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

2006 ◽  
Author(s):  
Kuldeep Shastri ◽  
Ramabhadran S. Thirumalai ◽  
Chad J. Zutter
Keyword(s):  
Futures Market ◽  
Information Revelation ◽  
Single Stock Futures

scholarly journals Information revelation in the futures market: Evidence from single stock futures

2008 ◽  
Vol 28 (4) ◽  
pp. 335-353 ◽  
Author(s):  
Kuldeep Shastri ◽  
Ramabhadran S. Thirumalai ◽  
Chad J. Zutter
Keyword(s):  
Futures Market ◽  
Information Revelation ◽  
Single Stock Futures

scholarly journals Does Equity Derivatives Trading Affect the Systematic Risk of the Underlying Stocks in an Emerging Market: Evidence from Pakistan’s Futures Market

2013 ◽  
Vol 18 (1) ◽  
pp. 63-80 ◽  
Author(s):  
Safi Ullah Khan ◽  
Zaheer Abbas
Keyword(s):  
Empirical Evidence ◽  
Emerging Market ◽  
Systematic Risk ◽  
Estimation Procedure ◽  
Futures Market ◽  
Control Group ◽  
Index Futures ◽  
Market Effects ◽  
Underlying Stocks ◽  
Single Stock Futures

This paper examines the behavior of beta coefficients (systematic risk) for underlying stocks around the introduction of single-stock futures (SSFs) contracts in the Pakistani market, by employing models that account for nonsynchronous and thin trading and varying market conditions as “bull” and “bear” markets. Unlike the results of earlier studies on US markets, the empirical evidence tends to support a decline in systematic risk for the majority of underlying stocks in the post-futures listings period. Nevertheless, similar to SSFs stocks, we also find empirical evidence of a decrease in systematic risk for many of the control group stocks. This indicates that changes in beta estimates for SSFs-listed stocks might not be induced by the introduction of SSFs contract trading, but could be attributed to other market-wide or industry changes that have affected the overall market. Several plausible reasons, such as lack of program trading activities normally associated with index futures, market microstructure differences between developed markets and a developing market such as Pakistan, and the capturing of the “bear” and “bull” market effects on stock betas in our estimation procedure could explain these different results for Pakistan’s market.

Analysis of Price Discovery Effect in the Single Stock Futures Market

2018 ◽  
Vol 26 (4) ◽  
pp. 425-463
Author(s):  
Woo–baik Lee
Keyword(s):  
Price Discovery ◽  
Futures Market ◽  
Spot Price ◽  
Vector Error Correction ◽  
Vector Error ◽  
Securities Transaction Tax ◽  
Market Linkage ◽  
The Difference ◽  
Transaction Taxes ◽  
Single Stock Futures

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.

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

2019 ◽  
Vol 118 (3) ◽  
pp. 137-152
Author(s):  
A. Shanthi ◽  
R. Thamilselvan
Keyword(s):  
Banking Sector ◽  
Stock Exchange ◽  
Research Work ◽  
Futures Market ◽  
Least Square ◽  
Time Varying ◽  
Hedge Ratio ◽  
Hedging Effectiveness ◽  
Out Of Sample ◽  
Optimal Hedge

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

The profitability of algorithmic trading systems based on football sentiment

2020 ◽  
Vol 42 (1) ◽  
pp. 33-46
Author(s):  
Raúl Gómez-Martínez ◽  
Camila Marqués-Bogliani ◽  
Jessica Paule-Vianez
Keyword(s):  
Financial Markets ◽  
Investment Decisions ◽  
Futures Market ◽  
Algorithmic Trading ◽  
Investment Strategies ◽  
Measurable Effect ◽  
Behavioural Finance ◽  
Trading Systems ◽  
European Football

Behavioural finance has shown that investment decisions are the result of not just rational but also emotional brain processes. On the assumption that emotions affect financial markets, it would seem likely that football results might have a measurable effect on financial markets. To test this, this study describes three algorithmic trading systems based exclusively on the results of three top European football teams (Juventus, Bayern München and Paris St Germain) opening long or short positions in the next market season of the futures market of the index of each country (MIB (Milano Italia Borsa), DAX (Deutscher Aktien Index) and CAC (Cotation Assistée en Continu). Depending on the outcome of the last game played a long position was taken after a victory and a short position after a draw or defeat. The results showed that the algorithmic systems were profitable in the case of Juventus and Bayern whereas in the case of PSG, the system was profitable, but in an inverse way. This study shows that investment strategies that take account of sports sentiment could have a profitable outcome.

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