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We will assess how governance and incentive problems contributed to Enron's rise and fall. A well-functioning capital market creates appropriate linkages of information, incentives, and governance between managers and investors. This process is supposed to be carried out through a network of intermediaries. We show that despite this elaborate corporate governance and intermediation network, Enron was able to attract large sums of capital to fund a questionable business model, conceal its true performance through a series of accounting and financing maneuvers, and hype its stock to unsustainable levels. While Enron presents an extreme example, it is also a useful test case for potential weaknesses in the U.S. capital market system. We believe that the problems of governance and incentives that emerged at Enron can also surface at many other firms, and may potentially affect the entire capital market. We will begin by discussing the evolution of Enron's business model in the late 1990s, the stresses that this business model created for Enron's financial reporting, and how key capital market intermediaries played a role in the company's rise and fall.

The search for a relation between environmental, social, and governance (ESG) criteria and corporate financial performance (CFP) can be traced back to the beginning of the 1970s. Scholars and investors have published more than 2,000 empirical studies and several review studies on this relation since then. The largest previous review study analyzes just a fraction of existing primary studies, making findings difficult to generalize. Thus, knowledge on the financial effects of ESG criteria remains fragmented. To overcome this shortcoming, this study extracts all provided primary and secondary data of previous academic review studies. Through doing this, the study combines the findings of about 2,200 individual studies. Hence, this study is by far the most exhaustive overview of academic research on this topic and allows for generalizable statements. The results show that the business case for ESG investing is empirically very well-founded. Roughly 90% of studies find a non-negative ESG-CFP relation. More importantly, the large majority of studies reports positive findings. We highlight that the positive ESG impact on CFP appears stable over time. Promising results are obtained when differentiating for portfolio and non-portfolio studies, regions, and young asset classes for ESG investing such as emerging markets, corporate bonds, and green real estate.

Spanish Abstract: La Prima de Riesgo del Mercado es uno de los parámetros financieros más investigados y controvertidos, y también uno de los que más confusión genera. Gran parte de la confusión se debe a que el término “Prima de Riesgo del Mercado” designa cuatro conceptos y realidades muy diferentes entre sí: a) Prima de Riesgo del Mercado Histórica (PRMH): es la diferencia entre la rentabilidad histórica de la bolsa (de un índice bursátil) y la de la renta fija.b) Prima de Riesgo del Mercado Esperada (PRME): es el valor esperado de la rentabilidad futura de la bolsa por encima de la de la renta fija. c) Prima de Riesgo del Mercado Exigida (PRMX): es la rentabilidad incremental que un inversor exige al mercado bursátil (a una cartera diversificada) por encima de la renta fija sin riesgo (required equity premium). Es la que se debe utilizar para calcular la rentabilidad exigida a las acciones. d) Prima de Riesgo del Mercado Implícita (PRMI): es la prima de riesgo del mercado exigida que se corresponde con el precio de mercado. Muchos autores y muchos profesionales de las finanzas suponen que la PRME es igual a la PRMH y a la PRMX. Se analizan los métodos propuestos por la literatura financiera para medirlo y se analiza la rentabilidad diferencial histórica de España y Estados Unidos.Conclusión principal: es imposible determinar “la” prima de riesgo “del mercado” porque tal número no existe debido a las heterogéneas expectativas y a las distintas rentabilidades exigidas de los inversores.English Abstract: The concept market risk premium is difficult to understand because it is used to designate three different concepts: 1. Required market risk premium. It is the incremental return of the market over the return of treasury bonds required by an investor. It is needed for calculating the required return to equity (cost of equity). 2. Historical market risk premium. It is the historical differential return of the stock market over treasury bonds. 3. Expected market risk premium. It is the expected differential return of the stock market over treasury bonds. Many authors and finance practitioners assume that expected market risk premium is equal to the historical market risk premium and to the required market risk premium. The CAPM assumes that the required market risk premium is equal to the expected market risk premium.The three concepts are different. The historical market risk premium is equal for all investors, but the required and the expected market risk premium are different for different investors. We also claim that there is no required market risk premium for the market as a whole: different investors use different required market risk premiums.

ESG is both extremely important and nothing special. It's extremely important because it's critical to long-term value, and so any practitioner or academic should take it seriously, not just those with "ESG" in their job title or list of research interests. Thus, ESG doesn't need a specialized term, as that implies it's niche. Considering long-term factors when valuing a company isn’t ESG investing; it’s investing.It's nothing special since it's no better or worse than other intangible assets that drive long-term value and create positive externalities for wider society, such as management quality, corporate culture, and innovative capability. The following implications follow:1. Companies shouldn't be praised more for improving their ESG performance than these other intangibles; investor engagement on ESG factors shouldn't be put on a pedestal compared to engagement on other value drivers. We want great companies, not just companies that are great at ESG. 2. Investors who greenwash are correctly being held to account. But so should other investors who fail to walk the talk, such as actively-managed funds that closet index or systematically underperform. Clients of non-ESG funds deserve the same protection as clients of ESG funds.3. Practitioners shouldn’t rush to do something special for ESG factors that they wouldn’t for other drivers of value, such as demand that every company tie executive pay to them, force a firm to report them even if not relevant for its particular business, or reduce complex intangibles to simple quantitative metrics. 4. Many of the controversies surrounding ESG become moot when we view it as a set of long-term value factors. It’s no surprise that ESG ratings aren’t perfectly correlated, because it’s legitimate to have different views on the quality of a company’s intangibles. We don’t need to get into angry fights between ESG believers and deniers, nor politicize the issues, because reasonable people can disagree on how relevant a characteristic is for a company’s long-term success.

We analyze 582 American companies using EVA, MVA, NOPAT and WACC data provided by Stern Stewart. For each of the 582 companies, we have calculated the 10-year correlation between the increase in the MVA (Market Value Added) each year and each year's EVA, NOPAT and WACC. For 296 (of the 582) companies, the correlation between the increase in the MVA each year and the NOPAT was greater than the correlation between the increase in the MVA ach year and the EVA. There are 210 companies for which the correlation with the EVA has been negative! The average correlation between the increase in the MVA and EVA, NOPAT and WACC was 16%, 21% and -21.4%. The average correlation between the increase in the MVA and the increases of EVA, NOPAT and WACC was 18%, 22.5% and -4.1%.We also find that the correlation between the shareholder return in 1994-1998 and the increase in the CVA (according to the Boston Consulting Group) of the world's 100 most profitable companies was 1.7%.

We propose a unified methodology to input non-linear views from any number of users in fully general non-normal markets, and perform, among others, stress-testing, scenario analysis, and ranking allocation. We walk the reader through the theory and we detail an extremely efficient algorithm to easily implement this methodology under fully general assumptions. As it turns out, no repricing is ever necessary, hence the methodology can be readily applied to books with complex derivatives. We also present an analytical solution, useful for benchmarking, which per se generalizes notable previous results. Code illustrating this methodology in practice is available through author's homepage.

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.

The CAPM is about expected return. If you find a formula for expected returns that works well in the real markets, would you publish it? Before or after becoming a billionaire?The CAPM is an absurd model because its assumptions and its predictions/conclusions have no basis in the real world. The use of CAPM is also a source of litigation: many professors, lawyers…get nice fees because many professionals use CAPM instead of common sense to calculate the required return to equity. Users of the CAPM make many illogical errors valuing companies, accepting/rejecting investment projects, evaluating fund performance, pricing goods and services in regulated markets, calculating value creation...It is important to differentiate between a fact (something that truly exists or happens: something that has actual existence; a true piece of information) and an opinion (what someone thinks about a particular thing). We all should try to explain a portion of “the world as it is”, not of “the world according to a wrong theory”. Ricardo Yepes, professor of philosophy of my university, wrote: “Learning means being able to keep perceiving reality as it truly is: complex - and not trying to fit every new experience into a closed and pre-conceived notion or overall scheme”. We may find out an investor’s expected return for IBM by asking him. However, it is impossible to determine the expected return for IBM of the market, because this parameter does not exist. Different investors have different cash flow expectations and different expected (and required) returns to equity. One could only talk of the expected return of the market if all investors had the same expectations. But investors do not have homogeneous expectations. Valuation is about required return. But there are persons, papers and books that mix (or assume that are equal) expected and required returns.Sections 11 and 12 show how to calculate required returns in a sensible way and how to use betas being a reasonable person.

The paper addresses flaws in the traditional application of Modern Portfolio Theory related to Strategic Asset Allocation. Estimates of parameters for portfolio optimization based on long-term observed average values are shown to be inferior to alternative estimates based on observations over much shorter time frames. An Adaptive Asset Allocation portfolio assembly framework is then proposed to coherently integrate portfolio parameters in a way that delivers substantially improved performance relative to SAA over the testing horizon.

We examine 24 global factor premiums across equity, bond, commodity and currency markets via replication and out-of-sample evidence between 1800 and 2016. Replication yields ambiguous evidence within a unified testing framework that accounts for p-hacking. Out-of-sample tests reveal strong and robust presence of the large majority of global factor premiums, with limited out-of-sample decay of the premiums. We find global factor premiums to be generally unrelated to market, downside, or macroeconomic risks in the 217 years of data. These results reveal significant global factor premiums that present a challenge to traditional asset pricing theories.

Alfred Cowles' (1934) test of the Dow Theory apparently provided strong evidence against the ability of Wall Street's most famous chartist to forecast the stock market. In this paper, we review Cowles' evidence and find that it supports the contrary conclusion -- that the Dow Theory, as applied by its major practitioner, William Peter Hamilton over the period 1902 to 1929, yielded positive risk-adjusted returns. A re-analysis of the Hamilton editorials suggests that his timing strategies yield high Sharpe ratios and positive alphas. Neural net modeling to replicate Hamilton's market calls provides interesting insight into the nature and content of the Dow Theory. This allows us to examine the properties of the Dow Theory itself out-of-sample.

Turning points are the Achilles' heel of time-series momentum portfolios. Slow signals fail to react quickly to changes in trend while fast signals are often false alarms. We analyze how momentum portfolios of various intermediate speeds, formed by blending slow and fast strategies, handle turning points. We find that the intersection of slow and fast signal directions possesses predictive information, including predictably negative returns when both signals are negative. We propose a novel decomposition of momentum strategy alpha, highlighting the role of volatility timing; and a mean-variance optimal dynamic speed-selection strategy with efficient out-of-sample performance across international equity markets.Journal of Financial Economics, forthcoming.This version includes the Internet Appendix.

We provide an overview of research on the stock trading behavior of individual investors. This research documents that individual investors (1) underperform standard benchmarks (e.g., a low cost index fund), (2) sell winning investments while holding losing investments (the “disposition effect”), (3) are heavily influenced by limited attention and past return performance in their purchase decisions, (4) engage in naïve reinforcement learning by repeating past behaviors that coincided with pleasure while avoiding past behaviors that generated pain, and (5) tend to hold undiversified stock portfolios. These behaviors deleteriously affect the financial well being of individual investors.

This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2018 for 59 countries. We got answers for 73 countries, but we only report the results for 59 countries with more than 5 answers. The change between 2015 and 2018 of the average Km (RF + MRP) used was higher than 1% for 22 countries (see table 5). Most of the respondents use for European countries a RF higher than the yield of the 10-year Government bonds. Due to “Quantitative Easing”, the RF and the MRP reported for some countries are negatively correlated (Spain, Germany, Netherlands, Norway). The coefficient of variation (standard deviation/average) of Km is lower than the one of MRP and RF for 35 countries. For the first time of this survey, 11 respondents provided - without being asked for - a different MRP for Spain and Catalonia (on average, 6,7% for Spain and 11,3% for Catalonia).

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.

Drawing on the academic literature in accounting, finance and economics, we analyze economic and policy factors related to the potential adoption of International Financial Reporting Standards (IFRS) in the U.S. We highlight the unique institutional features of U.S. markets to assess the potential impact of IFRS adoption on the quality and comparability of U.S. reporting practices, the ensuing capital market effects, and the potential costs of switching from U.S. GAAP to IFRS. We discuss the compatibility of IFRS with the current U.S. regulatory and legal environment as well as the possible effects of IFRS adoption on the U.S. economy as a whole. We also consider how a switch to IFRS may affect worldwide competition among accounting standards and standard setters, and discuss the political ramifications of such a decision on the standard setting process and on the governance structure of the International Accounting Standards Board. Our analysis shows that the decision to adopt IFRS mainly involves a cost-benefit tradeoff between (1) recurring, albeit modest, comparability benefits for investors, (2) recurring future cost savings that will largely accrue to multinational companies, and (3) one-time transition costs borne by all firms and the U.S. economy as a whole, including those from adjustments to U.S. institutions. We conclude by outlining several possible scenarios for the future of U.S. accounting standards, ranging from maintaining U.S. GAAP, letting firms decide whether and when to adopt IFRS, to the creation of a competing U.S. GAAP-based set of global accounting standards that could serve as an alternative to IFRS.

This paper characterizes the trading strategy of a large high-frequency trader (HFT). The HFT incurs a loss on its inventory but earns a profit on the bid-ask spread. Sharpe ratio calculations show that performance is very sensitive to cost of capital assumptions. The HFT employs a cross-market strategy as half of its trades materialize on a large incumbent market and the other half on a small, high-growth entrant market. Trade participation rates are 8.1% and 64.4%, respectively. In both markets, about four out of five of its trades are passive, i.e., its price quote was consumed by others.

We present “The Prayer”, a recipe of ten sequential steps for all portfolio managers, risk managers, algorithmic traders across all asset classes and all investment horizons, to model and manage the P&L distribution of their positions. For each of the ten steps of the Prayer, we introduce all the key concepts with precise notation; we illustrate the key concepts by means of a simple case study that can be handled with analytical formulas; we point the readers toward multiple advanced approaches to address the non-trivial practical problems of real-life risk modeling; and we highlight a non-exhaustive list of common pitfalls.

Motivated by studies of the impact of frictions on asset prices, we examine the effect of key components of time-series momentum strategies on turnover and performance. We show that more efficient volatility estimation and price trend detection can significantly reduce portfolio turnover by more than one third, without causing statistically significant performance degradation. We propose a novel implementation of the strategy that incorporates the pairwise signed correlations by means of a dynamic leverage mechanism. The correlation-adjusted variant outperforms the naive implementation of the strategy and the outperformance is more pronounced in the post-2008 period. Finally, using a transaction costs model for futures-based strategies that separates costs into roll-over and rebalancing costs, we show that our findings remain robust to the inclusion of transaction costs.

Previous research uses negative word counts to measure the tone of a text. We show that word lists developed for other disciplines misclassify common words in financial text. In a large sample of 10 Ks during 1994 to 2008, almost three-fourths of the words identified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts. We develop an alternative negative word list, along with five other word lists, that better reflect tone in financial text. We link the word lists to 10 K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings.

This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2023 for 80 countries. We got answers for 102 countries, but we only report the results for 80 countries with more than 6 answers.The paper also contains the links to previous years surveys, from 2008 to 2022.

Spanish Abstract: En los últimos años han proliferado consultoras, libros y documentos de investigación proponiendo distintos y, a veces, sorprendentes métodos para determinar el valor de las marcas. El primer y mayor problema con el que nos encontramos al valorar una marca es el definir qué es la marca y el acotar qué margen, qué volumen de ventas, qué gastos y qué inversiones son atribuibles a la marca. Se revisan varias valoraciones de marcas y se concluye que son muy poco fiables (muchísimo menos que las valoraciones de empresas) debido a la dificultad de definir qué flujos se deben a la marca y cuáles no. Sin embargo, sí es útil la identificación, la evaluación y la jerarquización de los brand value drivers, que constituyen una herramienta gerencial para crear marcas fuertes y con estabilidad en el tiempo.English Abstract: In this paper, we revise several methods used for valuing brands. Among them, those of Interbrand, Damodaran, Financial World, Houlihan Valuation Advisors, Market Facts, Young & Rubicam and CDB Research & Consulting.Our goal is to show the limitations of a number of the methods proposed for valuing brands and intellectual capital and, within the limits imposed by the brand's intrinsic reality, establish guidelines for value creation through the study of brands and intellectual capital. We also propose a scheme for identifying brand value drivers, that is, the parameters influencing the brand's value.

We compare execution quality of six brokerage accounts across five brokers by generating a sample of 85,000 simultaneous market orders. Commission levels and payment for order flow (PFOF) differ across our accounts. We find that execution prices vary significantly across brokers: the mean account-level round-trip cost ranges from –0.07% to –0.46% excluding any commissions. The dispersion is due to off-exchange wholesalers systematically giving different execution prices for the same trades to different brokers. Across brokers, variation in PFOF does not explain the large variation in price execution. We provide several suggestions for more informative disclosures on execution quality.

The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this article. Over the past two decades, I have seen many faces come and go, firms started and shut down. In my experience, there are ten critical mistakes underlying most of those failures. This paper is partly based on the book Advances in Financial Machine Learning (Wiley, 2018). The first chapter of this book is available at http://ssrn.com/abstract=3104847. A presentation can be found at http://ssrn.com/abstract=3031282.

The 'shadow banking system' at the heart of the current credit crisis is, in fact, a real banking system – and is vulnerable to a banking panic. Indeed, the events starting in August 2007 are a banking panic. A banking panic is a systemic event because the banking system cannot honor its obligations and is insolvent. Unlike the historical banking panics of the 19th and early 20th centuries, the current banking panic is a wholesale panic, not a retail panic. In the earlier episodes, depositors ran to their banks and demanded cash in exchange for their checking accounts. Unable to meet those demands, the banking system became insolvent. The current panic involved financial firms 'running' on other financial firms by not renewing sale and repurchase agreements (repo) or increasing the repo margin ('haircut'), forcing massive deleveraging, and resulting in the banking system being insolvent. The earlier episodes have many features in common with the current crisis, and examination of history can help understand the current situation and guide thoughts about reform of bank regulation. New regulation can facilitate the functioning of the shadow banking system, making it less vulnerable to panic.