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Value investors generally characterize themselves as the grown ups in the investment world, unswayed by perceptions or momentum, and driven by fundamentals. While this may be true, at least in the abstract, there are at least three distinct strands of value investing. The first, passive value investing, is built around screening for stocks that meet specific characteristics – low multiples of earnings or book value, high returns on projects and low risk – and can be traced back to Ben Graham’s books on security analysis. The second, contrarian investing, requires investing in companies that are down on their luck and in the market. The third, activist value investing, involves taking large positions in poorly managed and low valued companies and making money from turning them around. While value investing looks impressive on paper, the performance of value investors, as a whole, is no better than that of less “sensible” investors who chose other investment philosophies and strategies. We examine explanations for why "active" value investing may not provide the promised payoffs.

We analyze Berkshire Hathaway's equity portfolio over the 1976 to 2006 period and explore potential explanations for its superior performance. Contrary to popular belief, we find Berkshire Hathaway invests primarily in large-cap growth rather than "value" stocks. Over the period the portfolio beat the benchmarks in 27 out of 31 years, on average exceeding the S&P 500 Index by 11.14%, the value-weighted index of all stocks by 10.92%, and a Fama and French characteristic-based portfolio by 8.56% per year. Although beating the market in all but four years can statistically happen due to chance, incorporating the magnitude by which the portfolio beats the market makes a luck explanation extremely unlikely even after taking into account ex-post selection bias. We find that Berkshire Hathaway's portfolio is concentrated in relatively few stocks with the top five holdings averaging 73% of the portfolio value. While increased volatility is normally associated with higher concentration we show the volatility of the portfolio is driven by large positive returns and not downside risk. The market appears to under-react to the news of a Berkshire Hathaway stock investment since a hypothetical portfolio that mimics the investments at the beginning of the following month after they are publicly disclosed also earns significantly positive abnormal returns of 10.75% over the S&P 500 Index. Our evidence suggests the Berkshire Hathaway triumvirates of Warren Buffett, Charles Munger, and Lou Simpson posses' investment skill unlikely to be explained by Efficient Market Theory.

This paper outlines a financial statement analysis for use in equity valuation. Standard profitability analysis is incorporated, and extended, and is complemented with an analysis of growth. The perspective is one of forecasting payoffs to equities. So financial statement analysis is presented first as a matter of pro forma analysis of the future, with forecasted ratios viewed as building blocks of forecasts of payoffs. The analysis of current financial statements is then seen as a matter of identifying current ratios as predictors of the future ratios that drive equity payoffs. The financial statement analysis is hierarchical, with ratios lower in the ordering identified as finer information about those higher up. To provide historical benchmarks for forecasting, typical values for ratios are documented for the period 1963-1996, along with their cross-sectional variation and correlation. And, again with a view to forecasting, the time series behavior of many of the ratios is also described and their typical "long-run, steady-state" levels are documented.

The 1998 failure of Long-Term Capital Management (LTCM) is said to have nearly blown up the world's financial system. For such a near-catastrophic event, the finance profession has precious little information to draw from. By piecing together publicly available information, this paper draws lessons from risk management practices at LTCM. LTCM's strategies are analyzed in terms of the fund's Value at Risk (VAR) and the amount of capital necessary to support its risk profile. The paper shows that LTCM has severely underestimated its risk due to its reliance on short-term history and risk concentration. LTCM also provides a good example of risk management taken to the extreme. Using the same covariance matrix to measure risk and to optimize positions inevitably leads to biases in the measurement of risk. This approach also pushes the strategy to take positions that appear to generate ``arbitrage'' profits based on recent history but also represent bets on extreme events, like selling options. Overall, LTCM's strategy exploited the intrinsic weaknesses of its risk management system.

For many decades the only way to invest in volatility has been through trading options, futures, or variance swaps. But in recent years a number of volatility-related exchange traded Funds (ETFs) and Exchange Traded Notes (ETNs) have been launched which make volatility trading accessible to the retail investor and fund managers without the need to access futures markets. Our objective is to devise a trading strategy using them. We document where volatility returns come from, clearing up some misconception in the process. Then we illustrate five different strategies that will appeal to different investors. Four of the strategies are simple to describe and implement. All of the strategies have had extraordinary returns with high Sharpe Ratios and low correlation to the S&P5'08 in some cases negative correlation. The returns seems to be too good to be true – like picking up $100 bills in front of a steamroller – so we have a detailed discussion on the risks and the nature of the steamroller. We illustrate how these strategies can be incorporated into existing portfolios to reduce portfolio risk especially in times of crisis. They have positive exposure to the markets during good times and negative exposure during bad times. Unfortunately they do not always provide absolute returns and while reducing net portfolio drawdowns they can themselves have significant drawdowns. Still, we suggest that a traditional 60% equities, 40% bonds portfolio should be adjusted to 55% equities, 35% bonds, and 10% volatility.

This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2020 for 81 countries. We got answers for 87 countries, but we only report the results for 81 countries with more than 6 answers.Many respondents use for European countries a RF higher than the yield of the 10-year Government bonds. The coefficient of variation (standard deviation/average) of RF is higher than the coefficient of variation of MRP for the Euro countries.The paper also contains the links to previous years surveys, from 2008 to 2019. More than fifty respondents provided answers at the beginning of March and later, considering the coronavirus. Most of them increased MRP by 2%.

The size premium has been challenged along many fronts: it has a weak historical record, varies significantly over time, in particular weakening after its discovery in the early 1980s, is concentrated among microcap stocks, predominantly resides in January, is not present for measures of size that do not rely on market prices, is weak internationally, and is subsumed by proxies for illiquidity. We find, however, that these challenges are dismantled when controlling for the quality, or the inverse “junk”, of a firm. A significant size premium emerges, which is stable through time, robust to the specification, more consistent across seasons and markets, not concentrated in microcaps, robust to non-price based measures of size, and not captured by an illiquidity premium. Controlling for quality/junk also explains interactions between size and other return characteristics such as value and momentum.

Although risk management has been a well-ploughed field in financial modeling for over two decades, traditional risk management tools such as mean-variance analysis, beta, and Value-at-Risk do not capture many of the risk exposures of hedge-fund investments. In this article, I review several aspects of risk management that are unique to hedge funds - survivorship bias, dynamic risk analytics, liquidity, and nonlinearities - and provide examples that illustrate their potential importance to hedge-fund managers and investors. I propose a research agenda for developing a new set of risk analytics specifically designed for hedge-fund investments, with the ultimate goal of creating risk transparency while, at the same time, protecting the proprietary nature of hedge-fund investment strategies.

We show remote work led to large drops in lease revenues, occupancy, and market rents in the commercial office sector. We revalue New York City office buildings taking into account both the cash flow and discount rate implications of these shocks, and find a 46% decline in long run value. For all U.S. office markets combined, we find a $556.8 billion value destruction. Higher quality buildings were buffered against these trends due to a flight to quality, while lower quality office is at risk of becoming a stranded asset. These valuation changes have repercussions for financial stability and local public finances.

The ‘flash crash’ of May 6th 2010 was the second largest point swing (1,010.14 points) and the biggest one-day point decline (998.5 points) in the history of the Dow Jones Industrial Average. For a few minutes, $1 trillion in market value vanished. In this paper, we argue that the ‘flash crash’ is the result of the new dynamics at play in the current market structure. We highlight the role played by order toxicity in affecting liquidity provision, and we show that a measure of this toxicity, the Volume-Synchronized Probability of Informed Trading (VPIN)*, captures the increasing toxicity of the order flow in the hours and days prior to collapse. Since the ‘flash crash’ might have been avoided had liquidity providers remained in the marketplace, a solution is proposed in the form of a ‘VPIN contract’ which would allow them to dynamically monitor and manage their risks.

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. We also show that PER, EBITDA and Profit after Tax (the most commonly used parameters for multiples) were more volatile than equity value.We also provide additional evidence of the analysts' recommendations for Spanish companies: less than 15% of the recommendations are to sell. 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 enables us to gauge the valuation performed and identify differences between the firm valued and the firms it is compared with.

The equity risk premium is the price of risk in equity markets, and it is not just a key input in estimating costs of equity and capital in both corporate finance and valuation, but it is also a key metric in assessing the overall market. Given its 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 the equity risk premium, historical returns are used, with the difference in annual returns on stocks versus bonds, over a long period, comprising the expected risk premium. We note the limitations of this approach, even in markets with an abundance of data, like the United States, 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 are asked to assess the risk premium and the implied premium approach, where a forward-looking estimate of the premium is estimated using either current equity prices or risk premiums in other markets. In the next section, we 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 generate 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.

It is difficult to predict stock market returns but relatively easy to predict market volatility. But volatility predictions don't easily translate into return predictions since the two are largely uncorrelated. We put forward a framework that produces a formula in which returns become a function of volatility and therefore become somewhat more predictable. We show that this strategy produces excess returns giving us the upside of leverage without the downside. As a side-effect the strategy also smooths out volatility variation over time, reduces the kurtosis of daily returns, reduces maximum drawdown, and gives us a dynamic timing signal for tilting asset allocations between conservative and aggressive assets. It has been said that diversification is the only free lunch in investing. It appears that once you have diversified away some risk you can get a further free lunch by smoothing what risk remains.

This handbook in Sustainable Finance corresponds to the lecture notes of the course given at University Paris-Saclay, ENSAE and Sorbonne University. It covers the following chapters: 1. Introduction, 2. ESG Scoring, 3. Financial Performance of ESG Investing, 4. Sustainable Financial Products, 5. Impact Investing, 6. Voting Policy & Engagement, 7. Extra-financial Accounting, 8. Economic Modeling of Climate Change, 9. Climate Risk Measures, 10. Transition Risk Modeling, 11. Portfolio Optimization, 12. Physical Risk Modeling, 13. Climate Stress Testing, 14. Conclusion, 15. Appendix A Technical Appendix, 16. Appendix B Solutions to the Tutorial Exercises.

This study explores which asset classes add value to a traditional portfolio of stocks, bonds and cash. Next, we determine the optimal weights of all asset classes in the optimal portfolio. This study adds to the literature by distinguishing ten different investment categories simultaneously in a mean-variance analysis as well as a market portfolio approach. We also demonstrate how to combine these two methods. Our results suggest that real estate, commodities and high yield add most value to the traditional asset mix. A study with such a broad coverage of asset classes has not been conducted before, not in the context of determining capital market expectations and performing a mean-variance analysis, neither in assessing the global market portfolio.

This paper addresses 10 corporate finance topics that are not well treated (or not treated at all) in many Corporate Finance Books. The topics are: 1. Where the WACC equation comes from. 2. The WACC is not a cost. 3. How is the WACC equation when the value of debt is not equal to its nominal value. 4. Textbooks differ a lot on their recommendations regarding the equity premium. 5. The term equity premium is used to designate four different concepts. 6. Which Equity Premium do professors and practitioners use? 7. Calculated (historical) betas change dramatically from one day to the next. 8. Why many professors continue using calculated (historical) betas in class? 9. EVA does not measure Shareholder value creation. 10. The relationship between the WACC and the value of the tax shields (VTS).

This paper contains the statistics of the Equity Premium or Market Risk Premium (MRP) used in 2014 for 88 countries. We got answers for more countries, but we only report the results for 88 countries with more than 6 answers. 37% of the MRP used in 2014 decreased (vs. 2013) and 9% increased. Most previous surveys have been interested in the Expected MRP, but this survey asks about the Required MRP. The paper also contains the references used to justify the MRP, comments from 30 persons that do not use MRP, and comments from 53 persons that do use MRP.

We compile a novel dataset on mandatory environmental, social, and governance (ESG) disclosure around the world to analyze the stock liquidity effects of such disclosure mandates. We document a positive effect of ESG disclosure mandates on firm-level stock liquidity. The effects are strongest if the disclosure requirements are implemented by government institutions, not on a comply-or-explain basis, and coupled with strong enforcement by informal institutions. Firms with weaker information environments benefit more from ESG disclosure mandates. Our results support the view that ESG disclosure regulation improves the information environment and has beneficial capital market effects.

This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2019 for 69 countries. We got answers for 84 countries, but we only report the results for 69 countries with more than 8 answers.Due to “Quantitative Easing”, many respondents use for European countries a RF higher than the yield of the 10-year Government bonds. The coefficient of variation (standard deviation/average) of RF is, on average, 2.75 times higher than the CV of MRP for 24 European countries.For the second time of this survey, 9 respondents provided — without being asked for — a different MRP for Spain and Catalonia (on average, 6.4% for Spain and 11.5% for Catalonia).

Using survey data from a sample of senior investment professionals from mainstream (i.e. not SRI funds) investment organizations we provide insights into why and how investors use reported environmental, social and governance (ESG) information. Relevance to investment performance is the most frequent motivation for use of ESG data followed by client demand and product strategy, bringing change in companies, and then ethical considerations. Important impediments to the use of ESG information are the lack of reporting standards and as a result lack of comparability, reliability, quantifiability and timeliness. Among the different ESG investment styles, negative screening is perceived as the least investment beneficial while full integration into stock valuation and engagement are considered more beneficial but they are all practiced with equal frequency. Current practices of different ESG styles, especially screening, are driven by product and ethical considerations. In contrast, integration is driven by relevance to investment performance. Future practices of ESG styles are driven by relevance to investment performance, bringing change in companies, and concerns about data reliability.

The original promise of Nakamoto (2008) to provide the world with a better global means of payment has not materialized. Instead, the focus has increasingly shifted to Bitcoin as an investment asset promising high capital gains. Promoters of this investment vision put little effort relating Bitcoin to an economic function which would justify its valuation. While most economists argue that the Bitcoin boom is a speculative bubble that will eventually burst, we analyse in this paper the impact of a Bitcoin-positive scenario in which its price continues to rise in the foreseeable future. What sounds intuitively promising or at least not harmful is problematic: Since Bitcoin does not increase the productive potential of the economy, the consequences of the assumed continued increase in value are essentially redistributive, i.e. the wealth effects on consumption of early Bitcoin holders can only come at the expense of consumption of the rest of society. If the price of Bitcoin rises for good, the existence of Bitcoin impoverishes both non-holders and latecomers. While previous discussions on the redistributive effects of Bitcoin assumed that badly timed trading was a necessary condition for losses, this paper shows that neither poor timing of trades nor holding Bitcoin at all are necessary for impoverishment under a Bitcoin-positive scenario.

These slides are part of the Data & AI Reskilling seminar that presents ML and AI for banking. This lecture presents machine learning methods for dimensionality reduction

These slides are part of the Data & Ai reskilling seminar in Banking hosted by Dauphine and Ecoles des Mines PSL. They provide tips & tricks for times series analysis using machine learning in banking

The number of published event studies exceeds 500, and the literature continues to grow. We provide an overview of event study methods. Short-horizon methods are quite reliable. While long-horizon methods have improved, serious limitations remain. A challenge is to continue to refine long-horizon methods. We present new evidence that properties of event study methods can vary by calendar time period and can depend on event sample firm characteristics such as volatility. This reinforces the importance of examining event study statistical properties for non-randomly selected samples.

When talking about withdrawal rates in retirement it's hard to ignore the 4% rule. The origin of this rule goes back to the work of Bengen (1994, 1996, 1997, 2001) and Cooley, Hubbard and Walz (1998, 2011), more commonly known as the Trinity Study. The Trinity Study showed that withdrawing 4% of the portfolio value at the beginning of retirement and subsequently adjusting the withdrawals for inflation, will likely sustain a 30-year retirement in a portfolio comprised of 50-100% stocks and 0-50% bonds. This result is relevant to the average retiree with a horizon of only 30 years and not the typical early retiree with a much longer horizon, though. We perform extensive simulations and case studies targeted at early retirees and show that the longer horizon and today's expensive equity valuations will likely necessitate a lower initial withdrawal rate.