Research on Quantitative Investment Strategies Based on Deep Learning

Abstract

This paper takes 50 ETF options in the options market with high transaction complexity as the research goal. The Random Forest (RF) model, the Long Short-Term Memory network (LSTM) model, and the Support Vector Regression (SVR) model are used to predict 50 ETF price. Firstly, the original quantitative investment strategy is taken as the research object, and the 15 min trading frequency, which is more in line with the actual trading situation, is used, and then the Delta hedging concept of the options is introduced to control the risk of the quantitative investment strategy, to achieve the 15 min hedging strategy. Secondly, the final transaction price, buy price, highest price, lowest price, volume, historical volatility, and the implied volatility of the time segment marked with 50 ETF are the seven key factors affecting the price of 50 ETF. Then, two different types of LSTM-SVR models, LSTM-SVR I and LSTM-SVR II, are used to predict the final transaction price of the 50 ETF in the next time segment. In LSTM-SVR I model, the output of LSTM and seven key factors are combined as the input of SVR model. In LSTM-SVR II model, the hidden state vectors of LSTM and seven key factors are combined as the inputs of the SVR model. The results of the two LSTM-SVR models are compared with each other, and the better one is applied to the trading strategy. Finally, the benefit of the deep learning-based quantitative investment strategy, the resilience, and the maximum drawdown are used as indicators to judge the pros and cons of the research results. The accuracy and deviations of the LSTM-SVR prediction models are compared with those of the LSTM model and those of the RF model. The experimental results show that the quantitative investment strategy based on deep learning has higher returns than the traditional quantitative investment strategy, the yield curve is more stable, and the anti-fall performance is better.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

“…They applied the deep reinforcement learning (DRL) model to retrain neural networks. Fang, Chen, and Xue [42] developed a methodology to predict the exchange-trade-fund (EFT) options prices. Through integrating LSTM and support vector regression (SVR), they produce two models of LSTM-SVR for modeling the final transaction price, buy price, highest price, lowest price, volume, historical volatility, and the implied volatility of the time segment.…”

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

“…Methods Application [93] LSTM and AE Market investment [94] Hyper-parameter Option pricing in finance [95] LSTM and SVR Quantitative strategy in investment [96] R-NN and genetic method Smart financial investment Aggarwal and Aggarwal [93] designed a deep learning model applied to an economic investment problem with the capability of extracting nonlinear data patterns. They presented a decision model using neural network architecture such as LSTM, auto-encoding, and smart indexing to better estimate the risk of portfolio selection with securities for the investment problem.…”

Data Science in Economics

“…They applied the deep reinforcement learning (DRL) model to retrain neural networks. Fang, Chen, Xue [36] develop a methodology to predict the exchange-trade-fund (EFT) options prices. Integrating LSTM model and support vector regression (SVR), They firstly develop two models of LSTM-SVR I and LSTM-SVR II where in LSTM-SVR I the output of LSTM and the final transaction price, buy price, highest price, lowest price, volume, historical volatility, and the implied volatility of the time segment, that considered as factors affecting the price, added as the input of SVR model.…”