We model the multitasking nature of managerial incentives when ESG metrics are
introduced jointly with standard financial or accounting metrics in executive
compensation. Building on insights from multitasking theory, we predict that
pay-performance sensitivity or dollar delta of standard metrics should optimally
decrease when value-adding but less measurable ESG goals are introduced in executive
pay. Empirical tests support the existence of a significant opportunity cost for the
effort of executives to improve ESG metrics that firms mitigate by decreasing
incentives to achieve standard metrics. Consistently, the downward adjustment in
dollar delta of standard metrics is shown to be larger when the number of ESG metrics
increases, they are less material to the firm, or less measurable. This adjustment is not
offset by a simultaneous increase in the time vesting delta or the executive’s total
compensation. The tests show differential effect of E, S, and G metrics on the dollar
delta of standard metrics. In sharp contrast, there is no variation in the dollar delta of
standard metrics when a new standard metric (instead of an ESG metric) is
introduced. Overall, the evidence is consistent with efficient contracting in the
presence of multitasking when ESG metrics are introduced in executive compensation.

This study investigates the impact of Regulation 18 on green innovation within Chinese firms. Introduced in 2013 to sever political ties in corporate governance, Regulation 18 mandated the resignation of government officials from business roles. Studying Chinese listed firms from 2010 to 2016, we utilize a difference-in-differences (DiD) model to evaluate its effects. Our findings reveal a significant influence of Regulation 18 on green innovation. Non-State-Owned Enterprises (non-SOEs) in High Energy Consumption, High Pollution, or Overcapacity (HHO) industries experienced reduced green innovation post-regulation, consistent with the legitimacy theory and social perspective, indicating a positive correlation between political connections and green innovation. Conversely, State-Owned Enterprises (SOEs) in HHO industries exhibited increased green innovation, aligning with the resource curse theory and tunneling arguments, suggesting a negative correlation between political connections and green innovation. Furthermore, the analysis extends to corporate financial performance, revealing a decline for politically connected non-SOEs in HHO industries post-Regulation 18, while improvements are observed for SOEs in the same sector.

We explore the director labor market consequences of social media use. Specifically, we identify directors in S&P 1500 firms who are active on Twitter and examine various director labor market outcomes. We find directors, particularly females and minorities, on Twitter are more likely to gain an additional directorship, a larger directorship and a directorship in a new industry each year than those who are not on Twitter. These results hold when controlling for time invariant unobserved director characteristics and when using an instrumental variable approach to control for endogeneity. They are strongest for directors, primarily females and minorities, who engage more with other directors via social media. Shareholders show more support for social-media-active directors through a greater (lesser) percentage of votes casts “For” (“Against”) their election and through a greater stock price reaction to the announcement of first-time director appointments. These results suggest that social media can play an important role in reducing traditional labor market frictions and facilitating more opportunities for minority and female directors.

We use Target Date Funds (TDFs) to study how managers of funds behave when shielded from their investors' short-term needs. We document that asset managers exploit reduced investor attention to deliver lower performance quantifiable in 21% for an average investor holding the fund for 50 years. This underperformance is driven by fund families using TDFs to smooth the flow shocks of affiliated open-end funds and to boost fees by investing in the affiliated expensive share classes. We use the Pension Protection Act of 2006 as an exogenous shock that made TDFs the default investment option within 401(k) retirement plans.

The exponential growth and variety of studies on returns highlights the lack of comprehensive asset pricing theory for explicitly explaining the empirical data. The paper addresses this challenge by proposing a hierarchical state-based asset pricing model based on two interconnected solutions: the state space of assets and explanatory gain decomposition approach. As a result, the states of assets extend the conventional state of nature for bringing fundamental and macroeconomic characteristics into existing asset pricing models. Then, the decomposition approach tackles the complexity and heterogeneity of asset pricing by advancing all-in analyses with hierarchical piecewise finer-grained regressions. The direction is demonstrated with a multi-step analysis subsequently boosting the explanatory power of regressions between price-to-fundamental ratios and asset quality characteristics and resolving the weak correlation between the HML and RMW factors. Furthermore, the proposed model establishes a direct link between theory and empirics encompassing multi-dimensional data and growing stack of data science techniques.

It is assumed in the news-based model of stock pricing (NBSPM) that stock prices are determined with macroeconomic news (modeled with the total market return in the spirit of CAPM), industry news (modeled with the relevant industry ETF returns), and the company-specific news and momentum that are described using an optimal ARMA-GARCH model. In this work, the NBSPM accuracy for forecasting stock prices is compared with that of the momentum-enhanced five-factor Fama-French model. The results for a representative list of holdings of nine major US equity sector ETFs demonstrates superiority of the NBSPM in most cases.

We propose a new analytical model for the valuation of loan commitments and some of their main features including the MAC (Material Adverse Change) clause. A two-period contingent claims approach in continuous time is developed. The advantage of this approach is that it is based on rational economic considerations that are not based on utility functions.

When liabilities are deemed improbable or cannot be reliably estimated by management, they are not recorded on the balance sheet. Instead, they are disclosed qualitatively in the company's filings. Examples include obligations related to pending or future lawsuits, product liability, environmental matters, false advertising, or patent and copyright infringements. We refer to these obligations as intangible liabilities (IL). We construct a firm-level, text-based measure of IL. IL is positively related to firm size, volatility, and share turnover, and is negatively correlated with accounting performance, abnormal returns, and Tobin’s Q. IL also predictably varies across industries. Moreover, IL predicts future lawsuits against firms and the future deterioration of their reputations. Companies with higher IL trade at lower valuation ratios and have significantly higher future crash risks. A portfolio that is long on high IL and short on low IL yields an annual abnormal return of 3% after accounting for common factors. Overall, the results suggest that intangible liabilities are a significant determinant of firm value and stock returns.

We introduce an out-of-sample neural-network-based measure of firm-level operating leverage, which outperforms existing ones in capturing the elasticity of operating profits to gross profits. Strikingly, our analysis uncovers a non-monotonic—and potentially negative—relationship between operating leverage and the risk premium. This challenges conventional wisdom and contradicts explanations that link operating leverage to the value premium. A production-based asset pricing model incorporating both variable and fixed costs provides a possible rationale for these empirical findings. Furthermore, our analysis offers a fresh perspective on the idiosyncratic volatility premium by emphasizing the interplay between the operating hedge effect induced by variable costs and the operating leverage effect induced by fixed costs.

We investigate open-source innovation by public firms and the private value it generates for these firms. Unlike patents, which grant inventors exclusive rights to their inventions, open-source innovations can be used by anyone. Nevertheless, using an extensive dataset of public-firm activity on GitHub, we find that firms with open-source projects represent 68% of the U.S.~stock market across 86% of industries. We estimate the private value of all projects in our sample to be nearly $25 billion, with the average project generating $842,000. We find that projects with fully permissive licenses are generally less valuable and firms facing higher competition tend to generate less private value from their projects. We also find that the degree to which a project complements commercial products is not a primary driver of private value. Finally, open-source value is associated with a firm's substantial growth in terms of sales, profits, employment, and patenting, yet it also induces creative destruction. These results contribute to our understanding of the private value generated by innovation in the absence of legal excludability.

The interconnectedness of firms through various networks, such as production, credit, and competition, plays a critical role in determining firm-level and aggregate outcomes. However, data on these connections are often limited. This paper introduces a novel artificial intelligence methodology that extracts explicit firm relationship networks from financial news articles, providing comprehensive and interpretable data across multiple dimensions. Applying this methodology to New York Times articles since 1981, we generate extensive networks that predict key macroeconomic indicators. Our publicly accessible dataset offers valuable insights for future research on firm networks and aggregate fluctuations.

This paper analyzes investor search behavior across financial information providers by modeling demand within a multi-class classification framework. Examining searches on Bloomberg Terminals and EDGAR, I explore how subscription prices, expected stock returns, and investor income influence the selection of financial data sources. The findings offer insights into information-seeking behavior, retrieval patterns, and access dynamics, highlighting the economic factors that drive the use of proprietary and open-access financial platforms.

We derive an option-implied valuation of impermanent loss for liquidity providers on decentralized exchanges and quantify it based on traded option prices. We propose a model that values impermanent loss through the variance of the tokens' relative price. Since the relative price is not the price of a traded asset, we introduce a model for the distribution of the former and a valuation formula induced by a change of num'{e}raire. We show that impermanent loss arises from the tokens' individual risks and their correlation risk. These risks negatively impact pool sizes and explain the cross-sectional returns of liquidity pools.

I provide the first systematic analysis of collateral choices in one of the main short-term funding markets, the repurchase agreement (repo) market. Repos establish a natural connection between short-term and long-term funding markets as long-term bonds serve as collateral in short-term funding trades. In general collateral repos, banks can choose which bond they post as collateral out of a predefined list. In the aggregate, on-the-run bonds are more likely to be delivered than cheapest-to-post securities, which is surprising given that the former are more expensive. I rationalize those findings in a theoretical framework that links the repo to the bond market. My results are relevant for explaining bond market patterns that are different in the United States compared to the euro area.

This paper analyzes the effects of hidden liquidity by studying the introduction of iceberg orders at a large cryptocurrency exchange. Compared to other assets, cryptocurrencies often trade against both fiat currencies and pegged stablecoins. Considering the introduction of iceberg orders for trading pairs against the US dollar but not against a dollar-pegged stablecoin, this study finds that hidden liquidity is associated with increased quoting and trading activity. Larger average trade sizes suggest greater institutional participation. Liquidity improves through tighter spreads and deeper depth, while the price impact of trades declines. Realized spreads increase, indicating improved revenues for market makers while also offering enhanced execution conditions for liquidity takers. Price discovery also shifts significantly toward the markets accepting iceberg orders. Overall, our results suggest that hidden liquidity has positive effects on market quality.

Abstract:

This study examines the efficiency of Robo-Advisors within the broader context of Fintech in wealth and asset management, aiming to determine their performance relative to traditional asset management strategies and benchmarks. Positioned as a case study, the research explores the performance of a particular leading Robo-Advisor in Japan, decomposing returns into tactical and strategic components, alongside various risk metrics. The work is structured into three key parts: theoretical foundations, methodological development, and empirical analysis.

First a classification framework is proposed to capture the essential features of Robo-Advisory services globally. Then, the experiment is developed and conducted: Empirical results reveal that, across varied market conditions, Robo-Advisors do not consistently outperform a simple fixed-weight strategy, which holds assets at predetermined allocations without frequent adjustments. Furthermore, the study’s comparison with the Japanese Fund Market suggests no statistically significant difference in asset management outcomes between Robo-Advisors and conventional fund strategies.

Desing/Approach:

A case study methodology is employed, focusing on a leading Japanese Robo-Advisor. Theoretical foundation of the Robo-Advisor methodology and data from the Japanese Fund Association are utilized to construct a composite benchmark and evaluate returns, which are decomposed into tactical and strategic components. Statistical tests and risk metrics are applied to data to determine significant performance differences between the Robo-Advisor and traditional asset management strategies.

Most human decisions are taken intuitively, with a mix of reflection and emotions that is often impossible to disentangle. Economists model decisions explicitly as a mixture of objective and subjective elements: economic agents objectively (mathematically) optimize a subjective value function. Using this model, one can create situations where choice is independent of subjective value and demonstrate that humans often fail at the objective part of decision making. Algorithmic advisers can thus help humans, as they never fail at objective optimization.

However, since decision optimality depends both on correct optimization and on knowledge of the right subjective value function, machines who disregard the taste or “preferences” of the human on whose behalf they act, will make poor decisions. Thus, the performance of algorithmic advisers is crucially affected by the machine’s ability to learn about a particular human’s preferences. But will a human do better at communicating their preferences to a machine than at making their decisions themselves? We know humans fail at common tasks of deciding what to consume or invest in, but will they be less faulty at the even less natural task of communicating their preferences?

In the controlled environment of the economic laboratory – taken online via a platform to recruit a diverse set of participants (Prolific) – we induce a specific type of risk preferences and ask participants to create investment portfolios of a risky and a risk-free asset to maximize this preference, either directly or through a robotic adviser. To induce preferences, participant payoff is a fixed transformation of the probability distribution of risky asset payoffs, the payoff of the risk-free asset, and the participants’ chosen holdings of these two assets. Thus, participants do not face true risk: their payoff depends on the entire distribution of payoffs, not on realized payoff only. By controlling participant “risk preferences”, we can assess if the human-algorithm interaction leads to a correct treatment of the subjective part of decision making. In all experimental treatments, we vary the risk preferences we induce over time, so to see if participants react to and attempt to communicate these changes.

We have one treatment where participants choose portfolios on their own and three treatments where participants are advised by algorithms who elicit their human boss’s risk preference via a test (lottery choice). We ask if portfolio choices with or without the algorithmic adviser are better for the preferences we induce. To refine our question, we vary the frequency at which the algorithm elicits risk preferences from humans. This gives us three treatments with an algorithmic adviser, depending on whether the frequency of elicitation is equal, higher, or lower than the frequency at which we change participants’ risk preferences. We ask whether frequent communication allows for better fine-tuning of communicated preferences or, instead, adds noise due to, for example, a biased perception of past algorithm outcomes by the human.

The experiment, coded in oTree, will be preregistered on the platform AsPredicted and approved by the internal review board (IRB) of the University of Utah.

Authorised Participants (APs), primarily market makers, create and redeem ETF shares in response to investor demand, making their behaviour crucial for ETF liquidity and price alignment. We formulate a dynamic equilibrium model of APs' trading decisions, explicitly capturing their inventory management incentives and arbitrage motives, and derive predictions linking ETF mispricing to inventory risk and aggregate demand shocks. Using a novel regulatory dataset covering primary and secondary market trades for 128 ETFs between 2018 and 2022, we empirically validate our model's predictions. Results confirm that APs' real-time inventory positions and investor demand significantly explain ETF price deviations from net asset values (NAVs), offering insights beyond traditional economic and fundamental factors. Our model further clarifies APs' incentives and sheds light on mechanisms underlying the severe mispricing episodes observed in March 2020 across various ETF classes.

Higher ETF ownership lowers the volatility of corporate bonds returns, particularly small and less liquid bonds. The distinguishing features of ETF ownership– exchange trading and in-kind creation and redemption – have differential impacts. Secondary market trading, concentrated in just a few funds, serves as a liquidity buffer. The negative effect of ownership is heightened for bonds held by ETFs with greater trading volume and institutional ownership. In contrast, greater in-kind creation and redemption activity process mitigates the negative effect of ownership on volatility. Thus, ETF ownership serves as both a buffer and transmitter in corporate bond markets.

We use TRACE transactions data to assess trading activity and measure dealer markups on riskless principal trades in structured products. Median markups on such transactions with market values in the $5-$10 million range for MBS and ABS are just 0.03%, comparable to the 0.02% observed for Corporate bonds. Corresponding median markups are 0.10% for Agency CMO and 0.20% for Non-Agency CMO. Skewed markup distributions exist in all products, suggesting that customers are short-changed in a significant number of trades by opportunistic (“bandit”) dealers. The top quartile of both Agency and Non-Agency CMO riskless principal trades cross at markups above 1.0%, more than quadruple their median values. The top eighth of these paired trades cross at markups above 2.0%, more than nine times their median values.

The incidence of dealer banditry increased during the Pandemic crisis week beginning March 23, 2020. One bandit dealer made $54.5 million in excessive markups by buying 238 Non Agency CMO worth $1.732 billion from a single seller, while simultaneously splitting sales of these same positions among five counterparty accounts during a 12-minute “fire sale” on March 25, 2020. Benchmarks suggest this dealer also facilitated at least a 20% suppression of the fair value of these trades, benefiting the buying group while disadvantaging the seller by an extra $346 million. One of the buyers realized a $139.4 million capital gain (39% return on investment) after unwinding 35 days later in highly unusual “after hours” trades that also netted the dealer an extra $22.9 million in markup profits.

In sharp contrast to its near immediate dissemination of prices from Corporate bond, MBS, and ABS transactions, FINRA waits more than 18 months after the trade date to release data for CMO trades with transaction quantities equal to or greater than $1 million. We show that the 3/20/2017 rollout of reporting for CMO trades with transaction quantities less than $1 million appears to have reduced both the level and variability of markups in that segment. However, incidences of banditry appear to have increased for Non-Agency CMO trades with sizes of $1 million or more. The March 25, 2020, “crime scene” makes the costs of continuing to withhold reliable and timely information from customers all too real. We recommend that FINRA commence near real-time dissemination of TRACE transactions reports on all riskless principal trades in all structured products, including not only CMO but also Commercial Mortgage Backed Securities (CMBS), Collateralized Loan Obligations (CLO), and Collateralized Debt Obligations (CDO).

This paper investigates the puzzling negative empirical relationship between tail risk and expected return. Using Expected Shortfall as a measure of tail risk, this study decomposes it into elemental systematic and idiosyncratic components which allow for a deep probing of the relationship. The evidence suggests that while Expected Shortfall is an important determinant of expected returns, it earns a negative risk premium in stark contradiction of theory. After verifying empirically the negative tail risk premium anomaly, the paper investigates how the systematic and idiosyncratic components of tail risk influence expected returns and challenges prevailing explanations of tail risk premia. The negative tail risk premium anomaly is driven mainly by idiosyncratic Expected Shortfall. Moreover, contradicting recent findings in the literature which document that systematic tail risk has a positive impact on expected returns, the systematic Expected Shortfall is either negative or at times insignificantly positive. This is a new and puzzling finding. These findings contribute to a deeper understanding of the drivers behind tail risk anomalies and hold implications for both investment strategies and the interpretation of tail risk-return relationships.

Firms are increasingly exposed to cybersecurity risks. We examine whether lenders recognize and accordingly price firms’ cybersecurity risks. Our findings indicate that lenders on average charge a 4 to 12 basis points higher loan rate when a firm exhibits greater cybersecurity risk over time. Commercial banks tend to adopt a more stringent approach to pricing cybersecurity risks compared to non-bank lenders. Finally, the purchase of cybersecurity insurance by a firm does not mitigate the higher loan spreads associated with elevated cybersecurity risks.

Despite numerous contributions on the topic, the study of the dynamics that influences the risk of SME insolvency still finds remarkable interest. In recent years, the use of machine learning algorithms in this field has increased the predictive accuracy of credit risk models. Using a set of fourteen indicators derived from a proprietary dataset, our study compare the predictive effectiveness of different machine learning models, currently widely used in the literature and in credit risk applications, through the calculation of specific evaluation indicators (e.g., accuracy, precision, F1 score, ROC curve (AUC), precision/recall curve). In addition, we provide useful information on the role of financial and accounting indicators in providing warning lights to entrepreneurs and managers to anticipate and manage potential default risks through the implementation of a Feature Importance Analysis.