# Research on Quantitative Investment Strategies Based on Deep Learning
## 2 Literature Review
[PERSON] and [PERSON] [16] further improved on the basis of [PERSON], linking SVM with teaching-learning-based optimization (TLBO) which avoids user-specified control parameters that are required in other optimization methods. The feasibility and effectiveness of this hybrid model was evaluated by predicting the daily closing price of the Commodity Futures Index (COMDEX). They discovered that this model is more efficient than the PSO-SVM hybrid model and the standard support vector machine model. [PERSON] [21] designed a hybrid method based on back-propagation neural network (BPNN), feature selection technology and genetic programming (GP), using technical indicators to solve the stock/future price forecasting problem. By predicting the closing price of the spot monthly futures of the Taiwan Stock Exchange's Capital Weighted Index (TAIEX), the feasibility and effectiveness of the forecast are verified. In addition, the most important technical indicators can be determined by using a feature selection method based on the proposed simulation technique, or a preliminary GP prediction model. [PERSON] et al. [22] proposed a simple and effective options price prediction method based on neural network (NN) and SVR analysis. First, they improved the traditional options pricing method to make options prices prediction accessible. Secondly, the use of NNs and SVRs further reduced the prediction error of the parametric method. Since the traditional method simulated the trend of the actual options price, the prediction error of the nonlinear curve simulated in a mixed model of NNs and SVRs in the first stage can be further reduced in the second stage. Finally, a lot of experimental research on the Hong Kong options market data proved that NNs and SVRs can improve the prediction accuracy. With the further development of deep learning, researchers have also applied it into the financial forecasting field, where financial time series forecasting problems are also essentially time series problems. Considering that the performance of Recurrent Neural Networks (RNN) tend to surpass other methods, many researchers have focused their attention on RNN. [PERSON] et al. [23] used recurrent neural networks to form a deep learning methodology to forecast the price movement in future by using large-scale high-frequency data on Limit Order Books. [PERSON] et al. [24] modeled and predicted China stock returns by using LSTM. Compared with the random prediction method, the proposed LSTM model showed its power by improving the accuracy of the stock returns forecast. [PERSON] [25] proposed a sequential learning model that uses LTSM-RNN methods to predict individual stock prices with corporate behavioral event information and macroeconomic indices, displaying broad application prospects in stock price forecasting under the corporate behavior and corporate publishing variables. Besides, some researchers used other deep learning models; [PERSON] et al. [26] used Convolutional Neural Networks (CNNs) to form a deep learning methodology to forecast the price movements of stocks, using as input large-scale, high-frequency time-series derived from the order book of financial exchanges. [PERSON] et al. compared CNNs with other models like SVM and Multilateral Neural Networks to show that CNNs had better performance in their situation. However, compared with hybrid models, single models are usually unlikely to give a best result. Hence,Sun et al. [6] proposed a hybrid integration learning method of the AdaBoost model and the LSTM network to predict financial time series. The database was trained by AdaBoost model to obtain training samples, after which each of them was predicted by LSTM. Finally, the prediction results of all LSTM predictors were synthesized by the AdaBoost model to offer an integrated result. This AdaBoost-LSTM integrated learning method was illustrated to be superior to other single prediction models and integrated learning methods. In this paper, two different LSTM-SVR models are proposed to forecast the 50 ETF price.