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Article type: Research Article
Authors: Roy Choudhury, Ahana; * | Abrishami, Soheila | Turek, Michael | Kumar, Piyush
Affiliations: Department of Computer Science, Florida State University, FL, USA. E-mails: roychoud@cs.fsu.edu, abrisham@cs.fsu.edu, turek@cs.fsu.edu, piyush@cs.fsu.edu
Correspondence: [*] Corresponding author: Ahana Roy Choudhury, 3748 Biltmore Avenue, Tallahassee, FL-32311. Tel.: 205-566-4074; E-mail: roychoud@cs.fsu.edu.
Abstract: Financial time-series forecasting, and profit maximization is a challenging task, which has attracted the interest of several researchers and is immensely important for investors. In this paper, we present a deep learning system, which uses a variety of data for a subset of the stocks on the NASDAQ exchange to forecast the stock price. Our framework allows the use of a variational autoencoder (VAE) to remove noise and time-series data engineering to extract higher-level features. A Stacked LSTM Autoencoder is used to perform multi-step-ahead prediction of the stock closing price. This prediction is used by two profit-maximization strategies that include greedy approach and short selling. Besides, we use reinforcement learning as a third profit-enhancement strategy and compare these three strategies to offline strategies that use the actual future prices. Results show that the proposed methods outperform the state-of-the-art time-series forecasting approaches in terms of predictive accuracy and profitability.
Keywords: Financial time series prediction, stock price, LSTM autoencoder, feature engineering, reinforcement learning
DOI: 10.3233/AIC-200629
Journal: AI Communications, vol. 33, no. 2, pp. 75-92, 2020
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