用于股市预测的深度学习模型:综合比较分析

Md Salim Chowdhury, Norun Nabi, Md Nasir Uddin Rana, Mujiba Shaima, Hammed Esa, Anik Mitra, Md Abu Sufian Mozumder, Irin Akter Liza, Murshid Reja Sweet, Refat Naznin, Md Murshid
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引用次数: 1

摘要

本研究利用美国国家证券交易所(NSE)和纽约证券交易所(NYSE)两家著名证券交易所的数据,对用于股市预测的深度学习模型进行了全面的比较分析。四种深度神经网络架构--多层感知器(MLP)、循环神经网络(RNN)、长短期记忆(LSTM)和卷积神经网络(CNN)--在 NSE 数据上进行了训练和测试,重点是汽车行业的塔塔汽车公司。分析包括 NSE 的汽车、银行和 IT 等行业数据,以及 NYSE 的金融和石油行业数据。结果显示,深度神经网络架构在两个交易所的表现均优于传统线性模型 ARIMA。使用 ARIMA 预测 NSE 价值时获得的平均绝对百分比误差 (MAPE) 值明显高于神经网络得出的值,这表明深度学习模型具有卓越的预测能力。值得注意的是,CNN 架构在捕捉非线性趋势方面表现出色,尤其是在识别数据中的季节性模式方面。预测股票价格的可视化进一步支持了这一发现,展示了深度学习模型适应动态市场条件和辨别金融时间序列数据中复杂模式的能力。此外,还分析了不同神经网络架构遇到的挑战,例如在特定时间范围内识别某些模式的困难,从而深入了解了每种模型的优势和局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis
This study presents a comprehensive comparative analysis of deep learning models for stock market forecasting using data from two prominent stock exchanges, the National Stock Exchange (NSE) and the New York Stock Exchange (NYSE). Four deep neural network architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)—were trained and tested on NSE data, focusing on Tata Motors in the automobile sector. The analysis included data from sectors such as Automobile, Banking, and IT for NSE and Financial and Petroleum sectors for NYSE. Results revealed that the deep neural network architectures consistently outperformed the traditional linear model, ARIMA, across both exchanges. The Mean Absolute Percentage Error (MAPE) values obtained for forecasting NSE values using ARIMA were notably higher compared to those derived from the neural networks, indicating the superior predictive capabilities of deep learning models. Notably, the CNN architecture demonstrated exceptional performance in capturing nonlinear trends, particularly in recognizing seasonal patterns within the data. Visualizations of predicted stock prices further supported the findings, showcasing the ability of deep learning models to adapt to dynamic market conditions and discern intricate patterns within financial time series data. Challenges encountered by different neural network architectures, such as difficulties in recognizing certain patterns within specific timeframes, were also analyzed, providing insights into the strengths and limitations of each model.
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