Intelligent System for the Detection of Insider Trading in Indian Stock Market

Amosh Sapkota, Anand Kumar, A. Mathur
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Abstract

. Insider trading is a pervasive stock market malpractice that has existed since the inception of the security market. Insider trading is notoriously difficult for regulators worldwide to crack down on. India has a dismal track record when it comes to prosecuting insider traders. In the last three decades of Sebi’s existence, there hasn’t been a single conviction for insider trading. In this study, we mainly inspectthe features of insider trading by examiningkey indicators during the time length before the release of insider information. In our investigation, we proposed a methodology for detection of insider trading in Indian stock market. To start with, the insider trading cases that happened in the Indian financial exchange we collected corporate filing data from NSE website for each company of NIFTY 50, which has different columns related to price, action and person or organization doing that action from 1stJanuary to a day prior to the publicationof financial results of December quarter offiscal year 2020-21. On doing as such, we have seen that enormous exchange have been done prior to publicationof financial results in some companies, which can be suspected as insider trading. At that point, themachine learning algorithms were utilized for preparing and for foreseeing Insider trading. Then, the algorithms were used for training and for predicting insider trading. Finally, their performance wasmeasured, compared and accuracy was calculated.Experiments revealed that the recommended method successfully achieved the best accuracy. This could be amazingly helpful for detecting insider trading in future, not only in Indian stock market, but also in other stock exchanges. The proposed approach and results in this examination is of incredible importance for market controllers to improve their oversight proficiency and precision on insider trading.. machine learning, deep learning..
印度股票市场内幕交易智能检测系统
. 内幕交易是一种普遍存在的股票市场舞弊行为,自证券市场成立以来一直存在。众所周知,全球监管机构很难打击内幕交易。在起诉内幕交易者方面,印度的记录令人沮丧。在Sebi成立的过去30年里,没有一起内幕交易案被定罪。在本研究中,我们主要通过考察内幕信息发布前一段时间内的关键指标来考察内幕交易的特征。在我们的调查中,我们提出了一种检测印度股票市场内幕交易的方法。首先,发生在印度金融交易所的内幕交易案件,我们从NSE网站上收集了NIFTY 50每家公司的公司备案数据,其中有不同的栏目,涉及1月1日至2020-21年12月季度官方财务业绩公布前一天的价格、行动和个人或组织。在这样做的过程中,我们看到,在一些公司公布财务业绩之前,已经进行了大量的交换,这可能被怀疑是内幕交易。那时,机器学习算法被用于准备和预测内幕交易。然后,这些算法被用于训练和预测内幕交易。最后对它们的性能进行了测量、比较和精度计算。实验结果表明,该方法取得了较好的精度。这对未来发现内幕交易非常有帮助,不仅在印度股市,而且在其他股票交易所。本文提出的方法和结果对市场管理者提高对内幕交易的监管熟练度和精确度具有重要意义。机器学习、深度学习……
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