Financial knowledge influences the relationships between investment decisions and offers a base for safety [16] . It has been reported in studies that a person with more experience will have more knowledge than a person with less experience and these experience might be regarding age or investment [22] . Thus while making huge investment decisions, it is necessary to have financial knowledge.

There is line difference between financial knowledge and financial literacy. Financial knowledge means to know about finances or money and its values or having information about finances and money while financial literacy means that along with the knowledge about finances and having skills and abilities to utilize money or finances [23] [24] [25] . Investors sometimes feel confused and have problems in stock markets investment as they don’t have the basic knowledge about the market which makes it difficult for them to invest. Christelis et al. [26] and McArdle et al. [27] explained that more wealth leads to investing more in the market and has better chances of attaining profitability but chances of better returns multiply because of highly valued financial knowledge about the markets. Ameriks et al. [28] , Lusardi and Mitchell [20] also supported the relationship between the financial knowledge, financial literacy and investment decisions.

Risk perception can be defined “the perceived damage or loss from the future” but according to Douglas “Risk will entail the probability of an event combined with the magnitude of the losses and gains”. Individual perception of risk has an apparent relationship with investment decisions. Chance or gambling, financial investing, business related decisions, and a personal decision determines which decisions need to be taken by respondents. It will also show the degree of risk taking capacity [29] [30] . Personal decisions about financial investment are not determined by the familiarity and controllability on the market, but it is affected by the amount risk perceived by the investor [31] .

Diacon & Ennew [23] , Warneryd [24] , Jordan & Kaas [25] suggested that people have different perceptions about the risk and return. It depends upon the prior information, responses and knowledge with them. Diacon & Hasseldine [32] revealed that risk is an important factor that determines the results of investment decision and it further depends upon the category of knowledge that an investor possesses about the investment.

Recent studies have publicized that intuitive risk measures such as risk perception can be a better proxy for the intuition of investors about financial risks than can be variance and standard deviation [33] [34] . Rettinger and Hastie [35] , Weber et al. [36] and Baucells and Rata [37] illustrated that differences in risk taking capacity over various content domain. These can be the financial domain (e.g. investment decision) or the health domain (e.g. seat-belt usage), and further, it can be explained by differences in risk perceptions. More precisely, these studies show that risk perceptions vary substantially for different content domains.

The prediction of error forecast can be possible only if the share analyst has the Information at the time of issue of quarterly earnings. For example, past earnings revelations [38] and past returns of the market [39] have a significant and positive correlation with forecast error. Any Firms financial characteristics such as dividend payment ratios, book-to-market ratios [40] [41] , and book-tax differences [42] and accruals (Bradshaw et al., 2006) [43] , may also predict forecast errors. Forecasts sometimes also under-weight the information received from the consensus forecast [44] . The prior literature documents an instance in which analysts forecast outputs can calculate forecast error. The predicted sign of analyst’s revision with regards to the recent quarter’s earnings forecast has the positive correlation with revised forecast error [45] [46] . Researchers suggested that biases may also occur in longer-horizon forecasts. The forecasts made by an analyst for longer than one-quarter horizons are optimistic; this may further lead to the earnings realization on the forecast date [47] [48] . Brav and Lehavy 2003 [49] documented that share price target revisions usually have a large window for announcement returns. The further suggested that forecast regarding appreciation of share prices often contains some information about future returns and may also contain significant bias. Bradshaw et al. [50] revealed that analysts do not exhibit continual ability to forecast the share price appreciation. Lim [51] , Ke and Yu [48] have argued that analyst forecasts become optimistic at long-horizon and the pessimistic at short-horizon because of incentives receipt in favor with management. However, Jackson [52] , Beyer and Guttman [53] , Groysberg et al. [54] suggested that reason for optimism in share forecast is because of the encouragement to trade. On the other hand, Lin and McNichols, [55] argued the reason is to win investment banking business line. Researchers have also linked that the reason for under-weighting of analysts’ forecasts is the information incentives [44] [56] .

Since the innovation of first event study, numerous papers have demonstrated that premature identification of new information in the market can offer substantial profits. Inside traders by privileged information can consequently make excess returns by violating the assumption of the strong form of the market efficiency. Even the earliest research by Cowles [57] [58] , make it clear that investment professionals do not bang the market returns. Until the 1960s, there was confirmation on the performance of security analysts, that there was a relative gap in knowledge about the share returns achieved by portfolio managers. Treynor [59] and Sharpe [60] had developed the capital asset pricing model and make it clear that the CAPM can provide a benchmark or better returns for performance analysis.

The detailed analysis of one hundred and fifteen mutual funds for the period 1955-64 by Jensen [61] to judge the performance of fund managers’ finds that any extra advantage that portfolio manager earns is consumed by fees and expenses. Jensen concludes that “on usual, the fund’s managers apparently were not much successful in their trading activities.”

The Nobel laureate of 2013 “Fama” summarizes several subsequent studies of mutual fund and performance of institutional portfolio managers. Though few mutual fund managers have achieved trivial abnormal gross returns calculated before expenses, pension funds had underperformed the passive benchmarks by risk-adjusted returns. It is essential to note that efficient markets hypothesis doesn’t rule out for small abnormal returns, earned before fees and expenses. Analysts might therefore still have a motivation to acquire and act on received valuable information from the market, though investors would not expect to receive more than average returns.

Grossman and Stiglitz [62] formalize the idea mentioned above, with a more sensible model of equilibrium that it must put down some incentive for security analysis. They suggested that concept of market efficiency have to acknowledge the few possibility of market inefficiencies. The evidence gathered during the decade of the 1960s and 1970s appeared to be largely consistent with their view. Although it was clear that markets could not be utterly efficient in the strong form of efficiency, there was prominent support for the weak and semi-strong forms. Even the studies were done only on a single version of strong form efficiency that centers just on the performance of the investment managers.

A plethora of academic literature explores the reasons for the outperformance of insider traders and exclusively insider buying. According to a study done in Wharton School entitled “Estimating the returns to insider trading”; about one-fourth of the abnormal returns added within the initial five days after the trade and the rest half of abnormal returns accrue within the first month. The study had taken an all-inclusive sample of insider transactions made over the period of 22 years from 1975 to 1996. Jeng, Andrew Metrick, Richard Zeckhauser [63] estimated the returns gained by insiders when they trade with their own company’s stock. The study constructed a rolling “purchase portfolio” that consist of all shares acquired by insiders for a six-month period and a parallel “sale portfolio” that consist of all shares which are sold by insiders for six months. The six-month period was chosen to correspond with “short-swing” rule of the Securities and Exchange Act, 1934. This rule prohibits profit-taking capacity by insiders for counterbalancing the trades done within six months.

The study employed performance-evaluation methods to scrutinize the returns gained by the purchase and sale portfolios. This method capitulates a proxy for value-weighted returns gained by the insider transactions. The proxy will be weighed up at the beginning of the execution day of trade. The research had tried to avoid the statistical hitches that plague event studies for the long-term returns. New methods were designed to guesstimate the returns gained by insiders. The study is different from the previous literature of insider-trading, which were focused on “level of informativeness” of insider. Using a wide-ranging sample of all reported insider transactions starting from 1975 to 1996, they find that the purchase portfolio of insiders earns abnormal returns of additional 50 basis points per month. However, the sale portfolio is not able to earn abnormal returns. About one-fourth of the abnormal returns added within the initial five days after the trade and rest half of abnormal returns were accrued within the first month. The study focuses on portfolio-based approach and further allows for clear-cut decompositions of the performance of insiders signifies various characteristics. They further concluded that the abnormal returns in small firms are insignificantly different from large firms, and usually, the top executives didn’t earn higher abnormal returns as compared to other insiders of the firms.

Finnerty [64] suggested that the strong-form of the efficient market hypothesis is always concerned with the reflection of all available public and private information in a security’s price. In expressions of market participants, it states that no participants have higher expected profit as compared to others just because of the monopolistic access to information he has. A test of strong-form is done just to determine average abnormal return received by insiders from their market transactions. To determine if the market is effective will engross determining how an insider earns fare relative as compared to the rest of market in general.

Jaffe [65] documented that commonly called insiders are corporate officers, large stockholders and board of directors; they habitually command extensive attention in the market’s financial community. Investors are always interested in knowing the amount of special information that an insider possesses with him or her, and another area of interest is to know the profit they take home from such knowledge. The middle investors try to find out valuable information from the official summary of corporate or the transactions of insider trading; these are listed in the monthly report that summarizes the transactions of corporate officials. The conclusion comes that maximum of the trading rules is based on the available official information because the sample considered in the study shows the residuals are greater as compared to the transaction cost. L. Inmoo [66] collected the evidence of insider trading of all companies on Amex, NYSE, and Nasdaq during the 1975 to 1995 period. Results revealed that insider trading is common in more than half of the firms considered in the sample; there is the evidence of at least some insider activity in the period of study. However, usually the insider’s trade put a very little impact on market movement and few conditions arrives when their trades are reported to Securities and Exchange Commission (the SEC). Insiders in a summative sense are contrarian investors. Conversely, they can predict market movements in a better way as compared to simple contrarian strategies. Further, it is added that insiders may be able to predict cross-sectional returns of the stocks. The results showed that purchases of insiders are more informative as compared to sales of insiders. Insiders have better ability to forecast returns of smaller firms than a larger one. Lorie and Victor Niederhoffer [67] documented that SEC requires that insiders keep them informed regarding transactions made in the common equity stock and other convertible securities of their respective companies. “Insiders” are defined by them as the directors, officers, and owners of ten or more percent of the common equity stock of the listed companies on the New York Stock Exchange and American Stock Exchange. The SEC is interested in trading of insiders because of the belief that insiders ought not to exploit their exceptional opportunities regarding the developments in their companies for personal profit motives through short-term trading. Finally, the study expounded that the role of the intermediaries are much important in the market and they are controlling a large volume of trading to derive profitable trade. Rozeff and Zaman [68] believed that it is not startling that corporate insiders can earn profits from trading their stocks. However, it is surprising that outsiders can also earn abnormal returns just by imitating the insider trades using available information in the monthly publically reports. Studies suggest that the anomalous returns are well explained by the price to book size and price-earnings ratio effects. Domineering over these factors may reduce outsider profits by half of the net returns. There are two percent transactions costs that make outsider profits either zero or negative. Overall measured insider profits are reduced by controlling the factors like size and price/earnings effects. Hence, insider profits are reserved for approximately 3 percent per annum after removing 2 percent fee for transactions costs. Seyhun. H. N [69] examines the ability of aggregate insider trading to predict future stock returns. The research question raised that if insider trading can be recognized to bring changes in business conditions or changes in fundamentals. Results suggest that both conditions contribute towards the predictive capability of an insider trading.

Terrance [70] has revealed that disposition effect is influencing the market prices and had the greater financial significance for the individual investor. The study suggested that the individual’s behavior exhibits the disposition effects and hence they acquire the profitable stocks at a much higher rate as compared to unprofitable stock held in the market. The prospect theory given by Kahneman and Tversky’s [71] for investments is the extension of disposition effect. In prospect theory, if there are choices concerning two and three outcomes of lotteries, people perform just to maximize the “S”-shaped curve or value function. The value function is analogous to the standard utility function excluding that it has been defined on gains and losses relatively to the levels of wealth. The function reacts concave in the sphere of gains and convex for losses. It is steeper for losses as compared to gains, which involves that people are normally risk-averse. The value function is the reference point from we can measure the gains and losses. Usually the status quo is considered to the reference point; on the other hand, there are several situations in which gains and losses are usually coded as per the aspiration or expectation level that vary as per the status quo. “A person, who has not made peace with his losses is likely to accept gambles that would be unacceptable to him otherwise” [71] . Schlarbaum et al. [72] analyze the trading records of 2500 accounts at large retail brokerage unit for the period 1964 to 1970. They described the disposition effect by analyzing the rates of investor’s realized gains and losses relative to available opportunities they have. Badrinath and Lewellen [73] analyzed the another data set provided by the same retail brokerage unit for 3000 retail accounts over the period 1971 to 1979. The data set suggest that the recent investment comes from the discount brokers. By exploration of the discount brokerage records found from the sample, it can be suggested that the retail broker has put an influence on trading patterns of the market. The study had looked for evidence of trading motivated by the tax and suggested that the ratio stocks sold for a loss as compared to those sold for a gain are rising as the year passes. The study further evident that the tax-motivated trading is one of the biggest reason for the stock investment. Constantinides [74] illustrated the case of U.S. federal taxes from January 1987 to December 1993 and suggested that if there is transaction cost involved in a transaction, then no peculiarity or difference can occur between the long-term or short-term and tax rates and in such cases the investors must gradually increase their tax-loss selling transactions. Dyl [75] , Badrinath and Lewellen [73] and Lakonishok and Smidt [76] reported that investors sell more losing investments nearby the end of the financial year. Shefrin and Statman [77] recommended that investors prefer to sell their losers in the month of December as a tool of self-control measure. They suggested that investors are unwilling to sell investments for a loss but be acquainted with the tax benefits of doing it. The year end is the deadline for becoming conscious about these losses. Consequently each year, investors rearrange realizing losses till December whilst they involve themselves to sell losers earlier than the deadline passes away.

Since 1965, the efficient market hypothesis is a topic where a plethora of studies has been undertaken. Many studies mainly justify the aspects of the semi-strong and weak form of the efficient market hypothesis. But to justify the strong form of market efficiency very few studies have been undertaken to give the validation for the insider trading in the Indian stock market. Further, the majority of studies have used quantitative approaches to justify the performance of fund managers and the share brokers, but very few studies have been conducted based on interviewees based on their decisions and their perception. Hence, to bridge this gap the research has formulated a questionnaire based on several interviews. Interviews are intended at gaining a perceptive understanding of how share brokers, financial analyst, investment advisers and equity dealers work within the market today. Interviewees are asked precisely several questions about their investing firms: the types of services offered to them, the number of profitable financial transactions made by them, knowledge requirements for them, technical skills available with financial professionals, existing compliance and supervisory functions, types, and forms of compensation or emoluments they are getting for investment. Interviewees were also asked about the types of disclosures they are having while their investment strategy formulation. Based on the existing literature the study has put together the hypothesis regarding knowing the statistically significant relationship among investment decision, profit earning capacity, insider information, forecasting capabilities and risk bearing attitude of the financial professionals.

Initially, 472 questionnaires were distributed and 403 were received back. Out of these 21 questionnaires were incomplete and were omitted. Therefore 382 questionnaires were used for the study, representing a response rate of 85.38 percent. The names of respondents or organization’s names were not mentioned anywhere in the questionnaire to guarantee privacy and confidentiality. In Indian Equity segment, there were 6840 registered Stock Brokers (as on date Mar, 2015). Hence we have taken the 5 percent of the population size [79] to calculate the sample size (i.e. 342) and oversampling of 12 percent of the sample size (i.e. 41) has been done to solve the issue of sampling non-respondents suggested by [80] [81] [82] [83] . Hence, the sample size of 382 is considered for study. Demographic characteristic of respondents along with the standard deviation is mentioned in Table 1. The sample consists of following characteristics:

Multivariate analysis of variance represents the only single relationship between the dependent and independent variables. SEM can examine a series of dependence relationship simultaneously. It is particularly useful in testing theories that contain multiple equations involving dependence relationships. SEM is a family of statistical models that seek to explain the relationships among multiple variables. It examines the structure of interrelationships expressed in series of equation similar to a series of multiple regressions. SEM is based on two models, the measurement model that represents how measured variables come together to represent constructs and the structural model that shows how constructs are associated with each other.

Additionally, all the dimensions in our study have a Cronbach’s α greater than 0.7 (our results reveals 89.4 percent of reliability), which complies with the criterion proposed by Nunnally [85] and Wortzel [86] . Factor analysis is taken as a tool to verify the convergent validity of the questionnaire. We extract factors with an eigen value greater than 1, a cumulative explained variation greater than 50 percent, and a factor loading greater than 0.5 [87] . It also has discriminant validity, because the correlation coefficient of each of the two factors is lower than the Cronbach’s α of each dimension.

Questionnaire development process can be summarised in five broad stages.

Domain specification includes definitions, identifying factors that define customer relationship management, identifying dependent variable.

Generation of items or variables were based on literature review after identifying factors that leads to fulfil the objective of the study.

Third phase of development of questionnaire was pilot testing. Academicians and practitioners were asked to judge the items pooled through literature review.

A pre-test was conducted with 50 respondents to check the reliability of the questionnaire.

Reliability has been measured by the coefficient of alpha gained by Cronbach, 1951. The alpha term measures the internal consistency and reveals that it should be more than 0.7 [85] . However, for this study it was found .894 (i.e. shown in Table 2).

For the establishment of the relationship among the variables, we have formulated the aforesaid path diagram. The diagram signifies that there is a relationship among the variables considered in the study. The initial framework considered for the study is shown in Figure 1.

The path diagram created in the study and the initial conceptual framework are somewhat interlinked. The standardized regression weights gained by the study are mentioned in Table 7, Tables 14-18. The estimates range from 97 percent to 54 percent.

The weights of regression are found statistically significant at 5 percent level of significance. The unstandardized regression weight along with the construct

reliability (denoted by C.R.) and standard error (denoted by S.E.) is mentioned in the Table 8. The below table shows that p-value is significant for all the variables considered in the study. This highlights the presence of strong positive relationship among the variables and hence these confirmed construct are taken in the study.

Results also signify that the variables undertaken in the study are significantly correlated. Moreover, the covariances of the Group number 1 and the Default model is also found to be statistically significant. The corresponding results are reported in Table 9 and Table 10.

Ahire, Golhar and Waller [90] suggested that confirmatory factor analysis (CFA) always provides improved control for gauging unidimensionality (i.e., up to which extent the items related to a specific factor measures one single construct or antecedent) as compared to exploratory factor analysis (EFA). It is a more

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations.

systematic process of construct’s overall reliability and validation. Hence in the study, we have applied the CFA and EFA. Leary-Kelly and Vokurka [91] suggested that convergent validity signifies that multiple methods for measuring the data set will provide the same results ever. Construct validity involves the estimation of the operationalization of the target variables considered in the study. Further, it had been suggested that Unidimensionality, reliability, convergent validity and discriminant validity are the way to prove the measures of Construct validity. Byrne revealed that if comparative fit index (CFI) must be 0.90 or above then it is evident that there is unidimensionality in data. Hence, this scale also supports the study of Byrne [92] . Ahire, Golhar and Waller [90] presented in their study that if the reliability and unidimensionality exist in scale then further validation analysis will strengthen the results. These tests for the goodness

fit of model mentioned in Table 11 and validation analysis is given in Table 12 respectively. Hu and Bentler [93] and Hair et al. [88] have given the thresholds listed in the figure given:

Further, in order to determine the comparative importance of the five construct considered in the study regression analysis might become an effective tool.

No validity concerns―wahoo!

Hence to do the robustness testing we have considered the insider information as the dependent variable and rest all constructs as independent. Unstandardized Beta (β) coefficient provides the relative importance of the independent variable towards the dependent. The variable with the largest coefficient value represents the most important in terms of its influence on insider information and vice versa. The results for ordinary least square regression are mentioned in Table 13. The F statistics is found to be 49.488 and overall model is significant with 5 percent level of confidence. The percentage of variance in overall model explained by the independent variable studied together is 58.7 percent. All the factors in the study were found to be significant and stay on in the equation for explaining insider information of financial professionals. The results revealed that insider information is much revealed with the help of the forecasting capability a financial professional carries with him/her. The factor considered in the study refers that insider information altogether dependent upon the investment, forecasting, risk-bearing and profit earning capacity of an investor.