# Research on Quantitative Investment Strategies Based on Deep Learning
## 1 Introduction
[PERSON] [15] and [PERSON] [16] etc., all combine a variety of models to enhance prediction results by exploiting the characteristics of each model. The application of deep learning in financial markets not only lowers the difficulty of analysis and forecasting to a large extent, but also introduces new investment methods and ideas to investors. Based on the above ideas, this paper will adopt the deep learning and support vector machine approach, as well as using the specific data (50 ETF options data) to predict the 50 ETF price. During the research, the data is preprocessed, which involves the following procedures. First of all, the History Volatility (HV) model and the Implied Volatility (IV) model are combined to calculate the HV of 50 ETF and the IV of 50 ETF options. Then, they are selected as two input characteristics of the deep learning model, and the price prediction model of the 50 ETF options is constructed. Afterwards, the question of whether the deep learning shows a higher accuracy rate on this problem is studied, as well as whether the quantitative investment strategy can lead to a higher rate of return and lower drawdown. The experimental results show that compared with the traditional quantitative investment strategy, the quantitative investment strategy based on deep learning presents higher returns, a more stable yield curve, and better anti-fall performance. Therefore, the quantitative investment strategy based on deep learning has certain reference value for investors when making decisions. For the deep learning model, the Long Short-Term Memory (LSTM) that is built in this paper, there is still space to improve, and the combined Long Short-Term Memory- Support Vector Regression (LSTM-SVR) model improvessignificantly in prediction accuracy. At the same time, the quantitative investment strategy developed in this paper can withstand certain prediction errors, so that the quantitative investment strategy based on deep learning in this paper has a better performance in the options market. In contrast, in the stock and futures markets, errors are likely to cause huge losses. For the stock market, due to the particularity of China's stock market, only T + 1 trading transactions can be conducted, and this error may result in a certain amount of loss. With regard to the futures market, the loss caused by the error may even lead to a direct burst. Therefore, there is still a lot of work that needs to be done in research on quantitative investment strategies based on deep learning. The innovative points of this paper are as follows: (1) Based on the deep learning model LSTM, this paper combines LSTM and SVM to form the LSTM-SVR model. This model can reduce the predicted deviation value; that is, improve the accuracy of the prediction, especially during a relatively stable period. (2) The quantitative investment strategy designed in this paper introduces the concept of hedging, which guarantees Delta neutrality. The experiment proves that hedging boosts the quantitative investment strategy performance in terms of profitability and stability. (3) The quantitative investment strategy in this paper is conducted every 15 minutes, which is closer to the real trading market, so that the back-testing results can be more practical. (4) This paper combines the prediction result of the deep learning model LSTM-SVR, which acts as a signal _'diff'_ with the quantitative investment strategy to form a quantitative investment strategy based on the deep learning model, which exhibits a distinct optimization in terms of both yield and resilience. The paper is organized as follows: Section 2 offers a brief review of both the related domestic and foreign literature. Section 3 introduces the models and the quantitative investment strategy of this paper. Section 4 introduces the experimental simulation. Section 5 analyzes and compares the experimental results. Section 6 gives conclusions and prospects.