Knowledge that Transforms

This paper reexamines the beta-return relation through the lens of time-varying risk aversion. We show that the security market line (SML) depends critically on the level of aggregate risk aversion. During periods of high risk aversion, the SML exhibits a positive slope and an intercept that is statistically indistinguishable from zero, with investor sentiment playing only a minor role. During periods of low risk aversion, the SML slope becomes negative and the intercept is significantly positive. Investor sentiment affects the SML only when risk aversion is low. These patterns are robust across alternative portfolio constructions, longer investment horizons, and multiple measures of risk aversion.

Despite the growing integration of artificial intelligence (AI) into business models, studies of its impact on corporate climate change exposure remain scarce. Through an examination of AI-related innovations among US-listed firms from 2001 to 2019, we present compelling evidence that AI innovation effectively mitigates firms’ climate change exposure. In particular, it reduces firms’ exposure to regulatory and physical risks related to climate change through improved carbon management efficiency, with computer vision and control and planning being the most effective types in this context. Our findings are particularly pronounced for mature firms and those facing greater regulatory intervention. The results withstand rigorous tests that address endogeneity concerns. Our study provides strong support for firms to adopt AI innovations to achieve carbon neutrality, contributing to the ongoing discourse regarding AI trade-offs. Our findings also offer valuable insights into the development of climate risk mitigation strategies.

• We investigate illegal insider trading in China’s stock market. • Cross-provincial variation in legal quality affects insider-trading profitability and risk. • Stronger legal environments raise regulatory risk and enhance pricing efficiency. • Results show evidence consistent with deterrence in the form of fewer low-return trades. • Lower information efficiency enhances the positive impact of legal quality on insider-trading profitability. This study examines how provincial legal environments shape the profitability of illegal insider trading in China. Using 521 adjudicated insider-trading cases from 2006 to 2018, we hand-collect detailed information from court judgments and CSRC sanction documents to reconstruct holding-period returns and illicit gains. We combine these data with established provincial indices of legal development and firm-level measures of ex ante litigation risk to test whether legal risk is priced in illegal insider trades. We find that stronger provincial legal environments are associated with significantly higher per-trade profitability among illegal trades that insiders execute after accounting for enforcement risk. This pattern is consistent with a risk-compensation mechanism rather than a failure of enforcement, as stricter legal environments deter low-return trades and leave only trades with sufficiently high expected gains. Firm-level litigation exposure further strengthens this effect. The results remain robust to sample-selection corrections, alternative return measures and a range of heterogeneity tests. Overall, our findings show how institutional variation in enforcement shapes insider incentives and the risk–return trade-off of illegal trading.

This paper demonstrates practical arbitrage trading on the cryptocurrency market. It provides guidance on how to build a high-frequency trading system that benefits from exhibiting arbitrage opportunities. It reveals the algorithm of the trading bot that incorporates the order placement and execution strategy between decentral and central cryptocurrency exchanges. The arbitrage algorithm is implemented on two different blockchains that interact with Uniswap and Balancer folks. Current research explores arbitrage opportunities with back-testing models, the paper focuses on trades with realized arbitrage trades. Practical arbitrage includes all operational costs, liquidity constraints, direct effects on markets, and competition with peer arbitrage traders.

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