The third contribution of this paper is that we employ a high quality, high frequency dataset in combination with daily data. Trading activity typically varies systematically through the trading day (Wood et. al., 1985); it tends to abate in the middle of the day and peak towards the end. This is also true of the National Stock Exchange of India (NSE), which is the object of our study (Shah and Sivakumar, 2000). It follows that non-synchronous trading effects are likely to be amplified in the middle of the day, and be less significant at the end of the day. Evidently, such effects will be difficult to detect using data sets based exclusively on closing prices where there is a trading peak. We would therefore argue that intra-day data is essential to obtain satisfactory evidence of the effects of non-synchronicity.
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Table 1 provides details about year-wise trading value of different market segments in national stock exchange of India (in Rs. Crores). The growth in Eq- uity Futures & Options segment has been stupendous and is one of the main reasons why NSE figures as the 11th largest stock exchange in the world. Table 2 provides a snapshot of nifty 50 index performance since 2013-14. Volatility in percentage terms has been calculated as standard deviation of Natural Logarithm of returns for the respective month/year. The table gives us an idea of P/E Ratio of nifty 50 stocks from an emerging market like India. Figure 1 highlights daily closing price of nifty 50 index of national stock exchange of India before and af- ter implementation of GST.
In this paper we evaluate the impact of an unusual empirical market microstructure occurrence, namely: the suspension of opening and closing call auctions by the National Stock Exchange of India (NSE) on the 9th June 1999. The main impetus for this study is the research by Camilleri and Green (2009) which yielded contrasting, although highly significant results in terms of volatility changes around this auction suspension. A study of the auction suspension by the NSE presents the noteworthy advantage that it involves a comparison between two regimes which differ only in terms of the presence of the auction. This enables a relatively clear assessment of the impact of the suspension, given that other market features on the exchange remained unchanged.
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The study investigates the relationship of stock returns with the investors’ sentiment in the Indian securities market. The market volatility index (VIX) of the Chicago Board Options Exchange (CBOE) is a good indicator of future short- term volatility in equity markets. The VIX is referred to as the investor fear gauge since high levels of volatility index have coincided with high degrees of market turmoil in the U.S. (Whaley, 2000). Researchers are very much interested in analyzing how stock market volatility is influenced by negative shocks and its behavior attributed to positive returns and near zero-returns. The implied volatility index is the new measure of stock market volatility and measure of investor fear on market performance. The National Stock Exchange of India (NSE) is one of the emerging markets in the trading of equities and derivatives. Nifty NVIX, of the National Stock Exchange of India (NSE), can be considered as the emerging markets’ volatility index. It is the gauge of investors’ fear and greed with short-horizon. Moreover, Indian equity markets are efficient (Shaikh & Padhi, 2014; Padhi & Shaikh, 2014) and option based implied volatility outperforms the historical return volatility in forecasting of stock market volatility.
Most of the trading the Indian securities trade occurs on its two stock exchanges: The Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). The BSE was considered in 1875 and the NSE, on the other hand, was built up in 1992 and started trading 1994. The trading framework, trading hours, the settlement process is equivalent to both the exchanges. At the last check, the BSE had around 4,700 recorded firms, while the rival NSE had approximately 1,200. Out of all the registered companies on the BSE, just about 500 groups constitute more than 90% of its market capitalization; whatever is left of the gathering involves significantly illiquid shares.
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Capital market means the market for all the financial instruments, short term and long term as commercial, industrial and government paper. The capital market deals with capital. The capital market is a market where borrowing and lending of long term funds takes place. Capital markets deal in both debt and equity. The governments both central and state raise money in the capital market, through the issue of government securities. Capital markets refer to all the institutes and mechanisms of raising medium and long-term funds, through various instruments available like shares, debentures, bonds etc. The National Stock Exchange of India Limited has genesis in the report of the High Powered Study Group on Establishment of New Stock Exchanges, which recommended promotion of a National Stock Exchange by financial institutions (FIs) to provide access to investors from all across the country on an equal footing. Based on the recommendations, NSE was promoted by leading Financial Institutions at the behest of the Government of India and was incorporated in November 1992 as a tax-paying company unlike other stock exchanges in the country.NSC is able to radically transform the Indian Capital market during the decade of its existence. It has changed the mindset of all market players and has built investor confidence in the secondary markets. The NSE is different from most other stock exchanges in India where membership automatically implies ownership of the exchange. The ownership and management of NSE have been totally delinked from the right of trading members. This pattern has been adopted. Since broker, owned stock exchanges are also broker managed there is a clear conflict of interest. This is a structurally unstable model, as it inevitably leads to emergence of power groups, and investor interests invariably take a back seat
Returns generated by firms of different sizes have cross sectional variations even in similar external environment. Banz (1981) observed that smaller firms had higher risk adjusted returns than larger firms. Reinganum (1983), found that smaller firms systematically outperformed the larger ones after returns were adjusted for risk as measured by beta. Fama et al.(1993, 1995) indicate that size effect alone may not be able to explain the cross sectional variation in stock returns. The size is only one of the three common risk factors in the returns on stocks, the other two being market factors and book-to-market equity. However, Horowitz et al. (1996), find that the average monthly returns are approximately constant across size deciles. Berk (1997), also supports this view. He argues, if the size of firms is measured correctly, small firms do not necessarily earn higher returns than larger firms. The size enigma results from the part of market value that measures the firm’s discount rate and not from a relation between the size of firms and returns. Chieffe (2004), argues against the "small firm effect" and urges investors to exercise caution when buying small stocks.
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Akhter Mohiuddin Rather (2011), in his work, used prediction based neural networks approach for stock returns. An autoregressive neural network predictor was used to predict future stock returns. Various error metrics have been used to evaluate the performance of the predictor. Experiments with real data from National stock exchange of India (NSE) were employed to examine the accuracy of this method. Data from date 02-01-2007 till 22-03-2010 for: TCS, BHEL, Wipro, Axis Bank, Maruthi and Tata Steel were taken. The result was not accurate but he suggested the use of better neural predictive systems and training methods for minimizing the prediction errors for the future work. D. Ashok kumar et al. (2013) discussed some basic ideas of time series data, need of ANN, importance of stock indices, survey of the previous works and it investigates neural network models for time series in forecasting. In their study, for performance of between BSE100 stock market index and NIFTY MIDCAP50 stock market index is studied by neural network model and measured aggregation where observed viz., MAE, MAPE, PMAD, MSE and RMSE. The result shows that the performance is comparatively best. From the result they observed that an optimal feedback weighting factor learning rate is 0.28, momentum is 0.5 and epoch is 2960. The model achieved the lower prediction error and it may be fit into any stock market data.
Two of the most important topics in market microstructure are the design of trading protocols and the evaluation of their effectiveness. Since trading protocols provide the framework within which markets operate they play a central role in price formation and discovery (Madhavan, 2000). Evaluating the effectiveness of different protocols is therefore a key concern for market authorities and regulators. One of the most hotly debated issues in this area is the effectiveness of call auctions as compared to continuous trading systems or to hybrid protocols featuring both systems. In theory, call auctions provide an efficient mechanism for aggregating diverse information because trading does not take place until price discovery has occurred (Economides and Schwartz, 1995), whereas under continuous trading, price discovery and trading take place simultaneously implying that trades may occur at “false” prices (Schwartz, 2000). However, continuous trading involves greater immediacy and therefore less price risk than an auction. Since there is a delay in establishing the trading price during an auction, the “true” price may change between the submission and execution of an order (Madhavan, 1992). Therefore, Economides and Schwartz (1995) argue for a hybrid system: an opening call auction to aggregate overnight information efficiently, followed by continuous trading during the day. Some of the major industrial stock exchanges have introduced an auction to open and in some cases also to close the day’s trading (Ellul, Shin and Tonks, 2004).
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While several studies have shown that developing economies have a more volatile stock market than their more developed counterparts (Agarwal, Inclan, & Leal, 1999; Bekaert & Harvey, 1997; Santis & Imrohoroglu, 1997), little research exists to analyze how volatility in a country’s stock market changes as its economy and financial institutions develop over time. This paper analyzes this relationship by considering volatility in the Indian stock market. India has two major stock exchanges – the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) – with market capitalizations of US$2.273 trillion and US$2.298 trillion respectively as on 30 April, 2018 (The World Federation of Exchanges, 2018).
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Later on, when the trading mechanisms for derivatives were established within the NSE, SSFs were introduced to India’s investors. Anand Rathi, the ex-president of BSE, said in his interview with Business Line, “Badla combines all the economic functions of the capital market with the financial functions in one product. In other parts of the world, all this is available separately. The day all these products are available in India, I don't think anybody would need Badla.” Finally, Badla became history when banned on July 2, 2001. Since then, the equity trading system adopted rolling settlements, to be done on a T+5 basis. 2 On November 9, 2001, SSFs were formally listed, and began trading on the National Stock Exchange of India.
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Dr. Satyajit Dhar and Ms. Sweta Chhaochharia (2008) examines the market efficiency of three corporate announcements, stock split; bonus issue and right issue on share prices of companies listed in National stock exchange (NSE). The study was conducted from 1996- 2015 with an event window of 41 days. The methodology used was Average Abnormal return (AAR), Cumulative Average Abnormal Returns (CARR) and T-test to test the significance level. The study resulted in a significant positive abnormal return on bonus announcement day and a negative abnormal return on right issue and stock split shows the existence of significant positive abnormal return on announcement
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Three segments of the NSE trading platform were established one after another. The Wholesale Debt Market (WDM) commenced operations in June 1994 and the Capital Market (CM) segment was opened at the end of 1994. Finally, the Futures and Options segment began operating in 2000. Today the NSE takes the 14th position in the top 40 futures Exchanges in the world. In 1996, the National Stock Exchange of India launched S&P CNX Nifty and CNX Junior Indices that make up 100 most liquid stocks in India. CNX Nifty is a diversified index of 50 stocks from 25 different economy sectors. The Indices are owned and managed by India Index Services and Products Ltd (IISL) that has a consulting and licensing agreement with Standard & Poor's.
Poshakwale(2002) examined the random walk hypothesis of 100 actively traded stocks of Bombay Stock Exchange National Index from 1 January, 1990 to 30 November, 1998. The study finds that the daily return series do not conform to a random walk. Using BDSL 1 test statistics, the study revealed significant non linear dependence in the return in the form of Autoregressive Conditional Heteroskedasticity (ARCH) 2 . Sharma and Kennedy (1977) evaluated the stock price behavior of stock indices of the Bombay, London and New York Stock exchanges during 1963- 73 using run tests and spectral analysis. The study confirmed the random movements of the stock indices for all the three stock exchanges.
Financial markets play a crucial role in the foundation of a stable and efficient financial system of an economy. The stock markets and their indicators in the form of indices, reflect the potential, the direction and health of the economy. There is extensive group of macroeconomic variables that influences the stock prices in the share market. The literature provides plethora of studies performed in international and national context to examine the relationship between stock market and macroeconomic variables. The present study extends the existing literature in the Indian context. This study takes into consideration two macroeconomic variables – Inflation and Exchange Rate, and a widely used composite index of the Indian Stock Market– BSE Sensex.
There have been several efforts in the literature to extract as much information as possible from the financial networks. Most of the research has been concerned about the hierarchical structures, clustering, topology and also the behavior of the market network; but not a notable work on the network filtration exists. This paper proposes a stock market filtering model using the correlation - based financial networks in which network nodes represent the potential stocks and network edges indicate the correlation coefficients of corresponding stock pairs. The model is capable of reducing the basic market size while keeping the diversification and risk - return expectations fairly constant. The novelty of this research is to develop a new market network filtering method which exploits Minimum Spanning Tree (MST) to reduce the number of network nodes (graph order) rather than the links (graph size). The proposed method chooses the nodes (stocks) based on dangling ends of the constructed MST. In order to verify our proposed model, we applied the model on data of three stock markets: New York Stock Exchange (NYSE), Germany Stock Exchange (DAX) and Toronto Stock Exchange (TSE). In conclusion, the numerical results showed that our proposed model can make a subset of the stock market in which its performance can imitate the whole market with a rather considerable reduction in size; as a result, we can have a diversified subset of the market compatible with that of the whole market. The performance of the model is confirmed by comparing the portfolio of the filtered market network with the whole market portfolio using the complement of Herfindahl Index as a measure of diversification.
Calendar effect connotes the changes in security prices in stock market following certain trends based on seasonal effects. Such trends or consistent patterns occur at a regular interval or at a specific time in a calendar year. Presence of such anomalies in any stock market is the biggest threat to the concept of market efficiency as these anomalies may enable stock market participants beat the market by observing these patterns. This notion again violates the basic assumption of efficient market hypothesis (EMH) that no one can beat the market and earn the profit in excess of market.
Furthermore, the ARCH-family models are applied to explore the volatility and their impact is checked through the application of Johansen Co-integration approach. We would try to know whether the exchange rate fluctuations are causing the stock return volatility. Keeping in mind the interest of the policy makers and importance of stock market we would explore the dynamic relationship of exchange rate volatility to stock return volatility. For this purpose three Asian countries Pakistan, India and China (PIC) are taken as sample to check the relationship of exchange rate volatility to stock return volatility. The paper is divided in three sections. The second section start with the literature review, third section includes methodology and final section deal with the result and conclusions.
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Weak form efficiency: when in a market current prices of stocks already reflect past information of stocks regarding price and volume then it is called weak form efficient market. So it is not beneficial to do past study or technical analysis to predict future price movement. Everything is random. Semi- strong efficiency: A situation where current prices of stock already reflect past information plus publicly available information regarding company. So to do fundamental analysis to predict future price of stock is useless. Strong form of efficiency: A situation where stock prices fully reflect all relevant information that is public including insider information. Abnormal profits can’t be accessed in an efficient market.
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After implementing GARCH family models it is found that Nifty and all five sectors except IT are highly volatile and volatility moves in clusters. Significant ARCH and GARCH terms of these models indicate that current period variance of stock returns is conditional on previous period volatility in all five sectors except IT. Significant Leverage effect is captured in all sectors except FMCG sector in EGARCH model indicating negative shocks have larger impact on volatility than positive shocks. In EGARCH (1, 1) and TGARCH (1, 1) Auto and realty both have shown less volatility persistence means there is faster decay of volatility shocks in these two sectors. So the risk averse investors can invest in IT, auto and Realty sectors by avoiding bank and FMCG sectors stocks where volatility persist for a longer duration. In overall all five sectors are suitable to invest.
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