用线性回归分析孟加拉国股市，发现时间序列数据难建预测模型。
A Regression-Based Share Market Prediction Model for Bangladesh
- 基于达卡证券交易所数据做线性回归，识别影响股价的关键因素。
- 随机森林比线性模型表现更好，但时间序列无法构建有效线性预测模型。
- 适合关注新兴市场量化分析的投资者和研究者阅读。
股票市场是国家经济发展的重要领域。每天众多公司发行股票，投资者进行买卖。通常投资者偏好流动性较高的公司股票，而市场流动性与平均股价相关。本文对达卡证券交易所的股票市场数据进行了全面的线性回归分析，并将线性模型与随机森林模型在不同指标下进行对比，结果显示随机森林性能更优。同时，论文识别并解释了各因素对股价波动的个体显著性。研究还表明，时间序列数据不足以构建有效的线性预测模型。
原文摘要 · Abstract (English)
Share market is one of the most important sectors of economic development of a country. Everyday almost all companies issue their shares and investors buy and sell shares of these companies. Generally investors want to buy shares of the companies whose market liquidity is comparatively greater. Market liquidity depends on the average price of a share. In this paper, a thorough linear regression analysis has been performed on the stock market data of Dhaka Stock Exchange. Later, the linear model has been compared with random forest based on different metrics showing better results for random forest model. However, the amount of individual significance of different factors on the variability of stock price has been identified and explained. This paper also shows that the time series data is not capable of generating a predictive linear model for analysis.
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