Quarterly literature watch highlight   This edition’s quarterly literature watch features the article “Prediction Markets 2.0” (abstract and link below) which examines the evolution of prediction markets and whether they can incorporate nonpublic information ahead of official channels. Recently, a US soldier was indicted by the Department of Justice for allegedly using classified information about Operation Absolute Resolve, the US military mission to capture Nicolás Maduro, to place bets on Polymarket, netting over $400,000 in profits. This case directly embodies the tension the authors examine: prediction markets are extraordinarily efficient at absorbing insider knowledge.   Prediction market platforms such as Polymarket and Kalshi have led the recent surge in interest, with the notional value of contracts across all platforms reaching USD $13 billion in November 2025 – 130 times the volume recorded just nineteen months earlier. In prediction markets, contracts are written on observable future events (such as economic, political or sporting events) featuring binary payoff structures. The authors see these markets as an extension of derivatives markets, drawing comparisons to instruments such as Fed Fund futures or credit default swaps, whose values depend on the target rate announced by the Federal Reserve or the default of specific reference entities, respectively.   The authors believe that prediction markets are best understood not as a tool to “guess” the outcome of events but rather a mechanism that makes observable the process through which information is aggregated, evaluated, and incorporated into prices. They further state that the line of demarcation between traditional markets, derivatives markets and digital event-based markets has become increasingly blurred, exposing the inadequacy of traditional statistical and institutional classifications.

Prediction Markets 2.0 (English Version)

In recent years, prediction markets such as Polymarket have experienced rapid growth, returning to the center of the economic and financial debate as tools for aggregating information about future events. This paper analyzes the recent evolution of prediction markets, placing them within the broader context of derivatives markets and highlighting the informational content and signaling power of prices. After discussing the relationship between exchange-traded and over-the-counter markets, the paper examines several emblematic cases – including contracts on Ali Khamenei, Nicolás Maduro and Abu Musab al-Zarqawi – that illustrate how prediction market prices can incorporate relevant information very quickly, often ahead of its full dissemination through traditional information channels. The paper also compares the forecasting performance of prediction markets with that of opinion polls and selected structural models used to predict electoral outcomes. Finally, key regulatory issues are discussed, including insider trading and cryptocurrencies. The value of prediction markets lies less in the ex post accuracy of individual forecasts than in their ability to
make uncertainty about complex events observable and measurable, thereby providing a useful informational complement to public and private decision-making.

Barone, Emilio and Carli, Federico, Prediction Markets 2.0 (English Version) (January 23, 2026). Available at SSRN: http://dx.doi.org/10.2139/ssrn.6122726

Insider Trading

The Private Enforcer: Algorithmic Deterrence and the Shadow Tax on Insider Trading

Can transparent order-flow environments discipline informed trading even in the absence of
public enforcement? We study a DeFi market in which sandwich-capable MEV searchers impose immediate, state-dependent execution losses on aggressive informational trading. In a baseline equilibrium execution model, predation risk enters the trader’s problem through a predictable attack intensity and state-contingent jump/loss terms, generating an endogenous shadow tax on aggressive trading and a safe-harbor region of near-zero execution. We then discipline these reduced-form objects through a bot-side partial-observation impulse control problem, which rationalizes how greater trading aggressiveness and detectability increase predation exposure. As a pricing extension, we show that the same predation channel can load equilibrium prices with additional jump-driven tail risk under a projected Cox representation and an LP-based pricing kernel. Our contribution shows that transparent DeFi microstructure can implement a private deterrence technology with real distributional and pricing consequences

Lin, Tingyi and Yu, Karl and Lai, Ruoran and Qi, Weiyu, The Private Enforcer: Algorithmic Deterrence and the Shadow Tax on Insider Trading (June 07, 2025). Chicago Booth Center for Decision Research (Managerial & Organizational Behavior) Working Paper, Available at SSRN: https://ssrn.com/abstract=61523466 or http://dx.doi.org/10.2139/ssrn.6152346

The Misconceived Personal-benefit Requirement of Insider Trading Law: About the Insecurities Markets

In Dirks v. SEC and Salman v. United States, the Supreme Court held that, under Section 10(b) of the Securities Exchange Act, tipper/tippee liability arises only if the tippee confers a benefit on the tipper. The ostensible justification for this rule arises from a mistaken premise. The error is rooted in the so-called classical theory of insider trading. When a corporate insider trades on material, nonpublic corporate information, the Supreme Court has held that the insider faces liability because that person profited from the trade. Building on this mistaken premise, the Court has held that when a tipper receives a personal benefit from the tippee, that benefit functions as a proxy for the profits the insider would have made had the insider, rather than the tippee, traded. This Article argues that the Court has misconceived the nature of insider trading liability. When an insider trades on confidential corporate information, the wrongful act is making an unauthorized trade. Whether a corporate insider profits from the trade is irrelevant. Even if the insider loses money, the insider has nevertheless committed the same wrongful act: the trade itself. Thus, in a tipper/tippee situation, the tippee’s
trade stands as a proxy for the trade the tipper might have made. That trade is a deceptive and manipulative device, which violates section 10(b).

Davis, Kenneth Robert, The Misconceived Personal-benefit Requirement of Insider Trading Law: About the Insecurities Markets (January 15, 2025). Denver Law Journal, Available at SSRN: https://ssrn.com/abstract=6137326 or http://dx.doi.org/10.2139/ssrn.6137326

The Effect of Insider Trading Law on Firms’ Innovation Disclosures

We also document that stronger antitrust enforcement lowers the informativeness of insider trades. We examine whether insider trading law affects firms’ innovation disclosures. Our setting exploits United States v. Newman, a 2014 circuit court decision that made it more difficult for prosecutors to establish insider trading liability in some U.S. states. Using a difference-in-differences design, we find that firms headquartered in states affected by the decision increase their patenting rates and reduce their reliance on trade secrecy after the ruling. Patent disclosures also become more detailed, with more claims, figures, and numerical references. These effects are stronger for firms with greater incentives to mitigate agency problems and for firms with higher pre-ruling insider trading intensity. Overall, the evidence is consistent with agency theory: by making insider trading liability harder to establish, the ruling heightened agency costs, inducing firms to provide more transparent innovation disclosures to limit insiders’ ability to exploit private information.

Gu, Dingwei and Sun, Hanwen, Cost Information, Insider Trading, and Product Market Equilibrium (February 24, 2026). Contemporary Accounting Research, Accepted, Available at SSRN: https://ssrn.com/abstract=6297319 or http://dx.doi.org/10.2139/ssrn.6297319

Cost Information, Insider Trading, and Product Market Equilibrium

We study how insider trading based on private cost information affects product market outcomes when firms differ in cost variance. In our model, managers exploit firm-specific cost information to pursue short-term trading gains, leading them to adjust output decisions and reshape product market competition. We show that trading opportunities have heterogeneous effects on firms’ production and value: firms with high cost variance overproduce, whereas those with low cost variance underproduce; correspondingly, the value of firms with high cost variance rises, while that of firms with low cost variance declines. These results demonstrate how heterogeneous costs and private cost information create real economic consequences by linking insider trading incentives to distortions in product market competition and firm value.

Dai, Jiayu and Tseng, Kevin and Zeng, Jean and Zuo, Luo, The Effect of Insider Trading Law on Firms’ Innovation Disclosures  (March 05, 2026). Available at SSRN: https://ssrn.com/abstract=6347638 or http://dx.doi.org/10.2139/ssrn.6347638

Do corporate insiders trade on narrative disclosures? Evidence from tone management in the MD&A

This study investigates whether corporate insiders engage in opportunistic trading based on the tone of Management Discussions and Analysis (MD&A) disclosures. By utilizing a comprehensive dataset of insider trades and employing advanced textual analysis of MD&A disclosures, we examine how discrepancies between the tone of MD&A disclosures and firm fundamentals affect insider sales.
We find a positive relationship between abnormal tone in MD&A reports and insider stock sales, suggesting that insiders exploit the gap between optimistic narratives and the firm’s financial reality for personal gain. We further show that the effect of tone management on insider sales is more pronounced in firms with higher investor divergence and facing higher competitive pressure. Additionally, we show that regulatory oversight can significantly reduce insider trading activities.
Our findings advance the literature on managerial opportunism and corporate disclosure by demonstrating the significant role that tone management in MD&A sections plays in shaping insider trading behavior. They also encourage investors to critically assess the narratives presented in corporate disclosures.

Li, Xiyang and Liu, Lanlan and Li, Bin, Do corporate insiders trade on narrative disclosures? Evidence from tone management in the MD&A. Available at SSRN: https://ssrn.com/abstract=6333527 or http://dx.doi.org/10.2139/ssrn.6333527

Market Manipulation

Are Existing Preventive Measures that Regulators are Employing to Prevent Stock Market Manipulation Sufficient?

Stock market manipulation remains a persistent threat to market integrity, investor confidence, and economic stability, particularly as financial markets become increasingly digitised and accessible. This dissertation examines whether the preventive measures currently employed by regulators are sufficient to deter and mitigate modern forms of stock market manipulation. Drawing on peer-reviewed academic literature, regulatory frameworks, and real-world case studies, the research evaluates the effectiveness of existing controls across four key areas: online pump-and-dump schemes, insider trading, analyst-driven persuasion, and biased credit rating practices. Case studies, notably the GameStop short squeeze and the 2008 financial crisis, are used to assess how these measures perform under conditions of extreme market stress and coordinated behaviour. While certain tools demonstrate partial success in reducing volatility and information leakage, the research identifies significant structural weaknesses, including enforcement limitations, behavioural biases, and the adaptability of manipulators.

Kostanecka, Aleksandra, Are Existing Preventive Measures that Regulators are Employing to Prevent Stock Market Manipulation Sufficient? (June 01, 2022). Available at SSRN: https://ssrn.com/abstract=6004474 or http://dx.doi.org/10.2139/ssrn.6004474

Strategic Bot Activity in Financial Social Media

This paper examines whether automated social media accounts (“bots”) target stocks strategically or randomly. Using cashtagged Twitter data for U.S. equities from 2019 to 2022 and Botometer-based measures of automation, we document systematic patterns in bot activity across stocks and market conditions. Bots disproportionately engage with underperforming stocks and those experiencing elevated selling pressure, with responses that are significantly stronger following negative returns and high-volume down days. Analyst coverage amplifies these effects, while firm size plays little role, indicating that bots are drawn to visible stocks precisely when vulnerability is highest. Overall, the findings suggest that bots intensify market stress rather than merely reflect investor attention.

Paydarzarnaghi, Mahnaz and Rakowski, David A., Strategic Bot Activity in Financial Social Media (December 31, 2025). Available at SSRN: https://ssrn.com/abstract=6095926 or http://dx.doi.org/10.2139/ssrn.6095926

Market Manipulation, Shareholder Heterogeneity and Stock Repurchases

Corporations are significantly hindered in making repurchases by SEC Rule 10b-18, which considers all buybacks as involving potential market manipulation and artificially suppresses the prices that companies can pay for their shares in open market purchases. The main argument in this article is that, while passage of the Rule was an improvement on the prior regulatory regime which was characterized by massive uncertainty about the legality of literally every issuer repurchase program, the Rule is still far too restrictive on repurchases because it prohibits firms from repurchasing their shares at a premium over the bid or market price for the firm’s shares.

Macey, Jonathan R., Market Manipulation, Shareholder Heterogeneity and Stock Repurchases (February 15, 2026). Yale Law & Economics Research Paper No. 02152026, European Corporate Governance Institute – Law Working Paper No. 904/2026, Available at SSRN: https://ssrn.com/abstract=6241959 or http://dx.doi.org/10.2139/ssrn.6241959

Artificial Intelligence and Algorithmic Trading: Implications for Market Dynamics

The rapid advancement of artificial intelligence (AI) has significantly transformed financial markets, particularly through the widespread adoption of algorithmic trading systems. These systems utilize machine learning models, big data analytics, and automated decision-making processes to execute trades at speeds and scales previously unattainable by human traders. This paper explores the implications of AI-driven algorithmic trading on market dynamics, with a focus on liquidity, volatility, price discovery, and market efficiency. By reviewing existing literature and analyzing theoretical frameworks, the study highlights both the benefits and risks associated with AI-based trading mechanisms. While AI-enhanced algorithms contribute to improved liquidity and faster information processing, they also raise concerns regarding systemic risk, flash crashes, market manipulation, and regulatory challenges. The paper concludes by emphasizing the need for balanced regulatory oversight and ethical AI governance to ensure stable and fair financial markets in an increasingly automated trading environment.

Seth, Aman, Artificial Intelligence and Algorithmic Trading: Implications for Market Dynamics (January 28, 2026). Available at SSRN: https://ssrn.com/abstract=6143508 or http://dx.doi.org/10.2139/ssrn.6143508