Abstract
Stock price prediction is very important in financial decision-making, and it is also the most difficult part of economic forecasting. The factors affecting stock prices are complex and changeable, and stock price fluctuations have a certain degree of randomness. If we can accurately predict stock prices, regulatory authorities can conduct reasonable supervision of the stock market and provide investors with valuable investment decision-making information. As we know, the LSTM (Long Short-Term Memory) algorithm is mainly used in large-scale data mining competitions, but it has not yet been used to predict the stock market. Therefore, this article uses this algorithm to predict the closing price of stocks. As an emerging research field, LSTM is superior to traditional time-series models and machine learning models and is suitable for stock market analysis and forecasting. However, the general LSTM model has some shortcomings, so this paper designs a LightGBM-optimized LSTM to realize short-term stock price forecasting. In order to verify its effectiveness compared with other deep network models such as RNN (Recurrent Neural Network) and GRU (Gated Recurrent Unit), the LightGBM-LSTM, RNN, and GRU are respectively used to predict the Shanghai and Shenzhen 300 indexes. Experimental results show that the LightGBM-LSTM has the highest prediction accuracy and the best ability to track stock index price trends, and its effect is better than the GRU and RNN algorithms.
Highlights
As of December 16, 2019, there are 3765 listed companies in China’s Shanghai and Shenzhen stock markets, with a total market value of 57779.362 billion yuan [1]
At present, according to different theories of building stock price forecasting models, forecasting models can be divided into three categories: timeseries model, machine learning model, and deep learning model [4]
Deep learning is a modern tool for automatic feature extraction and prediction. It has strong adaptability and self-learning ability and does not need to show specific network relationships and mathematical models. It has made some progress in intelligent speech and image classification technology [7]. ere are a lot of schemes for the application of deep learning model in stock price forecasting model [8]
Summary
As of December 16, 2019, there are 3765 listed companies in China’s Shanghai and Shenzhen stock markets, with a total market value of 57779.362 billion yuan [1]. Deep learning is a modern tool for automatic feature extraction and prediction It has strong adaptability and self-learning ability and does not need to show specific network relationships and mathematical models. Researchers have applied deep learning theory to financial time-series forecasting [9]. (1) is paper designs a LightGBM-optimized LSTM model to realize short-term stock price prediction. (3) In order to verify its effectiveness compared with other deep network models such as RNN (Recurrent Neural Network) and GRU (Gated Recurrent Unit), the LightGBM-LSTM, RNN, and GRU are respectively used to predict the Shanghai and Shenzhen 300 indexes. Experimental results show that the LightGBM-LSTM has the highest prediction accuracy and the best ability to track stock index price trends, and its effect is better than the GRU and RNN algorithms. Is article is divided into five parts. e first part is an introduction to the research background; the second part is an introduction to the current research status; the third part is an introduction to the LightGBM-LSTM model algorithm; the fourth part shows the prediction effect of the LightGBMLSTM algorithm on stock prices, compared with the prediction effect of RNN and GRU algorithm; the fifth part is the conclusion of the article
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