Identification of Insider Trading Using Extreme Gradient Boosting and Multi-Objective Optimization

Wang, Chenguang; Li, Jie; Yu, Haoran; Tian, Hongyu; Zhang, Yu; Cui, Yong; Ma, Fangjie; Yang, Tianxiang

doi:10.3390/info10120367

Cited by 11 publications

(11 citation statements)

References 47 publications

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“…From 2000 to 2017, the greater part of the budget summaries of recorded organizations has been effectively obtained, including an aggregate of 32,283 fiscal reports of 3,025 recorded organizations. Based on perusing countless writing [16][17][18][19], this paper constructed six first-level indicators according to the above financial statement data, namely, solvency index, profitability index, operating ability index, development ability index, cash flow index, and risk level index. Under this, we figured out 25 auxiliary markers as indicated by the current writing on monetary danger and monetary extortion.…”

Section: Financial Index Constructionmentioning

confidence: 99%

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

Wei

2022

Wireless Communications and Mobile Computing

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At present, the process of security issuance in China has changed from examination and approval system to approval system. With the increasing stock that enters the capital market through the IPO, establishing an effective financial risk monitoring and control system of listed companies has great practical significance and application value. In the perspective of big data, this paper constructs a new practical financial early warning integration model. A total of 32,283 financial statements of 3,025 listed companies are collected from 2000 to 2017. First, this paper collected and built a financial crisis prediction database: a total of 32,283 financial statements of 3,025 listed companies occurred from year 2000 to 2017 and corresponding financial indicators including 6 primary indicators and 25 secondary indicators are collected. Next, the financial crisis model based on financial indicators is proposed and trained by the classic machine learning method such as random forest and gradient boosting decision tree. And then, through natural language processing and deep learning technology, this paper also builds a financial crisis prediction model based on financial statement text. Finally, an ensemble model is proposed and its performances are evaluated; the results indicate that the proposed ensemble model performed the best and significantly outperforms other benchmark methods for financial crisis prediction and identification, indicating that it can be employed as an intelligent identification system to enhance identification accuracy for early warning and identification of financial crisis of listed companies in China stock marketing.

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Section: Financial Index Constructionmentioning

confidence: 99%

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

Wei

2022

Wireless Communications and Mobile Computing

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“…For the detection of insider trading, research [1] employs Extreme Gradient Boosting and Multi-Objective Optimization. To begin, an integrated system of XGboost and NSGA-II was used to automatically derive insider trading cases that occurred in the Chinese stock market in the past, as well as their relevant indicators.…”

Section: Literature Surveymentioning

confidence: 99%

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

Sapkota¹,

Kumar²,

Mathur³

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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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..

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“…Recently, since Chen and Guestrin first proposed the eXtreme Gradient Boosting (XG-Boost) algorithm in 2016 [15], an increasing number of researchers have implemented it to solve the forecasting task, and great accuracies have been produced in many applications [16,17]. The most remarkable advantage of XGBoost is that the prediction accuracy and computing speed are significantly enhanced compared to the traditional gradient boosting algorithms [18][19][20]. In the field of financial markets, Huang et al predicted the intradaily market trends using an XGBoost-based method, and it successfully produced satisfactory forecasting performance [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method

et al. 2022

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Futures price-movement-direction forecasting has always been a significant and challenging subject in the financial market. In this paper, we propose a combination approach that integrates the XGBoost (eXtreme Gradient Boosting), SMOTE (Synthetic Minority Oversampling Technique), and NSGA-II (Non-dominated Sorting Genetic Algorithm-II) methods. We applied the proposed approach on the direction prediction and simulation trading of rebar futures, which are traded on the Shanghai Futures Exchange. Firstly, the minority classes of the high-frequency rebar futures price change magnitudes are oversampled using the SMOTE algorithm to overcome the imbalance problem of the class data. Then, XGBoost is adopted to construct a multiclassification model for the price-movement-direction prediction. Next, the proposed approach employs NSGA-II to optimize the parameters of the pre-designed trading rule for trading simulation. Finally, the price-movement direction is predicted, and we conducted the high-frequency trading based on the optimized XGBoost model and the trading rule, with the classification and trading performances empirically evaluated by four metrics over four testing periods. Meanwhile, the LIME (Local Interpretable Model-agnostic Explanations) is applied as a model explanation approach to quantify the prediction contributions of features to the forecasting samples. From the experimental results, we found that the proposed approach performed best in terms of direction prediction accuracy, profitability, and return–risk ratio. The proposed approach could be beneficial for decision-making of the rebar traders and related companies engaged in rebar futures trading.

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Identification of Insider Trading Using Extreme Gradient Boosting and Multi-Objective Optimization

Cited by 11 publications

References 47 publications

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

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

High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method

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