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Investigates whether the Bombay Stock Exchange behaves like an efficient market, or whether it displays a typical seasonal pattern. Takes mean daily returns between 1979 and 1990 to discover by regression that there is a December effect. Considers whether this is because of holidays, broker recommendations or window dressing by mutual fund managers. Notes a weekend effect like the New York Stock Exchange, and considers Bombay to be weak‐form efficient; but points out that banking and transport are nationalized in India but private in the USA. Explains that government regulation, which prevents foreign trading in Bombay or Indian trading on the New York Stock Exchange, has left the Bombay exchange autonomous, less affected by external factors and more by domestic conditions.

This paper examines market structure and efficiency of price transmittals in the two national stock exchanges of India: The Bombay Stock Exchange and the National Stock Exchange. Price movements in a large number of important stocks in both markets are considered. The framework used is the Johansen‐Juselius multivariate cointegration technique. It is discovered that price movements within each market are cointegrated. Short‐run ECM analysis shows that no stock in any market is exogenous, thus indicating that there is considerable feedback in short‐run price movements from each stock. Some short‐run price movements are stabilizing. The Bombay Stock Exchange and National Stock Exchange appear to be reasonably efficient markets.

The present study seeks to investigate the relative edge between the market microstructure of the two leading stock exchanges of the Indian capital market, that is BSE and NSE with a focus on analysing their trading mechanism, efficiency, liquidity and volatility.

We analyse the microstructure of BSE and NSE on the basis of: (1) trading mechanism – ownership structure, listing of securities, trading system and settlement and clearing process; (2) information efficiency using unit root test, serial correlation, runs test, variance ratio and the ARIMA model; (3) liquidity using trading statistics no. of listed Companies, market capitalisation, no. of trades etc. and (4) volatility using standard deviation and GARCH(1,1) model.

A comprehensive scrutiny on microstructure of BSE and NSE makes it evident that the two leading stock exchanges of India are mostly similar and leave no scope to choose between them. Both the exchanges are demutualised corporate entities with a fully automated trading system in an order-driven market, informationally inefficient as evidenced by the predictability of returns, have shown tremendously growing trading statistics and by and large a declining trend in volatility over the years.

Understanding the components of the microstructure black-box will provide the regulatory bodies with an intellectual framework to strengthen the market architecture. Both the exchanges will get aware of the dynamics of trading, can grow to be more competitive and attract more firms for listing and investors for trading of securities. Also, investors, portfolio managers and equity analysts will be able to make better investment strategies by understanding how the market works.

Research in the area of market microstructure has been severely neglected, especially in the context of the Indian market. India is the world's fastest growing economies and we have witnessed tremendous reforms in the capital market. The past two and a half decades have brought about several innovations via demutualisation, screen-based trading, emergence of clearing corporations, innovative financial products and intense use of IT in the Indian stock market. A spurt of reforms and the emerging environment make it crucial to deeply analyse the market structure and design of two premier stock exchanges of India – BSE and NSE.

Despite volumes of research on the efficient market hypothesis (EMH) over the last six decades, the results are inconclusive as some studies supported the hypothesis, and some studies rejected it. The study aims to examine the market efficiency of the Indian stock market.

For analysis, nine Bombay Stock Exchange (BSE) broad market indices were selected covering the study period from 01 January 2011 to 31 December 2020. The data collected for this study are daily open, high, low and closing prices of selected indices. The tools used in this study are: (1) unit root test to check the stationarity of time series, (2) descriptive statistics, (3) autocorrelation and (4) runs test.

The empirical findings of the study reveal that BSE broad market indices do not follow a random walk and Indian stock market is as weak-form inefficient.

The findings from this study provide several avenues for future research. One of the research implications is that anomalies in the statistical results by different academicians in the finance area need to be explained by future researchers.

Investment companies need to understand that extraordinary skills are required to beat the market to make abnormal returns. In an inefficient market where securities do not reflect the complete available information, it is challenging for the investment brokers to convince the customers about the portfolios they recommend to the public that the rate of return would be more than expected.

As economic growth is related to the growth in the financial sector, developing countries like India depend on the accuracy of the information. In the presence of asymmetric information, the fluctuations in the stock market would have serious harmful consequences on the economy.

Amid several controversies surrounding the EMH testing, this study is a modest attempt to provide evidence that the Indian stock market is in weak-form inefficient. However, it is essential to link investors' behaviour and trends observed in the financial sector to fully understand the implications of EMH.

The objective of this chapter is to study the symmetric and asymmetric impact of macroeconomic variables on the Indian stock prices (SPs) of the Bombay Stock Exchange index. This chapter further investigates whether the asymmetric impact of macroeconomic variables on SP is due to the impact of any tail events like the global financial recession. An autoregressive distribution lag and non-autoregressive distribution lag approach is used for the full sample covering the period from January 2000 to June 2019 and later this sample is further subdivided into before and after the crisis period to study the variations in result. The findings show that macroeconomic variables and SP have a symmetric relation in the long run whereas an asymmetric relationship in the short run when the whole sample is analyzed. However when data are segregated into “before and after” crisis period this relationship turns to be asymmetric in long run too, meaning that in the long run, the negative and positive changes in a macroeconomic variable do not affect SPs similarly.

Corporate governance is the system by which companies are directed and controlled. Boards of directors are responsible for the governance of their companies. The shareholders' role in governance is to appoint the directors and the auditors and to satisfy themselves that an appropriate governance structure is in place. The responsibilities of the board include setting the company's strategic aims, providing the leadership to put them into effect, supervising the management of the business and reporting to shareholders on their stewardship. The board's actions are subject to laws, regulations and scrutiny by the shareholders in general meeting.

The purpose of the paper is to empirically test the overconfidence hypothesis at Bombay Stock Exchange (BSE).

The study applies bivariate vector autoregression to perform the impulse-response analysis and EGARCH models to understand whether there is self-attribution bias and overconfidence behavior among the investors.

The study shows the empirical evidence in support of overconfidence hypothesis. The results show that the overconfident investors overreact to private information and underreact to the public information. Based on EGARCH specifications, it is observed that self-attribution bias, conditioned by right forecasts, increases investors’ overconfidence and the trading volume. Finally, the analysis of the relation between return volatility and trading volume shows that the excessive trading of overconfident investors makes a contribution to the observed excessive volatility.

The study focused on self-attribution and overconfidence biases using monthly data. Further studies can be encouraged to test the proposed hypotheses on daily data and also other behavioral biases.

Insights from the study suggest that the investors should perform a post-analysis of each investment so that they become aware of past behavioral mistakes and stop continuing the same. This might help investors to minimize the negative impact of self-attribution and overconfidence on their expected utility.

To the best of the authors’ knowledge, this is the first study to examine the investors’ overconfidence behavior at market-level data in BSE, India.

The study attempts to examine the impact of mandatory corporate social responsibility (CSR) spending and inclusion of firms into the environment, social and governance (ESG) index of BSE India on the performance of firms constituting firms under the Bombay Stock Exchange (BSE) 100 Index.

The stock prices of the firms were collected from the official website of BSE India for a total of 32 firms and the System Generalized Method of Moments (GMM) model was utilized for analyzing the data for the present study.

The study found that the investors in the Indian market do consider the CSR spending and ESG listing as a factor while framing the investment strategy; however, ESG listing is least preferred. Among the other variables, AGE, DPS, EPS and BVPS have a significant positive bearing on the firm's performance, while SIZE has a significant negative impact on the firm's performance.

Further investigation is needed to understand the factors that influence investment decision-making, including why investors tend to overlook CSR and environmental protection. Future research can identify ways to increase the importance of these factors in investment decision-making. Future research can explore the long-term impact of investing in socially responsible companies, including whether such investments lead to better long-term performance.

There is a need for increased awareness of the importance of CSR among investors. Educational programs and campaigns can be used to inform investors about the potential benefits of considering social responsibility factors in investment decision-making. Companies that prioritize CSR and environmental protection should distinguish themselves from competitors in the eyes of investors. This can lead to higher investment and potentially higher returns for these companies.

Since mandatory CSR expenditure and the launch of the ESG index by the BSE have been introduced in India recently, hardly any study in India has examined the impact of the same on the firm's performance.

This research paper aims to analyze the stock exchanges of developed, emerging and developing countries to investigate the volatility in stock markets and to evaluate the rate of mean reversion.

The stock exchanges included in the research are NASDAQ, Tokyo stock exchange, Shanghai stock exchange, Bombay stock exchange, Karachi stock exchange and Jakarta stock exchange. Secondary daily data from Bloomberg are used to conduct the research for the period from January 2011 to December 2018. Generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model was applied to examine volatility and the half-life formula was used to calculate mean reversion in days.

The research concluded that all the stock exchanges included in the research satisfy the assumptions of mean reversion. Developing countries have the lowest volatility while emerging countries have the highest volatility which means that the rate of mean reversion is fastest in developing countries and slowest in emerging countries.

Future studies can determine the reasons for fastest rate of mean reversion in developing countries and slowest rate of mean reversion in emerging countries.

Developing countries show the lowest mean reversion in days while the emerging countries show the highest mean reversion in days indicating that developing countries take less time to revert to their mean position.

The majority of previous studies on univariate volatility models are mostly on applications of the models. Only a few researchers have taken the robustness of the models into account when applying them in emerging countries and not in developed, developing and emerging countries in one place. This makes the current study unique and more rigorous.

This study examines return and volatility spillovers from the US and Japanese stock markets to three South Asian capital markets – (i) the Bombay Stock Exchange, (ii) the Karachi Stock Exchange and (iii) the Colombo Stock Exchange. We construct a univariate EGARCH spillover model that allows the unexpected return of any particular South Asian market to be driven by a local shock, a regional shock from Japan and a global shock from the USA. The study discovers return spillovers in all three markets, and volatility spillovers from the US to the Indian and Sri Lankan markets, and from the Japanese to the Pakistani market. Regional factors seem to exert an influence on these three markets before the Asian financial crisis but the global factor becomes more important in the post-crisis period.