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Construction of Quantitative Transaction Strategy Based on LASSO and Neural Network

Author

Listed:
  • Xu Wang
  • Jia-Yu Zhong
  • Zi-Yu Li

Abstract

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.

Suggested Citation

  • Xu Wang & Jia-Yu Zhong & Zi-Yu Li, 2017. "Construction of Quantitative Transaction Strategy Based on LASSO and Neural Network," Applied Economics and Finance, Redfame publishing, vol. 4(4), pages 134-144, July.
    • Handle: RePEc:rfa:aefjnl:v:4:y:2017:i:4:p:134-144
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    References listed on IDEAS

    as
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    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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