This study investigates trade-based manipulations of capital market instruments. The dataset of the study was gathered from 22 cases of manipulation in Borsa Istanbul (BIST) that occurred in the period between 2010 and 2015. We propose a machine learning approach consisting of supervised machine learning classification models to detect trade-based manipulation from the daily data of manipulated stocks. As a result of this study, supervised machine learning techniques are proven to be successful at detecting trade-based manipulations in trading networks based on the measurement methods of accuracy, sensitivity, and F1 score. We found that our proposed model has an F1 score of 91%, 95% sensitivity, and 93% accuracy in market manipulation detection.

本研究调查基于贸易的资本市场工具操纵。该研究的数据集是从 2010 年至 2015 年期间发生在伊斯坦布尔证券交易所 (BIST) 的 22 起操纵案例中收集的。我们提出了一种由监督机器学习分类模型组成的机器学习方法，以检测日常交易中的交易操纵被操纵股票的数据。作为这项研究的结果，基于准确性、灵敏度和 F1 分数的测量方法，监督机器学习技术被证明可以成功地检测交易网络中基于交易的操作。我们发现我们提出的模型在市场操纵检测中的 F1 分数为 91%，灵敏度为 95%，准确率为 93%。