CAGTRADE: Predicting Stock Market Price Movement with a CNN-Attention-GRU Model

IF 2.5 Q2 ECONOMICS Asia-Pacific Financial Markets Pub Date : 2024-06-17 DOI:10.1007/s10690-024-09463-w
Ibanga Kpereobong Friday, Sarada Prasanna Pati, Debahuti Mishra, Pradeep Kumar Mallick, Sachin Kumar
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Abstract

Accurately predicting market direction is crucial for informed trading decisions to buy or sell stocks. This study proposes a deep learning based hybrid approach combining convolutional neural network (CNN), attention mechanism (AM), and gated recurrent unit (GRU) to predict short-term market trends (1 day, 3 days, 7 days, 10 days) across different stock indices (BSE, HSI, IXIC, NIFTY, N225, SSE). The architecture dynamically weights the input sequence with the AM model, captures local patterns through CNN and effectively models long-term dependencies with GRU thus aiming to accurately classify either "buy" or "sell" positions of stocks. The model is assessed using classification and financial evaluation metrics involving accuracy, precision, recall, f1-score, annualized returns, maximum drawdown, and return on investment. It outperforms benchmark models, and different technical indicators including average directional index, rate of change, moving average convergence divergence, and the buy-and-hold strategy, demonstrating its effectiveness in various market conditions. The proposed model achieves an average accuracy of 98% in predicting the 1 day-ahead direction, and an average accuracy of 88.53% across all prediction intervals. The model was also validated using the wilcoxon signed rank test that further supported its significance over the benchmark models. The CAG model presents a comprehensive and intuitive approach to stock market trend prediction, with potential applications in real-world asset decision-making.

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CAGTRADE:利用 CNN-Attention-GRU 模型预测股市价格走势
准确预测市场走向对于做出买入或卖出股票的明智交易决策至关重要。本研究提出了一种基于深度学习的混合方法,将卷积神经网络(CNN)、注意力机制(AM)和门控递归单元(GRU)结合起来,预测不同股票指数(上证指数、恒生指数、IXIC、NIFTY、N225、上证指数)的短期市场趋势(1 天、3 天、7 天、10 天)。该架构利用 AM 模型对输入序列进行动态加权,通过 CNN 捕捉局部模式,并利用 GRU 对长期依赖关系进行有效建模,从而准确地对股票的 "买入 "或 "卖出 "位置进行分类。该模型采用分类和财务评估指标进行评估,包括准确率、精确度、召回率、f1-分数、年化收益率、最大缩水率和投资回报率。该模型的表现优于基准模型和不同的技术指标,包括平均方向性指数、变化率、移动平均收敛背离和买入并持有策略,证明了其在各种市场条件下的有效性。所提出的模型在预测 1 天前方向时的平均准确率为 98%,在所有预测区间内的平均准确率为 88.53%。该模型还通过威尔科克逊符号秩检验进行了验证,进一步证明了其优于基准模型的显著性。CAG 模型为股市趋势预测提供了一种全面而直观的方法,在现实世界的资产决策中具有潜在的应用价值。
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来源期刊
CiteScore
3.00
自引率
0.00%
发文量
34
期刊介绍: The current remarkable growth in the Asia-Pacific financial markets is certain to continue. These markets are expected to play a further important role in the world capital markets for investment and risk management. In accordance with this development, Asia-Pacific Financial Markets (formerly Financial Engineering and the Japanese Markets), the official journal of the Japanese Association of Financial Econometrics and Engineering (JAFEE), is expected to provide an international forum for researchers and practitioners in academia, industry, and government, who engage in empirical and/or theoretical research into the financial markets. We invite submission of quality papers on all aspects of finance and financial engineering. Here we interpret the term ''financial engineering'' broadly enough to cover such topics as financial time series, portfolio analysis, global asset allocation, trading strategy for investment, optimization methods, macro monetary economic analysis and pricing models for various financial assets including derivatives We stress that purely theoretical papers, as well as empirical studies that use Asia-Pacific market data, are welcome. Officially cited as: Asia-Pac Financ Markets
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