在股票盘中交易中运用深度学习

G. Taroon, Ayushi Tomar, Chinthakunta Manjunath, M. Balamurugan, Bikramaditya Ghosh, Addapalli V. N. Krishna
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引用次数: 4

摘要

准确的股票价格预测对股票投资者来说是一项重要的利益。任何公司的未来股票价值都是由股票市场预测决定的。对股票未来价格的成功预测可能会带来可观的利润;因此,投资者更喜欢准确的股价预测。虽然有许多不同的方法可以帮助预测股票价格,但本文将简要介绍深度学习模型,并比较LSTM模型及其变体。本研究的主要目的是提出一个最适合并可用于股票价格趋势预测的模型。本文主要研究二元分类问题,预测SPDR标准普尔500指数的下一分钟价格走势。对SPDR标准普尔500指数进行的测试实验表明,与标准LSTM模型相比,参数较少的LSTM模型的变体Slim LSTM1、Slim LSTM2和Slim LSTM3具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Employing Deep Learning In Intraday Stock Trading
Accurate stock price prediction is a significant benefit to the Stock investors. The future Stock value of any company is determined by Stock market prediction. A successful prediction of the stock’s future price could result in a significant profit; Hence investors prefer a precise Stock price prediction. Although there are many different approaches to helps in forecasting stock prices, this paper will briefly look into the deep learning models and compare LSTM model and its variants. The key intention of this study is to propose a model that is best suitable and can be implemented to forecasting trend of stock prices. This paper focuses on binary classification problem, predicting the next-minute price movement of SPDR S&P 500 index The testing experiments performed on the SPDR S&P 500 index reveals that the variants of LSTM models, Slim LSTM1, slim LSTM2, and Slim LSTM3 with less parameters, provide better performance when compared to the Standard LSTM Model.
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