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The last several decades have witnessed a shift away from a fully rational paradigm of financial markets toward one in which investor behavior is influenced by psychological biases. Two principal factors have contributed to this evolution: a body of evidence showing how psychological bias affects the behavior of economic actors; and an accumulation of evidence that is hard to reconcile with fully rational models of security market trading volumes and returns. In particular, asset markets exhibit trading volumes that are high, with individuals and asset managers trading aggressively, even when such trading results in high risk and low net returns. Moreover, asset prices display patterns of predictability that are difficult to reconcile with rational-expectations–based theories of price formation. In this paper, we discuss the role of overconfidence as an explanation for these patterns.

We propose a theoretically motivated factor model based on investor psychology and assess its ability to explain the cross-section of U.S. equity returns. Our factor model augments the market factor with two factors that capture long-and short-horizon mispricing. The long-horizon factor exploits the information in managers’ decisions to issue or repurchase equity in response to persistent mispricing. The short-horizon earnings surprise factor, which is motivated by investor inattention and evidence of short-horizon underreaction, captures short-horizon anomalies. This 3-factor risk-and-behavioral model outperforms other proposed models in explaining a broad range of return anomalies.

A common practice in the finance literature is to create characteristic portfolios by sorting on characteristics associated with average returns. We show that the resultant portfolios are likely to capture not only the priced risk associated with the characteristic but also unpriced risk. We develop a procedure to remove this unpriced risk using covariance information estimated from past returns. We apply our methodology to the five Fama-French characteristic portfolios. The squared Sharpe ratio of the optimal combination of the resultant characteristic-efficient portfolios is 2.13, compared with 1.17 for the original characteristic portfolios.

The book-to-market effect is often interpreted as evidence of high expected returns on stocks of \"distressed\" firms with poor past performance. We dispute this interpretation. We find that while a stock's future return is unrelated to the firm's past accounting-based performance, it is strongly negatively related to the \"intangible\" return, the component of its past return that is orthogonal to the firm's past performance. Indeed, the book-to-market ratio forecasts returns because it is a good proxy for the intangible return. Also, a composite equity issuance measure, which is related to intangible returns, independently forecasts returns.

Pricing greenhouse-gas (GHG) emissions involves making trade-offs between consumption today and unknown damages in the (distant) future. While decision making under risk and uncertainty is the forte of financial economics, important insights from pricing financial assets do not typically inform standard climate–economy models. Here, we introduce EZ-Climate, a simple recursive dynamic asset pricing model that allows for a calibration of the carbon dioxide (CO₂) price path based on probabilistic assumptions around climate damages. Atmospheric CO₂ is the “asset” with a negative expected return. The economic model focuses on society’s willingness to substitute consumption across time and across uncertain states of nature, enabled by an Epstein–Zin (EZ) specification that delinks preferences over risk from intertemporal substitution. In contrast to most modeled CO₂ price paths, EZ-Climate suggests a high price today that is expected to decline over time as the “insurance” value of mitigation declines and technological change makes emissions cuts cheaper. Second, higher risk aversion increases both the CO₂ price and the risk premium relative to expected damages. Lastly, our model suggests large costs associated with delays in pricing CO₂ emissions. In our base case, delaying implementation by 1 y leads to annual consumption losses of over 2%, a cost that roughly increases with the square of time per additional year of delay. The model also makes clear how sensitive results are to key inputs.

Hemorrhage from internal organs remains a critical challenge in both trauma care and surgical procedures, as existing hemostatic adjuncts frequently fail to provide consistent and effective bleeding control, particularly under conditions of active bleeding or impaired coagulation. Here, we develop and evaluate a Hemostatic Tough Adhesive (HTA) in controlled preclinical models of traumatic solid organ injury and compare its performance against leading commercially available hemostatic agents. The HTA consistently outperformed its counterparts, achieving 100% hemostasis in both liver and spleen injuries within an in vivo preclinical porcine model. In contrast, existing adjuncts exhibited variable and often incomplete efficacy. Beyond immediate hemostasis, the HTA demonstrated prolonged stability and biocompatibility during the postoperative wound healing phase. Notably, the HTA exhibited tissue surface adhesion energy several orders of magnitude greater than that of current hemostatic products, indicating its potential utility for surgical and trauma-related bleeding management. A Hemostatic Tough Adhesive (HTA) is developed and evaluated, achieving hemostasis in both liver and spleen injuries within an in vivo preclinical porcine model.

This study uses critical discourse analysis to explain how legislators determine the role and contributions of academic researchers in Congressional legislative hearings. The discursive practices that legislators use serve to construct the social identity of academic witnesses, characterize witnesses' qualifications, solicit information from witnesses, frame comments from witnesses, and amplify and mitigate witness testimony. The findings make visible the ways that legislators use the power of their positions to depict academic witnesses as both experts who offer independent knowledge and experts who validate or confirm a legislator's preferences and priorities. The results have implications for academics who seek to improve connections between research and policy, and academics who seek to further advance the production of knowledge of federal policymaking processes.

Firm sizes and book-to-market ratios are both highly correlated with the average returns of common stocks. Fama and French (1993) argue that the association between these characteristics and returns arise because the characteristics are proxies for nondiversifiable factor risk. In contrast, the evidence in this article indicates that the return premia on small capitalization and high book-to-market stocks does not arise because of the comovements of these stocks with pervasive factors. It is the characteristics rather than the covariance structure of returns that appear to explain the cross-sectional variation in stock returns.

This paper offers a model in which asset prices reflect both covariance risk and misperceptions of firms' prospects, and in which arbitrageurs trade against mispricing. In equilibrium, expected returns are linearly related to both risk and mispricing measures (e.g., fundamental/price ratios). With many securities, mispricing of idiosyncratic value components diminishes but systematic mispricing does not. The theory offers untested empirical implications about volume, volatility, fundamental/price ratios, and mean returns, and is consistent with several empirical findings. These include the ability of fundamental/price ratios and market value to forecast returns, and the domination of beta by these variables in some studies.

Risk and uncertainty are important in pricing climate damages. Despite a burgeoning literature, attempts to marry insights from asset pricing with climate economics have largely failed to supplement—let alone supplant—decades-old climate–economy models, largely due to their analytic and computational complexity. Here, we introduce a simple, modular framework that identifies core trade-offs, highlights the sensitivity of results to key inputs, and helps pinpoint areas for further work. Pricing greenhouse-gas (GHG) emissions involves making trade-offs between consumption today and unknown damages in the (distant) future. While decision making under risk and uncertainty is the forte of financial economics, important insights from pricing financial assets do not typically inform standard climate–economy models. Here, we introduce EZ-Climate, a simple recursive dynamic asset pricing model that allows for a calibration of the carbon dioxide ( C O 2 ) price path based on probabilistic assumptions around climate damages. Atmospheric C O 2 is the “asset” with a negative expected return. The economic model focuses on society’s willingness to substitute consumption across time and across uncertain states of nature, enabled by an Epstein–Zin (EZ) specification that delinks preferences over risk from intertemporal substitution. In contrast to most modeled C O 2 price paths, EZ-Climate suggests a high price today that is expected to decline over time as the “insurance” value of mitigation declines and technological change makes emissions cuts cheaper. Second, higher risk aversion increases both the C O 2 price and the risk premium relative to expected damages. Lastly, our model suggests large costs associated with delays in pricing C O 2 emissions. In our base case, delaying implementation by 1 y leads to annual consumption losses of over 2%, a cost that roughly increases with the square of time per additional year of delay. The model also makes clear how sensitive results are to key inputs.