SSRN Viewer

In the last two decades, over 75% of U.S. industries have experienced an increase in concentration levels. We find that firms in industries with the largest increases in product market concentration have enjoyed higher profit margins and more profitable M&A deals. At the same time, we do not find evidence of a significant increase in operational efficiency, which suggests that market power is becoming an important source of value. These findings are robust to the inclusion of private firms, factors that account for foreign competition, as well as to the use of alternative measures of concentration. We also show that the higher profit margins associated with an increase in concentration are reflected in higher returns to shareholders. Overall, our results suggest that the nature of U.S. product markets has undergone a shift that has potentially weakened competition across the majority of industries.

This paper examines how the listenability of corporate vocal disclosures affects firms’ information environment. Listenability refers to the ease with which humans can comprehend spoken language. We introduce a new measure of listenability by analyzing audio recordings of 56,989 quarterly earnings calls using an advanced, supervised machine learning model trained on 680,000 hours of labeled audio data. We find that enhanced listenability of the presentation portion of earnings conference calls is associated with higher quality manager-analyst interactions during the Q&A session of the call and better information production by analysts after the call. Further analyses reveal that higher listenability is associated with stronger price responsiveness, higher stock price informativeness, faster price formation, and lower bid-ask spread following the call. These results are consistent with clear and effective corporate oral communications facilitating a better information environment.

Spanish Abstract: Se pueden valorar empresas estacionales por descuento de flujos utilizando datos anuales, pero éstos requieren algunos ajustes. La forma correcta de valorarlas es utilizando datos mensuales. Los errores debidos a la utilización de datos anuales sin ajustar son grandes. Utilizar la deuda media y las NOF medias no proporciona una buena aproximación al valor de la empresa.English Abstract: The correct way of valuing seasonal companies by cash flow discounting is to use monthly data. We may use annual data, but it requires some adjustments. To adjust only by using average debt and average working capital requirements does not provide a good approximation. This paper values a company in which the seasonality is due to the purchases of raw materials: the company buys and pays all raw materials in the moth of December. We show that the equity value calculated using annual data without doing the adjustments understates the true value in a 45% if the valuation is done at the end of December, and overstates the true value in a 38% if the valuation is done at the end of November. The error of adjusting only by using average debt and average working capital requirements ranges from -17.9% to 8.5%.

The average MRP used by analysts in the USA (5.1%) was similar to the one used by their colleagues in Europe (5.0%). But the average MRP used by companies in the USA (5.3%) was smaller than the one used by companies in Europe (5.7%), and UK (5.6%). The dispersion of the MRP used was high, but lower than the one of the professors: the average range of MRP used by analysts (companies) for the same country was 5.7% (4.1%) and the average standard deviation was 1.7% (1.2%). These statistics were 7.4% and 2.4% for the professors. The paper also shows the MRP used in more than 20 countries and the MRP used in 2009. The paper also contains the references that analysts and companies use to justify their MRP, and comments from 89 respondents that illustrate the various interpretations of what is the required MRP.

The change in the cost of capital that results from a divestiture strategy can be closely approximated as a simple function of three parameters: (1) the fraction of socially conscious capital, (2) the fraction of targeted firms in the economy and (3) the return correlation between the targeted firms and the rest of the stock market. When calibrated to current data, we demonstrate that the impact on the cost of capital is too small to meaningfully affect real investment decisions. We empirically corroborate these small estimates by studying firm changes in ESG status and are unable to detect an impact of ESG divestiture strategies on cost of capital of treated firms. Our results suggest that to have impact, instead of divesting, socially conscious investors should invest and exercise their rights of control to change corporate policy.

We update our global evidence on the long-term realized equity risk premium, relative to both bills and bonds, in 19 different countries. Our study now runs from 1900 to the start of 2011. While there is considerable variation across countries, the realized equity risk premium was substantial everywhere. For our 19-country World index, over the entire 111 years, geometric mean real returns were an annualized 5.5%; the equity premium relative to Treasury bills was an annualized 4.5%; and the equity premium relative to long-term government bonds was an annualized 3.8%. The expected equity premium is lower, around 3% to 3½% on an annualized basis.

We provide a visual primer on copulas. We highlight and prove the most important theoretical results. We discuss implementation issues. Documented code is available for download.

Recent advances in machine learning are finding commercial applications across many industries, not least the finance industry. This paper focuses on applications in one of the core functions of finance, the investment process. This includes return forecasting, risk modelling and portfolio construction. The study evaluates the current state of the art through an extensive review of recent literature. Themes and technologies are identified and classified, and the key use cases highlighted. Quantitative investing, traditionally a leading field in adopting new techniques is found to be the most common source of use cases in the emerging literature.

We propose a conservative investment formula which selects 100 stocks based on three criteria: low return volatility, high net payout yield, and strong price momentum. We show that this simple formula gives investors full and efficient exposure to the most important factor premiums, and thus effectively summarizes half a century of empirical asset pricing research into one easy to implement investment strategy. With a compounded annual return of 15.1 percent since 1929, the conservative formula outperforms the market by a wide margin. It reduces downside risk and shows a positive return over every decade. The formula is also strong in European, Japanese and Emerging stock markets, and beats a wide range of other strategies based on size, value, quality, and momentum combinations. The formula is designed to be a practically useful tool for a broad range of investors and addresses academic concerns about ‘p-hacking’ by using three simple criteria, which do not even require accounting data.

We explain in a nontechnical fashion why dollar-neutral quant trading strategies, such as equities Statistical Arbitrage, suffered substantial losses (drawdowns) during the COVID-19 market selloff. We discuss: (i) why these strategies work during "normal" times; (ii) the market regimes when they work best; and (iii) their limitations and the reasons for why they "break" during extreme market events. An accompanying appendix (with a link to freely accessible source code) includes backtests for various strategies, which put flesh on and illustrate the discussion in the main text.

In this paper, we analyze the potential effects that the coronavirus, “COVID-19”, will have on the stock market and then we propose possible ways that an individual could profit off a market affected by a global viral outbreak. We look at past outbreaks and come to the conclusion that often markets will react adversely to these such incidents in the short run but that in the long run, markets eventually correct themselves and increase. In order to profit off of such a market, we propose shorting industries that will be immediately affected by the virus in the short run and then eventually buying back into those industries after their price has dropped significantly. Specifically, we look at the travel industry, technology industry, entertainment industry, and gold as potential areas where great profit can be made.

We address how value relevance of accounting information evolved as the new economy developed. Prior research concludes accounting information—primarily earnings—has lost relevance. We consider more accounting items and find no decline in combined value relevance from 1962 to 2018. We assess evolution in each item’s value relevance and find increases, most notably for items related to intangible assets, growth opportunities, and alternative performance measures, which are important in the new economy. The number of relevant items also increases. We also consider separately new economy, old economy profit, and old economy loss firms. The trends are more pronounced for, but extend beyond, new economy firms. We base inferences on a non-parametric approach that does not require specifying the valuation relation. Taken together, our findings reveal an evolution to a more nuanced, but not declining, relation between accounting information and share price.

I present evidence that a moving average (MA) trading strategy third order stochastically dominates buying and holding the underlying asset in a mean-variance-skewness sense using monthly returns of value-weighted decile portfolios sorted by market size, book-to-market cash-flow-to-price, earnings-to-price, dividend-price, short-term reversal, medium-term momentum, long-term reversal and industry. The abnormal returns are largely insensitive to the four Carhart (1997) factors and produce economically and statistically significant alphas of between 10% and 15% per year after transaction costs. This performance is robust to different lags of the moving average and in subperiods while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The MA strategy works just as well with randomly generated returns and bootstrapped returns. I also report evidence regarding the profitability of the MA strategy in seven international stock markets. The performance of the MA strategies also holds for more than 18,000 individual stocks from the CRSP database. The substantial market timing ability of the MA strategy appears to be the main driver of the abnormal returns. The returns to the MA strategy resemble the returns of an imperfect at-the-money protective put strategy relative to the underlying portfolio. Furthermore, combining several MA strategies into a value/equal-weighted portfolio of MA strategies performs even better and represents a unified framework for security selection and market timing.

There are three fundamental ways of testing the validity of an investment algorithm against historical evidence: a) the walk-forward method; b) the resampling method; and c) the Monte Carlo method. By far the most common approach followed among academics and practitioners is the walk-forward method. Implicit in that choice is the assumption that a given investment algorithm should be deployed throughout all market regimes. We denote such assumption the “all-weather” hypothesis, and the algorithms based on that hypothesis “strategic investment algorithms” (or “investment strategies”).The all-weather hypothesis is not necessarily true, as demonstrated by the fact that many investment strategies have floundered in a zero-rate environment. This motivates the problem of identifying investment algorithms that are optimal for specific market regimes, denoted “tactical investment algorithms.” This paper argues that backtesting against synthetic datasets should be the preferred approach for developing tactical investment algorithms. A new organizational structure for asset managers is proposed, as a tactical algorithmic factory, consistent with the Monte Carlo backtesting paradigm.

Over the years numerous portfolio performance measures have been proposed. In general they are designed to capture some particular enhancement that might result from active management. However, if a principal uses a measure to judge an agent, then the agent has an incentive to game the measure. Our paper shows that such gaming can have a substantial impact on a number of popular measures even in the presence of extremely high transactions costs. The question then arises as to whether or not there exists a measure that cannot be gamed? As this paper shows there are conditions under which such a measure exists and fully characterizes it. This manipulation-proof measure looks like the average of a power utility function, calculated over the return history. The case for using our alternative ranking metric is particularly compelling in the hedge fund industry, in which the use of derivatives is unconstrained and manager compensation itself induces a non-linear payoff and thus encourages gaming.

The seemingly never ending scandals in the world of finance with their damaging effects on value and human welfare argue strongly for an addition to the current paradigm of financial economics. We summarize here our new theory of integrity that reveals integrity as a purely positive phenomenon with no normative aspects whatsoever. Adding integrity as a positive phenomenon to the paradigm of financial economics provides actionable access (rather than mere explanation with no access) to the source of the behavior that has resulted in those damaging effects on value and human welfare; thereby significantly reducing that behavior. More generally we argue that this addition to the paradigm of financial economics will create significant increases in economic efficiency, productivity, and aggregate human welfare. Because integrity has generally been treated as a virtue (a normative phenomenon) the actual cause of the damaging effects of out-of-integrity behavior are hidden, resulting in assigning false causes to those effects. This keeps the actual source of these damaging effects invisible to us. As a result, in spite of all the attempts to police the false causes of these damaging effects, the out-of-integrity actions that are the source of these effects continue to be repeated. This new model of integrity makes the actual source of the damage available for all to see, and therefore to act on. Integrity as we define it (or the lack thereof) on the part of individuals or organizations has enormous economic implications for value, productivity, and quality of life. Indeed, integrity is a factor of production as important as labor, capital, and technology. Without a clear, concise, and most importantly, an actionable definition of integrity, economics is far less powerful than it can be. So too finance and management.

The value of active investment management is traditionally measured by alpha, beta, tracking error, and the Sharpe and information ratios. These are essentially static characteristics of the marginal distributions of returns at a single point in time, and do not incorporate dynamic aspects of a manager's investment process. In this paper, I propose a new measure of the value of active investment management that captures both static and dynamic contributions of a portfolio manager's decisions. The measure is based on a decomposition of a portfolio's expected return into two distinct components: a static weighted-average of the individual securities' expected returns, and the sum of covariances between returns and portfolio weights. The former component measures the portion of the manager's expected return due to static investments in the underlying securities, while the latter component captures the forecast power implicit in the manager's dynamic investment choices. This measure can be computed for long-only investments, long/short portfolios, and asset allocation rules, and is particularly relevant for hedge-fund strategies where both components are significant contributors to their expected returns, but only one should garner the high fees that hedge funds typically charge. Several analytical and empirical examples are provided to illustrate the practical relevance of these new measures.

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.

This paper develops a broad concept of systemic risk, the basic economic concept for the understanding of financial crises. It is claimed that any such concept must integrate systemic events in banking and financial markets as well as in the related payment and settlement systems. At the heart of systemic risk are contagion effects, various forms of external effects. The concept also includes simultaneous financial instabilities following aggregate shocks. The quantitative literature on systemic risk, which was evolving swiftly in the last couple of years, is surveyed in the light of this concept. Various rigorous models of bank and payment system contagion have now been developed, although a general theoretical paradigm is still missing. Direct econometric tests of bank contagion effects seem to be mainly limited to the United States. Empirical studies of systemic risk in foreign exchange and security settlement systems appear to be non-existent. Moreover, the literature surveyed reflects the general difficulty to develop empirical tests that can make a clear distinction between contagion in the proper sense and joint crises caused by common shocks, rational revisions of depositor or investor expectations when information is asymmetric ("information-based" contagion) and "pure" contagion as well as between "efficient" and "inefficient" systemic events. currency crises

The existence and the enforcement of insider trading laws in stock markets is a phenomenon of the 1990s. A study of the 103 countries that have stock markets reveals that insider trading laws exist in 87 of them, but enforcement - as evidenced by prosecutions - has taken place in only 38 of them. Before 1990, the respective numbers were 34 and 9. Does this matter? We find that the cost of equity in a country, after controlling for a number of other variables, does not change after the introduction of insider trading laws, but decreases significantly after the first prosecution.

I challenge Sharpe’s (1991) famous equality that “before costs, the return on the average actively managed dollar will equal the return on the average passively managed dollar.” This equality is based on the implicit assumption that the market portfolio never changes, which does not hold in the real world because new shares are issued, others are repurchased, and indices are reconstituted so even “passive” investors must regularly trade. Therefore, active managers can be worth positive fees in aggregate, allowing them to play an important role in the economy: helping allocate resources efficiently. Passive investing also plays a useful economic role: creating low-cost access to markets.

Over 5,000 popular technical trading rules are not consistently profitable in the 49 country indices that comprise the Morgan Stanley Capital Index once data snooping bias is accounted for. Each market has some rules that are profitable when considered in isolation but these profits are not statistically significant after data snooping bias adjustment. There is some evidence that technical trading rules perform better in emerging markets than developed markets, which is consistent with the finding of previous studies that these markets are less efficient, but this result is not strong. While we cannot rule out the possibility that these trading rules compliment other market timing techniques or that trading rules we do not test are profitable, we do show that over 5,000 trading rules do not add value beyond what may be expected by chance when used in isolation during the time period we consider.

Mutual fund returns strongly persist over multi-year periods - that is the central finding of this paper. Further, consumer and fund manager behavior both play a large role in explaining these long-term continuation patterns - consumers invest heavily in last-year's winning funds, and managers of these winners invest these inflows in momentum stocks to continue to outperform other funds for at least two years following the ranking year. By contrast, managers of losing funds appear reluctant to sell their losing stocks to finance the purchase of new momentum stocks, perhaps due to a disposition effect. Thus, momentum continues to separate winning from losing managers for a much longer period than indicated by prior studies. Even more surprising is that persistence in winning fund returns is not entirely explained by momentum - we find strong evidence that flow-related buying, especially among growth-oriented funds, pushes up stock prices. Specifically, stocks that winning funds purchase in response to persistent flows have returns that beat their size, book-to-market, and momentum benchmarks by two to three percent per year over a four-year period. Cross-sectional regressions indicate that these abnormal returns are strongly related to fund inflows, but not to the past performance of the funds - thus, casting some doubt on prior findings of persistent manager talent in picking stocks. Finally, at the style-adjusted net returns level, we find no persistence, consistent with the results of prior studies. On balance, we confirm that money is smart in chasing winning managers, but that a "copycat" strategy of mimicking winning fund stock trades to take advantage of flow-related returns appears to be the smartest strategy.

On April 16, 2012 the Argentine Federal Government decreed the intervention of YPF and expropriated of 51% of the YPF shares owned by Repsol. Repsol had a 57.4% stake. A tribunal will determine the compensation Repsol will receive for its YPF shares. You are required to help the tribunal. What is your best estimation of the compensation that Repsol should receive for its expropriated 51% of YPF shares? The case provides you with information and data to answer that question: transactions of YPF shares, analyst valuations of YPF, share price in the NYSE.La versión española de este artículo se puede encontrar en: http://ssrn.com/abstract=2183603.

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 article 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.