用财务数据+机器学习预测股市走势，准确率超70%
Leveraging Fundamental Analysis for Stock Trend Prediction for Profit
- 基于财务比率和折现现金流模型构建预测任务
- 逻辑回归模型在两项任务中分别达74.66%和72.85%准确率
- 适合关注长期投资与基本面分析的决策者
本文研究长短期记忆网络（LSTM）、一维卷积神经网络（1D CNN）和逻辑回归（LR）模型在基于基本面分析的股票趋势预测中的应用。与多数依赖技术或情感分析的研究不同，本研究聚焦公司财务报表和内在价值。基于2019至2023年跨多个行业的269个上市公司数据点，采用关键财务比率和折现现金流（DCF）模型，构建两个预测任务：年度股价差（ASPD）和当前股价与内在价值差（DCSPIV），分别评估年度盈利可能性和当前盈利能力。结果表明，逻辑回归模型表现最优，平均测试准确率分别为74.66%（ASPD）和72.85%（DCSPIV）。该研究填补了将基本面分析融入机器学习进行股票预测的文献空白，为学术研究与实际投资策略提供参考，凸显利用基本面数据进行长期趋势预测的潜力，助力组合管理者决策。
原文摘要 · Abstract (English)
This paper investigates the application of machine learning models, Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Networks (1D CNN), and Logistic Regression (LR), for predicting stock trends based on fundamental analysis. Unlike most existing studies that predominantly utilize technical or sentiment analysis, we emphasize the use of a company's financial statements and intrinsic value for trend forecasting. Using a dataset of 269 data points from publicly traded companies across various sectors from 2019 to 2023, we employ key financial ratios and the Discounted Cash Flow (DCF) model to formulate two prediction tasks: Annual Stock Price Difference (ASPD) and Difference between Current Stock Price and Intrinsic Value (DCSPIV). These tasks assess the likelihood of annual profit and current profitability, respectively. Our results demonstrate that LR models outperform CNN and LSTM models, achieving an average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV. This study contributes to the limited literature on integrating fundamental analysis into machine learning for stock prediction, offering valuable insights for both academic research and practical investment strategies. By leveraging fundamental data, our approach highlights the potential for long-term stock trend prediction, supporting portfolio managers in their decision-making processes.
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