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We use BERT, an AI-based algorithm for language understanding, to quantify regulatory climate risk disclosures and analyze their impact on the term structure in the credit default swap (CDS) market. Risk disclosures can either increase or decrease CDS spreads, depending on whether the disclosure reveals new risks or reduces uncertainty. Training BERT to differentiate between transition and physical climate risks, we find that disclosing transition risks increases CDS spreads after the Paris Climate Agreement of 2015, while disclosing physical risks decreases the spreads. In addition, we also find that the election of Trump had a negative impact on CDS spreads for firms exposed to transition risk. These impacts are consistent with theoretical predictions and economically and statistically significant.

This paper investigates the role of seasonal affective disorder (SAD) in the seasonal time-variation of stock market returns. SAD is an extensively documented medical condition whereby the shortness of the days in fall and winter leads to depression for many people. Experimental research in psychology and economics indicates that depression, in turn, causes heightened risk aversion. Building on these links between the length of day, depression, and risk aversion, we provide international evidence that stock market returns vary seasonally with the length of the day, a result we call the SAD effect. Using data from numerous stock exchanges and controlling for well-known market seasonals as well as other environmental factors, stock returns are shown to be significantly related to the amount of daylight through the fall and winter. Patterns at different latitudes and in both hemispheres provide compelling evidence of a link between seasonal depression and seasonal variation in stock returns: Higher latitude markets show more pronounced SAD effects and results in the Southern Hemisphere are six months out of phase, as are the seasons. Overall, the economic magnitude of the SAD effect is large.

Using text from 200 million pages of 13,000 US local newspapers and machine learningmethods, we construct a 170-year-long measure of economic sentiment at the countryand state levels, that expands existing measures in both the time series (by more than acentury) and the cross-section. Our measure predicts GDP (both nationally and locally),consumption, and employment growth, even after controlling for commonly-used predictors,as well as monetary policy decisions. Our measure is distinct from the informationin expert forecasts and leads its consensus value. Interestingly, news coverage has becomeincreasingly negative across all states in the past half-century.

We introduce a new framework to integrate liquidity risk, funding risk and market risk, which goes beyond the simple bid-ask spread overlay to a VaR number. In our approach, we overlay a whole distribution of liquidity uncertainty to each future market-risk scenario. Then we allow for the liquidity uncertainty to vary scenario by scenario, depending on what liquidation policy or funding policy is implemented in that scenario. The result is one easy-to-interpret and easy-to-implement formula for the total liquidity-plus-market-risk P&L distribution. Using this formula we can stress-test different market risk P&L distributions and different scenario-dependent liquidation policies and funding policies; compute total risk and decompose it into a novel liquidity-plus-market risk formula; and define a liquidity score as a monetary measure of portfolio liquidity. Our approach relies on three pillars: first, the literature on optimal execution, to model liquidity risk as a function of the actual trading involved; second, an analytical conditional convolution, to blend market risk and liquidity/funding risk; third the Fully Flexible Probabilities framework, to model and stress-test market risk even in highly non-normal portfolios with complex derivatives. Our approach can be implemented efficiently with portfolios of thousand of securities. The code for the case study is available for download

We document fragile demand for socially responsible investments (SRI) by retail mutual fund investors. Using COVID-19 as an economic shock, we show funds with higher sustainability ratings experienced sharper declines in retail flows during the pandemic, controlling for fund characteristics. The decline in retail SRI fund flows is sharper than that of institutional flows, more pronounced when economies are hit harder by COVID-19, and unlikely to be driven by fund performance, past flows and size, or shifting investor attention. Corroborated by out-of-sample survey evidence, our findings highlight high sensitivity of SRI demand by retail investors with respect to income shocks.

Spanish Abstract: En esta monografía se introduce en la valoración de los activos financieros de renta fija: la utilización de los tipos cupón-cero y los tipos a plazo implícitos, la estimación del rendimiento esperado, la valoración de los bonos amortizables anticipadamente (con una referencia a la valoración con modelo binomial con tipos de interés negativos y positivos), la valoración de las obligaciones convertibles (normales, cupón-cero y CoCos), la valoración de la obligación convertible y rescatable anticipadamente, la valoración de los bonos con warrants y la valoración de los CDS mediante la teoría de opciones.English Abstract: This monograph shows an introduction to the valuation of fixed income securities: valuation through coupon-zero rates and implied forward rates, estimation of the yearly return, valuation of bonds with embedded options (call, put, convertibles and mix of them; with a reference to binomial lattice with positive and negative interest rates). Also the CDS valuation is analyzed using the options theory.

Machine learning (ML) models for predicting stock returns are typically trained on one-month forward returns. While these models show impressive full-sample gross alphas, their performance net of transaction costs post 2004 is close to zero. By training on longer prediction horizons and using efficient portfolio construction rules, we demonstrate that ML-based investment strategies can still yield significant positive net returns. Longer-horizon strategies select slower signals and load more on traditional asset pricing factors but still unlock unique alpha. We conclude that design choices are critical for the success of ML models in real-life applications.

Using a large sample of U.S. firms over the period 1964–2007, we find that conditional conservatism is associated with the lower likelihood of a firm’s future stock price crashes. This finding holds for multiple measures of conditional conservatism and crash risk and it is robust to controlling for other known determinants of crash risk and firm fixed effects. Moreover, we find that the relation between conservatism and crash risk is more pronounced for firms with higher information asymmetries. Overall, our results are consistent with the notion that conditional conservatism limits managers’ incentive and ability to overstate performance and hide bad news from investors, which, in turn, reduces stock price crash risk.

As we move from manufacturing to service based economies, an increasing large proportion of the firms that we value derive their value from intangible assets ranging from technological patents to human capital. In this paper, we focus on a few variables that make valuing these service companies different from conventional manufacturing firms. The first is that accountants routinely miscategorize operating and capital expenses, when firms invest in intangible assets. Thus, R&D expenses, which are really capital expenses, are treated as operating expenses, thus skewing both reported profit and capital values. The second is that firms with intangible assets are more likely to use options and restricted stock to compensate employees and the accounting treatment of this compensation can also affect earnings and cash flows. In this paper, we look at how best to correct for the accounting errors and the consequences for valuation.

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high dimensional problems. For a (potentially misspecified) standalone model, it provides reliable price of risk estimates for both tradable and non-tradable factors, and detects those weakly identified. For competing factors and (possibly non-nested) models, the method automatically selects the best specification -- if a dominant one exists -- or provides a Bayesian model averaging (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors, and find that the BMA-SDF outperforms existing models in- and out-of-sample.

We measure reputational losses by examining a firm's stock price reaction to the announcement of a major operational loss event. If the firm's market value declines by more than the announced loss amount, this is interpreted as a reputational loss. We find that market values fall one-for-one with losses caused by external events, but fall by over twice the loss percentage in cases involving internal fraud. We find that for firms with weak shareholder rights, there is not a significant difference between internal fraud and non-internal fraud events on market returns; however, for firms with strong shareholder rights, while we do not find evidence that the market reacts more than one-to-one for non-internal fraud announcements, we find strong and robust evidence that the market does fall more than one-to-one for internal fraud announcements. These results are consistent with there being a reputational impact for losses due to internal fraud while externally-caused losses have no reputational impact.

We analyze a sample of 330 firms making unaudited disclosures required by Section 302 and 383 firms making audited disclosures required by Section 404 of the Sarbanes-Oxley Act. We find that Section 302 disclosures are associated with negative announcement abnormal returns of -1.8 percent, and that firms experience an abnormal increase in equity cost of capital of 68 basis points. We conclude that Section 302 disclosures are informative and point to lower credibility of disclosing firms' financial reporting. In contrast, we find that Section 404 disclosures have no noticeable impact on stock prices or firms' cost of capital. Further, we find that auditor quality attenuates the negative response to Section 302 disclosures and that accelerated filers - larger firms required to file under Section 404 - have significantly less negative returns (-1.10 percent) than non-accelerated filers (-4.22 percent). The findings have implications for the debate about whether to implement a scaled securities regulation system for smaller public companies: material weakness disclosures are more informative for smaller firms that likely have higher pre-disclosure information uncertainty.

I test the stock market efficiency with respect to the information in the texts of annual reports. More specifically, I examine the implications of corporate annual reports' risk sentiment for future earnings and stock returns. I measure the risk sentiment of annual reports by counting the frequency of words related to risk or uncertainty in the 10-K filings. I find that an increase in risk sentiment is associated with lower future earnings: Firms with a larger increase in risk sentiment have more negative earnings changes in the next year. Risk sentiment of annual reports can predict future returns in a cross-sectional setting: Firms with a large increase in risk sentiment experience significantly negative returns relative to those firms with little increase in risk sentiment in the twelve months after the annual report filing date. A hedge portfolio based on buying firms with a minor increase in risk sentiment of annual reports and shorting firms with a large increase in risk sentiment generates an annual Alpha of more than 10% measured using the four-factor model including the Fama-French three factors and the momentum factor.

We introduce a simple approach to managing portfolio interest rate risk that is consistent and performs well across different interest rate regimes, including when interest rates are low or even negative. Inspired by Black (1995), this approach uses a novel inverse-call transformation methodology to convert interest rates into shadow rates. We show that this methodology is more appropriate than the standard normal and lognormal models for forecasting and managing the distribution of the profits and losses of portfolios affected by the term structure of interest rates, producing more reliable forecasts and thus risk estimates for purposes of both internal and regulatory risk management.

Trustees of pensions, charities, and personal trusts invest tens of trillions of dollars of other people’s money subject to a sacred trust known in the law as fiduciary duty. Recently, these trustees have come under increasing pressure to use environmental, social, and governance (ESG) factors in making investment decisions. ESG investing is common among investors of all stripes, but many trustees have resisted its use on the grounds that doing so may violate the fiduciary duty of loyalty. Under the “sole interest rule” of trust fiduciary law, a trustee must consider only the interests of the beneficiary. Accordingly, a trustee’s use of ESG factors, if motivated by the trustee’s own sense of ethics or to obtain collateral benefits for third parties, violates the duty of loyalty. On the other hand, some academics and investment professionals have argued that ESG investing can provide superior risk-adjusted returns. On this basis, some have even argued that ESG investing is required by the fiduciary duty of prudence. Against this backdrop of uncertainty, this Article examines the law and economics of ESG investing by a trustee. We differentiate “collateral benefits” ESG from “risk-return” ESG, and we provide a balanced assessment of the theory and evidence about the possibility of persistent, enhanced returns from risk-return ESG.We show that ESG investing is permissible under American trust fiduciary law if two conditions are satisfied: (1) the trustee reasonably concludes that ESG investing will benefit the beneficiary directly by improving risk-adjusted return; and (2) the trustee’s exclusive motive for ESG investing is to obtain this direct benefit. In light of the current theory and evidence on ESG investing, we accept that these conditions could be satisfied under the right circumstances, but we reject the claim that the duty of prudence either does or should require trustees to use ESG factors. We also consider how the duty of loyalty should apply to ESG investing by a trustee if such investing is authorized by the terms of a trust or the beneficiaries, or is consistent with a charity’s purpose, clarifying with an analogy to whether a distribution would be permissible under similar circumstances. We conclude that applying the sole interest rule (as tempered by authorization and charitable purpose) to ESG investing is normatively sound.

“Financial Behavior: An Overview” is the introduction chapter for the book Financial Behavior: Players, Services, Products, and Markets edited by H. Kent Baker, Greg Filbeck, and Victor Ricciardi. The book provides a synthesis of the theoretical and empirical literature on the financial behavior of major stakeholders, financial services, investment products, and financial markets. The book offers a different way of looking at financial and emotional well-being and processing beliefs, emotions, and behaviors related to money. It presents important insights about cognitive and emotional biases that influence various financial decision-makers, services, products, and markets. The chapter also provides an overview of the emerging research topics covered in Financial Behavior and the abstract descriptions for the remaining 29 chapters of the book by 50 contributors.

Building upon the fundamental notion of hierarchy, the "Hierarchical Risk Parity" (HRP) and the "Hierarchical Clustering based Asset Allocation" (HCAA), the Hierarchical Equal Risk Contribution Portfolio (HERC) aims at diversifying capital allocation and risk allocation. HERC merges and enhances the machine learning approach of HCAA and the Top-Down recursive bisection of HRP. In more detail, the modified Top-Down recursive division is based on the shape of dendrogram, follows an Equal Risk Contribution allocation and is extended to downside risk measures such as conditional value at risk (CVaR) and Conditional Drawdown at Risk (CDaR). The out-of-sample performances of hierarchical clustering based portfolios are evaluated across two empirical datasets, which differ in terms of number of assets and composition of the universe (multi-assets and individual stocks). Empirical results highlight that HERC Portfolios based on downside risk measures achieve statistically better risk-adjusted performances, especially those based on the CDaR.

We use forward-looking information from option prices to estimate option-implied correlations and to construct an option-implied predictor of factor betas. With our implied market betas, we find a monotonically increasing risk-return relation, not detectable with standard rolling-window betas, with the slope close to the market excess return. Our implied betas confirm a risk-return relation consistent with linear factor models, because, when compared to other beta approaches: (i) they are better predictors of realized betas, and (ii) they exhibit smaller and less systematic prediction errors. The predictive power of our betas is not related to known relations between option-implied characteristics and returns.

In a sample of 399 firms from Indonesia, Korea, Malaysia, the Philippines, and Thailand, firm-level differences in variables related to corporate governance had a significant impact on firm performance during the East Asian financial crisis of 1997-1998. First, indicators of higher disclosure quality (having ADRs and auditors from Big Six accounting firms) are associated with significantly better stock price performance during the crisis. Second, higher outside (but not managerial) ownership concentration led to better performance during the crisis. Finally, diversified firms, particularly those with high variation in investment opportunities across divisions, performed worse than single-segment firms during the crisis.

This paper discusses the information content and potential measurement biases in hedge fund benchmarks. Hedge fund indices built from databases of individual hedge funds will inherit their measurement biases. In addition, broad-based indices mask the diversity of individual hedge fund return characteristics. Consequently, these indices are less informative for investors seeking diversification from traditional asset classes through the use of hedge funds. This paper proposes a different approach to constructing hedge fund benchmarks. It is based on the simple idea that the most direct way of measuring hedge fund performance is to observe the investment experience of hedge fund investors themselves. In terms of measurement biases, returns of funds-of-hedge funds can deliver a cleaner estimate of the investment experience of hedge fund investors. In terms of risk characteristics, indices of funds-of-hedge funds is more indicative of the demand side dynamics driven by hedge fund investors' preferences. Therefore, indices of funds-of-hedge funds can provide additional valuable information to the assessment of the hedge fund industry's performance.

We analyze the history of the equity risk premium from surveys of U.S. Chief Financial Officers (CFOs) conducted every quarter from June 2000 to March 2014. The risk premium is the expected 10-year S&P 500 return relative to a 10-year U.S. Treasury bond yield. While the risk premium sharply increased during the financial crisis peaking in February 2009, the premium has decreased to a level of 3.73% which is only slightly higher than the long-term average. However, the total market return forecast is a modest 6.43%. The survey also provides measures of cross-sectional disagreement about the risk premium, skewness, and a measure of individual uncertainty. Consistent with the last four quarters of surveys, CFOs see more downside risks than upside risks. In addition, we find that dispersion of beliefs is above the long-term average as well as individual uncertainty. We also present evidence on the determinants of the long-run risk premium. Our analysis suggests the level of the risk premium closely tracks both market volatility (reflected in the VIX index) as well as credit spreads. However, the most recent data show a divergence between VIX and the risk premium.

Abstract Using novel earnings calendar data, we show that firms' advanced scheduling of earnings announcement dates foreshadows their earnings news. Firms that schedule later-than-expected announcement dates subsequently announce worse news than those scheduling earlier-than-expected announcement dates. Despite scheduling disclosures being observable weeks ahead of earnings announcements, we show equity markets fail to reflect the information in these disclosures until the announcement itself. By also showing that option markets respond efficiently to `volatility-timing' information embedded in the same scheduling disclosures, we provide novel evidence markets fail to react to information about future earnings despite investors immediately trading on the underlying signal.

The integration of algorithmic trading with reinforcement learning, known as AI-powered trading, has significantly impacted capital markets. This study employs a theoretical laboratory characterized by information asymmetry and imperfect competition, where informed AI speculators serve as the subjects of our simulation experiments. It explores how AI technology impacts market power, information rents, price informativeness, market liquidity, and mispricing. Our findings show that informed AI speculators can autonomously learn to sustain collusive supra-competitive profits without any form of agreement, communication, intention, or any interactions that might violate traditional antitrust regulations. AI collusion robustly emerges from two distinct mechanisms: one through price-trigger strategies ("artificial intelligence") when price efficiency and noise trading risk are both low, and the other through self-confirming bias in learning ("artificial stupidity") under other conditions.

To assess stock market informational efficiency with minimal data snooping, we take the view of a statistician with little knowledge of finance. The statistician uses techniques like least squares to estimate peer-implied fair values from the market values of replicating portfolios with the same accounting statements as the company being valued. Divergence of a company’s peer-implied value estimate from its market value represents mispricing, motivating a convergence trade that earns risk-adjusted returns of up to 10% per year and is economically significant for both large and small cap firms. The rate of convergence decays to zero over the subsequent 34 months.

We review extensive evidence about how psychological biases affect investor behavior and prices. Systematic mispricing probably causes substantial resource misallocation. We argue that limited attention and overconfidence cause investor credulity about the strategic incentives of informed market participants. However, individuals as political participants remain subject to the biases and self-interest they exhibit in private settings. Indeed, correcting contemporaneous market pricing errors is probably not government's relative advantage. Government and private planners should establish rules ex ante to improve choices and efficiency, including disclosure, reporting, advertising, and default-option- setting policies. Especially, government should avoid actions that exacerbate investor biases.