用RNN和LSTM预测印度股市股票同步性，准确率98%。
Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach
- 通过相位图与交叉量化分析，将股价同步转化为时序数据
- 20只大盘股21年数据验证，分类准确率达98%，F1为0.83
- 适合量化交易与金融风险管控研究者参考
本研究提出一种基于机器学习与非线性时间序列分析的股票价格同步性预测新方法。为捕捉股价间的复杂非线性关系，采用递归图（RP）与交叉递归定量分析（CRQA）。将交叉递归图（CRP）数据转化为时序格式后，引入循环神经网络（RNN）与长短期记忆网络（LSTM），实现对股票价格同步性的回归与分类预测。方法应用于印度市场20只高市值股票在21年期间的数据。结果表明，该方法可有效预测股价同步性，分类准确率达到0.98，F1得分为0.83，为制定高效交易策略与风险管理系统提供重要参考。
原文摘要 · Abstract (English)
Our research presents a new approach for forecasting the synchronization of stock prices using machine learning and non-linear time-series analysis. To capture the complex non-linear relationships between stock prices, we utilize recurrence plots (RP) and cross-recurrence quantification analysis (CRQA). By transforming Cross Recurrence Plot (CRP) data into a time-series format, we enable the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks for predicting stock price synchronization through both regression and classification. We apply this methodology to a dataset of 20 highly capitalized stocks from the Indian market over a 21-year period. The findings reveal that our approach can predict stock price synchronization, with an accuracy of 0.98 and F1 score of 0.83 offering valuable insights for developing effective trading strategies and risk management tools.
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