 3 8. 3 3-7 lakhs 20 23. 8 23. 8 32. 1 7-10 lakks 32 38. 1 38. 1 70. 2 above 10 lakhs 25 29. 8 29. 8 100. 0 Total 84 100. 0 100. 0 Table 3. 1 (d) Figure 1 Above graph shows that 2. 4% of the respondents are Doctors, 20. 2%of the respondents are Engineers, 4. % of the respondents are Businessmen, 46% of the respondents are Students, 26. 2% of the respondents have other occupations. 24 3. 1. 5 Occupation Occupation: Cumulative Frequency Valid Doctor Percent Valid Percent Percent 2 2. 4 2. 4 2. 4 Engineer 17 20. 2 20. 2 22. 6 Student 39 46. 4 46. 4 69. 0 4 4. 8 4. 8 73. 8 Other 22 26. 2 26. 2 100. 0 Total 84 100. 0 100. 0 Business Table 3. 1 (e) Above graph shows that 2. 4% of the respondents are Doctors, 20. 2%of the respondents are Engineers, 4. 8% of the respondents are Businessmen, 46% of the respondents are Students, 26. 2% of the respondents have other occupations. 5 3. 2 Testing of Hypothesis 3. 2. 1 Chi Square Analysis 3. 2. 1. 1 Age group and investing decision Chi-Square Tests Asymp. Sig. (2Value df sided) a 87 .005 114. 582 Pearson Chi-Square 87 .025 5. 295 1 .021 125. 044 Likelihood Ratio Linear-by-Linear Association N of Valid Cases 84 a. 119 cells (99. 2%) have expected count less than 5. The minimum expected count is . 01. Table 3. 2 (a) The Chi-Square value for the associationbetween age and investing decision of investorduring market volatility was obtained as 125. 004 with 87 degrees of freedom and a Significance Probability o f 0. 005 which is lower than 0. 05.

On the evidence of this data there would appear to be that there is association between age and investing decision of investor during market volatility and hence our null hypothesis is accepted. 3. 2. 1. 2 Age and investor portfolio Chi-Square Tests Asymp. Sig. (2Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases Df sided) 77. 680a 39 .000 75. 247 39 .000 2. 840 1 .092 84 a. 55 cells (98. 2%) have expected count less than 5. The minimum expected count is . 01. Table 3. 2 (b) 26 The Chi-Square value for the associationbetween age and investor portfolio during market volatility was obtained as 77. 80 with 39 degrees of freedom and a Significance Probability o f 0. 000 which is lower than 0. 05. On the evidence of this data there would appear to be that there is association betweenage and investor portfolio during market volatility and hence our null hypothesis is accepted. 3. 2. 1. 3 Age and investor motivation Chi-Square Tests Asymp. Sig. (2Value Df sided) Pearson Chi-Square 53. 502 a 45 .180 Likelihood Ratio 54. 373 45 .160 Linear-by-Linear Association 5. 088 1 .024 N of Valid Cases 84 a. 61 cells (95. 3%) have expected count less than 5. The minimum expected count is . 01. Table 3. 2 (c)

The Chi-Square value for the associationbetween age and investor motivation for investing during market volatility was obtained as 53. 502 with 45 degrees of freedom and a Significance Probability o f 0. 180 which is lower than 0. 05. On the evidence of this data there would appear to be that there is no association betweenage and investor motivation for investing during market volatility and hence our null hypothesis is rejected. 3. 2. 1. 4 Gender and investing decision Chi-Square Tests Asymp. Sig. (2Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases a. Df sided) 62. 222a 29 .000 70. 878 29 .000 3. 75 1 .055 84 58 cells (96. 7%) have expected count less than 5. The minimum expected count is . 25. Table 3. 2 (d) 27 The Chi-Square value for the associationbetween gender and investing decision of investorduring market volatility was obtained as 62. 222 with 29 degrees of freedom and a Significance Probability o f 0. 000 which is lower than 0. 05. On the evidence of this data there would appear to be that there is association between gender and investing decision of investor during market volatility and hence our null hypothesis is accepted. 3. 2. 1. 5 Gender and investor portfolio Chi-Square Tests Asymp. Sig. (2Value Df sided) 30. 86a Likelihood Ratio Linear-by-Linear Association 13 .004 34. 017 Pearson Chi-Square 13 .001 3. 780 1 .052 N of Valid Cases 84 a. 22 cells (78. 6%) have expected count less than 5. The minimum expected count is . 25. Table 3. 2 (e) The Chi-Square value for the associationbetween gender and investor portfolio during market volatility was obtained as 30. 286 with 13 degrees of freedom and a Significance Probability o f 0. 004 which is lower than 0. 05. On the evidence of this data there would appear to be that there is association betweengender and investor portfolio during market volatility and hence our null hypothesis is accepted. . 2. 1. 6 Gender and investor motivation Chi-Square Tests Asymp. Sig. (2Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases Df sided) a 15 .026 31. 996 15 .006 4. 990 1 .025 27. 285 84 a. 28 cells (87. 5%) have expected count less than 5. The minimum expected count is . 25. Table 3. 2 (f) 28 The Chi-Square value for the associationbetween gender and investor motivation for investing during market volatility was obtained as 27. 285 with 15 degrees of freedom and a Significance Probability o f 0. 026 which is lower than 0. 5. On the evidence of this data there would appear to be that there is no association betweenage and investor motivation for investing during market volatility and hence our null hypothesis is accepted. 3. 2. 1. 7 Annual income and investing decision Chi-Square Tests Asymp. Sig. (2Value sided) 123. 120a .007 87 .003 28. 577 Likelihood Ratio 87 127. 088 Pearson Chi-Square 1 .000 Linear-by-Linear Association N of Valid Cases a. df 84 119 cells (99. 2%) have expected count less than 5. The minimum expected count is . 08. Table 3. 2 (g)

The Chi-Square value for the associationbetween annual income and investing decision of investorduring market volatility was obtained as 123. 120 with 87 degrees of freedom and a Significance Probability o f 0. 007 which is lower than 0. 05. On the evidence of this data there would appear to be that there is association between annual income and investing decision of investor during market volatility and hence our null hypothesis is accepted. 3. 2. 1. 8 Annual income and investor portfolio Chi-Square Tests Asymp. Sig. (2Value Df sided) a 39 .000 Likelihood Ratio 83. 290 39 .000 Linear-by-Linear Association 6. 283 1 .000 Pearson Chi-Square N of Valid Cases a. 77. 176 84 55 cells (98. 2%) have expected count less than 5. The minimum expected count is . 08. Table 3. 2 (h) 29 The Chi-Square value for the associationbetween annual income and investor portfolio during market volatility was obtained as 77. 176 with 39 degrees of freedom and a Significance Probability o f 0. 000 which is lower than 0. 05. On the evidence of this data there would appear to be that there is association betweenannual income and investor portfolio during market volatility and hence our null hypothesis is accepted. . 2. 1. 9 Annual income and investor motivation Chi-Square Tests Asymp. Sig. (2Value Df sided) Pearson Chi-Square 91. 668a 45 .000 Likelihood Ratio 100. 311 45 .000 30. 809 1 .000 Linear-by-Linear Association N of Valid Cases 84 a. 63 cells (98. 4%) have expected count less than 5. The minimum expected count is . 08. Table 3. 2 (i) The Chi-Square value for the associationbetween annual income and investor motivation for investing during market volatility was obtained as 91. 668 with 45 degrees of freedom and a Significance Probability o f 0. 000 which is lower than 0. 05.

On the evidence of this data there would appear to be that there is no association betweenage and investor motivation for investing during market volatility and hence our null hypothesis is accepted. 30 3. 2. 2 Correlation Analysis 3. 2. 2. 1 Investor’s Investment and Dimensions of Market volatility Correlations According to you following reasons result in volatile market avgcmpnypro avginvstmnt Avgglblscen Spearman’s rho Avginvstmnt Correlation 1. 000 ** .504 f conditions: avgmktxpct ** .558 ** .588 [Inflation] .377 ** Coefficient Sig. (2-tailed) N . .000 .000 .000 .000 84