Enhancing profit from stock transactions using neural networks
| Autor: | Ahana Roy Choudhury, Piyush Kumar, Soheila Abrishami, Michael Turek |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Artificial Intelligence Computer science 020208 electrical & electronic engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Profit (economics) Stock (geology) Industrial organization |
| Zdroj: | AI Communications. 33:75-92 |
| ISSN: | 1875-8452 0921-7126 |
| DOI: | 10.3233/aic-200629 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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