Stock Index Futures

This paper investigates which events of World War II (WWII) the US investors (at that time) considered as turning points (structural breaks) of the war. The empirical study employs daily Dow Jones industrial average stock index and volatility from January 1939 to December 1945 and applies structural shift oriented test to determine endogenously the structural breaks during the WWII period. Results show that the majority of the wartime events (on and off the battlefield) labelled important by historians did result in structural breaks in both price movement and stock returns volatility (risk). These results have major implications for investors of the present and future. 1 Tsiakas is able to show that events during trading and non-trading hours both have substantial effect on both stock price and volatility. During wartime, events can take place during trading and non-trading hours. 2 According to Mazouz et al. (2009) investors under-react to both positive and negative shocks, irrespective of the magnitude of the shocks.

Stock Market is unpredictable phenomenon and is hard to digest that it provides complete information about the listed stocks and market. The investments made by the investors and their returns are directly based on the up and down movements of the indices of stock exchanges in which the buying securities are listed. Most individuals engage in transaction buying and selling securities (stocks in particular), do so under the general assumption that the securities they are buying are worth more than the price that they are paying, while securities that they are selling are worth less than the selling price. But if markets are efficient and current prices fully reflect all information, then buying and selling securities is an attempt to outperform the market and will effectively be a game of chance rather than skill. The Efficient Market Hypothesis states that at any given time, security prices fully reflect all available information basing on various factors like company disclosures, company announcements, dividend policy of company, company fundamentals and change in government policy etc and these factors are also used as a tool to predict the future prices of stocks. Thus this study is to test the efficient market hypothesis on Bombay stock exchange during the pre recession period from 4 th Jan to 24 th Dec. 2008. In this study by implementing modern tool like "Run test and Autocorrelation" on BSE Sensex we will find out whether there is any relationship between the future prices of stock and their past performance through efficient market hypothesis. In last finding, conclusion and recommendation will be given. The finding of study will be useful to investors but it may differ from other study depending upon selected time period.

Despite abundant research which focuses on estimating the level of return on stock market index, there is a lack of studies examining the predictability of the direction / sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, we evaluate the efficacy of several multivariate classification techniques relative to a group of level estimation approaches. Specifically, we conduct time series comparisons between the two types of models on the basis of forecast performance and investment return. The tested classification models, which predict direction based on probability, include linear discriminant analysis, logit, probit, and probabilistic neural network. On the other hand, the level estimation counterparts, which forecast the level, are exponential smoothing, multivariate transfer function, vector autoregression with Kalman filter, and multilayered feedforward neural network. Our comparative study also measures the relative strength of these models with respect to the trading profit generated by their forecasts. To facilitate more effective trading, we develop a set of threshold trading rules driven by the probabilities estimated by the classification models. Empirical experimentation suggests that the classification models outperform the level estimation models in terms of predicting the direction of the stock market movement and maximizing returns from investment trading. Further, investment returns are enhanced by the adoption of the threshold trading rules.

This paper investigates which events of World War II (WWII) the US investors (at that time) considered as turning points (structural breaks) of the war. The empirical study employs daily Dow Jones industrial average stock index and volatility from January 1939 to December 1945 and applies structural shift oriented test to determine endogenously the structural breaks during the WWII period. Results show that the majority of the wartime events (on and off the battlefield) labelled important by historians did result in structural breaks in both price movement and stock returns volatility (risk). These results have major implications for investors of the present and future. 1 Tsiakas is able to show that events during trading and non-trading hours both have substantial effect on both stock price and volatility. During wartime, events can take place during trading and non-trading hours. 2 According to Mazouz et al. (2009) investors under-react to both positive and negative shocks, irrespective of the magnitude of the shocks.

This paper examines the impact of trading in the FTSE-100 Stock Index Futures on the volatility of the underlying spot market. To examine the relationship between information and volatility (as subject neglected in previous studies) the GARCH family of techniques is used. The results suggest that futures trading has led to increased volatility, but that the nature of volatility has not changed post-futures. The finding of price changes being integrated pre-futures, but being stationary post-futures, implies that the introduction of futures has improved the speed and quality of information flowing to the spot market. * Corresponding author. The authors gratefully acknowledge A. Damell, A. Foster, I. Garrett, J. Hunter and J Rougier and two anonymous referees for helpful comments. We would also like to thank G. Constantinides and A. Malliaris for fruitful discussions. The usual disclaimer applies.

This study examines the performance of short and long hedgers using four stock index futures contracts traded at the Taiwan Futures Exchange. We compare the optimal hedge ratios and resulting hedge performances based on three risk measures: variance, extended Gini, and lower partial moment. We find that long hedgers achieve greater hedging performance than short hedgers for both the minimum-extended Gini and minimum-lower partial moment hedge ratios. These results are observed in both in-sample and post-sample analyses. We also find that the minimum-extended Gini hedge ratio dominates the lower partial hedge ratio in terms of post-sample hedging performance.

The no arbitrage relation between futures and spot prices implies an analogous relation between futures and spot volatilities as measured by daily range. Long memory features of the range-based volatility estimators of the two series are analyzed, and their joint dynamics are modeled via a fractional vector error correction model (FVECM), in order to explicitly consider the no arbitrage constraints. We introduce a two-step estimation procedure for the FVECM parameters and we show the properties by a Monte Carlo simulation. The out-of-sample forecasting superiority of FVECM, with respect to competing models, is documented. The results highlight the importance of giving fully account of long-run equilibria in volatilities in order to obtain better forecasts. Keywords. Range-based volatility estimator, Long memory, Fractional cointegration, Fractional VECM, Stock Index Futures.

Prediction in any field is a complicated, challenging and daunting process. Employing traditional methods may not ensure the reliability of the prediction. In this paper, we are reviewing the possibility of applying two well-known techniques neural network and data mining in stock market prediction. As neural network is able to extract useful information from a huge data set and data mining is also able to predict future trends and behaviors. Therefore, a combination of both these techniques could make the prediction much reliable.

Investing in stock markets is a decisive role for every investor. Speculation in the market makes an investor distressed about his investment. Hence predicting the exact stock market price at high accuracy helps investors to invest wisely to yield more returns. Most of the literature has been carried in predicting the stock market prices using various Logistic regression, ARIMA and machine learning techniques. This paper aims at predicting the stock indices of developed markets and emerging markets using deep learning neural network techniques i.e., Artificial Neural Networks (ANN) and Recurrent Neural Network (RNN). The data consists of daily prices open, close, high, and low, volume of NIFTY 50, S&P 500, New York Stock Index, Korean Stock Index and Dow Jones Index Jan 2014 to July 2019. The open price of the index is fed as input to the models. This study predicts the next day index and gives a comparative analysis between the two models based on the accuracy of prediction. Based on the performance of the models, the best model would be suggested to the investors for the investors

This paper examines the relationship between trading volume,and returns in Greek stock index futures market. For both available stock index futures contracts of the Athens Derivatives Exchange (ADEX), we study GARCH effects in our data and test how well these effects are explained by trading ,volume ,(under both GARCH and GMM). For FTSE/ASE-20, trading volume contributes significantly in explaining

We use recently proposed tests to extract jumps and cojumps from three types of assets: stock index futures, bond futures, and exchange rates. We then characterize the dynamics of these discontinuities and informally relate them to U.S. macroeconomic releases before using limited dependent variable models to formally model how news surprises explain (co)jumps. Nonfarm payroll and federal funds target announcements are the most important news across asset classes. Trade balance shocks are important for foreign exchange jumps. We relate the size, frequency and timing of jumps across asset classes to the likely sources of shocks and the relation of asset prices to fundamentals in the respective classes.

This paper reports empirical evidence on stock index futures pricing based on about four years of synchronous hourly data from the UK. Reported index values are based on firm quotes. Identified arbitrage opportunities are therefore actually exploitable and economically significant. The analysis controls for cash market settlement procedures. The results show that ex-ante trading rules would have generated attractive profits after transaction costs and after the risks of dividend uncertainties, marking to market and possible delays in execution are considered. The far contract and the near contract tend to be mispriced in the same direction. The mild tendency of futures to be overpriced in rising markets and underpriced in falling markets appears to be unimportant. Finally, significant positive relationships are observed between the absolute magnitude of mispricing and time to maturity and between mispricing and index option implied volatility.

Intraday volatility for the Eurodollar, the Euro/dollar foreign exchange rate, and the E-mini S&P 500 futures contracts traded on a continuous 23-hour schedule on the Chicago Mercantile Exchange Globex electronic platform is studied. Volatility transmission in a single market across different regions is mainly explained by intraregion volatility (heat waves); interregion volatility (meteor showers) plays a secondary role. The joint impact of liquidity variables such as volume and open interest on volatility is also analyzed. Volume tends to increase volatility, but open interest does not affect it. The results are explained by the type of trading venue. Unlike floor-based trading systems, in electronic markets open interest does not seem to provide additional information on market liquidity and its relation to volatility beyond any information contributed by volume. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:313– 334, 2008

The development of the global economy presupposes the stable functioning of all its constituent parts, institutions, and markets. Among such markets of the global economy, a special place is occupied by the financial market, which is able to redistribute financial resources. However, such redistribution must be protected from various risks, unfavorable development of negative situations. To solve such problems, the concept of stock indices is used, which reflects the dynamics of price changes for different groups of securities. Knowing the dynamics of the values of stock indices, one can analyze and predict the development of the financial market as a whole. It is also necessary to take into account the relationship of stock indices and the relationship between the components of various stock indices. Based on this, the paper examines a qualitative assessment of the relationship between the components of the world's leading stock indices in reflecting the dynamics of the world financial market. It is proposed to consider such a qualitative estimate taking into account the wavelet coherence. To this end, the paper proposes a general approach to considering a qualitative assessment of the relationship between the components of the world's leading stock indices. The paper considers real data in the context of various world stock indices, presents research results, and draws general conclusions.

This paper investigates possible spill over effects on the Spot Market due to the initiation of Futures contracts in three different financial markets. According to many analysts there still exists a puzzle regarding the stabilization or destabilization effects of futures contracts. Although the speculative forces (uninformed investors) tend to destabilize the market, rational hedging strategies and the transition of risk allow for stabilization shift. In order to investigate this issue, many researchers during the last decade, have utilized the GARCH framework enriched to capture many stylized financial features, such as the asymmetric response to news and leptokurtosis.

Investing in stock markets is a decisive role for every investor. Speculation in the market makes an investor distressed about his investment. Hence predicting the exact stock market price at high accuracy helps investors to invest wisely to yield more returns. Most of the literature has been carried in predicting the stock market prices using various Logistic regression, ARIMA and machine learning techniques. This paper aims at predicting the stock indices of developed markets and emerging markets using deep learning neural network techniques i.e., Artificial Neural Networks (ANN) and Recurrent Neural Network (RNN). The data consists of daily prices open, close, high, and low, volume of NIFTY 50, S&P 500, New York Stock Index, Korean Stock Index and Dow Jones Index Jan 2014 to July 2019. The open price of the index is fed as input to the models. This study predicts the next day index and gives a comparative analysis between the two models based on the accuracy of prediction. Based on the performance of the models, the best model would be suggested to the investors for the investors.

We conduct a rigorous analysis to find out the speed of adjustments in futures and spot indices on NSE NIFTY 50 in short run as a part of examining the efficiency of financial futures market in India. Towards that, we undertake the analysis of long run and short run efficiencies separately, and use Engle-Granger's Error Correction Mechanism (ECM) so that a clear picture of short run efficiency in terms of speed of adjustments can emerge. The rigour manifests in the analysis of 412538 data points that breed from (i) the choice of five different time intervals spanning from 1-minute to 120-minutes using high frequency data, and (ii) examining 100 lags of 1-minute interval. Long run efficiency is a precondition for examining short run efficiency. We document an excellent state of affairs for long run efficiency across all five intervals of time. Most of the price discovery takes place in futures market, and the spot market follows it with a lag of 9 minutes, effectively. However, it takes 35 minutes to completely return to the desired relationship once a drift has taken place. The increase in the speed of adjustments, as compared to the speed documented in previous studies can be attributed to the large-scale adoption of the high frequency (i.e. algorithmic) trading, in recent times. Our findings suggest that traders can effectively use the near month contract of NIFTY 50 Futures to hedge their open positions in the index or any other stock. JEL Classification: G10, G14, G19

This paper investigates possible spillover effects on the spot market due to the initiation of derivatives markets. Although speculation, which is apparent because of the low transaction cost of derivatives markets, produces noise in the financial system, the speculative forces and the rational hedging strategies jointly contribute to the price discovery process. Furthermore, the demand and supply forces are responsible for the financial system equilibrium and high volatility regimes imply high rates for the accumulation of new information. According to many analysts, there still exists a puzzle regarding the stabilization or destabilization effect of Futures contracts’ onset, which is both country and model specific. For that reason, this paper examines empirically the case of three European countries by the application not only of conventional structural break analysis, but also of regime shift analysis in order to account for the timing of possible spillover effects, and hence, to overcome the conflicting results of the extant literature. Furthermore, the proposed econometric methodology considers some stylized financial facts such as the leverage effect, the time varying risk premium, the decomposition of the day of the week effect, the leptokurtosis and the skewness of the returns’ distributions. For the purposes of our analysis we use data from the UK, Spain and Greek capital markets and according to the empirical findings, there exists a significant stabilization effect, which is negatively associated with the level of efficiency and completeness of the capital markets examined. However, in some cases there exists a short run destabilization effect for 1 or 2 months.

Investing in stock markets is a decisive role for every investor. Speculation in the market makes an investor distressed about his investment. Hence predicting the exact stock market price at high accuracy helps investors to invest wisely to yield more returns. Most of the literature has been carried in predicting the stock market prices using various Logistic regression, ARIMA and machine learning techniques. This paper aims at predicting the stock indices of developed markets and emerging markets using deep learning neural network techniques i.e., Artificial Neural Networks (ANN) and Recurrent Neural Network (RNN). The data consists of daily prices open, close, high, and low, volume of NIFTY 50, S&P 500, New York Stock Index, Korean Stock Index and Dow Jones Index Jan 2014 to July 2019. The open price of the index is fed as input to the models. This study predicts the next day index and gives a comparative analysis between the two models based on the accuracy of prediction. Based on the performance of the models, the best model would be suggested to the investors for the investors.

The Purpose of the study is to examine the impact of derivative trading on stock market volatility. This study is based on both closing and opening price returns as well. The sample data consist of daily opening and closing price returns of S & P CNX Nifty, Nifty Junior and S & P 500 index of 5 years from January 1 2009 to December 26 2014.The study uses GARCH model to capture nature of volatility over time and volatility clustering phenomenon of data. The evidences suggest that there is no significant change in the volatility of S &P CNX Nifty, but the structure of volatility has changed to some extent. However, results show mixed effect. These results can assist investors in making investment decision. It also helps to identify need for regulation. The study also found that the new information is assimilated into prices more rapidly than before, and there is a decline in the persistence of volatility since the inception of futures trading JEL Classification: G10.

This paper investigates the intraday and day-of-the-week seasonalities, as well as the non-linear dynamics of the two most traded American stock Index futures, i.e. the E-mini SP500 and the E-mini Nasdaq100, quoted on the Chicago Mercantile Exchange. The dataset covers both European and American trading hours. We employ the periodic autoregressive model together with the asymmetric periodic GARCH (PAR -APGARCH), with a flexible distribution given by the Skew-T, which allows for both conditional skewness and kurtosis. Differently from previous approaches, we directly model the strong seasonalities in the behavior of the conditional mean, volatility, skewness and kurtosis, and we avoid to use the common seasonal adjustment procedures, which can generate misleading inferences and blur the characteristics of the data (see Franses 1996, Osborn 2004).

This paper studies price volatility of the Nikkei 225 stock index and its futures contracts traded on SIM EX. OSE and CME. We address the following three questions: What effect does the introduction of a new futures market have on volatility in the underlying spot and existing futures markets'? Is price volatility affected by market structure and regulations'? and Is there an expiration day effect'? We find that the introduction of a new futures contract has no significant effect on volatility in the stock market. Evidence, too weak to suggest that tighter regulations could have a stabilizing effect and no expiration day effect.

In this paper, we examine the nature of transmission of stock returns and volatility between the U.S. and Japanese stock markets using futures prices on the S&P 500 and Nikkei 225 stock indexes. We use stock index futures prices to mitigate the stale quote problem found in the spot index prices and to obtain more robust results. By employing a two-step GARCH approach, we find that there are unidirectional contemporaneous return and volatility spillovers from the U.S. to Japan. Furthermore, the U.S.'s influence on Japan in returns is approximately four times as large as the other way around. Finally, our results show no significant lagged spillover effects in both returns and volatility from the Osaka market to the Chicago market, while a significant lagged volatility spillover is observed from the U.S. to Japan.

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.

This paper reports empirical evidence on stock index futures pricing based on about four years of synchronous hourly data from the UK. Reported index values are based on firm quotes. Identified arbitrage opportunities are therefore actually exploitable and economically significant. The analysis controls for cash market settlement procedures. The results show that ex-ante trading rules would have generated attractive profits after transaction costs and after the risks of dividend uncertainties, marking to market and possible delays in execution are considered. The far contract and the near contract tend to be mispriced in the same direction. The mild tendency of futures to be overpriced in rising markets and underpriced in falling markets appears to be unimportant. Finally, significant positive relationships are observed between the absolute magnitude of mispricing and time to maturity and between mispricing and index option implied volatility.

This paper examines hedging effectiveness in Greek stock index futures market. We focus on various techniques to estimate variance reduction from constant and time-varying hedge ratios. For both available stock index futures contracts of the Athens Derivatives Exchange (ADEX), we employ a variety of models to derive and estimate the effectiveness of hedging. We measure hedging effectiveness using three different methods: (i) the OLS method, (ii) the method of , and (iii) the method suggested by . In both cases for Greek stock index futures, the hedge ratio from M-GARCH model provides greater variance reduction, in line with similar findings in the literature. These findings are helpful to risk managers dealing with Greek stock index futures.

Hedging is claimed to be of fundamental importance in managing the risk of an investment portfolio. Several techniques to assess the effectiveness of a hedge have been suggested in the literature. While these techniques hold theoretical appeal, there is little empirical evidence as to their usefulness. This paper provides an empirical comparison of three measures of hedge effectiveness in the context of hedging market risk using the Australian All Ordinaries Share Price Index Futures contract. The three measures are portfolio S.D. ranking, the Howard and D'Antonio [Howard, C.T., D'Antonio, L.J., 1987. A risk return measure of hedging effectiveness: a reply. J. Finan. Quant. Anal. 22(3), 377-381.] measure and the Lindahl [Lindahl, M., 1991. Risk-return hedging effectiveness measures for stock index futures. J. Futures Markets 11(4), 399-409.] measure. The results indicate that the selection of the particular measure of hedge effectiveness has an impact on the assessment of hedged portfolios. Further, the paper highlights problems that arise in the application of the Lindahl [Lindahl, M., 1991. Risk-return hedging effectiveness measures for stock index futures, J. Futures Markets 11(4), 399-409.] and Howard and D'Antonio [Howard, C.T., D'Antonio, L.J., 1987. A risk return measure of hedging effectiveness: a reply, J. Finan. Quant. Anal. 22(3), 377-381.] measures. : S 1 0 4 2 -4 4 4 X ( 0 1 ) 0 0 0 3 6 -6

This study examines the returns, relative to the S&P 500, on cash indices and futures tracking smaller stocks around the turn of the year. While we control for volatility clustering, return autocorrelation in small stock indices, and other calendar effects, our main focus is the evolution of the turn of the year effect through time: in particular, whether the effect is smaller or takes place earlier subsequent to the introduction of the S&P Midcap and Russell 2000 futures in 1993. We find that evidence of a traditional turn of the year effect, in both cash and futures, is confined to the pre-1993 period. Post-1993, there are no abnormal returns during the turn of the year window as a whole. Interestingly, returns in this period remain high on the last trading day of December, but they are negative We thank an anonymous referee and the editor for valuable comments and suggestions on earlier versions of the paper. Any remaining errors are our own responsibility. often observe significant abnormal returns prior to the traditional turn of the year, i.e., in the pre-Christmas and post-Christmas windows. Taken together, our results suggest that market participants may be eliminating the turn of the year effect with the aid of two new futures contracts that are well suited to this purpose.

PurposeThe purpose of this paper is to examine the effects of the current global crisis on the integration and co‐movements of selected stock index futures markets.Design/methodology/approachTime series techniques of cointegration and weekly data covering the period from January 2001 to December 2009 were used in this study. The period of analysis was divided into two periods, namely the pre‐crisis period (January 2001‐July 2007) and during crisis period (August 2007‐December 2009).FindingsNo evidence was found of cointegration among the stock index futures markets in both periods. Accordingly, the 2007 subprime crisis does not seem to affect the long‐run co‐movements among the stock index futures markets.Practical implicationsThe stock index futures markets provide opportunity for the potential benefits from international portfolio diversification and hedging strategies even after the subprime crisis. The stock index futures significantly extended the variety of investment and risk...

The issue of market linkages (and price discovery) between stock indices and the lead-lag relationship are topics of interest to financial economists, financial managers and analysts. The lead-lag relationship analysis should take into account both the short and long-run investor. From a portfolio diversification perspective, the first type of investor is generally more interested in knowing the comovement of stock returns at higher frequencies, that is, short-run fluctuations, while the latter concentrates on the relationship at lower frequencies, that is, long-run fluctuations. The study uses a technique known as the ‘wavelet approach’ which has been very recently imported to finance from engineering sciences. Daily return data covering the period from June 2005 to December 2011 for MSCI stock indices of East Asian countries (Japan, China, Korea, Taiwan and Hong Kong) are analyzed. We examine the following empirical question: Is the global leadership of the US financial market sha...

This study reports new findings on the behavior of index futures (FKLI: code name of Kuala Lumpur Index Futures contract) prices and also records the effect of a major financial crisis on the prices. Since the inception of trading in 1995, the FKLI has been selling at a discount, which gradually increased till early 1997; further, at the onset of the financial crisis in July 1997, FKLI prices were at a high premium relative to its theoretical values. This significant mispricing of the contract declined after the initial overreaction to the crisis. Herding behavior during crisis, liquidity constraint and imposition of trading restrictions are some plausible explanations for the mispricing. This study also investigates whether trades by foreign investors had any impact when compared with prices by domestic investors. We find that foreign investors had a negative influence on permanent price changes while the domestic investors had a positive effect.

1984(b)), and Arditti, Ayadin, Mattu, and Rigskee (1986) documents the existence of substantial and sustained deviations between actual stock index futures prices and theoretical values. Based on these findings, Merrick (1989) and Finnerty and Park (1988) attempt to demonstrate the profitability of arbitrage related program trading strategies. On the other hand, Saunders and Mahajan (1988) adopt an alternative approach and conclude that stock index futures are priced efficiently. One limitation of empirical work in this area is that it relates only to stock index futures contracts traded within the USA. The fact that earlier work involves repeated analysis of data sets pertaining to the same economic and institutional environment, albeit for different sample periods, means that (whilst the work is of great interest and enables a better understanding of index futures contract pricing to be developed) it needs to be externally validated. A second limitation of earlier empirical work in this area is that even though there appears to be a broad consensus that observed mispricing is often sufficient to span the transaction cost bounds and offer arbitrage possibilities, this is not substantiated with formal evidence on actual transaction costs. In this respect the evidence of Stoll and Whaley (1987) is largely anecdotal, while Merrick (1989) uses an ad hoc estimate and Finnerty and Park (1988) ignore transaction costs. This article seeks to partially bridge these "gaps" in the literature by reporting the results of a comprehensive analysis of a totally new set of data relating to the UK FTSE-100 stock index futures contract traded on the London International Financial Futures Exchange (LIFFE). The results are set into perspective by an analysis of the relevant transaction costs. To provide direct comparability with previous work, the present study seeks to replicate, as closely as possible, many of the tests The authors acknowledge the financial assistance of the Center for the Study of Futures Markets, Columbia University. They are grateful for comments received from Michael Brennan, and participants at conferences and seminars at the universities of Edinburgh, Dundee, Wales, Warwick, and the London School of Economics. The authors are also grateful to the Institute of Quantitative Investment Research (INQUIRE) for their encouragement in the form of the prize for the best paper at the annual conference, Brighton, October 1989. Any remaining errors are, of course, the responsibility of the authors.

This analyzes concerning empirically appraise the effects and effect of stock market growth on the economic increase in two areas, in particular, South Asia and Malaysia. The researcher used market capitalization, total fee traded ratio, and turnover ratio as signs on inventory market improvement whilst GDP through a capital increase worth is back because of standardized monetary improvement. The linear dashboard facts methodology is utilized over the yearly facts about 1996-2018 according to locate out concerning the phenomenon. The effect is then compared at some stage in the countries concerning both regions. The experimental findings point out that inventory need enhancement contributes after half extent between the financial expand on the South Asian region but its impact concerning Malaysian neighborhood discovered in imitation which is very much significant.