• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.15-37
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• 2006

In this paper, we discuss a data mining approach to detection of stock price manipulation in the Korean stock market. First of all, we review current methods which is being exercised in the Korean stock market as well as in the US stock market. And then we apply data mining techniques to the problem using data from the Korean stock market and discuss the results along with their implications.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.707-715
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• 2021

This article examines the potential impact of the Covid-19 Lockdown on earnings manipulation and stock market sensitivity to earnings announcements. It also explores the effects of earnings manipulation after the COVID-19 outbreak on the share price sensitivity to the earnings disclosures. The study uses a quantitative method to analyze the financial data consisting of 87 firms listed on the Iraq Stock Exchange for the period from 2018 to 2020, which constitutes a total of (174 observations). We used Ohlson (1995) model to estimate financial market reaction and sensitivity to earnings manipulation fluctuations and accounting information. The results show that companies practice earnings manipulation to maintain earnings over a time series, which means a negative impact of earnings manipulation on all earnings measures' value relevance (EPS, BVS, and CFS). Accordingly, earnings manipulation negatively influences investor behavior in the financial market, based mainly on financial reporting. The value relevance of financial reports has also decreased because of the COVID-19 outbreak and related economic Lockdown. These results reflect a long-term adverse impact of earnings manipulation on investor behavior and financial statements reliability.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.41-47
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• 2017

This study is a preceding research designed to deduct an institutional supplementary measure that minimizes any inevitable side effects from the improvement of artificial intelligence (AI) technology, which is the core element of the Fourth Industrial Revolution. In this specific case in which the Robo-Advisor, the representative type of AI-applied technology, was hacked by a third party and ended up manipulating prices, the study was intended to examine the responsibility relationship of the current legal framework. Although the current legal framework strictly prohibits acts such as hacking and manipulation, it was confirmed that if the Robo-Advisor management firm acts in compliance with protection measures regarding hacking, the firm is free from any legal liabilities and there is insufficient legal protection available for ordinary investors with grand-scale damage from price manipulation Based on this study, further studies are needed to derive more institutional supplementary measures on overcoming these problems.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.907-920
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• 2016

This study suggests an outlier detection algorithm that uses quantile autoregressive model in time series data, eventually applying it to actual stock manipulation cases by comparing its performance to existing methods. Studies on outlier detection have traditionally been conducted mostly in general data and those in time series data are insufficient. They have also been limited to a parametric model, which is not convenient as it is complicated with an analysis that takes a long time. Thus, we suggest a new algorithm of outlier detection in time series data and through various simulations, compare it to existing algorithms. Especially, the outlier detection algorithm in time series data can be useful in finding stock manipulation. If stock price which had a certain pattern goes out of flow and generates an outlier, it can be due to intentional intervention and manipulation. We examined how fast the model can detect stock manipulations by applying it to actual stock manipulation cases.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.4
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• pp.1640-1647
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• 2012

The allocation to third parties in increase of capital is increasing in KOSDAQ firms. With this trend, they cause many problems which involves stock price manipulation. Under this condition, this study analyzes stock price reaction by event study to 197 cases of 81 KOSDAQ companies that allocated to third parties in increase of capital between the year of 2007 and 2009. And we find determinants of cumulative abnormal return by using multiple regression. Results of this research are as follows. First, in case of excess return of (-5, +5), it reveals positive excess return significantly at 1% significance level during 4 days before payment day(event day). But it reveals negative excess return significantly at 1% significance level during 5 days after payment day. Second, in case of excess return of (-40, +40), it reveals positive excess return significantly at 1% significance level during 40 days before payment day(event day). But it reveals negative excess return significantly at 1% significance level during 40 days after payment day. Third, in case of excess return of (0, 1 year), it reveals negative excess return significantly at 1% significance level during 1 year after payment day. Fourth, significant determinant of cumulative abnormal return to (-5, +5) was firm size with positive effect. Significant determinants of cumulative abnormal return to (-40, +40) were reserve ratio and debt ratio. Reserve ratio has positive effect But debt ratio has negative effect. Significant determinants of cumulative abnormal return to (0, 1 year) were firm size, debt ratio, reserve ratio. equity ratio to large shareholder. Firm size, debt ratio, equity ratio to large shareholder have negative effect. But reserve ratio has positive effect.