A Time Series Model Based on Deep Learning and Integrated Indicator Selection Method for Forecasting Stock Prices and Evaluating Trading Profits

Ching‐Hsue Cheng · Ming-Chi Tsai · Chin Chun Chang

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Stock Market Forecasting Methods Forecasting Techniques and Applications Hydrological Forecasting Using AI

Abstract

A stock forecasting and trading system is a complex information system because a stock trading system needs to be analyzed and modeled using data science, machine learning, and artificial intelligence. Previous time series models have been widely used to forecast stock prices, but due to several shortcomings, these models cannot apply all available information to make a forecast. The relationship between stock prices and related factors is nonlinear and involves nonstationary fluctuations, and accurately forecasting stock prices is not an easy task. Therefore, this study used support vector machines (linear and radial basis functions), gene expression programming, multilayer perceptron regression, and generalized regression neural networks to calculate the importance of indicators. We then integrated the five indicator selection methods to find the key indicators. Next, we used long short-term memory (LSTM) and gated recurrent units (GRU) to build time series models for forecasting stock prices and compare them with the listing models. To evaluate the effectiveness of the proposed model, we collected six different stock market data from 2011 to 2019 to evaluate their forecast performance based on RMSE and MAPE metrics. It is worth mentioning that this study proposes two trading policies to evaluate trading profits and compare them with the listing methods, and their profits are pretty good to investors. After the experiments, the proposed time series model (GRU/LSTM combined with the selected key indicators) exhibits better forecast ability in fluctuating and non-fluctuating environments than the listing models, thus presenting an effective reference for stakeholders.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2021Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)1995Random Walks in Stock Market Prices 2001Application of support vector machines in financial time series forecasting 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2014Stock Price Prediction Using the ARIMA Model 2011Forecasting the Equity Risk Premium: The Role of Technical Indicators 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies

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