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We examine return premia associated with the level, slope, and curvature of the yield curve over time and across countries from a novel perspective by borrowing pricing factors from other asset classes. Measures of value, momentum, and carry, when applied to bonds, provide a rich description of bond return premia: subsuming pricing information from the yield curve’s first three principal components, as well as priced factors unspanned by yield information, such as macroeconomic growth, inflation, and the Cochrane and Piazzesi (2005) factor. These characteristics provide new economic intuition for what drives bond return premia, where value, measured by a bond’s yield relative to a fundamental anchor of expected inflation, subsumes a “level” factor. Momentum, which reveals recent yield trends, and carry, which captures expected future yields if the yield curve does not change, subsume information about expected returns from the slope and curvature of the yield curve. These characteristics describe both the cross-section and time-series of yield curve premia and connect to return predictability in other asset classes, suggesting a unifying asset pricing framework.

Duration and DV01 (dollar duration) measure price sensitivity and provide the basic risk measure for bonds, swaps, and other fixed income instruments. When valuing instruments off a yield curve, duration and DV01 naturally extend to a vector of partial DV01s or durations (key rate durations) and these are widely used in the finance industry. But partial DV01s or durations can be measured with respect to different rates: forwards, par rates, zero yields, or others. This paper reviews the concepts of partial DV01 and duration and then discusses a simple method for transforming partial DV01s between different rate bases and provides examples. The benefit of this transformation method is that it only requires calculating the risk of a small set of alternate instrument and does not require re-calculating the original portfolio risk. (This paper is also available in an interactive version with enhanced digital content - see references.)

We provide a broad empirical investigation of momentum strategies in the foreign exchange market. We find a significant cross-sectional spread in excess returns of up to 10% p.a. between past winner and loser currencies. This spread in excess returns is not explained by traditional risk factors, it is partially explained by transaction costs and shows behavior consistent with investor under- and over-reaction. Moreover, cross-sectional currency momentum has very different properties from the widely studied carry trade and is not highly correlated with returns of benchmark technical trading rules. However, there seem to be very effective limits to arbitrage which prevent momentum returns from being easily exploitable in currency markets.

This paper explores the application of machine learning methods to financial statement analysis. We compare a range of models in the machine learning repertoire in their ability to predict the sign and magnitude of abnormal stock returns around earnings announcements based on past financial statement data alone. Random Forests produce the most accurate forecasts and the highest abnormal returns. (Nonlinear) neural network-based models perform relatively better for predictions of extreme market reactions, while the linear methods are relatively better in predicting moderate market reactions. Long-short portfolios based on model predictions generate sizable abnormal returns, which seem to decay over time. Abnormal returns are robust to various risk factors and load in expected ways on size, value and accruals. Analysing the underlying economic drivers of the performance of the Random Forests, we find that the models select as most important predictors financial variables required to forecast free cash flows and firm characteristics that are known cross-sectional predictors of stock returns.

We document that firms' management of accounting earnings increased steadily from 1987 until the passage of the Sarbanes Oxley Act (SOX), with a significant increase during the period prior to SOX, followed by a significant decline after passage of SOX. However, the increase in earnings management preceding SOX was primarily in poorly performing industries. We also show that the informativeness of earnings increased steadily over time, and there was no significant change in earnings informativeness following the passage of SOX. Further, we find that earnings management increased the absolute informativeness of earnings, but reduced the informativeness for a given earnings surprise, as well as reduced the abnormal return for a given amount of earnings surprise. Finally, the evidence supports the hypothesis that the opportunistic behavior of managers, primarily related to the fraction of compensation derived from options, was significantly associated with earnings management in the period preceding SOX.

Since decades, the financial industry has experienced a continuous evolution in service delivery due to digitalization. This evolution is characterized by expanded connectivity and enhanced speed of information processing both at the customer interface and in back-office processes. Recently, there has been a shift in the focus of digitalization from improving the delivery of traditional tasks to introducing fundamentally new business opportunities and models for financial service companies. Digital Finance encompasses a magnitude of new financial products, financial businesses, finance-related software, and novel forms of customer communication and interaction — delivered by FinTech companies and innovative financial service providers. Against this backdrop, the research on finance and information systems has started to analyze these changes and the impact of digital progress on the financial sector. Therefore, this article reviews the current state of research in Digital Finance that deals with these novel and innovative business functions. Moreover, it gives an outlook on potential future research directions. As a conceptual basis for reviewing this field, the Digital Finance Cube, which embraces three key dimensions of Digital Finance and FinTech, i.e., the respective business functions, the technologies and technological concepts applied as well as the institutions concerned, is introduced. This conceptualization supports researchers and practitioners when orientating in the field of Digital Finance, allows for the arrangement of academic research relatively to each other, and enables for the revelation of the gaps in research.

Investing can be characterized as a data science problem. While investment firms have attracted scientific talent, they have done a poor job at developing it. Firms hire specialists, but entice them to become generalists (e.g., portfolio managers). Under the ubiquitous silo/platform structure, quants succumb to the Sisyphean trap, and do not achieve their full potential. A research lab structure offers a unique environment for developing scientists, by means of: (a) co-specialization, working in a highly cooperative lab environment; (b) tackling well-defined open investment problems; and (c) applying the scientific method.

This paper quantifies the impact of Robinhood traders on the US equity market. Within a structural model, we estimate retail and institutional demand curves and derive aggregate pricing implications via market clearing. The inelastic nature of institutional demand allows Robinhood traders to have a substantial effect on stock prices. Despite their small market share of 0.15%, Robinhood traders account for 10% of the cross-sectional variation in stock returns during the second quarter of 2020. Furthermore, without the surge in retail trading activity, the aggregate market capitalization of the smallest size quintile of stocks would have been 20% lower.

This paper aims to examine one of the most important issues in the international corporate laws, which is the issue of share repurchases known as treasury shares. This paper investigated corporate laws, stock exchange regulations, research literatures and stock trading volume for repurchase activities, covering a sample of thirty-five countries developed and emerging markets. The study found that there is an increasing movement in the world stock market towards adopting or deregulating the share repurchase activities, More than half of the selected sample witnessed a change in the related laws (especially corporate laws) of share repurchases during the period 1995 to 2000. Moreover, there is a toleration to use the treasury share in enhancing the stock market during stock crises and extraordinary market price volatility. Based on upon findings, a model has been suggested and articulated to be considered as a stabilization instrument for stock marketing.

We build cross-sections of asset returns for a given set of characteristics, that is, managed portfolios serving as test assets, as well as building blocks for tradable risk factors. We use decision trees to endogenously group similar stocks together by selecting optimal portfolio splits to span the Stochastic Discount Factor, projected on individual stocks. Our portfolios are interpretable and well diversified, reflecting many characteristics and their interactions. Compared to combinations of dozens (even hundreds) of single/double sorts, as well as machine learning prediction-based portfolios, our cross-sections are low-dimensional yet have up to three times higher out-of-sample Sharpe ratios and alphas.

How to analyze and visualize the propagation law of risk in a multi-dimensional market. Commented code is available for download

We assess how investors’ willingness-to-pay (WTP) for sustainable investments responds to the social impact of those investments, using a framed field experiment. While investors have a substantial WTP for sustainable investments, they do not pay significantly more for more impact. This also holds for dedicated impact investors. When investors compare several sustainable investments, their WTP responds to relative but not to absolute levels of impact. Regardless of investments' impact, investors experience positive emotions when choosing sustainable investments. Our findings suggest that the WTP for sustainable investments is primarily driven by an emotional rather than a calculative valuation of impact.

The purpose of this paper is to provide a systematic empirical comparison of the different proxy variables used to measure information asymmetry. We construct different information asymmetry measures based on a firm's growth opportunities, the market microstructure of the firm's stock, and analysts' forecasts of a firm's earnings per share and examine their correlations under different sampling situations. We also study the ability of the microstructure-based measures, which can be calculated at any point in time, to detect trends in corporate finance proxies, which are measured quarterly. We find that the market microstructure measures tend to be highly correlated with one another. Moreover, they are related to firm characteristics that ex ante should be associated with information asymmetry. The market microstructure measures tend to be related to analyst forecast errors only for large, widely followed firms. Our results also indicate that monthly changes in the microstructure measures of information asymmetry are significantly correlated with annual changes in the corporate finance proxy variables.

The roles played by idiosyncratic risk and liquidity in determining stock returns have recently received a great deal of attention. However, recent empirical tests have not examined the interaction between these two factors. As others have shown (and this paper confirms) stocks idiosyncratic risk and liquidity are negatively correlated. To what extent then is each variable responsible for the observed cross sectional patterns in stock returns? Overall, using monthly data, the paper finds that stock returns are increasing with the level of idiosyncratic risk and decreasing in a stock's liquidity. However, while both liquidity and idiosyncratic risk play a role in determining returns, the impact of idiosyncratic risk is much stronger and often eliminates liquidity's explanatory power. The point estimates indicate that a one standard deviation change in idiosyncratic risk has between 2.5 and 8 times the impact of a corresponding change in liquidity on cross sectional expected returns.

This paper conducts a textual analysis of earnings call transcripts to quantify climate risk exposure at the firm level. We construct dictionaries that measure physical and transition climate risks separately and identify firms that proactively respond to climate risks. Our validation analysis shows that our measures capture firm-level variations in respective climate risk exposure. Firms facing high transition risk, especially those that do not proactively respond, have been valued at a discount in recent years as aggregate investor attention to climate-related issues has been increasing. We document differences in how firms respond through investment, green innovation, and employment when facing high climate risk exposure.

Using a sample of 1,018 Sovereign Wealth Fund (SWF) equity investments in publicly traded firms and a control sample of 5,975 transactions by private-sector financial institutions over 1980-2012, we find that announcement-period abnormal returns of SWF investments are positive, but lower than those of comparable private-sector investments by approximately 2.67 percentage points. We do not find evidence of long-term stock price performance of SWF investment targets differing from that of private-sector investment targets. We do find, however, significant differences among SWFs which are only partially captured by the short-term market reaction: firms acquired by passive funds tend to underperform over the following three years, while positive abnormal returns are associated with actively monitoring SWFs. We conclude that SWFs’ corporate governance role tends to affect the value of the firm.

Just over 20 years have passed since the publication of Carhart’s landmark 1997 study on mutual funds. Its conclusion—that the data did “not support the existence of skilled or informed mutual fund portfolio managers”—was the capstone of an academic literature beginning with Jensen (1968) that formed the ‘conventional wisdom’ that active management does not create value for investors. In this paper, we review the literature on active mutual fund management since the publication of Carhart (1997) to assess the extent to which current research still supports the conventional wisdom. Our review of the most recent literature suggests that the conventional wisdom is too negative on the value of active management.

This paper develops a simple formula for approximating Tobin's q. The formula requires only basic financial and accounting information. Results of a series of regressions comparing our approximate q values with those obtained via Lindenberg and Ross' (1981) more theoretically correct model indicate that at least 96.6% of the variability of Tobin's q is explained by approximate q.

The Fintech Action Plan (see now also Digital Finance Strategy) and the Sustainable Finance Strategy both represent important pillars of the current EU policy agenda. Nonetheless, the two areas have been treated as separate for a long time, while they present certain common features and great potential when combined. In particular, Fintech appears able to respond to some shortcomings in the current sustainable finance framework (e.g. access to retail financing, ESG disclosure, verification and ratings, etc.). The relevance of the link between sustainability, finance and technology has also been evidenced by the COVID-19 pandemic crisis, which has urged all countries to re-think the models traditionally deployed and rely more on technology and sustainability. However, Fintech still raises per se relevant legal issues that need to be addressed to fulfil its promises and potential in the sustainable finance sector. The present paper aims at starting a debate about “Green Fintech” in order to effectively connect the two worlds and spur the research in such a new and promising area.

Using trends in firm fundamentals, we find that there is a fundamental momentum in the stock market. Buying stocks in the top quintile of fundamental trends and selling stocks in the bottom earns a monthly average return of 0.85%, comparable to price momentum. Combining both price and fundamental momentum produces a twin momentum, which earns an average return that exceeds their sum and is difficult to explain by short-sell impediments. Our results not only support the view that fundamental analysis is as important as technical analysis, but also indicate that trends contain incremental information beyond often used lagged fundamental predictors.

We introduce a general class of interest rate models in which the value of pure discount bonds can be expressed as a functional of some (low-dimensional) Markov process. At the abstract level this class includes all current models of practical importance. By specifying these models in Markov-functional form, we obtain a specification which is efficient to implement. An additional advantage of Markov-functional models is the fact that the specification of the model can be such that the forward rate distribution implied by market option prices can be fitted exactly, which makes these models particularly suited for derivatives pricing. We give examples of Markov-functional models that are fitted to market prices of caps/floors and swaptions.

We examine the valuation performance of a comprehensive list of pricing multiples. We find that multiples derived from forward earnings explain stock prices remarkably well for most firms: pricing errors are within 15 percent of stock prices for about half of our sample. In terms of relative performance, the following general rankings are observed: 1) forward earnings measures, 2) historical earnings measures, 3) cash flow measures and book value of equity (tied), and 4) sales. Contrary to the popular view that different industries have different ?best? multiples, we find that these overall rankings are observed consistently for almost all industries examined. Adjusting the ratio formulation typically followed in practice to allow for an intercept offers some improvement, especially for multiples that perform poorly. No improvement is observed, however, when we consider more complex measures of intrinsic value based on short-cut residual income models (where forward earnings are combined with book values, estimated discount rates, and generic terminal value estimates).

This essay provides a retrospective view on our co-authored paper, Ball and Brown (1968). The retrospective was commissioned by Gregory Waymire, then President of the American Accounting Association. It describes how we both came to be PhD students at the University of Chicago and set about researching the relation between earnings and share prices. It outlines the background against which we conducted the research, including the largely a priori accounting research literature at the time and the electric atmosphere and radical new ideas then in full bloom at Chicago. We describe some of the principal research choices we made, and their strengths and weaknesses. We also describe the reception our research received and how the related literature subsequently unfolded.

In this paper we investigate the claim that hedge funds offer investors a superior risk-return trade-off. We do so using a continuous time version of Dybvig's (1988a, 1988b) payoff distribution pricing model. The evaluation model, which does not require any assumptions with regard to the return distribution of the funds in question, is applied to the monthly returns of 77 hedge funds and 13 hedge fund indices over the period May 1990-April 2000. The results show that as a stand-alone investment hedge funds do not offer a superior risk-return profile. We find 12 indices and 72 individual funds to be inefficient, with the average efficiency loss amounting to 2.76% per annum for indices and 6.42% for individual funds. Part of the inefficiency cost of individual funds can be diversified away. Funds of funds, however, are not the preferred vehicle for this as their performance appears to suffer badly from their double fee structure. Looking at hedge funds in a portfolio context results in a marked improvement in the evaluation outcomes. Seven of the 12 hedge fund indices and 58 of the 72 individual funds classified as inefficient on a stand-alone basis are capable of producing an efficient payoff profile when mixed with the S&P 500. The best results are obtained when 10-20% of the portfolio value is invested in hedge funds

I present closed-form solutions for prices, portfolios, and beliefs in a model where four types of investors trade assets over time: naive investors who learn via a social network, ``fanatics'' possibly spreading fake news, and rational short- and long-term investors. I show that fanatic and rational views dominate over time, and their relative importance depends on their following by influencers. Securities markets exhibit social network spillovers, large effects of influencers and thought leaders, bubbles, bursts of high volume, price momentum, fundamental momentum, and reversal. The model sheds new light on the GameStop event, historical bubbles, and asset markets more generally.