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We examine whether corporate governance mechanisms, especially the market for corporate control, affect the profitability of firm acquisitions. We find that acquirers with more anti-takeover provisions experience significantly lower announcement-period stock returns than other acquirers. We also find that acquiring firms operating in more competitive industries or separating the positions of CEO and chairman of the board experience higher abnormal announcement returns. Our results support the hypothesis that managers protected by more anti-takeover provisions face weaker discipline from the market for corporate control and thus, are more likely to indulge in empire-building acquisitions that destroy shareholder value. They provide a partial explanation for why anti-takeover provision indices of Gompers, Ishii and Metrick and others are negatively correlated with shareholder value.

We propose a new framework for pricing assets, derived in part from the traditional consumption-based approach, but which also incorporates two long-standing ideas in psychology: the prospect theory of Kahneman and Tversky (1979), and the evidence of Thaler and Johnson (1990) and others on the influence of prior outcomes on risky choice. Consistent with prospect theory, the investor in our model derives utility not only from consumption levels but also from changes in the value of his financial wealth. He is much more sensitive to reductions in wealth than to increases, the "loss-aversion" feature of prospect utility. Moreover, consistent with experimental evidence, the utility he receives from gains and losses in wealth depends on his prior investment outcomes; prior gains cushion subsequent losses -- the so-called "house-money" effect -- while prior losses intensify the pain of subsequent shortfalls. We study asset prices in the presence of agents with preferences of this type and find that our model reproduces the high mean, volatility, and predictability of stock returns. The key to our restuls is that the agent's risk-aversion changes over time as a function of his investment performance. This makes prices much more volatile than underlying dividends, and together with the investor's loss-aversion, leads to large equity premia. Our results obtain with reasonable values for all parameters.

Equity index covered calls have historically provided attractive risk-adjusted returns largely because they collect equity and volatility risk premia from their long equity and short volatility exposures. However, they also embed exposure to an uncompensated risk, a naïve equity market reversal strategy. This paper presents a novel performance attribution methodology, which deconstructs the strategy into these three identified exposures, in order to measure each’s contribution to the covered call’s return. The covered call’s equity exposure is responsible for most of the strategy’s risk and return. The strategy’s short volatility exposure has had a realized Sharpe ratio close to 1.0, but its contribution to risk has been less than 10 percent. The equity reversal exposure is responsible for about one-quarter of the covered call’s risk, but provides little reward. Finally, we propose a risk-managed covered call strategy that hedges the equity reversal exposure in an attempt to eliminate this uncompensated risk. Our proposed strategy improved the covered call’s Sharpe ratio, and reduced its volatility and downside equity beta.

Trend rules are widely used to infer whether financial markets show an upward or downward trend. By taking suitable long or short positions, one can profit from a continuation of these trends. Conventionally, trend rules are based on moving averages (MAs) of prices rather than returns, which obscures how much weight is assigned to different historical time periods. In this paper, we show how to uncover the underlying historical weighting schemes of price MAs and combinations of price MAs. This leads to surprising and useful insights about popular trend rules, for example that some trend rules have inverted information decay (i.e., distant returns have more weight than recent ones) or hidden mean-reversion patterns. This opens the possibility for improving the trend rule by analyzing the added value of the mean reversion part. We advocate designing trend rules in terms of returns instead of prices, as they offer more flexibility and allow for adjusting trend rules to autocorrelation patterns in returns.

This paper takes a first step towards demonstrating that overall trading volume in equity markets is excessive, by showing that it is excessive for a particular group of investors: those with discount brokerage accounts. One possible cause of excessive trading is overconfidence. Overconfident investors will trade too frequently, that is, the gains overconfident investors realize through trade will be less than they anticipate and may not even offset the costs of trading. By analyzing trading records for 10,000 accounts at a large discount brokerage house, I test whether the securities these investors purchase outperform those they sell by enough to cover the costs of trading. I find the surprising result that, on average, the securities they purchase actually underperform those they sell. This is the case even when trading is not apparently motivated by liquidity demands, tax-loss selling, portfolio rebalancing, or a move to lower-risk securities. I examine return patterns before and after transactions. Return patterns before purchases and sales can be explained by the difficulty of the search for securities to buy, investors' tendency to let their attention be directed by outside sources, the disposition effect, and investors' reluctance to sell short.

Long‐horizon investors have an edge. They can ride out short‐term fluctuations in risk premiums, profit from periods of elevated risk aversions and short‐term mispricing, and they can pursue illiquid investment opportunities. The turmoil we have seen in the capital markets over the last decade has increased the competitive advantage of a long investment horizon. Unfortunately, the two biggest mistakes of long‐horizon investors - procyclical investments and misalignments between asset owners and managers - negate the long‐horizon advantage. Long‐horizon investors should harvest many sources of factor risk premiums, be actively contrarian, and align all stakeholders so that long‐horizon strategies can be successfully implemented. Illiquid assets can, but do not necessarily, play a role for long-horizon investors, but investors should demand high premiums to compensate for bearing illiquidity risk and agency issues.

Traditional risk factor models indicate that hedge funds capture pre-fee alphas of 6% to 10% per annum over the period from 1996 to 2012. At the same time, the hedge fund return series is not reliably distinguishable from the returns of mechanical S&P 500 put-writing strategies. We show that the high excess returns to hedge funds and put-writing are consistent with an equilibrium in which a small subset of investors specialize in bearing downside market risks. Required rates of return in such an equilibrium can dramatically exceed those suggested by traditional models, affecting inference about the attractiveness of these investments.

Deep learning is an active area of research in machine learning. I train deep feedforward neural networks (DFN) based on a set of 68 firm characteristics (FC) to predict the US cross-section of stock returns. After applying a network optimization strategy, I find that DFN long-short portfolios can generate attractive risk-adjusted returns compared to a linear benchmark. These findings underscore the importance of non-linear relationships among FC and expected returns. The results are robust to size, weighting schemes and portfolio cutoff points. Moreover, I show that price related FC, namely, short-term reversal and the twelve-months momentum, are among the main drivers of the return predictions. The majority of FC play a minor role in the variation of these predictions.

I review 100 finance and valuation textbooks published between 1979 and 2008 (Brealey, Myers, Copeland, Damodaran, Merton, Ross, Bruner, Bodie, Penman, Weston, Arzac...) and find that their recommendations regarding the equity premium range from 3% to 10%, and that several books use different equity premia in different pages. Some confusion arises from not distinguishing among the four concepts that the word equity premium designates: Historical equity premium, Expected equity premium, Required equity premium and Implied equity premium. Finance professors should clarify the different concepts of equity premium and convey a clearer message about their sensible magnitudes.

The CAPM and Fama and French (FF) three-factor pricing model are subjected to a series of tests using out of sample data, free from many of the biases present in typical testing. Data is collected from the Value Line Investment Survey allowing for survivorship and look-ahead biases to be eliminated. Further, it is argued that simple portfolio selection rules and the use of data from a source commonly consulted by investors minimizes data snooping concerns. Stronger support is found for the CAPM than that reported by FF (1992, 1996). Size and book-to-market equity (B/M) are insignificant in cross-sectional regressions, and Gibbons, Ross, and Shanken (1989) F-tests cannot reject the appropriateness of the CAPM when stocks are sorted into size and B/M portfolios. Beta, on the other hand, emerges as a powerful anomaly. Neither the CAPM nor the FF model can explain the high (low) returns on low (high) beta portfolios.

In February and March of 1999, we surveyed 392 CFOs about the cost of capital, capital budgeting, and capital structure. The survey consisted of 14 main questions, most with subparts - over 100 questions in total. Although the survey was anonymous, we also collected information on 12 characteristics of the firms and management. We asked questions about firm size, foreign sales, industry, CEO education, age of the CEO, CEO tenure, ownership, whether the firm paid dividends, whether the firm was regulated, and the proportion of common stock that the top three executives owned if all their options were exercised. We also collected information on debt-equity ratios and debt ratings. The analysis, published in the 2001 Journal of Financial Economics (http://ssrn.com/abstract=220251), showed that many survey responses differed by the firm and management characteristics. Our research left much of the data unexplored. In particular, in only one instance in the paper did we perform question-conditional analysis. That is, given a particular response to one question, how does that impact the response on another question. For example, is it the case that those CFOs that use real options analysis in project evaluation decisions also value financial flexibility in capital structure? Given the large number of inquiries we have received for the survey data, we now publicly release the raw data files so that other researchers can conduct question-conditional analysis. Using these data, researchers should be able to obtain CFO survey evidence related to specific aspects of their own research agendas, as well as perform more detailed analysis of the survey responses.

Motivated by the implications from a stylized self-contained general equilibrium model incorporating the effects of time-varying economic uncertainty, we show that the difference between implied and realized variation, or the variance risk premium, is able to explain a non-trivial fraction of the time series variation in post 1990 aggregate stock market returns, with high (low) premia predicting high (low) future returns. Our empirical results depend crucially on the use of "model-free,'' as opposed to Black-Scholes, options implied volatilities, along with accurate realized variation measures constructed from high-frequency intraday, as opposed to daily, data. The magnitude of the predictability is particularly strong at the intermediate quarterly return horizon, where it dominates that afforded by other popular predictor variables, like the P/E ratio, the default spread, and the consumption-wealth ratio (CAY).

This article demonstrates theoretically that without imposing any structure on the underlying forcing process, the model-free CBOE volatility index (VIX) does not measure market expectation of volatility but that of a linear moment-combination. Particularly, VIX undervalues (overvalues) volatility when market return is expected to be negatively (positively) skewed. Alternatively, we develop a model-free generalized volatility index (GVIX). With no diffusion assumption, GVIX is formulated directly from the definition of log-return variance, and VIX is a special case of the GVIX. Empirically, VIX generally understates the true volatility, and the estimation errors considerably enlarge during volatile markets. The spread between GVIX and VIX follows a mean-reverting process.

Changes in stock returns arise from changes in expected future cash flow growth and expected future discount rates. However, which variables proxy for those changes remains unknown. This paper considers twenty-five variables that are arranged into five groups and examines both in-sample predictability as well as out-of-sample forecasting. Existing research typically considers either one, or a small selection of variables, prominent within this is the dividend price ratio and interest rates. We consider variables that span the categories of financial ratios, macro, labour market and housing variables as well a group referred to as others, which incorporates measures of sentiment and leverage. In-sample results show that significance arises in variables across these five groups. Of note, price ratios, GDP acceleration, inflation, unemployment and consumer sentiment feature prominently, with the purchasing managers index, housing variables and leverage also represented. Thus, predictive variables appear across the different categories. In conducting out-of-sample forecasts, we utilise a range of forecast performance measures and consider single model and combined forecasts. The results show that, with one exception, the combined model forecasts outperform the single model forecasts across all measures. This supports the view that a range of variables from across the economy can help predict future stock returns.

This paper assesses whether shareholders drive the environmental and social (E&S) performance of firms worldwide. Across 41 countries, we find that institutional ownership is positively associated with E&S performance with additional tests suggesting this relation is causal. Our evidence shows that institutions are motivated by both financial and social returns. Investors increase firms’ E&S performance following shocks that reveal financial benefits to E&S. In cross-section, investors increase firms’ E&S performance when they come from countries where there is a strong community belief in the importance of E&S issues, but not otherwise. Overall, these results indicate that investors drive firms’ E&S performance around the world and transplant their local social norms in that process.

We use information from over two million trading strategies that are randomly generated using real data, and from strategies that survive the publication process to infer the statistical properties of the set of strategies that could have been studied by researchers. Using this set, we compute t-statistic thresholds that control for multiple hypothesis testing when searching for anomalies, at 3.84 and 3.38 for time-series and cross-sectional regressions, respectively. We estimate the expected proportion of false rejections that researchers would produce if they failed to account for multiple hypothesis testing to be 45.3%.

The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a mean-variance optimizer. In its place, we suggest using the matrix obtained from the sample covariance matrix through a transformation called shrinkage. This tends to pull the most extreme coefficients towards more central values, thereby systematically reducing estimation error where it matters most. Statistically, the challenge is to know the optimal shrinkage intensity, and we give the formula for that. Without changing any other step in the portfolio optimization process, we show on actual stock market data that shrinkage reduces tracking error relative to a benchmark index, and substantially increases the realized information ratio of the active portfolio manager.

This study examines the effect of age on risk tolerance. The life-cycle investment hypothesis is tested using the 1983-89 panel of the Survey of Consumer Finances. Household wealth is defined as the sum of human capital and net worth. Risk tolerance is measured by the ratio of risky assets to total wealth. Risk tolerance increases with age when other variables are controlled.

Viewing the detection of earnings management from the perspective of a crime scene investigator sheds new light on prior research on earnings management and its close relative, earnings quality. Ball and Shivakumar (2007) and Teoh et al. (1998) are used to illustrate the application of seven components of a crime scene investigation to earnings management research.

This paper provides a review of the main features of asset pricing models. The review includes single-factor and multi-factor models, extended forms of the Capital Asset Pricing Model (CAPM) with higher-order co-moments and asset pricing models conditional on time varying volatility models.

The paper constitutes a discussion of the trend around the rise of art as an alternative investment. With financial markets in turmoil, art as an alternative asset class is being incorporated into portfolios in the interest of diversification. Art's low correlation with the equities market and desirable risk and reward ratio, as price appreciation defies all logic, makes it an attractive investment. The volatility, irrationality and illiquidity of the art market make it hard to compare to more conventional investments. The paper will look at how investors are treating art as an asset class and how art compares to more traditional assets such as equities and bonds.

We estimate that the prevalence of illegal insider trading is at least four times greater than the number of prosecutions. Using structural estimation methods that account for incomplete non-random detection and all US prosecuted insider trading cases, we estimate that insider trading occurs in 1-in-5 mergers and acquisitions and in 1-in-20 earnings announcements. We find that insider trading is more likely when the information is more valuable, more people are in possession of the information, and in more liquid stocks. Detection and prosecution are more likely when there are abnormal trading patterns and more regulatory resourcing.

High-risk stocks do not have higher returns than low-risk stocks in all major stock markets. This paper provides a comprehensive overview of this low-risk effect, from the earliest asset pricing studies in the nineteen seventies to the most recent empirical findings and interpretations since. Volatility appears to be the main driver of the anomaly, which is highly persistent over time and across markets, and which cannot be explained by other factors such as value, profitability, or exposure to interest rate changes. From a practical perspective we argue that low-risk investing requires little turnover, that volatilities are more important than correlations, that low-risk indices are suboptimal and vulnerable to overcrowding, and that other factors can be efficiently integrated into a low-risk strategy. Finally, we find little evidence that the low-risk effect is being arbitraged away, as many investors are either neutrally positioned, or even on the other side of the low-risk trade.

This report consists of two parts. The first part is a research note describing a comprehensive analysis of the realized return differential between small and large firms (i.e., the "size premium") in a sample of European stocks during the period between 1990 and 2018. This study establishes that during the period under examination small European stocks earned a statistically and economically significant size premium relative to large stocks. The observed size premium is robust to controlling for size-related beta differences as well as to changes in the metric used to measure size. The second part of the report presents a large collection of exhibits – and a brief description of the procedures and assumptions underlying these exhibits – that can help the analyst in estimating the cost of equity capital for European businesses using various measures of firm size. The study described in this report was commissioned by Duff & Phelps LLC.

If investors are more averse to the risk of losses on the downside than of gains on the upside, investors ought to demand greater compensation for holding stocks with greater downside risk. Downside correlations better capture the asymmetric nature of risk than downside betas, since conditional betas exhibit little asymmetry across falling and rising markets. We find that stocks with high downside correlations with the market, which are correlations over periods when excess market returns are below the mean, have high expected returns. Controlling for the market beta, the size effect, and the book-to-market effect, the expected return on a portfolio of stocks with the greatest downside correlations exceeds the expected return on a portfolio of stocks with the least downside correlations by 6.55% per annum. We find that part of the profitability of investing in momentum strategies can be explained as compensation for bearing high exposure to downside risk.