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Spanish Abstract: El CAPM es un modelo absurdo porque sus hipótesis y sus conclusiones/predicciones son opuestas a la realidad (describen “un mundo” que no es el nuestro). La hipótesis más extravagante es que los inversores tienen expectativas homogéneas (todos esperan la misma rentabilidad y la misma volatilidad de todas las acciones) y la predicción más contraria a la realidad es que la cartera de renta variable de todos los inversores es idéntica en su composición: todas las acciones del mercado (“el mercado”). Es imposible determinar “la prima de riesgo del mercado” y “la beta de mercado de una empresa” porque tales números no existen debido a las heterogéneas expectativas de los inversores. De la hipótesis absurda de “expectativas homogéneas”, se derivan otros absurdos que se muestran en el apartado 3. Y si se aplica parcialmente surge la esquizofrenia del apartado 4.English Abstract: The CAPM is obviously contrary to common sense. The assumptions are senseless and the conclusions are untrue. It is an enormous error to use the historical beta as a proxy for the expected beta. First, because it is almost impossible to calculate a meaningful beta because historical betas change dramatically from one day to the next; second, because very often we cannot say with a relevant statistical confidence that the beta of one company is smaller or bigger than the beta of another; third, because historical betas do not make much sense in many cases: high-risk companies very often have smaller historical betas than low-risk companies; fourth, because historical betas depend very much on which index we use to calculate them.

Spanish Abstract: La conclusión fundamental es que los múltiplos casi siempre tienen una gran dispersión. Por este motivo las valoraciones realizadas por múltiplos son casi siempre muy cuestionables. Sin embargo, los múltiplos sí son útiles en una segunda fase de la valoración: una vez realizada la valoración por descuento de flujos, una comparación con los múltiplos de empresas comparables (en actividad, entorno y crecimiento) permite calibrar la valoración realizada e identificar diferencias entre la empresa valorada y las comparables. También pueden ser útiles para calcular el valor terminal en una valoración por descuento de flujos, siempre que el múltiplo utilizado tenga una magnitud y una interpretación “sensatas”.English Abstract: This paper focuses on equity valuation using multiples. Our basic conclusion is that multiples nearly always have broad dispersion, which is why valuations performed using multiples may be highly debatable. We revise the 14 most popular multiples and deal with the problem of using multiples for valuation: their dispersion. 1,200 multiples from 175 companies illustrate the dispersion of multiples of European utilities, English utilities, European constructors, hotel companies, telecommunications, banks and Internet companies. However, multiples are useful in a second stage of the valuation: after performing the valuation using another method, a comparison with the multiples of comparable firms (in activity, environment and growth) enables us to gauge the valuation performed and identify differences between the firm valued and the firms it is compared with.

Much has changed since penalty-free Roth conversions were inaugurated in 2010. Tax rates have gone up and down. The re-characterization provision went away. Heirs can no longer stretch out inherited Roth accounts over a lifetime. Medicare surcharges were expanded and began to adjust for inflation. The age to begin Required Minimum Distributions was pushed out to age 72 and the IRS changed the RMD divisor tables to further slow the pace of distribution. Given these developments it seemed worthwhile to re-examine the rationale for Roth conversions. That effort exposed multiple flaws in conventional wisdom: • Future tax rates need not be higher for a conversion to pay off;• Nor is it all that helpful to pay the tax on conversion from outside funds;• Nor are Roth conversions especially beneficial for top bracket taxpayers as compared to middle class taxpayers;• Rather, the greatest benefit accrues to taxpayers who can make the conversion partly in the zero percent tax bracket, i.e., during a year with no other taxable income.While the benefits from a Roth conversion are often small and slow to arrive, a Roth conversion will almost always pay off if given enough time, i.e., for life spans that extend past 90 and so long as annual distributions from converted amounts are not taken. Roth conversions work because of compounding, which requires the conversion to be left undisturbed for a long time. The paper elucidates the role played by the mathematics of compounding in underwriting the success of Roth conversions. [This paper is under revision; see notes at front of mss.]

We measure the contributions to risk of a set of factors, strategies, or investments, based on "Minimum-Torsion Bets", namely a set of uncorrelated factors, optimized to closely track the factors used to allocate the portfolio. We then introduce a novel definition of contributions to risk, which generalizes the "marginal contributions to risk", traditionally used in banks for risk budgeting and in asset management to build risk parity strategies. The Minimum-Torsion Bets allow us to also introduce a natural diversification score, the Effective Number of Minimum-Torsion Bets, which we use to measure and manage diversification. We discuss the advantages of the Minimum-Torsion Bets over the traditional approach to diversification based on marginal contributions to risk. We present two case studies, a security-based investment in the stocks of the S&P 500, and a factor-based investment in the five Fama-French factors.

We propose a theory based on investor overconfidence and biased self-attribution to explain several of the securities returns patterns that seem anomalous from the perspective of efficient markets with rational investors. The theory is based on two premises derived from evidence in psychological studies. The first is that individuals are overconfident about their ability to evaluate securities, in the sense that they overestimate the precision of their private information signals. The second is that investors' confidence changes in a biased fashion as a function of their decision outcomes. The first premise implies overreaction to private information arrival and underreaction to public information arrival. This is consistent with (1) post-corporate event and post-earnings announcement stock price 'drift', (2) negative long-lag autocorrelations (long-run 'overreaction'), and (3) excess volatility of asset prices. Adding the second premise leads to (4) positive short-lag autocorrelations ('momentum'), and (5) short-run post-earnings announcement 'drift,' and negative correlation between future stock returns and long-term measures of past accounting performance. The model also offers several untested empirical implications and implications for corporate financial policy. Presentation slides available at : http://ssrn.com/abstract=3181607

The global multi-asset market portfolio contains important information for strategic asset-allocation purposes. First, it shows the relative value of all asset classes according to the global financial investment community, which one could interpret as a natural benchmark for financial investors. Second, this portfolio may also serve as the starting point for investors who use a framework in the spirit of Black and Litterman (1992), or for investors who follow adaptive asset-allocation policies as advocated by Sharpe (2010). We estimate the invested global market portfolio for the period 1990-2012 by estimating the market capitalization for the eight asset classes: equities, private equity, real estate, high-yield bonds, emerging-market debt, investment-grade credits, government bonds and inflation-linked bonds. For the main asset categories - equities, real estate, non-government bonds and government bonds - we extend the period to 1959-2012. We provide these annual historical estimates in tabular form so that practitioners and academics can easily use these historical data going forward. To our knowledge, we are the first to document the global multi-asset market portfolio at these levels of detail for such a long period of time.

We compare performances of the two standard portfolio insurance methods: the Option Based Portfolio Insurance (OBPI) and the Constant Proportion Portfolio Insurance (CPPI). First we examine basic properties of these two strategies and compare them by means of various criteria: comparison of their payoffs, possible property of stochastic dominance, expectations, variances, skewness and kurtosis of their returns, and some of the quantiles of their returns. We prove that the OBPI method can be analyzed as a kind of CPPI where the multiple is allowed to vary. We then study the properties of this varying multiple. In a second section, we analyze more deeply both method's dynamic properties. We turn our attention to the dynamics management involved by these two strategies. Although the pure OBPI do not require any management by the buyer (if the put or call option is available on the market), we can calculate the "greeks" of its call part. We derive the "greeks" of the CPPI and show the very different nature of the dynamic properties of the two strategies.

In this paper we extend the timeseries momentum (or trendfollowing) model towards a generalized momentum model, called Flexible Asset Allocation (FAA). This is done by adding new momentum factors to the traditional momentum factor R based on the relative returns among assets. These new factors are called Absolute momentum (A), Volatility momentum (V) and Correlation momentum (C). Each asset is ranked on each of the four factors R, A, V and C. By using a linearised representation of a loss function representing risk/return, we are able to arrive at simple closed form solutions for our flexible asset allocation strategy based on these four factors. We demonstrate the generalized momentum model by using a 7 asset portfolio model, which we backtest from 1998-2012, both in- and out-of-sample.

This document has 100 questions from students, alumnae and other persons (judges, clients,...). They are useful to clarify some useful concepts in finance. Most of the questions have a clear answer. The document also has short answers to all questions.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. In this course, we discuss scientifically sound ML tools that have been successfully applied to the management of large pools of funds.

Pump-and-dump schemes (P&Ds) pervade the cryptocurrency market. We find that P&Ds trigger short-term episodes featuring dramatic increases in prices, volume, and volatility. Prices peak within minutes and quick reversals follow. The evidence we document, including price run-ups before P&Ds start, implies that significant wealth transfers between insiders and outsiders occur. Exploiting two natural experiments in which exchanges altered their P&D policies, we show that P&Ds are detrimental to cryptocurrency liquidity and prices. Interestingly, gambling and overconfident investors often participate in P&Ds and some investors exhibit naïve reinforcement learning. We also describe how our findings shed light on manipulation theories.

As companies and investors globalize, we are increasingly faced with estimation questions about the risk associated with this globalization. When investors invest in China Mobile, Infosys or Vale, they may be rewarded with higher returns, but they are also exposed to additional risk. When Siemens and Apple push for growth in Asia and Latin America, they clearly are exposed to the political and economic turmoil that often characterize these markets. In practical terms, how, if at all, should we adjust for this additional risk? We will begin the paper with an overview of overall country risk, its sources and measures. We will continue with a discussion of sovereign default risk and examine sovereign ratings and credit default swaps (CDS) as measures of that risk. We will extend that discussion to look at country risk from the perspective of equity investors, by looking at equity risk premiums for different countries and consequences for valuation. In the fourth section, we argue that a company’s exposure to country risk should not be determined by where it is incorporated and traded. By that measure, neither Coca Cola nor Nestle are exposed to country risk. Exposure to country risk should come from a company’s operations, making country risk a critical component of the valuation of almost every large multinational corporation. In the final section, we will also look at how to move across currencies in valuation and capital budgeting, and how to avoid mismatching errors.

Identifying the factors that drive the cross-section of expected returns is challenging for at least three reasons. First, the choice of testing approach (time-series versus cross-sectional) will deliver different sets of factors. Second, varying test portfolio sorts changes the importance of candidate factors. Finally, given the hundreds of factors that have been proposed, test multiplicity must be dealt with. We propose a new method that makes measured progress in addressing these key challenges. We apply our method in a panel regression setting and shed some light on the puzzling empirical result that the market factor drives the bulk of the variance of stock returns, but is often knocked out in cross-sectional tests. In our setup, the market factor is not eliminated. Further, we bypass arbitrary portfolio sorts and instead execute our tests on individual stocks | with no loss in power. Finally, our bootstrap implementation, which allows us to impose the null hypothesis of no cross-sectional explanatory power, naturally controls for the multiple testing problem.The on-line appendix appears in this version of the paper.Forthcoming, Journal of Financial Economics

We present an effcient algorithm for interpolation and extrapolation of a discrete set of European option prices into a an arbitrage consistent full double continuum in expiry and strike of option prices. The method is based on an application of the fully implicit finite difference method and related to the local variance gamma model of Carr (2008). In a numerical example we show how the model can fitted to all quoted prices in the SX5E option market (12 expiries, each with roughtly 10 strikes) in 0.05 seconds of CPU time.

A significant topic within the behavioral finance literature is the notion of perceived risk pertaining to novice investors (i.e. individuals, finance students) and investment professionals (i.e. financial planners, security analysts). The author provides an overview of the concepts of risk, perception, and risk perception with the financial scholar in mind. There is also a presentation on the behavioral finance concepts and themes that might influence an individual's perception of risk for different types of financial services and investment products. The next section presents a discussion of the significant risk perception research in the social sciences namely from psychology. This research work from psychology (i.e., risk perception studies in risky situations and hazardous activities) is the behavioral foundation for a substantial amount of the current contributions within the behavioral accounting and behavioral finance literature. In particular, the work of the Decision Research scholars including Paul Slovic and his co-authors on risk perception studies that have crossed over from psychology to the disciplines of behavioral accounting and behavioral finance (i.e. behavioral risk characteristics from psychology that are applied within a financial/investment decision making context). Within the last section of this paper, the author reveals the first of its kind thorough review of the academic research studies on perceived risk/risk perception from the disciplines of behavioral accounting since 1975 and behavioral finance since the late 1960s. This literature review incorporates 12 works from behavioral accounting and 71 endeavors from behavioral finance. In addition, the behavioral finance literature review section also includes approximately 10 narrative research reviews from risk perception studies in behavioral economics. A major facet of this paper was to bring together all the previous studies in the risk perception literature for the purpose of conducting a study based on the academic foundation of the main themes, research approaches, and findings from this collection of studies.

This study examines European stock market reactions to 16 events associated with the adoption of International Financial Reporting Standards (IFRS) in Europe. European IFRS adoption represented a major milestone towards financial reporting convergence yet spurred controversy reaching the highest levels of government. We find an incrementally positive reaction for firms with lower quality pre-adoption information, which is more pronounced in banks, and with higher pre-adoption information asymmetry, consistent with investors expecting net information quality benefits from IFRS adoption. We find an incrementally negative reaction for firms domiciled in code law countries, consistent with investors’ concerns over enforcement of IFRS in those countries. Finally, we find a positive reaction to IFRS adoption events for firms with high quality pre-adoption information, consistent with investors expecting net convergence benefits from IFRS adoption.

For nearly three decades, scientific studies have explored momentum investing strategies and observed stable excess returns in various financial markets. However, the trading strategies typically analyzed in such research are not accessible to individual investors due to short selling constraints, nor are they profitable due to high trading costs. Incorporating these constraints, we explore a simplified momentum trading strategy that only exploits excess returns from topside momentum for a small number of individual stocks. Building on US data from the New York Stock Exchange from July 1991 to December 2010, we analyze whether such a simplified momentum strategy outperforms the benchmark after factoring in realistic transaction costs and risks. We find that the strategy can indeed work for individual investors with initial investment amounts of at least $5,000. In further attempts to improve this practical trading strategy, we analyze an overlapping momentum trading strategy consisting of a more frequent trading of a smaller number of “winner” stocks. We find that increasing the trading frequency initially increases the risk-adjusted returns of these portfolios up to an optimal point, after which excessive transaction costs begin to dominate the scene. In a calibration study, we find that, depending on the initial investment amount of the portfolio, the optimal momentum trading frequency ranges from bi-yearly to monthly.

Social media has become a popular venue for individuals to share the results of their own analysis on financial securities. This paper investigates the extent to which investor opinions transmitted through social media predict future stock returns and earnings surprises. We conduct textual analysis of articles published on one of the most popular social-media platforms for investors in the United States. We also consider the readers’ perspective as inferred via commentaries written in response to these articles. We find that the views expressed in both articles and commentaries predict future stock returns and earnings surprises.

Factor investing has failed to live up to its many promises. Its success is compromised by three problems that are often underappreciated by investors. First, many investors develop exaggerated expectations about factor performance as a result of data mining, crowding, unrealistic trading cost expectations, and other concerns. Second, for investors using naive risk management tools, factor returns can experience downside shocks far larger than would be expected. Finally, investors are often led to believe their factor portfolio is diversified. Diversification can vanish, however, in certain economic conditions, when factor returns become much more correlated. Factor investing is a powerful tool, but understanding the risks involved is essential before adopting this investment framework.

In this paper we examine whether and how accounting information about a firm manifests in its cost of capital, despite the forces of diversification. We build a model that is consistent with the CAPM and explicitly allows for multiple securities whose cash flows are correlated. We demonstrate that the quality of accounting information can influence the cost of capital, both directly and indirectly. The direct effect occurs because higher quality disclosures reduce the firm's assessed covariances with other firms' cash flows, which is non-diversifiable. The indirect effect occurs because higher quality disclosures affect a firm's real decisions, which likely changes the firm's ratio of the expected future cash flows to the covariance of these cash flows with the sum of all the cash flows in the market. We show that this effect can go in either direction, but also derive conditions under which an increase in information quality leads to an unambiguous decline the cost of capital.

We construct optimal portfolios of mutual funds whose objectives include socially responsible investment (SRI). Comparing portfolios of these funds to those constructed from the broader fund universe reveals the cost of imposing the SRI constraint on investors seeking the highest Sharpe ratio. This SRI cost depends crucially on the investor's views about asset pricing models and stock-picking skill by fund managers. To an investor who believes strongly in the CAPM and rules out managerial skill, i.e. a market-index investor, the cost of the SRI constraint is typically just a few basis points per month, measured in certainly-equivalent loss. To an investor who still disallows skill but instead believes to some degree in pricing models that associate higher returns with exposures to size, value, and momentum factors, the SRI constraint is much costlier, typically by at least 30 basis points per month. The SRI constraint imposes large costs on investors whose beliefs allow a substantial amount of fund-manager skill, i.e., investors who rely heavily on individual funds' track records to predict future performance.

Traditionally smile models have been assessed according to how well they fit market option prices across strikes and maturities. However, the pricing of most of the recent exotic structures, such as reverse cliquets or Napoleons, is more dependent on the assumptions made for the future dynamics of implied vols than on today’s vanilla option prices. In this article we study examples of some popular classes of models, such as stochastic volatility and Jump/Lévy models, to highlight structural features of their dynamic properties.

Credit risk is the distribution of financial losses due to unexpected changes in the credit quality of a counterparty in a financial agreement. We review the structural, reduced form and incomplete information approaches to estimating joint default probabilities and prices of credit sensitive securities.

In this, the third paper in the Economists’ Hubris series, we highlight the shortcomings of academic thought in developing models that can be used by financial institutions to institute effective enterprise-wide risk management systems and policies. We find that pretty much all of the models fail when put under intense scientific examinations and that we still have a long way to go before we can develop models that can indeed be effective. However, we find that irrespective of the models used, the simple fact that the current IT and operational infrastructures of banking institutions does not allow the management to obtain a holistic view of risk and the silos they sit within means that instituting an effective enterprise-wide risk management system is as of today nothing more than a panacea. The main worry is that it is not only academics who fail to realize this fact, practitioners also believe that these models work even without having a holistic view of the risks within their organizations. In fact, we can state that this is the first paper in which we highlight not only the hubris exhibited by economists but also the hubris of practitioners who still believe that they are able to accurately measure and manage the risk of the institutions they manage, monitor, or regulate.

Within the past two years, important advances have been made in modeling credit risk at the portfolio level. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Less progress has been made, however, with comparative analyses. Direct comparison often is not straightforward, because the different models may be presented within rather different mathematical frameworks. This paper offers a comparative anatomy of two especially influential benchmarks for credit risk models, the RiskMetrics Group's CreditMetrics and Credit Suisse Financial Product's CreditRisk. We show that, despite differences on the surface, the underlying mathematical structures are similar. The structural parallels provide intuition for the relationship between the two models and allow us to describe quite precisely where the models differ in functional form, distributional assumptions, and reliance on approximation formulae. We then design simulation exercises which evaluate the effect of each of these differences individually.