基于lstm的深度学习股票预测模型及预测优化模型

IF 2.3 Q3 MANAGEMENT
Akhter Mohiuddin Rather
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引用次数: 0

摘要

本文提出了一种新的基于时间序列的股票价格预测方法和一种新的基于预测结果的投资组合模型。为此,在基于长短期记忆的深度神经网络上实现了一种新的回归方案。一旦获得预测,就用于构建投资组合,或者更具体地说,用于预测投资组合。对从印度国家证券交易所获得的NIFTY-50股票数据进行了大量的实验。结果表明,该模型优于各种标准预测模型和各种标准投资组合优化模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LSTM-based Deep Learning Model for Stock Prediction and Predictive Optimization Model

A new method of predicting time-series-based stock prices and a new model of an investment portfolio based on predictions obtained is proposed here. For this purpose, a new regression scheme is implemented on a long-short-term-memory-based deep neural network. The predictions once obtained are used to construct an investment portfolio or more specifically a predicted portfolio. A large set of experiments have been carried on stock data of NIFTY-50 obtained from the National stock exchange of India. The results confirm that the proposed model outperforms various standard predictive models as well as various standard portfolio optimization models.

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CiteScore
2.70
自引率
10.00%
发文量
15
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