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We study time-series momentum (trend-following) strategies in bonds, commodities, currencies and equity indices between 1960 and 2015. We find that momentum strategies performed consistently both before and after 1985, periods which were marked by strong bear and bull markets in bonds respectively. We document a number of important risk properties. First, that returns are positively skewed, which we argue is intuitive by drawing a parallel between momentum strategies and a long option straddle strategy. Second, performance was particularly strong in the worst equity and bond market environments, giving credence to the claim that trend-following can provide equity and bond crisis alpha. Putting restrictions on the strategy to prevent it being long equities or long bonds has the potential to further enhance the crisis alpha, but reduces the average return. Finally, we examine how performance has varied across momentum strategies based on returns with different lags and applied to different asset classes.

We define low-latency activity as strategies that respond to market events in the millisecond environment, the hallmark of proprietary trading by high-frequency trading firms. We propose a new measure of low-latency activity that can be constructed from publicly-available NASDAQ data to investigate the impact of high-frequency trading on the market environment. Our measure is highly correlated with NASDAQ-constructed estimates of high-frequency trading, but it can be computed from data that are more widely-available. We use this measure to study how low-latency activity affects market quality both during normal market conditions and during a period of declining prices and heightened economic uncertainty. Our conclusion is that increased low-latency activity improves traditional market quality measures — lowering short-term volatility, decreasing spreads, and increasing displayed depth in the limit order book. Of particular importance is that our findings suggest that increased low-latency activity need not work to the detriment of long-term investors in the current market structure for U.S. equities.

It is a big mistake to use betas calculated from historical data to compute the required return to equity. It is a mistake for seven reasons: because betas calculated from historical data change considerably from one day to the next; because calculated betas depend very much on which stock index is used as the market reference; because calculated betas depend very much on which historical period is used to calculate them; because calculated betas depend on what returns (monthly, daily,...) are used to calculate them; because very often we do not know if the beta of one company is lower or higher than the beta of another; because calculated betas have little correlation with stock returns; and because the correlation coefficients of the regressions used to calculate the betas are very small.

This paper examines whether auditor fees are associated with earnings management and the market reaction to the disclosure of auditor fees. Using data collected from proxy statements, we present evidence that non-audit fees are positively associated with small positive earnings surprises, the magnitude of absolute discretionary accruals, and the magnitude of income-increasing and income-decreasing discretionary accruals. In contrast, audit fees are negatively associated with these earnings management indicators. These results are robust to a variety of alternative variable definitions and model specifications. Specifically, contrary to the claims of Ashbaugh et al. (2002), the results are robust to the use of performance-matched discretionary accruals. Moreover, contrary to the claims of Francis and Ke (2002), the results for small positive earnings surprises are robust regardless of whether the comparison group is all other earnings surprises or small negative earnings surprises. Our final set of results provide evidence of a significant negative association between non-audit fees and share values on the date the fees were disclosed, although the effect is small in economic terms.

It is not uncommon in private company and acquisition valuations to see large premiums attached to estimated value to reflect the 'value of control'. But what, if any, is the value of control in a firm, and if it exists, how do we go about estimating it? In this paper, we examine the ingredients of the control premium. In particular, we argue that the value of controlling a firm has to lie in being able to run it differently (and better). Consequently, the value of control will be greater for poorly managed firms than well run ones. The value of control has wide ranging implications beyond acquisitions. We show that the expected likelihood of control changing is built into the price of every publicly traded company and that this provides a way of measuring the payoff to strong corporate governance. We also argue that getting a better handle on the value of control can allow us to better explain the differences between voting and non-voting share prices and the minority discount in private company valuations.

Most practitioners favour a one factor model (CAPM) when estimating expected return for an individual stock. For estimation of portfolio returns academics recommend the Fama and French three factor model. The main objective of this paper is to compare the performance of these two models for individual stocks. First, estimates for individual stock returns based on CAPM are obtained using different time frames, data frequencies, and indexes. It is found that five years of monthly data and an equal-weighted index, as opposed to the commonly recommended value-weighted index, provide the best estimate. However performance of the model is very poor; it explains on average three percent of differences in returns. Then, estimates for individual stock returns are obtained based on the Fama and French model using five years of monthly data. This model, however, does not do much better; independent of the index used it explains on average five percent of differences in returns. These results provide a possible explanation for why CAPM is used so extensively by practitioners; the additional cost associated with Fama and French is not justified. However, they also bring into question the use of either model for estimation of individual stock returns.

Many individual investors, mutual funds and institutions trade as if dividends and capital gains are disconnected attributes, not fully appreciating that dividends result in price decreases. Behavioral trading patterns (e.g. the disposition effect) are driven by price changes instead of total returns. Investors rarely reinvest dividends, and trade as if they are a separate, stable income stream. Analysts fail to account for the effect of dividends on price, leading to optimistic price forecasts for dividend-paying stocks. Demand for dividends is systematically higher in periods of low interest rates and poor market performance, leading to lower returns for dividend-paying stocks.

IT progress and its application to the financial industry have inspired central banks and academics to analyse the merits of central bank digital currencies (CBDC) accessible to the broad public. This paper first reviews the advantages and risks of such CBDC. It then discusses two prominent arguments against CBDC, namely (i) risk of structural disintermediation of banks and centralization of the credit allocation process within the central bank and (ii) risk of facilitation systemic runs on banks in crisis situations. Two-tier remuneration of CBDC is proposed as solution to both issues, and a comparison is provided with a simple cap solution and the solution of Kumhof and Noone (2018). Finally, the paper compares the financial account implications of CBDC with the ones of crypto assets, Stablecoins, and narrow bank digital money, in a domestic and international context.

Understanding the ongoing credit crisis or panic requires understanding the designs of a number of interlinked securities, special purpose vehicles, and derivatives, all related to subprime mortgages. I describe the relevant securities, derivatives, and vehicles to show: (1) how the chain of interlinked securities was sensitive to house prices; (2) how asymmetric information was created via complexity; (3) how the risk was spread in an opaque way; and (4) how trade in the ABX indices (linked to subprime bonds) allowed information to be aggregated and revealed. These details are at the heart of the origin of the Panic of 2007. The events of the panic are described.

The tendency of some investors to hold on to their losing stocks, driven by prospect theory and mental accounting, creates a spread between a stock's fundamental value and its equilibrium price, as well as price underreaction to information. Spread convergence, arising from the random evolution of fundamental values and updating of reference prices, generates predictable equilibrium prices that will be interpreted as possessing momentum. Cross-sectional empirical tests are consistent with the model. A variable proxying for aggregate unrealized capital gains appears to be the key variable that generates the profitability of a momentum strategy. Past returns have no predictability for the cross-section of returns once this variable is controlled for.

Is gold a hedge against sudden changes in stock and bond returns, or does it instead have a subtly different property, that of being a safe haven? This paper addresses these two interlinked questions. A safe haven is defined as a security that is uncorrelated with stocks and bonds in case of a market crash. This is counterpoised against a hedge, defined as a security that is uncorrelated with stocks or bonds on average. We study constant and time-varying relationships between stocks, bonds and gold in order to investigate the existence of a hedge and a safe haven. The empirical analysis examines US, UK and German stock and bond returns and their relationship with gold returns. We find that gold is a hedge against stocks on average and a safe haven in extreme stock market conditions. This finding suggests that the existence of a safe haven enhances the stability and resiliency of financial markets since it reduces investors' losses at times when a reduction is needed the most. A portfolio analysis further shows that the safe haven property is extremely short-lived so that an investor buying gold one day after a shock loses money.

We disentangle the effects of treatment, selection, and financial engineering on investor returns in private equity deals using a narrative approach for identification. Exploiting confidential textual data contained in pre-deal investment memos and value creation plans, we show that PE firms create value for investors by selecting companies that are about to outperform (akin to stock picking) and by helping portfolio companies improve production through CAPEX and acquisitions, but not by financial engineering. Tracking the post-investment implementation of value creation plans using confidential monitoring reports, we show that successful execution increases investor returns over and above the return-boosting effects of successful stock picking.

In the period 1991-2010, the S&P 500 destroyed value for the shareholders ($4.5 trillion). In 1991-1999 it created value ($5.1 trillion), but in 2000-2010 it destroyed $9.6 trillion. The market value of the S&P 500 was $2.8 trillion in 1991 and $11.4 trillion in 2010.We also calculate the created shareholder value of the 500 companies during the 18-year period 1993-2010. The top shareholder value creators in that period have been Apple ($212bn), Exxon Mobil (86), IBM (78), Altria Group (70) and Chevron (67). The top shareholder value destroyers in that period have been American Intl Group ($-217), Pfizer (-188), General Electric (-183), Bank of America (-170), Citigroup (-169) and Time Warner (-130). 41% of the companies included in the S&P 500 in 2004 or 2010 created value in 1993-2010 for their shareholders, while 59% destroyed value. We define created shareholder value and provide the created shareholder value of the 633 companies that were in the S&P 500 in December 2004 or in December 2010.

A broad class of exotic interest rate derivatives can be valued simply by adjusting the forward interest rate. This adjustment is known in the market as convexity correction. Various ad hoc rules are used to calculate the convexity correction for different products, many of them mutually inconsistent. In this paper we put convexity correction on a firm mathematical basis by showing that it can be interpreted as the side-effect of a change of probability measure. This provides us with a theoretically consistent framework to calculate convexity corrections. Using this framework we provide exact expressions for libor in arrears, and diff swaps. Furthermore, we propose a simple method to calculate analytical approximations for general instances of convexity correction.

Modern Finance has dominated the area of financial economics for at least four decades. Based on a set of strong but highly unrealistic assumptions its advocates have produced a range of very influential theories and models. Nonetheless, in the last two decades a new academic school of thought has emerged that refutes the key assumption of a homo economicus; an assumption that represents the cornerstone for the development of the theory of efficient markets. The first empirical evidence against efficient markets in the mid-eighties signalled the beginning of a fierce debate between these two schools of thought. This paper gives an overview of the key arguments of these two distinctive academic doctrines.

Itô calculus deals with functions of the current state whilst we deal with functions of the current path to acknowledge the fact that often the impact of randomness is cumulative. We express the differential of the functional in terms of adequately defined partial derivatives to obtain an Itô formula. We develop an extension of the Feynman-Kac formula to the functional case and an explicit expression of the integrand in the Martingale Representation Theorem, providing an alternative to the Clark-Ocone formula from Malliavin Calculus. We establish that under certain conditions, even path dependent options prices satisfy a partial differential equation in a local sense.

We study whether institutional investors that sign the Principles for Responsible Investment (PRI), a commitment to responsible investing, exhibit better portfolio-level environmental, social, and governance (ESG) scores. Signatories outside the US have superior ESG scores than non-signatories, but US signatories have at best similar ESG ratings, and worse scores if they have underperformed recently, are retail-client facing, and joined the PRI late. US signatories do not improve the ESG scores of portfolio companies after investing in them. Commercial motives, uncertainty about fiduciary duties, and lower ESG market maturity explain why US-domiciled PRI signatories do not follow through on their responsible investment commitments.

Spanish Abstract: El PER es la magnitud más utilizada en bolsa, y es el resultado de dividir el precio de todas las acciones (capitalización bursátil) entre el beneficio de la empresa. También se puede calcular dividiendo el precio de cada acción entre el beneficio por acción. Se presenta la evolución del PER de empresas españolas, y de las bolsas de UK, USA y España desde 1990.También se muestra la relación del PER y del tipo de interés de la deuda pública a largo plazo. Los factores que más afectan al PER son los tipos de interés, la rentabilidad exigida a las acciones (Ke) y el crecimiento esperado del beneficio y de los flujos para las acciones.Se calcula el valor de una acción debido al crecimiento esperado y se descompone el PER en: PER sin Crecimiento y PER debido al crecimiento esperado.English Abstract: PER (Price to Earnings Ratio) is the most used magnitude in equity markets. We analyze the relationship among PER, required return and expected growth. We show the evolution of the PER of US, UK and Spain equity markets since 1990. The PER is affected by interest rates, required return to equity and expected growth of net income and equity cash flow.We also split the PER in two components: the PER due to expected growth and the PER without growth.

We show that machine learning methods, in particular extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock and labor market related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both of these channels.

Recent studies have underscored the need for market participants to develop reliable methods of measuring risk. One increasingly popular technique is the use of "value-at-risk" models, which convey estimates of market risk for an entire portfolio in one number. The author explores how well these models actually perform by applying twelve value-at-risk approaches to 1,000 randomly chosen foreign exchange portfolios. Using nine criteria to evaluate model performance, he finds that the approaches generally capture the risk that they set out to assess and tend to produce risk estimates that are similar in average size. No approach, however, appears to be superior by every measure.

If the distribution of a financial variable is highly non-normal, as is the case for the monthly return of some hedge funds or options, how do we compute the projected annualized skewness and kurtosis? We address this question in greater generality, projecting all the summary statistics of the financial variables, in addition to skewness and kurtosis, to arbitrary horizons, in addition to one year. Fully documented MATLAB code is also provided.

While the bulk of the research on the financial performance of mergers and acquisitions has focused on stock returns around the merger announcement, a surprisingly large set of papers has also examined long-run stock returns following acquisitions. We review this literature, concluding that long-run performance is negative following mergers, though performance is non-negative (and perhaps even positive) following tender offers. However, the effects of both methodology (see Lyon, Barber and Tsai (1999)) and chance (see Fama (1998)) may modify this conclusion. Two explanations of under-performance (speed of price-adjustment and EPS myopia) are not supported by the data, while two other explanations (method of payment and performance extrapolation) receive greater support.

Following previous work on calibration of multi-factor local stochastic volatility models to market smiles, we show how to calibrate exactly any such models. Our approach, based on McKean’s particle method, extends to hybrid models, for which we provide a Malliavin representation of the effective local volatility. We illustrate the efficiency of our algorithm on hybrid local stochastic volatility models.

Backtesting stands as a cornerstone technique in the development of systematic investment strategies, but its successful use is often compromised by methodological pitfalls and common biases. These shortcomings can lead to false discoveries and strategies that fail to perform out-of-sample. This article provides practitioners with guidance on adopting more reliable backtesting techniques by reviewing the three principal types of backtests (walk-forward testing, the resampling method, and Monte Carlo simulations), detailing their unique challenges and benefits. Additionally, it discusses methods to enhance the quality of simulations and presents approaches to Sharpe ratio calculations which mitigate the negative consequences of running multiple trials. Thus, it aims to equip practitioners with the necessary tools to generate more accurate and dependable investment strategies.

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.