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 NASDAQexchange 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.

中文翻译：

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使用神经网络提高股票交易的利润

财务时间序列预测和利润最大化是一项艰巨的任务，已吸引了几位研究人员的兴趣，对投资者而言极为重要。在本文中，我们提供了一个深度学习系统，该系统使用纳斯达克交易所股票一部分的各种数据来预测股票价格。我们的框架允许使用可变自动编码器（VAE）来消除噪声，并通过时序数据工程来提取更高级别的功能。堆叠式LSTM自动编码器用于执行股票收盘价的多步提前预测。两种预测方法可用于两种获利最大化策略，包括贪婪法和卖空法。除了，我们将强化学习作为第三种提高利润的策略，并将这三种策略与使用实际未来价格的离线策略进行比较。结果表明，所提出的方法在预测准确性和获利能力方面均优于最新的时间序列预测方法。