Stock Price Manipulation Research Articles

Product Market Competition and Predatory Stock Price Manipulation

We develop a model in which feedback effects from equity markets to firms' access to external finance allow uninformed traders to profit by short selling a firm's stock while going long on its competitors. Because this strategy distorts the investment incentives of the firm targeted by short selling to the benefit of its rivals, we label it predatory stock price manipulation. Our model shows that predatory stock price manipulation leads to inefficient market concentration. Our analysis further unveils product market competition as a channel through which buy orders increase manipulation profits, providing new insights into the regulation of short sales.

Review of the Causes of 1907 Panic and Aftermath

The Panic of 1907 is often known as the Panic that initiated the development of the Federal Reserve System (Bordo, 1985). The Panic of 1907 was “The beginning of the end of unregulated capital markets and banking system without the lender of the last resort in the United States.” (Fohlin, Gehrig and Haas, 2015, page 2). Numerous causes lead to the Panic of 1907 including: shadow banking, the San Francisco earthquake and fire, stock price manipulation, seasonal agriculture fluctuations, an outflow of gold, and higher interest rates. This paper reviews the primary literature on these causes, and how they led to the Panic of 1907 and the subsequent regulations culminating in the Federal Reserve Bank.

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation

Stock price manipulation uses illegitimate means to artificially influence market prices of several stocks. It causes massive losses and undermines investors’ confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme using supervised learning but lacks the adaptive capability to capture different manipulative strategies. This begets the assumption of model parameter values specific to the underlying manipulation scheme. In addition, supervised learning requires the use of labelled data which is difficult to acquire due to confidentiality and the proprietary nature of trading data. The proposed research establishes a detection model based on unsupervised learning using Kernel Principal Component Analysis (KPCA) and applied increased variance of selected latent features in higher dimensions. A proposed Multidimensional Kernel Density Estimation (MKDE) clustering is then applied upon the selected components to identify abnormal patterns of manipulation in data. This research has an advantage over the existing methods in overcoming the ambiguity of assuming values of several parameters, reducing the high dimensions obtained from conventional KPCA and thereby reducing computational complexity. The robustness of the detection model has also been evaluated when two or more manipulative activities occur within a short duration of each other and by varying the window length of the dataset fed to the model. Validation on multiple datasets and a comprehensive assessment of the model performance has been conducted without providing any prior information about the location of the manipulation. The results show a significant performance enhancement in terms of the F-measure values and a significant reduction in false alarm rate (FAR) has been achieved.

Buying Manipulation around Seasoned Equity Offerings

We show that seasoned equity offerings (SEOs), in which secondary share-offering size greatly exceeds market share turnover, provide a unique opportunity for stock price manipulation by speculators holding restricted shares. The proposed new theory is consistent with several puzzling empirical findings, for example, the positive abnormal stock return between announcement and issuance and the long-term poorer returns of issuing firms after SEOs. The manipulation is characterized by a strong buying in the secondary market prior to the issuance, which drives up the market price and thereby the offering price of restricted shares, and a large amount of liquidation of restricted shares in the SEO. Our model predicts that favorable market reactions with speculator's manipulative intent after SEO announcement could occur, which naturally leads to a successful issuance and over-investment based long-term underperformance after SEO.

Analysis of Market Manipulation from the Perspective of Behavioral Finance

Behavioral finance is a hot issue in the field of international finance in recent years. It draws on the research results of psychology, behavioral science and sociology, and systematically studies the investment decision-making behavior of investors under irrational assumptions and its impact on asset pricing. From the perspective of behavioral finance, this paper discusses how investors are influenced by information in the market manipulation phenomenon to make irrational decisions, and the market manipulation behavior artificially distorts the normal price of the securities market, making the price and value of the securities seriously deviate, resulting in false supply and demand, misleading the flow of funds, can not truly reflect the relationship between market supply and demand, and damage the interests of investors. Therefore, strengthening the supervision of stock price manipulation and formulating relevant regulatory measures are of great significance for controlling risks, enhancing market effectiveness, and making the securities market healthy and smooth.

Captured by Mutual Funds: Do Cross-Audits Undermine Auditor Independence?

We analyze the impact of cross-audits between companies and mutual funds on auditors’ reporting decisions. We document that companies are more likely to receive favorable audit opinions when they appoint the same auditor as their mutual fund blockholders. In cross-sectional evidence consistent with expectations, we find that the role that cross-audits play is concentrated where the mutual fund has stronger incentives to orchestrate stock price manipulation and the auditor is more likely to be captured by the mutual fund. We further show that investors fail to detect the impact of cross-audits on audit opinions in the short-term, which enables mutual funds to exploit this temporary mispricing through stock trading. In compensation, shared auditors captured by mutual funds benefit from higher audit fees and more audit business. Our evidence suggesting that market irregularities accompany cross-audits has major implications for stock market regulators.

Role of clustering based on density to detect patterns of stock trading deviation

The pattern of deviation patterns can be identified from the results of cluster transactions and transactions that are transaction irregularities, will be detected. DBSCAN as a density-based clustering algorithm forms clusters that agglomerate and make it easier to detect unclustered data, which is considered as data noise (data outlier). The nature of density in the data clamping process will make it easier to determine noise data objects.The DBSCAN has two parameters, Eps and MinPts. The values entered in both parameters play a role in forming clusters. Stock trading transactions are stated as data objects to be clustered. The noise from clustering with DBSCAN shows outlier transactions, which have diferrent pattern with ordinary transactions. In the results of this clustering, the stock transaction pattern which includes outliers is obtained, marking the close occurs. This result can help to detect stock price manipulation in outlier transactions carried out by securities brokers

Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning

With the rapid development of the stock markets in developing countries, determining how to efficiently detect stock price manipulation activities to protect the interests of ordinary investors is really an important problem. Previous studies have introduced machine learning techniques into stock price manipulation detection and achieved better experimental results than traditional multivariate statistical techniques. Some characteristic features show statistically significant differences between manipulated and non-manipulated stocks, but this complementary information has rarely been considered in the manipulation detection model. The main contribution of our research work is the design of a novel RNN-based ensemble learning (RNN-EL) framework that combine trade-based features derived from trading records and characteristic features of the list companies to effectively detect stock price manipulation activities. Based on prosecuted manipulation cases reported by the China Securities Regulatory Commission (CSRC), we built a specific dataset containing labeled samples with trading data and characteristic information to conduct empirical experiments. The experimental results show that our proposed method outperforms state-of-the-art approaches in detecting stock price manipulation by an average of 29.8% in terms of AUC value. The managerial implication of our work is that government regulators can apply the proposed methodology to efficiently identify suspicious trading behaviors among huge amounts of trading activities in time to take action to ensure a fair trading environment.

선물·옵션 만기일의 주가조작 행위에 대한 실증분석

선물·옵션 만기일의 주가조작 행위에 대한 실증분석

Managerial Compensation and Stock Price Manipulation

ABSTRACTThis paper studies the role of optimal managerial compensation in reducing uncertainty about manager reporting objectives. It is shown that, paradoxically, firm owners allow managers with higher propensity to manipulate the short‐term stock price to push for higher powered and more short‐term‐focused equity incentives. Such managers also work harder, and manipulate more, but may not generate higher firm profits. The model is consistent with existing empirical findings about the relationship between manipulation and equity pay, suggesting that heterogeneity in manager manipulation propensities may be an important driver of heterogeneity in pay. Novel testable predictions are developed.

Fake News: Evidence from Financial Markets

Using a unique dataset of fake paid-for articles obtained from an SEC investigation, we examine the impact of fake news in financial markets. In addition to the known fake articles, we use a linguistic algorithm to detect deception in expression for a much larger set of news content with unknown authenticity. The known fake articles are used to validate the algorithm. We find increases in abnormal trading volume and temporary price impact following fake news for small firms, but no impact for large firms. Following the SEC’s announced investigation, we find a marked decrease in the probability of false content and a decrease in reaction by investors to all news, but especially content with less authenticity. These findings are most pronounced for small firms with high retail ownership. Finally, small firms engage in press releases, 8-K filings, and insider trading that coincide with fake articles, consistent with concerns of coordinated stock price manipulation. No such patterns are observed for large firms. Our setting provides a unique opportunity to quantify fake news’ impact that avoids the joint hypothesis problem plaguing tests of informational efficiency.

Are Internet message boards used to facilitate stock price manipulation? Evidence from an emerging market, Thailand

AbstractThe impact of messages posted on the top three financial discussion boards in Thailand is investigated using an event study approach. Heightened message board posting activity for small firms is associated with a strong pre‐event share price run‐up over many days and a partial post‐event return reversal, consistent with manipulative activity whereby traders sometimes ‘talk up’ small‐firm prospects on message boards and then dump them. Close to 90 percent of the price falls associated with bad discussion board news get reversed within 75 days, consistent with short sellers utilising heightened self‐serving message board activity when targeting companies for short selling.

Do institutions trade ahead of false news? Evidence from an emerging market

Many studies examine the use of false news as a method of stock price manipulation. Empirical research shows, for example, that false news generates persistent abnormal returns and affects trading volume. Studies also show that institutions often know about news before it breaks. However, the role institutions play in false news events is still unclear. To understand that role, we track institutional order flow around the release of false news in the Chinese stock market. We find that institutions seem to have early information about false news releases. Their prerelease order flows predict false news sentiments and market reactions. We further find that the early information may come from the media outlets reporting the false news. We also find evidence that institutions reverse their positions several days after the denial releases rather than when the news breaks. In turn, our evidence shows which trading patterns could bring more potential profits than reversing right on the news breaks. Our results provide unique insights into price movements and institutional reactions on false news, as well as evidence regarding regulating institutions, media platforms, and information manipulation in the stock market.

Stock Price Manipulation: The Role of Intermediaries

We model a scenario in which there are three types of investors: fundamentalists, speculators, and trend-followers and an intermediary who cares about his reputation. Fundamentalists are rational investors with long horizons who are interested in the dividend stream. Speculators are rational investors who have short horizons and are interested in profiting from short-term price movements or capital gains. Trend-followers are behavioral investors who extrapolate price trends, and, consequently, are late entrants in the market. We show that an informed intermediary (broker) can manipulate demand (consequently stock price) without losing his reputation when there is information asymmetry. We also show that there is a trade-off between broker level competition for reputation and market liquidity. Broker level competition checks manipulation, but it adversely affects market liquidity.

Managerial gaming of stock and option grants

AbstractIn this paper, we examine managerial gaming of different types of equity grants, both at the initial award of the equity grants (front‐end gaming) and the unwinding of the equity holdings in the future (back‐end gaming). We find that the potential gains from stock price manipulation vary substantially across different types of equity grants. While traditional stock option grants are less vulnerable to front‐end gaming, they are more vulnerable to back‐end gaming than other types of equity grants (e.g., restricted stock grants). To prevent or discourage managerial gaming, firms should preset all terms of the equity grant in advance and link its future payoff to average stock prices (e.g., by granting Asian stock options).

UNDERSTANDING INVESTORS’ BEHAVIOR DURING STOCK PRICE MANIPULATION: A CASE OF INDONESIA’S STOCK MARKET

Penelitian ini mencoba untuk mencari pola tekstual yang berkaitan dengan perilaku investor dan sentimen selama manipulasi harga saham di Bursa Efek Indonesia. Kami mengusulkan analisis text mining untuk menunjukkan proses penggalian informasi dari berita dan media rilis Tweeter selama periode 2000 sampai 2014. Kami mengambil tiga sampel perusahaan dari PT AGIS Tbk (TMPI), PT Garda Tujuh Buana Tbk (GTBO) dan PT Bumi Resources Tbk (BUMI) yang telah ditunjukkan mengalami manipulasi harga saham. Perusahaan-perusahaan ini mengalami harga saham apresiasi abnormal dan diikuti dengan penurunan tajam dari sekitar 90 persen dalam waktu singkat. Informasi yang diekstrak dari Tweeter dan analisis sentimen memberikan bukti bahwa berita dan media rilis cenderung menghasilkan umum istilah kunci negatif seperti "terjun", "drop", dan "jatuh"; serta istilah yang terkait dengan laporan laba rugi seperti "kehilangan", "keuntungan", "bersih", "penjualan" dan "pendapatan" .Ini menunjukkan bahwa sebagian besar investor akan memiliki bias negatif dan menderita lebih ketika harga saham mengalami signifikan menurun. Hal ini juga mengungkapkan bahwa investor lebih cupet pada laporan laba rugi daripada menganalisis gambaran besar dari penilaian dasar perusahaan secara keseluruhan. Dengan demikian kita berpendapat bahwa basis pengetahuan ini dan istilah kunci dapat digunakan untuk mencerminkan karakteristik umum dari perilaku investor selama manipulasi harga saham di pasar saham Indonesia.This study attempts to search textual patterns that are associated with investor behavior and sentiment during stock price manipulation in Indonesia’s Stock Market. We propose text mining analysis to demonstrate the process of extracting information from news and media release of Tweeter over the period of 2000 to 2014. We took three sample firms of PT AGIS Tbk (TMPI), PT Garda Tujuh Buana Tbk (GTBO) and PT Bumi Resources Tbk (BUMI) that have been indicated experiencing stock price manipulation. These firms experienced an abnormal stock price appreciation and followed with a sharp decline of about 90 per cent within short period of time. The extracted information from Tweeter and sentiment analysis provide evidence that news and media releases tend to yield common negative key terms such as “plunge”, “drop”, and “fall”; as well as terms related to income statements such as “loss”, “profit”, “net”, “sales” and “revenue”.It shows us that most investors would have negative bias and suffer more when the stock price experienced a significant decline. It also reveals that investors are more short-sighted on the profit and loss statements rather than analyzing the big picture of corporate fundamental valuation as a whole. We thus argue that these knowledge bases and key terms could be used to reflect common characteristics of investor behavior during stock price manipulation in the Indonesia’s stock market.

Are Chinese Investors Really Different? Evidence from Stock Splits in the U.S. and China

Although China is now the world’s second largest stock market, in many ways, it is still an emerging market. Relatively unsophisticated retail investors are the largest group of traders and there remain heavy restrictions on short selling. This combination increases the potential for unsavory actors to take corporate actions that artificially inflate stock prices and expropriate retail investors. By analyzing stock return and trading account records, we uncover evidence consistent with stock price manipulation around stocks splits and find that the victims are likely to be retail investors with smaller account holdings. Finally, we report a link between “suspicious” splits and off-exchange insider transactions.

Legal liability of the management firm on hacked Robo-Advisor's stock price manipulation

Legal liability of the management firm on hacked Robo-Advisor's stock price manipulation

The Differential Role of R&D and SG&A for Earnings Management and Stock Price Manipulation

This study predicts and documents differential roles for RD investors also overvalue low discretionary SG&A and high discretionary R&D during manipulation years. These results highlight the importance of separating R&D and SG&A in analyzing firms’ real earnings management decisions. While cutting SG&A inflates earnings and stock prices, and thus is considered a feasible earnings management tool, cutting R&D is not a viable option when managers desire to signal growth and maintain high stock market valuations.