Stock Futures Market Research Articles

Determinants of the successful single stock futures market in Thailand

Thailand’s Single Stock Futures market has grown recently over the last ten years, evidenced by its 8th place in top 10 exchanges in the world by number of single stock futures traded in 2021. Since the main goal of any futures exchange is to list a successful contact, it is important to demonstrate the determinants of the success of Single Stock Futures. This study uses the sample consisting of 89 companies, on which stocks are underlying for Single Stock Futures in the period between January 2017 and December 2021, and finds that the best fitting method in modelling determinants of the success of Single Stock Futures is the fixed effects model. As expected, the results confirm the existence of a positive relationship between characteristics of underlying stock, including size, volatility, and liquidity, and the successful futures contract. Furthermore, the findings show the negative effects of the first year of contract trading and the tightened daily price limit of Single Stock Futures in response to the COVID-19 pandemic situation on contract success. AcknowledgmentThe author is grateful to the Department of Economics, Faculty of Economics, Kasetsart University for financial support to conduct this research.

Herding and spillover effects in the Indian commodity futures market

PurposeThe study examines herding in the Indian stock and commodity futures market including agricultural, metal and energy commodities. Herding is studied under various market conditions: rising and declining, high and low volatility. The study also examines spillover effects of herding.Design/methodology/approachThe study adapts the cross-sectional absolute deviation model given by Chang et al. (2000) to examine herding in Indian stock and commodity futures markets.FindingsThe results of the study indicate absence of herding among commodity futures under all market conditions except for the declining market where herding is present among energy futures. The investors investing in agricultural and energy commodities have a higher tendency to herd during high volatility days as compared to low volatility days. Further, the study of herding spillover effects indicates that the price fluctuations in metal commodities affect herding in agricultural and energy commodities.Research limitations/implicationsThe results can help market participants to diversify the risk by investing in agricultural, metal and energy futures along with the stocks.Originality/valueMajority of the previous studies explore herding among stocks and ignore commodities especially agricultural commodities. This study attempts to fill the gap by studying herding among various commodity futures. To the best of our knowledge this is the first study to explore herding spillover effects in the Indian stock and commodity futures market.

Simulating Multi-Asset Classes Prices Using Wasserstein Generative Adversarial Network: A Study of Stocks, Futures and Cryptocurrency

Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of the stock market, futures market and cryptocurrency market. We train our model on various datasets, including the Hong Kong stock market, Hang Seng Index Composite stocks, precious metal futures contracts listed on the Chicago Mercantile Exchange and Japan Exchange Group, and cryptocurrency spots and perpetual contracts on Binance at various minute-level intervals. We quantify the difference of generated results (836,280 data points) and original data by MAE, MSE, RMSE and K-S distances. Results show that WGAN-GP can simulate assets prices and show the potential of a market simulator for trading analysis. We might be the first to look into multi-asset classes in a systematic approach with minute intervals across stocks, futures and cryptocurrency markets. We also contribute to quantitative analysis methodology for generated and original price data quality.

Expert Study of the Execution of the Terms of an Investment Life Insurance Contract

A significant decrease in bank deposits’ profitability forces citizens to look for more profitable investment options, including purchasing various financial products. One such product is an investment life insurance contract. However, investors’ expectations are not always fully met since dealing with financial instruments, such as options, requires a certain of financial literacy, understanding of the futures stock market’s organization and functioning. Arising disputes are often resolved in court. To obtain answers to economical questions, a forensic examination is appointed and carried out.Forensic analysis on this topic has some features that are discussed in the article. The paper gives a list of objects for expert research and their brief characteristics. The main sources of reference information on quotations of financial instruments are provided. Some concepts and economic categories related to the stock market and derivatives are considered. The Russian and English terms used by the experts during the study are presented and explained.Using an example from expert practice, the authors explain the logic of the analysis of an option contract and show the sequence of actions that makes it possible to calculate the investment income amount. The article justifies the conditions that restrict the experts when carrying out calculations and should be taken into account when forming and studying the expert opinions.

Noise Trading and Single Stock Futures: Modifying Sentana & Wadhwani’s Model

Derivatives,and their influence on the dynamics of underlying stock markets,is an interesting topic of debate, which predates their introduction. The unresolved influence of derivatives on their underlying stock markets still intrigues many. In this regard, researchers/stake holders are still curious about the (de)stabilizing influence of derivatives on the overall market. In disposition of these observations, two contradicting hypothesis have been studied widely and have remained the focus of attention in several theoretical and empirical studies. These hypotheses are explained in several ways. Among many, one explanation refers to the destabilizing influence of derivatives,due to the enhanced involvement of noise traders, after the introduction of derivatives.This aspect remains the topic of discussion for this study. After the formal introduction of the SSFs (Single Stock Futures) in Pakistan, this topic became a cause of concern for the stakeholders of this market as well. Hence, this study attempts to tap into this aspect of the de(stabilization) debate,by proposing a modified version of the famous Sentana & Wadhwani (1982)model. In order to tap the potential shortcomings of the S&W model, this study contributes to the extant literature in several ways: 1) It adds the feature of trading volume in the model to analyze and study the potential movement of noise traders from spot to futures market,due to the ease of trading that the futures markets offer, 2) the new, modified model adds a lagged term for returns in order to tap the potential asynchronous inefficiencies, 3) it considers the Generalized Error Distribution (GED) instead of the Gaussian Distribution, in order to realize the fact that returns are not normally distributed. Generally speaking, the modified version of the model not only extends the original model in terms of its explanation, but also empirically tests this aspect in the Single Stock Futures (SSFs) market of Pakistan. This model tested whether SSFs promote,or inhibit the noise trading post-SSFs. After putting it to test, the newer model did not report any negative or positive impact of the introduction of SSFs on the underlying stocks. This may conclude that the proclaimed (de)stabilizing role of the SSFs,in the context of Pakistan,is not justified.

Risk-adjusted Returns from Statistical Arbitrage Opportunities in Indian Stock Futures Market

Statistical arbitrage is a trading strategy that employs time series methods to identify relative mispricing between securities based on the expected values of these assets. The Pairs Trading, one of the techniques of statistical arbitrage, is a market neutral trading strategy. The main objective of this paper is to investigate the profitability and risks of pairs trading strategy for various stocks. The daily future prices of stocks traded and listed on NSE over 2011–2017 are used on rolling basis to compute the performance based on the selection of pairs through minimizing the sum of squared deviation (distance method) and the selection based on cointegration tests (cointegration method) for identifying stocks suited for pairs trading strategies. The pairs trading strategy is performed in two stages: the formation period and the trading period. The strategy is created by long position in one stock and short position in other stock of the pair identified. To examine the risk of pairs trading and the drivers of returns, the portfolio returns are risk-adjusted using Fama and French (J Financ Econ 33:3–56, 1993) three factor asset pricing model. The study reveals that pairs trading in related stocks is significantly profitable with average annualized profitability of up to 34% including transaction costs. The evidence of pairs trading profits in stock futures supports the view that these profits reflect compensation to arbitrageurs for enforcing the law of one price in similarly related markets to ensure market efficiency. Indian financial markets are maturing and are attracting sizable retail and institutional investments. Advanced applications like the one presented in this study are of significance for the investors and investment consultants so that they can benefit from such trading strategies.

Noise Trading and Single Stock Futures: Modifying Sentana & Wadhwani's Model

Derivatives, and their influence on the dynamics of underlying stock markets, is an interesting topic of debate, which predates their introduction. The unresolved influence of derivatives on their underlying stock markets still intrigues many. In this regard, researchers/stakeholders are still curious about the (de)stabilizing influence of derivatives on the overall market. In disposition of these observations, two contradicting hypothesis have been studied widely and have remained the focus of attention in several theoretical and empirical studies. These hypotheses are explained in several ways. Among many, one explanation refers to the destabilizing influence of derivatives, due to the enhanced involvement of noise traders, after the introduction of derivatives. This aspect remains the topic of discussion for this study. After the formal introduction of the SSFs (Single Stock Futures) in Pakistan, this topic became a cause of concern for the stakeholders of this market as well. Hence, this study attempts to tap into this aspect of the de(stabilization) debate, by proposing a modified version of the famous Sentana & Wadhwani (1982) model. In order to tap the potential shortcomings of the S&W model, this study contributes to the extant literature in several ways: 1) It adds the feature of trading volume in the model to analyze and study the potential movement of noise traders from spot to futures market, due to the ease of trading that the futures markets offer, 2) the new, modified model adds a lagged term for returns in order to tap the potential asynchronous inefficiencies, 3) it considers the Generalized Error Distribution (GED) instead of the Gaussian Distribution, in order to realize the fact that returns are not normally distributed. Generally speaking, the modified version of the model not only extends the original model in terms of its explanation, but also empirically tests this aspect in the Single Stock Futures (SSFs) market of Pakistan. This model tested whether SSFs promote, or inhibit the noise trading post-SSFs. After putting it to test, the newer model did not report any negative or positive impact of the introduction of SSFs on the underlying stocks. This may conclude that the proclaimed (de)stabilizing role of the SSFs, in the context of Pakistan, is not justified. This may also imply that the stringent regulatory frameworks, post the Global Financial Crisis, (GFC) for the resumed SSFs, are not justified and require revision.1) It adds the feature of trading volume in the model to analyze and study the potential movement of noise traders from spot to futures market, due to the ease of trading that the futures markets offer, 2) the new, modified model adds a lagged term for returns in order to tap the potential asynchronous inefficiencies, 3) it considers the Generalized Error Distribution (GED) instead of the Gaussian Distribution, in order to realize the fact that returns are not normally distributed. Generally speaking, the modified version of the model not only extends the original model in terms of its explanation, but also empirically tests this aspect in the Single Stock Futures (SSFs) market of Pakistan. This model tested whether SSFs promote, or inhibit the noise trading post-SSFs. After putting it to test, the newer model did not report any negative or positive impact of the introduction of SSFs on the underlying stocks. This may conclude that the proclaimed (de)stabilizing role of the SSFs, in the context of Pakistan, is not justified. This may also imply that the stringent regulatory frameworks, post the Global Financial Crisis, (GFC) for the resumed SSFs, are not justified and require revision.

Does trade size restriction affect trading behavior? Evidence from Indian single stock futures market

AbstractAlgorithmic traders use their advantage of speed to execute a large number of small‐sized trades in a very short time. In the presence of a minimum trading unit (MTU) restriction, they are forced to trade at the smallest possible sizes, often restricted by the MTU. Using a novel data set of single stock futures market obtained from the National Stock Exchange of India, we show that the MTU restriction acts as a binding constraint for traders while optimizing trade sizes. Contrary to expectation, we find weak evidence that liquidity is positively impacted by the contract size revision.

Optimal Hedge Ratio and Hedging Effectiveness in Stock Futures Market: Evidence from National Stock Exchange, India

The major objective of the study is to examine the performance of optimal hedge ratio and hedging effectiveness in stock futures market in National Stock Exchange, India by estimating the following econometric models like Ordinary Least Square (OLS), Vector Error Correction Model (VECM) and time varying Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model by evaluating in sample observation and out of sample observations for the period spanning from 1st January 2011 till 31st March 2018 by accommodating sixteen stock futures retrieved through www.nseindia.com by considering banking sector of Indian economy. The findings of the study indicate both the in sample and out of sample hedging performances suggest the various strategies obtained through the time varying optimal hedge ratio, which minimizes the conditional variance performs better than the employed alterative models for most of the underlying stock futures contracts in select banking sectors in India. Moreover, the study also envisage about the model selection criteria is most important for appropriate hedge ratio through risk averse investors. Finally, the research work is also in line with the previous attempts Myers (1991), Baillie and Myers (1991) and Park and Switzer (1995a, 1995b) made in the US markets

Analysis of Price Discovery Effect in the Single Stock Futures Market

This paper examines the price dynamics in the single stocks futures and spot markets. In order to enhance the liquidity of the stock futures market, Korea Exchange introduced the liquidity provider in 2014, and exempted the securities transaction taxes on stocks sold for hedging purposes of liquidity provider from 2015. This study performed a vector error correction model (VECM) based on spot-futures market linkage to evaluate the effectiveness of the liquidity policy by examining the difference in the price discovery around the event. The main empirical analysis results are summarized as follows. First, a statistically significant sample of price discovery over the entire period was evident in the interrelationship between spot and futures. This implies that stock futures have information effect equivalent to spot price, which is different from the previous studies in which futures lead the spot price discovery significantly as in the case of KOSPI200 futures market. Second, the tendency of feedback between spot and futures is consistent in price discovery even after introduction of liquidity provider and exemption of securities transaction tax. Overall, empirical results suggest that the effectiveness of the stock futures market policy is limited during the sample period and the additional measures to enhance the long term activation are needed.

Spot and Futures Stock Market in India: Co-integration

Spot and Futures Stock Market in India: Co-integration

Are single stock futures used as an alternative during a short‐selling ban?

The response of the single stock futures (SSF) market to a short‐selling ban is investigated. The hypothesis is that traders use SSF as a substitute instrument for short‐selling. A significant increase in SSF trading activity is documented, accompanied by narrower spreads. SSF market volatility did not react during the ban, which suggests that the increased trading activity did not weaken SSF market quality. The quality of the underlying market during the ban period is also assessed, with the results suggesting that changes in SSF market activity had neither positive nor negative effects on the stocks’ liquidity, volatility, and volume.

개별주식선물시장의 거래대금에 따른 정보이전효과에 관한 연구

The study investigates the lead-lag relations by analyzing the temporal relationship between the returns of single stock futures and those of spot. Five single stock futures are selected among total 25 SSFs listed in Korea Exchanges according to the market amount. The market amount of selected samples is about 71% of total single stock futures. They are assumed as the representatives of the single stock futures market. So far, a lot of papers have been published related to the price discovery. Most of previous researches have verified the US stock market leads to other countries’ stock markets in the broad perspective. On the other hand, many papers have tried to show the information spillover effect between futures markets and spot markets with various futures products such as stock index futures, physical product futures,interest futures, exchange futures. In particular, most papers specified to stock index futures markets which are successful in introduction of index stock futures haven’t shown the consistent results, which futures markets lead spot markets. Domestic researches related to information spillover effects have focused on KOSPI 200 futures index market. These results also haven't suggested consistent performances similar to other previous results. This paper tries to analyze the relationship between returns of single stock futures and those of stocks on the microscopic aspects. The research employes five single stock futures like SK hynix, Samsung Electronics, Hyundai Motors, SK innovation, LG Electronics. The sample periods are from December 14,2009 to February 15, 2012. 542 observations both from returns of spot and those of nearby single stock futures are calculated taking the natural log of the price at t by the price at t-1. VAR model is used to catch the dynamic information spillover effects. The paper approaches three areas in detail. Is there information spillover effects between the returns of single stock futures and those of spots? How long do the information spillover effects go on? How much of the movements in one market can be explained by innovation in other market? To get the answers the above questions, we employed the Granger-causality test, impulse response test, and variance decomposition in order. In general, we take two steps to use the time series model, the unit root test for stationarity and cointegration test for long-run equilibrium. Fortunately, return data has been testified to be stationary through many previous researches. The main results with returns of the single stock futures and spots as follows. First, the returns of SSFs and spot of Hyundai Motors are highest owing to the growth of motor’s industry, but those of LG Electronics are negative due to low performance of smart phone area. The standard deviation of Samsung Electronics is lowest, but the standard deviations of SK Hynix and SK Innovation are highest due to ownership problem and unstable oil industries. It is very important to estimate the lags of VAR model. We figure out the lags with Schwarz’s Bayesian information criterion (BIC) used generally. SK Hynix, SK Innovation and LG Electronics show the lowest values of BIC at 2 lags. and Samsung Electronics and Hyundai Motors suggest the lowest values of BIC at 3 lags. So VAR(3) models for Samsung Electronics and Hyundai Motors, VAR(2) models for SK Hynix, SK Innovation and LG Electronics are employed. Second, the return of SK Hynix futures tends to lead that of spot at the 10% significant level and the return of SK spot also leads that of futures at the 10% significant level. The return of Samsung Electronics futures alone predicts that of spot at 10% significant level. The return of Hyundai Motors futures tends to lead that of spot at the 5% significant level and the return of Hyundai Motors spot also leads that of futures at the 10% significant level.

Trading Strategies based on Pattern Recognition in Stock Futures Market using Dynamic Time Warping Algorithm

Traditional technical analysis is forecasting the up and down trends in the stock market. However, it is difficult to apply technical analysis directly because it relies on human experience to select optimal strategies for individual stocks. Thus, a stock market trading system has been developed to apply optimal investment strategies by many traders given various market situations. Many pattern recognition methodologies have been used to develop successful strategies in this trading system, and the various strategies were generated using an expanded technical analysis. The aim of this study is to construct a pattern-recognition-based trading system (PRTS) for providing suitable investment strategies in the stock futures market. The PRTS uses a dynamic time warping (DTW) algorithm to recognize various market patterns and provides investment strategies using diverse technical indicators for the market. Through empirical studies, we show that DTW yields an efficient PRTS for various market conditions.

A Study on the Price Discovery and Asymmetric Volatility Spillovers between Single-Stock Futures and Spot Markets: Focused on Korea‘s 4 Financial Holding Companies

This paper investigated the price discovery and asymmetric volatility spillover effects between single stock futures and spot markets. For this purpose we employ 4 largest Korean financial holding companies's daily data covering the period from May 7, 2008 to the end of December, 2010. We introduce the Nelson (1991)'s Exponential GARCH models and the major empirical results are as follows; First, according to Johansen co-integration test, there is a long run relationship between the level variables of 4 financial holding companies' futures and cash markets. Second, based on Granger causality test, 3 financial holding companies's futures contracts among 4 have an impact on the spot returns at a significant level. Third, financial holding companies' futures and spot markets are influenced at 10% to 27% by previous price changes of each market. Fourth, there is a asymmetric volatility spillover effects in 4 financial holding companies futures markets. From this result we infer that individual futures and spot markets in banking area are more sensitive to bad news than good news. These empirical results are consistent with the those of Sakthivel and Kamaiah (2010), Chan et al.(1991), Lien and Tse (2000), Yang et al.(2001) and from these results we infer that 4 single stock futures market are more efficient than those of there spot markets.

Testing Alternative Models for Forecasting Volatility in Stock Futures Market: A Linear Approach

Thanks to numerous empirical research studies, a general consensus has been reached on examining the various alternative models for forecasting and modeling volatility in stock futures contracts by using out-of-sample forecast according to statistic and risk management evaluation criteria. The dataset were retrieved from National Stock Exchange (NSE) website terminal for the period from April 1, 2003 and ending on December 31, 2008. The forecasting models that are considered here ranges from Random Walk, Linear Regression, Moving Average, Autoregressive Models. In order to evaluate the forecasting performance of different models we use two forecasting error statistics by considering the root mean square error (RMSE) and the mean absolute percentage error (MAPE) for testing the return characteristics. Our findings suggest that, according to RMSE statistics the autoregressive model and linear regression models rationally shared and ranked first for out-of-sample forecasts in the linear models. In addition, one cannot conclude that the success or failure of a particular type of forecasting model applied to one market carries over to a different market, because the size and liquidity of a market can affect the quality of volatility forecasts. Finally, one can learn more about the market movement and return volatility through studying the non linear approach.

The Naked Truth from the Single Stock Futures Market

In this paper, we use a novel high-frequency data set, provided by the OneChicago single stock futures exchange, to study the impact of short sale restrictions on asset prices. Contrary to Battalio and Scultz (2006), we find many violations to the put call parity relation and to the cost of carry model even when using perfectly synchronous futures and stock prices. Most of these violations occur in a direction consistent with stocks being short-sale constrained. Examining the future put call parity relation that involves no equity, we find that futures and options are fairly priced, relative to each other. Constructing an implied intra-day measure of stock's short-sale rebate rate from stock and futures prices, we show that, contrary to many critics' claims, the SEC's ban of naked short selling on 19 financial stocks in July 2008 was highly effective in raising the overall cost of shorting. After remaining at the zero level for a week, on the first day the ban was effective, the average implied short sale rebate rate on the 19 financial firms instantly dropped to -4% p.a. and eventually -8% p.a. within a couple of days.

An Explanation of Efficient Market Hypothesis and Unbiasedness Using Markov Switching Framework

This paper uses Indian stock futures data to explore efficient market hypothesis and unbiasedness. Having experienced voluminous transactions within a short time span after its establishment, the Indian stock futures market provides an unparalleled case for exploring these issues involving expectation and efficiency. Besides analyzing efficiency hypothesis and unbiasedness of stock futures market using cointegration and error correction model, the degree of efficiency is further investigated after explicitly modeling the underlying state of the market (expansion or contraction) through the first-order Markov switching set-up. The results based on Markov switching analysis show that relatively longer time horizon is more effective in eliminating arbitrage opportunities than the short run.

Information Revelation in the Futures Market: Evidence from the Single Stock Futures Market

This paper analyzes 31 months of data on 137 single-stock futures (SSFs). The results indicate that SSFs contribute approximately 24 percent of the price discovery for underlying stocks. Information revelation in the SSFs market increases with the ratio (futures to stock market) of volumes, decreases with the ratio of spreads, and decreases with the volatility in the stock market. Moreover, the quality of the market for the underlying stocks improves substantially following the introduction of the SSFs market, with the largest improvement occurring on days with SSFs trading. Evidence also suggests that there exists both market- and security-level learning in the SSFs market which is associated with greater efficiency over time.

Financial Innovations and Market Efficiency: The Case of Single Stock Futures

Market efficiency improves for stocks that are listed on the newly established single stock futures (SSF) exchanges. After identifying information associated with large price changes, we show that the number of unexplained large stock returns decreases for SSF firms in comparison to the pre-SSF period, and to the matched non-SSF sample. The reduction is positively related to the extent of trading activity in the single stock futures market.