用LSTM和量子LSTM预测巴基斯坦股市，量子模型表现更优。
Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement
- 对比传统LSTM与量子版QLSTM在股市预测中的表现。
- QLSTM在预测卡拉奇股市指数时误差更低，稳定性更强。
- 适合对金融预测与量子机器学习感兴趣的读者。
近年来，金融分析师尝试构建模型以预测股票价格指数的走势。在经济、社会和政治环境模糊的背景下，如巴基斯坦，这一任务尤为困难。本研究采用高效的机器学习模型——长短期记忆网络（LSTM）和量子长短期记忆网络（QLSTM），基于2004年2月至2020年12月期间的26个经济、社会、政治及行政指标的月度数据，对卡拉奇证券交易所（KSE）100指数进行预测。通过比较LSTM与QLSTM对KSE 100指数的预测值与实际值之间的差异，结果显示，QLSTM在预测精度和稳定性方面具有潜在优势，表明其在股票市场趋势预测中具备应用前景。
原文摘要 · Abstract (English)
In recent years, financial analysts have been trying to develop models to predict the movement of a stock price index. The task becomes challenging in vague economic, social, and political situations like in Pakistan. In this study, we employed efficient models of machine learning such as long short-term memory (LSTM) and quantum long short-term memory (QLSTM) to predict the Karachi Stock Exchange (KSE) 100 index by taking monthly data of twenty-six economic, social, political, and administrative indicators from February 2004 to December 2020. The comparative results of LSTM and QLSTM predicted values of the KSE 100 index with the actual values suggested QLSTM a potential technique to predict stock market trends.
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