用LSTM模型预测尼日利亚股市收益，准确率超90%。
Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique
- 采用LSTM处理尼日利亚股市历史数据，进行时间序列建模。
- 在可靠数据下，模型预测准确率超过90%。
- 适合关注金融时序预测与深度学习应用的研究者。
投资者和股市分析师在预测股票回报并做出明智投资决策方面面临重大挑战。股票回报的可预测性可增强投资者信心，但实现仍具难度。为此，本研究使用长短期记忆（LSTM）模型预测未来股市走势，基于尼日利亚证券交易所（NSE）的历史数据集，经过清洗与归一化后构建LSTM模型。通过性能指标评估，并与人工神经网络（ANN）和卷积神经网络（CNN）等其他深度学习模型对比。实验结果表明，在可靠数据训练下，LSTM模型对未来股价与回报的预测准确率超过90%。研究结论认为，若训练得当，LSTM模型可用于解决金融时间序列相关问题。未来研究应探索将LSTM与CNN等技术结合，构建混合模型以降低单一模型带来的风险。
原文摘要 · Abstract (English)
Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. The model was evaluated using performance metrics and compared with other deep learning models like Artificial and Convolutional Neural Networks (CNN). The experimental results showed that the LSTM model can predict future stock market prices and returns with over 90% accuracy when trained with a reliable dataset. The study concludes that LSTM models can be useful in predicting financial time-series-related problems if well-trained. Future studies should explore combining LSTM models with other deep learning techniques like CNN to create hybrid models that mitigate the risks associated with relying on a single model for future equity stock predictions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。