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I investigate the impact of COVID-19 cases and related deaths on the US stock market (Dow Jones and S&P500 indices), allowing for changes in trading volume and volatility expectations, as well as day-of-the-week effects. The results, based a GARCH(1,1) model and data from April 8, 2019 to April 9, 2020, suggest that changes in the number of cases and deaths in the US and six other countries majorly affected by the COVID-19 crisis do not have an impact on the US stock market returns, apart from the number of reported cases for China. However, there is evidence of a positive impact, for some countries, on the conditional heteroscedasticity of the Dow Jones and S&P500 returns. VAR models suggest that the number of reported deaths in Italy and France have a negative impact on stock market returns, and a positive impact on the VIX returns. Finally, Markov-Switching models suggest that at the end of February 2020 the magnitude of the negative impact of the VIX on stock market returns increased threefold.

This Article presents the legal literature’s first detailed analysis of the inner workings of Initial Coin Offerings. We characterize the ICO as an example of financial innovation, placing it in kinship with venture capital contracting, asset securitization, and (obviously) the IPO. We also take the form seriously as an example of technological innovation, where promoters are beginning to effectuate their promises to investors through computer code, rather than traditional contract. To understand the dynamics of this shift, we first collect contracts, “white papers,” and other contract-like documents for the fifty top-grossing ICOs of 2017. We then analyze how such projects’ software code reflected (or failed to reflect) their contractual promises. Our inquiry reveals that many ICOs failed even to promise that they would protect investors against insider self-dealing. Fewer still manifested such contracts in code. Surprisingly, in a community known for espousing a technolibertarian belief in the power of “trustless trust” built with carefully designed code, a significant fraction of issuers retained centralized control through previously undisclosed code permitting modification of the entities’ governing structures. These findings offer valuable lessons to legal scholars, economists, and policymakers about the roles played by gatekeepers; about the value of regulation; and the possibilities for socially valuable private ordering in a relatively anonymous, decentralized environment.

Management and disclosure of environmental, social and governance (ESG) issues have received substantial interest over the last decade. In this paper, we outline a framework of how ESG issues become financially material, affecting corporate profitability and valuation. We argue that understanding this process is important both for actors driven by financial or societal motives. The former group, which includes companies and return-first investors, can use the framework to make resource allocation decisions based on expectations about future materiality thereby enhancing risk-adjusted returns. The latter group, which includes regulators, NGOs, and impact-first investors, can use the framework to design and implement interventions that create market-based incentives for companies and investors to align their behavior with social and environmental outcomes.

Spanish Abstract: Las burbujas especulativas no son un fenómeno nuevo. Se mencionan varias burbujas históricas (el ferrocarril, las autopistas, las líneas aéreas, la radio, la televisión, el teléfono, Levitz, Home Shopping Network, OM Scott, MCI, LTCM, Boston Chicken, TelePizza…). Se analiza la evolución de algunas empresas (Terra, Amazon, America Online, Microsoft, Yahoo, empresas de B2B, brokers online,…), pero nos centramos en la valoraciones de Terra (llegó a ser la segunda empresa española por capitalización tras Telefónica) y Amazon. English Abstract: In this paper, we review twelve valuations of Terra performed by Spanish and non-Spanish bank analysts and brokers. Terra started trading on the stock market in November 1999. The placement price was 13 euros per share (11.81 for retailers). In February 2000, its price stood at 139.75 euros. Between November 1999 and February 2000, Terra provided a return of 975% for its shareholders. However, by December 2000, the share price had plummeted to 11.6 euros, 8.3% of its February high. The average annual volatility of the Terra share was almost 100%. If you can't find a rational explanation for a share to continue rising, you can be sure that it will fall. To become a millionaire, you must sell your shares at the right time. A website is not necessarily a business. Selling below cost gets you lots of customers, but not much money.

Can an asset manager plan the optimal timing for her/his hedging strategies given market conditions? The standard approach based on Markowitz or other more or less sophisticated financial rules aims to find the best portfolio allocation thanks to forecasted expected returns and risk but fails to fully relate market conditions to hedging strategies decision. In contrast, Deep Reinforcement Learning (DRL) can tackle this challenge by creating a dynamic dependency between market information and hedging strategies allocation decisions. In this paper, we present a realistic and augmented DRL framework that: (i) uses additional contextual information to decide an action, (ii) has a one period lag between observations and actions to account for one day lag turnover of common asset managers to rebalance their hedge, (iii) is fully tested in terms of stability and robustness thanks to a repetitive train test method called anchored walk forward training, similar in spirit to k fold cross validation for time series and (iv) allows managing leverage of our hedging strategy. Our experiment for an augmented asset manager interested in sizing and timing his hedges shows that our approach achieves superior returns and lower risk.

We analyze in detail calibration and pricing performed within the framework of local stochastic volatility LSV models, which have become the industry market standard for FX and equity markets. We present the main arguments for the need of having such models, and address the question whether jumps have to be included. We include a comprehensive literature overview, and focus our exposition on important details related to calibration procedures and option pricing using PDEs or PIDEs derived from LSV models. We describe calibration procedures, with special attention given to usage and solution of corresponding forward Kolmogorov PDE/PIDE, and outline powerful algorithms for estimation of model parameters. Emphasis is placed on presenting practical details regarding the setup and the numerical solution of both forward and backward PDEs/PIDEs obtained from the LSV models. Consequently we discuss specifics (based on our experience and best practices from literature) regarding choice of boundary conditions, construction of nonuniform spatial grids and adaptive temporal grids, selection of efficient and appropriate finite difference schemes (with possible enhancements), etc. We also show how to practically integrate specific features of various types of financial instruments within calibration and pricing settings. We consider all questions and topics identified as most relevant during the selection, calibration and pricing procedures associated with local stochastic volatility models, providing answers (to the best of our knowledge), and present references for deeper understanding and for additional perspectives. In a nutshell, it is our intention to present here an effective roadmap for a successful LSV journey.

In Velez-Pareja and Tham (2001), we presented several different ways to value cash flows. First, we apply the standard after-tax Weighted Average Cost of Capital, WACC to the free cash flow (FCF). Second, we apply the adjusted WACC to the FCF, and third we apply the WACC to the capital cash flow. In addition, we discount the cash flow to equity (CFE) with the appropriate returns to levered equity. We refer to these four ways as the "discounted cash flow (DCF)" methods. In recent years, two new approaches, the Residual Income Method (RIM) and the Economic Value Added (EVA) have become very popular. Supporters claim the RIM and EVA are superior to the DCF methods. It may be case that the RIM and EVA approaches are useful tools for assessing managerial performance and providing proper incentives. However, from a valuation point of view, the RIM and EVA are problematic because they use book values from the balance sheet. It is easy to show that under certain conditions, the results from the RIM and EVA exactly match the results from the DCF methods. Velez-Pareja 1999 reported that when using relatively complex examples and book values to calculate Economic Value Added (EVA), the results were inconsistent with Net Present Value (NPV). Tham 2001, reported consistency between the Residual Income Model (RIM) and the Discounted Cash Flow model (DCF) with a very simple example. Fernandez 2002 shows examples where there is consistency between DCF, RIM and EVA. He uses a constant value for the cost of levered equity capital and in another example constant debt. Young and O'Byrne, 2001, show simple examples for EVA but do not show the equivalence between DCF and EVA. Ehrbar (1998) uses a very simple example with perpetuities and shows the equivalence between EVA and DCF. Lundholm and O'Keefe, 2001, show this equivalence with an example with constant Ke. Tham 2001, commented on their paper. Stewart, 1999, shows the equivalence between DCF and EVA with an example using a constant discount rate. Copeland, et al, show an example with constant WACC and constant cost of equity even with varying debt and assuming a target leverage that is different to the actual leverage. In general, textbooks do not specify clearly how EVA should be used to give consistent results. In this teaching note using a complex example with varying debt, varying leverage and terminal (or continuing value), we show the consistency between DCF, RIM and EVA. We stress what Velez-Pareja 1999 and Fernandez 2002 said: for a single period, RI or EVA does not measure value. We have to include expectations and market values in the calculation of discount rates and hence values.

This study documents annual returns of the invested global multi-asset market portfolio, using a newly constructed unique dataset which basically covers the whole invested market. We analyze returns as well as risk over the period from 1960 to 2017. The market realizes a compounded real return in US dollars of 4.45% with a standard deviation of 11.2% from 1960 until 2017. The compounded return is 3.39 percentage points above the riskless rate. We report returns in ten base currencies, which shows that real compounded returns vary from 2.87% to 4.93% and maximum cumulative drawdowns range from 34.9% to 56.6%.

We divide hedging methods between single-period and multi-period. After reviewing some well-known hedging algorithms, two new procedures are introduced, called Dickey-Fuller Optimal (DFO), Mini-Max Subset Correlation (MMSC). The former is a multi-period, cointegration-based hedging method that estimates the holdings that are most likely to deliver a hedging error absent of unit root. The latter is a single-period method that studies the geometry of the hedging errors and estimates a hedging vector such that subsets of its components are as orthogonal as possible to the error. We test each method for stability and robustness of the derived hedged portfolio. Results indicate that DFO produces estimates similar to the Error Correction Method, but more stable. Likewise, MMSC estimates are similar to Principal Component Analysis but more stable. Finally, a generalized Box-Tiao Canonical Decomposition (BTCD) method is proposed, which is of the multi-period class. BTCD estimates are also very stable, and cannot be related to any of the aforementioned methodologies. Finally, we find that all three advanced hedging methods (MMSC, BTCD, DFO) perform well.

We document and quantify the negative impact of trend breaks (i.e., turning points in the trajectory of asset prices) on the performance of standard monthly trend-following strategies across several assets and asset classes. In the years of the U.S. economy’s expansion following the global financial crisis of 2008, we find an increase in the frequency of trend breaks, which helps explain the lower performance of these trend strategies during this period. We illustrate how to repair such strategies using a dynamic trend-following approach that exploits the return-forecasting properties of the two types of trend breaks: market corrections andrebounds.Goulding, Christian L. and Harvey, Campbell R. and Mazzoleni, Michele, Breaking Bad Trends (14 January 2024). Financial Analysts Journal, 2024, 80(1): 84-98. DOI: 10.1080/0015198X.2023.2270084Also see our related paper: Momentum Turning Points

The English version of this paper can be found at http://ssrn.com/abstract=2176728 Spanish Abstract: El 16 de abril de 2012, el Gobierno Argentino expropio a Repsol un 51% de las acciones de YPF que eran de su propiedad. Repsol tenía el 57,4% de las acciones de YPF. Un tribunal determinará la compensación que Repsol debe recibir por sus acciones de YPF expropiadas. El lector debe ayudar al tribunal: ¿cuántos dólares debería pagar el gobierno argentino a Repsol por la expropiación? El caso proporciona información y datos para responder a esta pregunta: precio en bolsa de YPF, precios objetivo de varios analistas, estatutos de YPF sobre el precio a pagar, transacciones anteriores de acciones de YPF... English Abstract: 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.

Mean-Variance portfolios are optimal in-sample, however they tend to perform poorly out-of-sample (even worse than the 1/N naïve portfolio!) We introduce a new portfolio construction method that substantially improves the Out-Of-Sample performance of diversified portfolios. The full paper is available at: http://ssrn.com/abstract=2708678.

Flow toxicity can be measured in terms of the probability that a liquidity provider is adversely selected by informed traders. In previous papers we introduced the concept of Volume-synchronized Probability of Informed Trading (the VPIN* metric), and provided a robust estimation procedure. In this study, we discuss the asymmetric impact that an incorrect estimation of the VPIN metric has on a market maker’s performance. This asymmetry may be part of the explanation for the evaporation of liquidity witnessed on May 6th 2010. To mitigate that undesirable behavior, we present the specifications of a VPIN contract, which could be used to hedge against the risk of higher than expected levels of toxicity, as well as to monitor such risk. Among other applications, it would also work as an execution benchmark, and a price discovery mechanism, since it allows for the externalization of market participants’ views of future toxicity.

While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity, in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving, robot learning, and on a more conceptual side games solving like Go. This paper aims to bridge the gap between these two approaches by showing Deep Reinforcement Learning (DRL) techniques can shed new lights on portfolio allocation thanks to a more general optimization setting that casts portfolio allocation as an optimal control problem that is not just a one-step optimization, but rather a continuous control optimization with a delayed reward. The advantages are numerous: (i) DRL maps directly market conditions to actions by design and hence should adapt to changing environment, (ii) DRL does not rely on any traditional financial risk assumptions like that risk is represented by variance, (iii) DRL can incorporate additional data and be a multi inputs method as opposed to more traditional optimization methods. We present on an experiment some encouraging results using convolution networks.

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 material is part of Cornell University's ORIE 5256 graduate course at the School of Engineering.

As the ESG finance field and the use of ESG data in investment decision‐making continue to grow, we seek to shed light on several important aspects of ESG measurement and data. This article is intended to provide a useful guide for the rapidly rising number of people entering the field. We focus on the following:• The sheer variety, and inconsistency, of the data and measures, and of how companies report them. Listing more than 20 different ways companies report their employee health and safety data, the authors show how such inconsistencies lead to significantly different results when looking at the same group of companies.• ‘Benchmarking,’ or how data providers define companies' peer groups, can be crucial in determining the performance ranking of a company. The lack of transparency among data providers about peer group components and observed ranges for ESG metrics creates market‐wide inconsistencies and undermines their reliability.• The differences in the imputation methods used by ESG researchers and analysts to deal with vast ‘data gaps’ that span ranges of companies and time periods for different ESG metrics can cause large ‘disagreements’ among the providers, with different gap‐filling approaches leading to big discrepancies.• The disagreements among ESG data providers are not only large, but actually increase with the quantity of publicly available information. Citing a recent study showing that companies that provide more ESG disclosure tend to have more variation in their ESG ratings, the authors interpret this finding as clear evidence of the need for ‘a clearer understanding of what different ESG metrics might tell us and how they might best be institutionalized for assessing corporate performance.’What can be done to address these problems with ESG data? Companies should ‘take control of the ESG data narrative’ by proactively shaping disclosure instead of being overwhelmed by survey requests. To that end, companies should ‘customize’ their metrics to some extent, while at the same time seeking to self‐regulate by reaching agreement with industry peers on a ‘reasonable baseline’ of standardized ESG metrics designed to achieve comparability. Investors are urged to push for more meaningful ESG disclosure by narrowing the demand for ESG data into somewhat more standardized, but still manageable metrics. Stock exchanges should consider issuing—and perhaps even mandating—guidelines for ESG disclosures designed in collaboration with companies, investors, and regulators. And data providers should come to agreement on best practices and become as transparent as possible about their methodologies and the reliability of their data.

Individual investors who hold common stocks directly pay a tremendous performance penalty for active trading. Of 66,465 households with accounts at a large discount broker during 1991 to 1996, those that traded most earned an annual return of 11.4 percent, while the market returned 17.9 percent. The average household earned an annual return of 16.4 percent, tilted its common stock investment toward high-beta, small, value stocks, and turned over 75 percent of its portfolio annually. Overconfidence can explain high trading levels and the resulting poor performance of individual investors. Our central message is that trading is hazardous to your wealth.

We investigate whether ESG ratings predict future ESG news and the associated market reactions. We find that the consensus rating predicts future news, but its predictive ability diminishes for firms with large disagreement between raters. Relation between news and market reaction is moderated by the consensus rating. In the presence of high disagreement between raters, the relation between news and market reactions weakens while the rating with most predictive power predicts future stock returns. Overall, while rating disagreement hinders the incorporation of value relevant ESG news into prices, ratings predict future news and proxy for market expectations of future news.

Recent studies show that volatility-managed equity portfolios realize higher Sharpe ratios than portfolios with a constant notional exposure. We show that this result only holds for “risk assets”, such as equity and credit, and link this to the so-called leverage effect for those assets. In contrast, for bonds, currencies, and commodities the impact of volatility targeting on the Sharpe ratio is negligible. However, the impact of volatility targeting goes beyond the Sharpe ratio: it reduces the likelihood of extreme returns, across all asset classes. Particularly relevant for investors, “left-tail” events tend to be less severe, as they typically occur at times of elevated volatility, when a target-volatility portfolio has a relatively small notional exposure. We also consider the popular 60-40 equity-bond “balanced” portfolio and an equity-bond-credit-commodity “risk parity” portfolio. Volatility scaling at both the asset and portfolio level improves Sharpe ratios and reduces the likelihood of tail events.

The rate of factor production in the academic research is out of control. We document over 400 factors published in top journals. Surely, many of them are false. We explore the incentives that lead to factor mining and explore reasons why many of the factors are simply lucky findings. The backtested results published in academic outlets are routinely cited to support commercial products. As a consequence, investors develop exaggerated expectations based on inflated backtested results and are then disappointed by the live trading experience. We provide a comprehensive census of factors published in top academic journals through January 2019. We also offer a link to a Google sheet that has detailed information on each factor, including citation information and download links. Finally, we propose a citizen science project that allows researchers to add to our database both published papers as well as working papers. Here is the updated analysis:

Our study investigates trading in options that expire on the same day-so-called "0DTE" options-through the lens of retail investors. Using the recently activated Automated Price Improvement Mechanism to identify option trades that originate from retail investors, we find a strong preference for 0DTE options. Today, more than 75% of their trades in S&P 500 options involve 0DTE contracts. Despite benefiting from lower e↵ective spreads due to the price improvement mechanism, retail investors experience substantial losses. We explore the factors influencing how retailers are positioned in this market, and identify several drivers for their poor performance, both in the aggregate and at the level of individual trades.

We present new evidence on the persistence of U.S. buyout and venture capital (VC) fund performance using high quality cash-flow data sourced from Burgiss’s large sample of institutional investors. Using ex post returns, we find that persistence of VC funds has remained remarkably strong. We also find persistence in buyout performance, but this has become weaker for funds raised post-2000. However, when we focus on the information available to investors ex ante – previous fund performance at the time of fundraising, rather than final fund performance – we find little evidence of persistence for buyouts but continued strong persistence for VC funds.

The number of factors allegedly driving the cross-section of stock returns has grown steadily over time. We explore how much this ‘factor zoo’ can be compressed, focusing on explaining the available alpha rather than the covariance matrix of factor returns. Our findings indicate that about 15 factors are enough to span the entire factor zoo. This evidence suggests that many factors are redundant but also that merely using a handful of factors, as in common asset pricing models, is insufficient. While the selected factor styles remain persistent, the specific style representatives vary over time, underscoring the importance of continuous factor innovation.

Policymakers often express concern that herding by financial market participants destabilizes markets and increases the fragility of the financial system. This paper provides an overview of the recent theoretical and empirical research on herd behavior in financial markets. It addresses the following questions: What precisely do we mean by herding? What could be the causes of herd behavior? What success have existing studies had in identifying such behavior? And what effect does herding have on financial markets?

We introduce Kinetic Component Analysis (KCA), a state-space application that extracts the signal from a series of noisy measurements by applying a Kalman Filter on a Taylor expansion of a stochastic process. We show that KCA presents several advantages compared to other popular noise-reduction methods such as Fast Fourier Transform (FFT) or Locally Weighted Scatterplot Smoothing (LOWESS): First, KCA provides band estimates in addition to point estimates. Second, KCA further decomposes the signal in terms of three hidden components, which can be intuitively associated with position, velocity and acceleration. Third, KCA is more robust in forecasting applications. Fourth, KCA is a forward-looking state-space approach, resilient to structural changes. We believe that this type of decomposition is particularly useful in the analysis of trend-following, momentum and mean-reversion of financial prices. An instrument exhibits financial inertia when its price acceleration is not significantly greater than zero for long periods of time. Our empirical analysis of 19 of the most liquid futures worldwide confirms the presence of strong inertia across all asset classes. We also argue that KCA can be useful to market makers, liquidity providers and faders for the calculation of their trading ranges.