Research on Stock Index Futures Arbitrage Strategy Based on Convolutional Neural Network

2021 ◽  
Vol 10 (02) ◽  
pp. 557-567
Author(s):  
鹏飞 岳
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Arbitrage Strategy

scholarly journals Cost-benefit analysis of trading strategies in the stock index futures market

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Xiong Xiong ◽  
Yian Cui ◽  
Xiaocong Yan ◽  
Jun Liu ◽  
Shaoyi He
Keyword(s):  
Evaluation System ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Trading Strategies ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Four Dimensions ◽  
Arbitrage Strategy ◽  
Impact Cost

Abstract With the introduction of many derivatives into the capital market, including stock index futures, the trading strategies in financial markets have been gradually enriched. However, there is still no theoretical model that can determine whether these strategies are effective, what the risks are, and how costly the strategies are. We built an agent-based cross-market platform that includes five stocks and one stock index future, and constructed an evaluation system for stock index futures trading strategies. The evaluation system includes four dimensions: effectiveness, risk, occupation of capital, and impact cost. The results show that the informed strategy performs well in all aspects. The risk of the technical strategy is relatively higher than that of the other strategies. Moreover, occupation of capital and impact cost are both higher for the arbitrage strategy. Finally, the wealth of noise traders is almost lost.

scholarly journals Futures Trend Strategy Model Based on Recurrent Neural Network

2018 ◽  
Vol 5 (4) ◽  
pp. 95
Author(s):  
Ru Zhang ◽  
Chenyu Huang ◽  
Shaozhen Chen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Investment Performance ◽  
Index Futures ◽  
Model Based ◽  
Strategy Model ◽  
Global Futures

In recent years, quantitative investment has been widely used in the global futures market, and its steady investment performance has also been recognized by domestic futures investors. This paper takes the CSI-300 stock index futures as the research object and constructs a futures trend strategy model based on recurrent neural network. Furthermore, this paper back tests the strategy at different periods, different transaction costs and different parameters. The results show that the strategy model has strong profitability and robustness.

scholarly journals Construction of Quantitative Transaction Strategy Based on LASSO and Neural Network

2017 ◽  
Vol 4 (4) ◽  
pp. 134 ◽  
Author(s):  
Xu Wang ◽  
Zi-Yu Li ◽  
Jia-Yu Zhong
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Trading Strategy ◽  
Stock Index ◽  
Good Effect ◽  
Stock Index Futures ◽  
Expected Return ◽  
Index Futures ◽  
Statistical Field ◽  
Input Variables

Since the establishment of the securities market, there has been a continuous search for the prediction of stock price trend. Based on the forecasting characteristics of stock index futures, this paper combines the variable selection in the statistical field and the machine learning to construct an effective quantitative trading strategy. Firstly, the LASSO algorithm is used to filter a large number of technical indexes to obtain reasonable and effective technical indicators. Then, the indicators are used as input variables, and the average expected return rate is predicted by neural network. Finally, based on the forecasting results, a reasonable quantitative trading strategy is constructed. We take the CSI 300 stock index futures trading data for empirical research. The results show that the variables selected by LASSO are effective and the introduction of LASSO can improve the generalization ability of neural network. At the same time, the quantitative trading strategy based on LASSO algorithm and neural network can achieve good effect and robustness at different times.

Program trading and its risk analysis based on agent-based computational finance

2015 ◽  
Vol 02 (02) ◽  
pp. 1550014 ◽  
Author(s):  
Xiong Xiong ◽  
Hailiang Yuan ◽  
Wei Zhang ◽  
Yongjie Zhang
Keyword(s):  
Stock Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Experimental Conditions ◽  
Price Expectation ◽  
Agent Based ◽  
Index Futures ◽  
Artificial Stock Market ◽  
Arbitrage Strategy

Program trading originates from combination trading technology in 70's in America. It was popular, but once it was considered as root of disaster. Nowadays, there are many divergences on program trading risk in international academic world. This essay is to analyze program trading on risk of stock market. The method adopts computational experiment to build artificial stock market under various experimental conditions. The research will consider two strategies: combination insurance strategy and arbitrage strategy to inspect stock index futures' influences on artificial stock market. Through contrast experiments, it finds that program trading will cause abnormal fluctuation of stock market in short-term period but it will have slight impact on fluctuation of stock market in long-term period. On the whole, stock index futures reduce price fluctuation of spot market. Besides, the research finds that combination insurance strategy will increase short selling expectation in pessimistic market to accelerate market collapse when the market gives the same downside price expectation and the market should consider the influence of combination insurance strategy.

Volatility and Trading Demands in Stock Index Futures

CFA Digest ◽  
2003 ◽  
Vol 33 (3) ◽  
pp. 101-102
Author(s):  
Frank T. Magiera
Keyword(s):  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures
Sign in / Sign up

Export Citation Format

Share Document