基于长短期记忆递归神经网络的股票价格预测

C. Jeenanunta, Rujira Chaysiri, L. Thong
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引用次数: 11

摘要

本文研究了泰国SET50指数前五名公司的日股价预测。采用具有长短期记忆(LSTM)的递归神经网络(RNN)来预测下一个交易日的股票价格(高、低、开、收盘)。采用深度信念网络(Deep Belief Network, DBN)与LSTM进行比较。测试数据为来自SET50指数的call、SCB、SCC、KBANK和PTT。选择这5只股票的目的是比较模型在不同波动率的股票中的表现。本文对SET50指数中的5只股票进行了两次实验。第一个实验比较了不同训练数据长度的MAPE。实验采用1年、3年和5年的训练数据进行。PTT和SCC股票给出了5年训练数据的最小MAPE误差中值。KBANK、SCB和CPALL股票给出的一年期训练数据的MAPE误差中值最低。在第二个实验中,回望和输入的次数是不同的。一次回顾和四个输入的结果对股票价格预测有最好的效果。通过对不同技术的比较,结果表明LSTM在CPALL、SCB和KTB下的性能最好,误差小于2%。DBN在PTT和SCC下的性能最好,误差小于2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stock Price Prediction With Long Short-Term Memory Recurrent Neural Network
In this paper, we investigate the prediction of daily stock prices of the top five companies in the Thai SET50 index. A Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) is applied to forecast the next daily stock price (High, Low, Open, Close). Deep Belief Network (DBN) is applied to compare the result with LSTM. The test data are CPALL, SCB, SCC, KBANK, and PTT from the SET50 index. The purpose of selecting these five stocks is to compare how the model performs in different stocks with various volatility. There are two experiments of five stocks from the SET50 index. The first experiment compared the MAPE with different length of training data. The experiment is conducted by using training data for one, three, and five-year. PTT and SCC stock give the lowest median value of MAPE error for five-year training data. KBANK, SCB, and CPALL stock give the lowest median value of MAPE error for one-year training data. In the second experiment, the number of looks back and input are varied. The result with one look back and four inputs gives the best performance for stock price prediction. By comparing different technique, the result show that LSTM give the best performance with CPALL, SCB, and KTB with less than 2% error. DBN give the best performance with PTT and SCC with less than 2% error.
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