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This paper lays out alternative equity valuation models that involve forecasting for finite periods and shows how they are related to each other. It contrasts dividend discounting models, discounted cash flow modes, and "residual income" models based on accrual accounting. It shows that some models that are apparently different yield the same valuation. It gives the general form of the terminal value calculation in these models and shows how this calculation serves to correct errors in the model. It also shows that all models can be interpreted as providing a particular specification of the terminal value for the dividend discount model and in so doing provides the terminal value calculation for the dividend discount formula.

Anomalies are empirical results that seem to be inconsistent with maintained theories of asset-pricing behavior. They indicate either market inefficiency (profit opportunities) or inadequacies in the underlying asset-pricing model. The evidence in this paper shows that the size effect, the value effect, the weekend effect, and the dividend yield effect seem to have weakened or disappeared after the papers that highlighted them were published. At about the same time, practitioners began investment vehicles that implemented the strategies implied by some of these academic papers. The small-firm turn-of-the-year effect became weaker in the years after it was first documented in the academic literature, although there is some evidence that it still exists. Interestingly, however, it does not seem to exist in the portfolio returns of practitioners who focus on small-capitalization firms. All of these findings raise the possibility that anomalies are more apparent than real. The notoriety associated with the findings of unusual evidence tempts authors to further investigate puzzling anomalies and later to try to explain them. But even if the anomalies existed in the sample period in which they were first identified, the activities of practitioners who implement strategies to take advantage of anomalous behavior can cause the anomalies to disappear (as research findings cause the market to become more efficient).

Evaluating how factor premia vary over time and across asset classes is challenging due to limited time series data, especially outside of U.S. equities. We examine four prominent factors across six asset classes over a century. We find little evidence for arbitrage activity influencing returns, though some novel evidence of overfitting biases. We identify meaningful time variation in factor risk-adjusted returns that appears unrelated to macroeconomic risks, supporting other theories of dynamic return premia. Attempting to capture this variation, we evaluate various factor timing strategies, but find relatively modest predictability that likely fails to overcome implementation frictions.

This paper provides a practitioner-oriented review of the accrual anomaly in Sloan (1996) and related subsequent research. We begin with two simple examples that illustrate the computation and interpretation of accruals. We next review Sloan's (1996) original paper and related subsequent research corroborating Sloan's interpretation of the accrual anomaly. We next summarize research providing alternative explanations for the accrual anomaly. We finish with a brief discussion of the practical implications of this research.

Much of the extant literature predicts market returns with “simple” models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.

Financial ML offers the opportunity to gain insight from data:* Modelling non-linear relationships in a high-dimensional space* Analyzing unstructured data (asynchronous, categorical)* Learning complex patterns (hierarchical, non-parametric)* Focusing on predictability over parametric adjudication* Controlling for overfitting (early-stopping, cross-validation)At the same time, Finance is not a plug-and-play subject as it relates to machine learning. Modelling financial series is harder than driving cars or recognizing faces.In this presentation, we will review a few important financial ML applications.For the full paper, see https://ssrn.com/abstract=3365271.

This study examines the impact of ESG ratings on fund holdings, stock returns, and firm behavior. First, we show that among five major ESG ratings, only MSCI ESG can explain the holdings of US funds with an ESG mandate. We document that downgrades in the MSCI ESG rating substantially reduce firms' ownership by such funds, while upgrades increase it. However, this response in ownership is slow, unfolding gradually over a period of up to two years. This suggests that fund managers use ESG ratings mainly to comply with ESG mandates rather than treating them as updates to firms' fundamentals. Accordingly, we also find a slow and persistent response in stock returns. For a one-year holding period, downgrades lead to an abnormal return of -2.37%. For upgrades, we find a positive but weaker effect. Yet, the extent to which ESG ratings matter for the real economy seems limited. We find no significant effect of up- or downgrades on firms' subsequent capital expenditure. We find that firms adjust their ESG practices following rating changes, but only in the governance dimension.

We use the popular television show Mad Money hosted by Jim Cramer to test theories of attention and limits to arbitrage. Stock recommendations on Mad Money constitute attention shocks to a large audience of individual traders. We find that stock recommendations lead to large overnight returns which subsequently reverse over the next few months. The spike-reversal pattern is strongest among small, illiquid stocks that are hard-to-arbitrage. Using daily Nielsen ratings as a direct measure of attention, we find the overnight return is strongest when high income viewership is high. We also find weak price effects among sell recommendations. Taken together, the evidence supports the retail attention hypothesis of Barber and Odean (2008) and illustrates the potential role of media in generating mispricing.

We model investing that considers environmental, social, and governance (ESG) criteria. In equilibrium, green assets have low expected returns because investors enjoy holding them and because green assets hedge climate risk. Green assets nevertheless outperform when positive shocks hit the ESG factor, which captures shifts in customers' tastes for green products and investors' tastes for green holdings. The ESG factor and the market portfolio price assets in a two-factor model. The ESG investment industry is largest when investors' ESG preferences differ most. Sustainable investing produces positive social impact by making firms greener and by shifting real investment toward green firms.

We reconcile the major approaches in the literature to benchmark cash flow-based returns of private equity investments against public markets, a.k.a. 'Public Market Equivalent' methods. We show that the existing methods to calculate annualized excess returns are heuristic in nature, and propose an advanced approach, the 'Direct Alpha' method, to derive the precise rate of excess return between the cash flows of illiquid assets and the time series of returns of a reference benchmark. Using real-world fund cash flow data, we finally compare the major PME approaches against Direct Alpha to gauge their level of noise and bias.

The Fourier transform is an important tool in Financial Economics. It delivers real time pricing while allowing for a realistic structure of asset returns, taking into account excess kurtosis and stochastic volatility. Fourier transform is also rather abstract and therefore off-putting to many practitioners.The purpose of this paper is to explain the working of the fast Fourier transform in the familiar binomial option pricing model. We argue that a good understanding of FFT requires no more than some high school mathematics and familiarity with roulette, bicycle wheel, or a similar circular object divided into equally sized segments. The returns to such a small intellectual investment are overwhelming.

Equity risk premiums are a central component of every risk and return model in finance and are a key input into estimating costs of equity and capital in both corporate finance and valuation. Given their importance, it is surprising how haphazard the estimation of equity risk premiums remains in practice. We begin this paper by looking at the economic determinants of equity risk premiums, including investor risk aversion, information uncertainty and perceptions of macroeconomic risk. In the standard approach to estimating equity risk premiums, historical returns are used, with the difference in annual returns on stocks versus bonds over a long time period comprising the expected risk premium. We note the limitations of this approach, even in markets like the United States, which have long periods of historical data available, and its complete failure in emerging markets, where the historical data tends to be limited and volatile. We look at two other approaches to estimating equity risk premiums - the survey approach, where investors and managers ar asked to assess the risk premium and the implied approach, where a forward-looking estimate of the premium is estimated using either current equity prices or risk premiums in non-equity markets. We also look at the relationship between the equity risk premium and risk premiums in the bond market (default spreads) and in real estate (cap rates) and how that relationship can be mined to generated expected equity risk premiums. We close the paper by examining why different approaches yield different values for the equity risk premium, and how to choose the “right” number to use in analysis. (In an addendum, we also look at equity risk premiums during the market crisis, starting on September 12, 2008 through December 31, 2008, and then track the shift the changes through September 30, 2009.)

The various macro econometrics model for inflation are helpless when it comes to the pricing of inflation derivatives. The only article targeting inflation option pricing, the Jarrow Yildirim model, relies on non observable data. This makes the estimation of the model parameters a non trivial problem. In addition, their framework do not examine any relationship between the most liquid inflation derivatives instruments: the year to year and zero coupon swap. To fill this gap, we see how to derive a model on inflation, based on traded and liquid market instrument. Applying the same strategy as the one for a market model on interest rates, we derive no-arbitrage relationship between zero coupon and year to year swaps. We explain how to compute the convexity adjustment and what relationship the volatility surface should satisfy. Within this framework, it becomes much easier to estimate model parameters and to price inflation derivatives in a consistent way.

In this, the fourth article in the economists’ hubris paper series we look at the contributions of academic thought to the field of asset management. We find that while the theoretical aspects of the modern portfolio theory are valuable they offer little insight into how the asset management industry actually operates, how its executives are compensated, and how their performances are measured. We find that very few, if any, portfolio managers look for the efficiency frontier in their asset allocation processes, mainly because it is almost impossible to locate in reality, and base their decisions on a combination of gut feelings and analyst recommendations. We also find that the performance evaluation methodologies used are simply unable to provide investors with the necessary tools to compare portfolio managers’ performances in any meaningful way. We suggest a novel way of evaluating manager performance which compares a manager against himself, as suggested by Lord Myners. Using the concept of inertia, an asset manager’s end of period performance is compared to the performance of their portfolio assuming their initial portfolio had been held, without transactions, during this period. We believe that this will provide clients with a more reliable performance comparison tool and might prevent unnecessary trading of portfolios. Finally, given that the performance evaluation models simply fail in practice, we suggest that accusing investors who look for raw returns when deciding who to invest their assets with is simply unfair.

Changes in oil prices predict stock market returns worldwide. In our thirty year sample of monthly returns for developed stock markets, we find statistically significant predictability for twelve out of eighteen countries as well as for the world market index. Results are similar for our shorter time series of emerging markets. We find no evidence that our results can be explained by time varying risk premia. Even though oil price shocks increase risk, investors seem to underreact to information in the price of oil: a rise in oil prices does not lead to higher stock market returns, but drastically lowers returns. For instance, an oil price shock of one standard deviation (around 10 percent) predictably lowers world market returns by one percent. Oil price changes also significantly predict negative excess returns. Our findings are consistent with the hypothesis of a delayed reaction by investors to oil price changes. In line with this hypothesis the relation between monthly stock returns and lagged monthly oil price changes becomes substantially stronger once we introduce lags of several trading days between monthly stock returns and lagged monthly oil price changes.

The term decentralized finance (DeFi) refers to an alternative financial infrastructure built on top of the Ethereum blockchain. DeFi uses smart contracts to create protocols that replicate existing financial services in a more open, interoperable, and transparent way. This paper highlights opportunities and potential risks of the DeFi ecosystem. I propose a multi-layered framework to analyze the implicit architecture and the various DeFi building blocks, including token standards, decentralized exchanges, decentralized debt markets, blockchain derivatives, and on-chain asset management protocols. I conclude that DeFi still is a niche market with certain risks but that it also has interesting properties in terms of efficiency, transparency, accessibility, and composability. As such, DeFi may potentially contribute to a more robust and transparent financial infrastructure. (JEL G15, G23, E59)

Understanding fundamental human tendencies can help financial planners and advisers recognize behaviors that may interfere with clients achieving their long-term goals. The authors describe several well-established behavioral biases and suggest how to overcome them.

This PhD thesis analyses hedge fund strategies in detail by decomposing hedge fund performance figures. Our aim is to present hedge funds, to understand what managers expect to do and to understand how they make or destroy value over time. In order to achieve this objective, we develop a multi-factor performance analysis model, use it over several time periods and improve it over time. This model aims to determine both whether hedge funds create pure alpha over time (alpha over classical markets) and whether there is persistence in hedge fund returns over time. Following this, I analyse another specific aspect of hedge funds, their neutrality relative to equity markets in order to validate hedge fund managers' claims that they are market neutral. Finally, we develop new efficient frontier measures, which not only include returns and volatility, but also skewness and kurtosis in order to determine whether hedge funds are really beneficial to investors.

We present a simple procedure to construct credit curves by bootstrapping a hazard rate curve from observed CDS spreads. The hazard rate is assumed constant between subsequent CDS maturities. In order to link survival probabilities to market spreads, we use the JP Morgan model, a common market practice. We also derive approximate closed formulas for "cumulative" or "average" hazard rates and illustrate the procedure with examples from observed credit curves.

In the last decade, firms have increasingly turned to offering employees options and restricted stock (often with restrictions on trading) as part of compensation packages. Some of this trend can be attributed to the entry of young, cash poor technology firms into the market, many of which have to use equity because they have no choice. However, many larger market cap firms that can afford to pay cash compensation have used stock based compensation as a way of aligning managerial interests with stockholder interests. In this paper, we begin by looking at motives, good and bad, for using equity based compensation, and trends over the last few years. We then turn to the accounting rules, old and new, that govern how equity compensation is recorded and reported. Finally, we consider how best to incorporate employee options and restricted stock - both past and prospective - into discounted cash flow and relative valuation models.

The motivation for this paper is to show the usefulness of the information contained in the open-to-close (day) and close-to-open (night) periods compared to the more frequently used close-to-close period. To show this we construct two versions of a contrarian strategy, where the worst performing shares during the day (resp. night) are bought and held during the night (resp. day). We show that the strategies presented here generate a significant alpha and their returns cannot be solely explained by the factors derived from Fama and French (1993) 3-factor model and a modified 5-factor model introduced by Carhart (1997). Even after we account for the bid-ask bounce effect the returns generated are significant and consistent. The information ratios of the two strategies mentioned for the entire period 2000-2010 vary between 1.59 and 6.70 depending on the capitalization of stocks. Overall, we show that opening prices contain information that is not generally fully utilized yet. The strategy proposed uses this information to add value and extract a significant alpha which cannot be explained by market factors.

We consider a gradient boosting decision trees (GBDT) approach to predict large S&P 500 price drops from a set of 150 technical, fundamental and macroeconomic features. We report an improved accuracy of GBDT over other machine learning (ML) methods on the S&P 500 futures prices. We show that retaining fewer and carefully selected features provides improvements across all ML approaches. Shapley values have recently been introduced from game theory to the field of ML. They allow for a robust identification of the most important variables predicting stock market crises, and of a local explanation of the crisis probability at each date, through a consistent features attribution. We apply this methodology to analyze in detail the March 2020 financial meltdown, for which the model offered a timely out of sample prediction. This analysis unveils in particular the contrarian predictive role of the tech equity sector before and after the crash.

In this paper we analyze whether good corporate governance leads to higher common stock returns and enhances firm value in Europe. Throughout this study we use Deminor Corporate Governance Ratings for companies included in the FTSE Eurotop 300. Following the approach of Gompers, Ishii and Metrick (2003) we build portfolios consisting of well-governed and poorly governed companies and compare their performance. We also examine the impact of corporate governance on firm valuation. Our results show a positive relationship between these variables and corporate governance. This relationship weakens substantially after adjusting for country differences. Finally, we analyze the relationship between corporate governance and firm performance, as approximated by Net-Profit-Margin (NPM) and Return-on-Equity (ROE). Surprisingly, and contrary to Gompers, Ishii and Metrick (2003), we find a negative relationship between governance standards and these earnings based performance ratios for which we discuss possible implications.

In corporate finance and investment analysis, we assume that there is an investment with a guaranteed return that offers both firms and investors a “risk free” choice. This assumption, innocuous though it may seem, is a critical component of both risk and return models and corporate financial theory. But what if there is no risk free investment? During the banking crisis of 2008, this question came to the fore, as investors began questioning the credit worthiness of US treasuries, UK gilts and German bonds. In effect, the fear that governments can default, hitherto restricted to risky, emerging markets, had seeped into developed markets as well. In this paper, we examine why governments may default, even on local currency bonds, and the consequences. We also look at how best to estimate a risk free rate, when no default free entity exists, and the effects on both investors and firms. In particular, we argue that the absence of a risk free investment will make investors collectively more risk averse, thus reducing the prices of all risky assets, and induce firms to borrow less money and pay out lower dividends.

We take up Cochrane’s (2011) challenge to identify the firm characteristics that provide independent information about average U.S. monthly stock returns by simultaneously including 94 characteristics in Fama-MacBeth regressions that avoid overweighting microcaps and adjust for data snooping bias. We find that while 12 characteristics are reliably independent determinants in non-microcap stocks during 1980-2014 as a whole, return predictability fell sharply in 2003 such that just two characteristics have been independent determinants since then. Outside of microcaps, the hedge returns to exploiting characteristics-based predictability have also been insignificantly different from zero since 2003.