在同一个行业内的库存之间,在库存预测中应用倾向性得分匹配方法
1School of Communication and Information Engineering, Shanghai University, Shanghai, China.
PeerJ. Computer science
|March 4, 2024
概括
这项研究引入了倾向分数匹配 (PSM) 来分析股票相互依赖,通过整合整个行业的数据,显著提高了制药股票价格预测的准确性. 该研究使用改进的粒子群优化 (IPSO) 和长短期记忆 (LSTM) 网络来增强预测模型.
科学领域:
- 金融计量经济学 金融计量经济学
- 计算金融是一种计算金融.
- 在金融领域的机器学习.
背景情况:
- 现有的股票价格预测模型往往忽略了同一个行业内的股票之间的关键相互依存关系.
- 这种监督限制了预测准确性,因为股票往往会影响对方的表现.
- 倾向性得分匹配 (PSM) 是一种因果推断技术,在股票相互依赖性研究中未得到充分利用.
研究的目的:
- 通过对制药行业内部的相互依存关系进行会计核算,调查股价预测.
- 引入和评估倾向性得分匹配 (PSM) 在分析库存相互依存性的应用.
- 通过将PSM与改进的粒子群优化 (IPSO) 和长短期内存 (LSTM) 网络集成,提高预测准确性.
主要方法:
- 使用 Stata 来识别显著相关的药品库存 (化学,生物制药,传统中医药),使用对待 (ATT) 值的平均治疗效应.
- 应用倾向性得分匹配 (PSM) 来将目标库存与子行业内的非目标库存联系起来.
- 集成加权非目标库存数据与目标库存数据用于改进的粒子群优化-长期短期记忆 (IPSO-LSTM) 预测模型中的验证.
主要成果:
- 通过PSM将同一子行业内非目标库存的数据纳入PSM显著提高了IPSO-LSTM模型的预测准确性.
- 该研究表明,考虑股票相互依赖对整体股票价格预测绩效产生了积极的影响.
- 确定了显著的相关性,并在不同的制药子行业内有效地应用了PSM.
结论:
- 倾向分数匹配 (PSM) 是分析和将股票相互依赖纳入预测模型的一个有价值的工具.
- 对整个行业的股票关系进行会计,可以明显提高股票价格预测的准确性.
- 这项研究为金融市场分析提供了一种新的方法,通过因果推理技术提高机器学习模型的性能.
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