印度股票市场的数据挖掘:建立低风险、击败市场的投资组合

Q3 Economics, Econometrics and Finance Finance: Theory and Practice Pub Date : 2023-10-23 DOI:10.26794/2587-5671-2023-27-5-115-127
S. R. Mitragotri, N. Patel
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引用次数: 0

摘要

在过去的50年里,商业学者已经确定了300多个可能影响股票回报的决定因素。然而,我们仍然不知道是否所有的回报决定因素都同样重要,或者是否有一组较小的决定因素对股票回报有不成比例的更大影响。挖掘历史数据能帮助我们找到对股票收益有不成比例的更高影响的这组较小的收益决定因素吗?利用印度市场的历史数据,我们建立了一个庞大的投资数据库,其中有超过74,000笔投资,分布在132个月的时间里。从这个数据库中,使用“关联规则挖掘”方法,我们能够挖掘出一组强大的“关联规则”,这些“关联规则”指向一组较小的“回报决定因素”,这些决定因素在高于指数回报的投资中更常见。通过使用“关联规则挖掘”,我们从37个回报决定因素中找出了一小部分关键的回报决定因素,这些决定因素在印度超过指数回报的投资中最常见。根据这些“关联规则”创建的投资组合的投资组合风险低于市场风险,并提供优于指数的回报。使用这些关联规则创建的“样本外”投资组合的投资组合“贝塔”小于1,并且在印度市场的所有持有期间提供的回报都大大超过市场回报。通过本文,我们演示了投资组合经理如何挖掘“关联规则”并构建投资组合,而不限制可以包含在筛选过程中的因素的数量。
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Data Mining in Indian Equity Markets: building low Risk, Market beating Portfolios
Over the last five decades, business academics have identified over 300 determinants that potentially influence stock returns. However, we still do not know whether all return determinants are equally important, or whether there is a smaller set of determinants that has a disproportionately larger influence on stock returns. Can mining historical data help us find this smaller set of return determinants that has a disproportionately higher influence on stock returns? Using historical data from the Indian market, we build a large database of investments with more than 74,000 investments spread over a period of 132 months. From this database, using “association rule mining” method, we are able to mine a strong set of “association rules” that point to a smaller set of “return determinants” that are seen more frequently in investments that beat index returns. From a pool of thirty-seven return determinants, using “association rule mining”, we were able to find out a small set of key return determinants that are seen most frequently in investments that beat index returns in India. Portfolios created from these “association rules” have a portfolio risk lower than the market risk and provide index-beating returns. “Out-of-sample” portfolios created using these association rules have portfolio “Beta” less than one and provide returns that beat the market returns by a significant margin for all holding periods in the Indian market. Through this paper, we demonstrate how portfolio managers can mine “association rules” and build portfolios without any limits on the number of factors that can be included in the screening process.
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来源期刊
Finance: Theory and Practice
Finance: Theory and Practice Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
1.30
自引率
0.00%
发文量
84
审稿时长
8 weeks
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