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This article studies an economy with borrowers (firms or individuals) under costly default. Borrowers defaulting under adverse economic conditions may, despite incurring default costs, emerge as wealthier than nonborrowers. Asset substitution is generally not pronounced, although a larger risk exposure by borrowers may also occur, and then binary options emerge as useful credit derivatives. The asset-value dynamics are endogenously determined and shown to exhibit stochastic mean and volatility, in contrast to many credit risk models. In equilibrium, the market level is increased (decreased) in economic downturns (upturns) by the presence of credit risk.

We estimate effects of voluntary and mandatory disclosure of carbon emissions on stock returns and volatility using a large global sample of publicly listed firms. We find that voluntary disclosure of scope 1 emissions by companies results in lower stock returns relative to non-disclosing companies. However, a cost of disclosing emissions is increased divestment by institutional investors. We also find that U.K. mandatory carbon disclosure rules for publicly traded companies resulted in lower stock-level uncertainty. The effect of these mandatory disclosure rules also spilled over into other markets, especially those with close geographic and economic proximity, and companies in the same industry.

This study shows that U.S. individual investors hold under-diversified portfolios, where the level of under-diversification is greater among younger, low-income, less-educated, and less-sophisticated investors. The level of under-diversification is also correlated with investment choices that are consistent with over-confidence, trend-following behavior, and local bias. Furthermore, investors who over-weight stocks with higher volatility and higher skewness are less diversified. In contrast, there is little evidence that portfolio size or transaction costs constrains diversification. Under-diversification is costly to most investors, but a small subset of investors under-diversify because of superior information.

As part of the Securities and Exchange Commission’s revision of Regulation S-K, which lays out reporting requirements for publicly listed companies, many investors proposed the mandatory disclosure of sustainability information in the form of environmental, social and governance (ESG) data. However, progress is contingent on collecting evidence regarding which sustainability disclosures are financially material. To inform this issue, we examine materiality standards developed by the Sustainability Accounting Standards Board (SASB). Firms voluntarily disclosing more SASB-identified sustainability information exhibit greater price informativeness, while the disclosure of non-SASB information does not relate to informativeness. The results are robust to a changes analysis and a difference-in-differences analysis that exploits the staggered release of SASB standards across different industries over time. We also document stronger results for firms with higher exposure to sustainability issues, poorer sustainability ratings, greater institutional and socially responsible investment fund ownership, and coverage from analysts with lower portfolio complexity.

This study investigates whether investors are willing to trade off wealth for societal benefits. We take advantage of unique institutional features of the municipal securities market to provide insight into this question. Since 2013, over $23 billion green bonds have been issued to fund eco-friendly projects. Comparing green securities to nearly identical securities issued for non-green purposes by the same issuers on the same day, we observe economically identical pricing for green and non-green issues. In contrast to a number of recent theoretical and experimental studies, we find that in real market settings investors appear entirely unwilling to forgo wealth to invest in environmentally sustainable projects. When risk and payoffs are held constant and are known to investors ex-ante, investors view green and non-green securities by the same issuer as almost exact substitutes. Thus, the greenium is essentially zero.

We analyze cross-sectional and time series information from forty-six equity markets around the world, to consider whether short sales restrictions affect the efficiency of the market, and the distributional characteristics of returns to individual stock and market indices. We construct two measures of price efficiency that quantify the asymmetric response of individual stock returns to negative vs. positive information, and find that prices incorporate information faster in countries where short sales are allowed and practiced. This evidence is consistent with more efficient price discovery at the individual security level. A common conjecture by regulators is that short sales restrictions can reduce the relative severity of a market panic. We test this conjecture by examining the skewness of market returns. We find some evidence that in markets where short selling is either prohibited or not practices, market returns display significantly less negative skewness. However, at the individual stock level, short sales restrictions appear to make no difference.

The root cause of the financial crisis that erupted in 2008 is psychological. In the events which led up to the crisis, heuristics, biases, and framing effects strongly influenced the judgments and decisions of financial firms, rating agencies, elected officials, government regulators, and institutional investors. Examples involving UBS, Merrill Lynch, Citigroup, Standard & Poor’s, the SEC, and end investors illustrate this point. Among the many lessons to be learned from the crisis is the importance of focusing on the behavioral aspects of organizational process.

In this paper we present a new approach to incorporate dynamic default dependency in intensity-based default risk models. The model uses an arbitrary default dependency structure which is specified by the Copula of the times of default, this is combined with individual intensity-based models for the defaults of the obligors without loss of the calibration of the individual default-intensity models. The dynamics of the survival probabilities and credit spreads of individual obligors are derived and it is shown that in situations with positive dependence, the default of one obligor causes the credit spreads of the other obligors to jump upwards, as it is experienced empirically in situations with credit contagion. For the Clayton copula these jumps are proportional to the pre-default intensity. If information about other obligors is excluded, the model reduces to a standard intensity model for a single obligor, thus greatly facilitating its calibration. To illustrate the results they are also presented for Archimedean copulae in general, and Gumbel and Clayton copulae in particular. Furthermore it is shown how the default correlation can be calibrated to a Gaussian dependency structure of CreditMetrics-type.

We study coordinated engagements by a prominent international network of long-term shareholders cooperating to influence firms on environmental and social issues. A two-tier engagement strategy, combining lead investors with supporting investors, is effective in successfully achieving engagement goals, and is followed by improved target performance and increased investor fund flows. An investor is more likely to lead the collaboration when it has higher stakes in and exposure to the target, and when the target is geographically and/or culturally closer. Success rates are elevated when lead investors are domestic, and when more coalition investors are from countries with high social norms.

We present a simple model of systemic risk and show how each financial institution’s contribution to systemic risk can be measured and priced. An institution’s contribution, denoted systemic expected shortfall (SES), is its propensity to be undercapitalized when the system as a whole is undercapitalized, which increases in its leverage, volatility, correlation, and tail-dependence. Institutions internalize their externality if they are “taxed” based on their SES. Through several examples, we demonstrate empirically the ability of components of SES to predict emerging systemic risk during the financial crisis of 2007-2009.

Do investors obtain their long term returns smoothly and steadily over time, or is their long term performance largely determined by the return of just a few outliers? How likely are investors to successfully predict the best days to be in and out of the market? The evidence from 15 international equity markets and over 160,000 daily returns indicates that a few outliers have a massive impact on long term performance. On average across all 15 markets, missing the best 10 days resulted in portfolios 50.8% less valuable than a passive investment; and avoiding the worst 10 days resulted in portfolios 150.4% more valuable than a passive investment. Given that 10 days represent less than 0.1% of the days considered in the average market, the odds against successful market timing are staggering.

The Entropy Pooling approach is a versatile theoretical framework to process market views and generalized stress-tests into an optimal "posterior" market distribution, which is then used for risk management and portfolio management. Entropy Pooling can be implemented non-parametrically or parametrically. The non-parametric implementation with historical scenarios is more suitable for risk management applications. Here introduce the parametric implementation of Entropy Pooling under a factor structure, which we name Factor Entropy Pooling. The factor structure reduces the dimension of the problem and linearizes the parameter space, allowing for fast computation of the posterior market distribution. We apply Factor Entropy Pooling to two portfolio construction problems. First, we use the Factor Entropy Pooling to construct the "implied returns", i.e. a market distribution consistent with a target optimal portfolio, such as maximum diversification/risk parity, or the CAPM equilibrium. Our approach improves on the implied returns a-la-Black-Litterman, and the ensuing distribution can be used as the starting point for further portfolio construction. Second, we use Factor Entropy Pooling to construct and backtest quantitative systematic trading strategies based on ranking views, or "portfolios from sorts". Unlike standard approaches, Factor Entropy Pooling closely ties to the actual empirical data.

This Article posits that the essential role of securities regulations is to create a competitive market for information traders (analysts). The Article advances two related theses - one descriptive and the other normative. Descriptively, it demonstrates that securities regulation is specifically designed to facilitate and protect the work of analysts. Normatively, the Article shows that analysts are the only group that can best underwrite efficient and liquid capital markets and, hence, it is the group securities regulation should strive to protect. By protecting analysts, securities regulations enhance efficiency and liquidity in financial markets. This protection, in turn, benefits other types of investors by reducing transaction costs. Furthermore, by protecting analysts, securities regulation represents the highest form of market integrity by ensuring accurate pricing to all investors, and improves the allocation of resources in the economy. Securities regulation may be divided into three broad categories: disclosure duties; restrictions on fraud and manipulation; and restrictions on insider trading - each of which contributes to the creation of a vibrant market for analysts. Disclosure duties reduce analysts - costs of gathering information, and diminish the ability of analysts to produce biased analyses in exchange for pay. Restrictions on fraud and manipulation lower analysts' cost of verifying the credibility of information, and enhance analysts' ability to make accurate predictions. Finally, restrictions on insider trading protect analysts from competition from insiders that would undercut the ability of analysts to recoup their investment in information, and thereby drive analysts out of the market. Thus, the effect of securities regulation is to develop and secure a competitive market for analysts. Moreover, a competitive market for analysts reduces management agency costs. While courts can discern fraud or illegal transfers, they are ill-equipped to evaluate the quality of business decisions. Judicial oversight can curtail breaches of the duty of loyalty but not breaches of the duty of care; the tasks of curbing breaches of the duty of care and restraining inefficient investments are performed by analysts. Furthermore, a competitive analysts' market generates positive externalities for the rest of the economy by improving the information market and facilitating the operations of the investment banking industry. Our account has important implications for several policy debates. First, our account supports the system of mandatory disclosure. We show that while market forces may provide management with an adequate incentive to disclose at the initial public offering (IPO) stage, they cannot be relied on to effect optimal disclosure thereafter. Second, our analysis categorically rejects the calls to limit disclosure duties to hard information and self-dealing by management. Third, our analysis supports Basic v. Levinson and the use of the fraud-on-the-market presumption in all fraud cases regardless of how efficient financial markets are. Fourth, our analysis suggests that in cases involving corporate misstatements, the appropriate standard of care should, in principle, be negligence, not fraud.

This paper uses broad samples of value-weighted and equally-weighted returns to document the fact that abnormally high rates of return on small-cap stocks continued to be observed during the month of January. The January effect in small cap stock returns is remarkably consistent over time, and does not appear to have been affected by passage of the Tax Reform Act of 1986. This finding adds new perspective to the traditional tax-loss selling hypothesis, and suggests the potential relevance of behavioral explanations. After a generation of intensive study, the January effect is alive and well, and continues to present a daunting challenge to the Efficient Market Hypothesis.

In this paper we study the multivariate return properties of a large variety of commodity futures. We find that between commodity groupings (such as metals, energy, etc.) correlations are very low and mostly insignificant whereas within groups they tend to be much stronger. In addition, commodity futures are roughly uncorrelated with stocks and bonds. Still, correlations may vary somewhat over the different phases of the business cycle, suggesting that not all commodities make equally good diversifiers at all times. Copula-based tests do not indicate any deviant behaviour in the tails of the joint return distribution of commodity futures and stocks or bonds. Contrary to equities and bonds, we show that commodity futures returns are positively correlated with unexpected inflation (i.e. 25% on average with CPI inflation as opposed to -30% for equities and -50% for bonds). There are significant differences between the various commodities, however, with energy, metals, cattle, and sugar offering the best hedging potential. Altogether, assuming that the observed regularities will persist, our results confirm that a well-balanced commodity futures portfolio could offer a worthwhile diversification service to the typical traditional investment portfolio.

Can a self-proclaimed Socially Responsible Fund (SRF) whose objective is to maximize assets under management improve social welfare? We study this question in a general equilibrium two-sector model incorporating financial intermediation, negative externalities due to firms’ emissions, and investors' social preferences, which are of two kinds: (a) private benefits from investing in low-emission footprint equities (``value alignment''), and (b) utility from causing improvement in social welfare (``impact''). We analyze the equilibrium size and strategies of the SRF. When investors with value-alignment preferences are in large proportion in the population we show that the SRF invests in the low-emission sector, while requiring invested companies to use low-emission suppliers. This ``Scope 3 strategy'' attracts both types of investors and indirectly induces lower emissions by acting on the supply-chain. In some other scenarios, the SRF adopts a dual-fund strategy that separates the two types of investors: One fund, focussed on the clean sector, caters to investors with value-alignment preferences, while another, which invests in the higher-emission sector, appeals to impact investors by imposing reduced direct emissions to invested companies.

Statistical arbitrage is a popular trading strategy employed by hedge funds and proprietary trading desks, built on the statistical notion of cointegration to identify profitable trading opportunities. Given the revolutionary shift in markets represented by high frequency trading (HFT), it is unsurprising that risks and rewards have changed. This paper explores the effect of HFT volume on statistical arbitrage profitability, and reports three trends in the data. First, higher levels of comovement due to HFT cause more stock pairs to be cointegrated. Second, profitability from statistical arbitrage remains steady among the deciles with the most HFT. Third, the range of profitability is larger in more recent years. These findings suggest that HFT increases correlation and volatility and have a direct impact on statistical arbitrage trading strategies.

This paper introduces stochastic volatility to the Libor market model of interest rate dynamics. As in Andersen and Andreasen (2000a) we allow for non-parametric volatility structures with freely specifiable level dependence (such as, but not limited to, the CEV formulation), but now also include a multiplicative perturbation of the forward volatility surface by a general mean-reverting stochastic volatility process. The resulting model dynamics allow for modeling of non-monotonic volatility smiles while explicitly allowing for control of the stationarity properties of the resulting model dynamics. Using asymptotic expansion techniques, we provide closed-form pricing formulas for caps and swaptions that are robust, accurate, and well-suited for both model calibration and general mark-to-market of plain-vanilla instruments. Monte Carlo schemes for the proposed model are proposed and examined.

Cyclical and commodity companies share a common feature, insofar as their value is often more dependent on the movement of a macro variable (the commodity price or the growth in the underlying economy) than it is on firm specific characteristics. Thus, the value of an oil company is inextricably linked to the price of oil just as the value of a cyclical company is tied to how well the economy is doing. Since both commodity prices and economies move in cycles, the biggest problem we face in valuing companies tied to either is that the earnings and cash flows reported in the most recent year are a function of where we are in the cycle, and extrapolating those numbers into the future can result in serious misvaluations. In this paper, we look at the consequences of this dependence on cycles and how best to value companies that are exposed to this problem.

The Chicago Board Options Exchange (CBOE) recently redesigned its widely followed VIX volatility index. While the new VIX is conceptually more appealing than its predecessor, the CBOE's implementation of the index is flawed. Using option prices simulated under typical market conditions, we show that the CBOE procedure may underestimate the true volatility by as much as 198 index basis points or overestimate it by as much as 79 index basis points. As each index basis point is worth $10 per VIX futures contract, these errors are economically significant. More importantly, these errors exhibit predictable patterns in relations to volatility levels. We propose a simple solution to fix the problems, based on a smooth interpolation-extrapolation of the implied volatility function. This alternative method is accurate and robust across a wide range of model specifications and market conditions.

Motivated by the time-series insights of Daniel and Moskowitz (2016), we investigate the link between expected skewness and momentum in the cross-section. The alpha of skewness-enhanced (-weakened) momentum is about twice (half) as large as the traditional alpha. These findings are driven by the short leg. Portfolio sorts, Fama-MacBeth regressions, and the market reaction to earnings announcements suggest that expected skewness is an important determinant of momentum. Due to the simplicity of the approach, its economic magnitude, its existence among large stocks, and the success of risk management, the results are difficult to reconcile with the efficient market hypothesis.

In this short teaching note I explain why we subtract the change in working capital from the proper item (Earnings before interest and taxes (EBIT) or Net income) in the Income Statement. I show in detail how departing from the sales revenues and the cost of goods sold we have to subtract the change in working capital. This explanation might be seen as unnecessary given it is a common practice. However, my experience in teaching this subject indicates that some additional explanations are needed.

We propose a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the Markowitz approach: the ability to handle higher moments and estimation error. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to incorporate higher order moments in portfolio selection. Further, our comparison to other methods where parameter uncertainty is either ignored or accommodated in an ad hoc way, shows that our approach leads to higher expected utility than the resampling methods that are common in the practice of finance.

While regulators emphasize the need for machine-readable corporate disclosures, we examine how improvements in machine readability of textual and numerical information affect the human readability of these disclosures. Relative to the 2009 XBRL mandate that required a separate XBRL exhibit of financial statement numbers and footnotes, the 2019 Inline XBRL (iXBRL) regulation improves the machine readability of both textual and numerical content throughout corporate filings. Utilizing the iXBRL mandate as a quasi-exogenous shock to machine readability, we observe a negative effect of machine readability on human readability. In addition, we document that following the iXBRL regulation, disclosures become less informative to retail investors, who generally have less ability to process corporate disclosures with machines and who are more reliant on human readability, and that they reduce ownership in stocks impacted by the iXBRL regulation. Further evidence suggests the reduction in human readability is driven by both lower incentive to allocate effort toward making disclosures human-readable and reduced attention to human readability. Our results are robust to a regression discontinuity design and an alternative difference-in-differences design. Overall, our findings indicate that improved machine readability has implications for the human processing of disclosures.

Economists have traditionally viewed futures prices as fully informative about future economic activity and asset prices. We argue that open interest could be more informative than futures prices in the presence of hedging demand and limited risk absorption capacity in futures markets. We find that movements in open interest are highly pro-cyclical, correlated with both macroeconomic activity and movements in asset prices. Movements in commodity market interest predict commodity returns, bond returns, and movements in the short rate even after controlling for other known predictors. To a lesser degree, movements in open interest predict returns in currency, bond, and stock markets.