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Recent years have seen a dramatic shift from mutual funds into hedge funds even though hedge funds charge management fees that have been decried as outrageous. While expectations of superior returns may be responsible for this shift, this article shows that mutual funds are more expensive than commonly believed. Mutual funds appear to provide investment services for relatively low fees because they bundle passive and active funds management together in a way that understates the true cost of active management. In particular, funds engaging in closet or shadow indexing charge their investors for active management while providing them with little more than an indexed investment. Even the average mutual fund, which ostensibly provides only active management, will have over 90% of the variance in its returns explained by its benchmark index. This article derives a method for allocating fund expenses between active and passive management and constructs a simple formula for finding the cost of active management. Computing this active expense ratio requires only a fund's published expense ratio, its R-squared relative to a benchmark index, and the expense ratio for a competitive fund that tracks that index. At the end of 2004, the mean active expense ratio for the large-cap equity mutual funds tracked by Morningstar was 7%, over six times their published expense ratio of 1.15%. More broadly, funds in the Morningstar universe had a mean active expense ratio of 5.2%, while the largest funds averaged a percent or two less.

We study a number of large international military conflicts since World War II where we establish a news analysis as a proxy for the estimated likelihood that the conflict will result in a war. We find that in cases when there is a pre-war phase, an increase in the war likelihood tends to decrease stock prices, but the ultimate outbreak of a war increases them. In cases when a war starts as a surprise, the outbreak of a war decreases stock prices. We show that this paradox cannot be explained by uncertainty about investment decisions, nor by the expectation about a quick end of the war or ambiguity aversion. A connection of this puzzling phenomenon to mean-variance preferences of investors is suggested.

Strategic use of a reverse mortgage can improve retirement outcomes. The benefits are non-linear in nature, as they relate to the synergies created by reducing sequence risk for portfolio withdrawals and to the non-recourse aspects of reverse mortgages that can potentially allow a client to spend more than the value of their home. This article explores six different methods for incorporating home equity into a retirement income plan through the use of a reverse mortgage. Generally, strategies which spend the home equity more quickly increase the overall risk for the retirement plan. More upside potential is generated by delaying the need to take distributions from investments, but more downside risk is created because the home equity is used quickly without necessarily being compensated by sufficiently high market returns. Meanwhile, opening the line of credit and that start of retirement and then delaying its use until the portfolio is depleted creates the most downside protection for the retirement income plan. This strategy allows the line of credit to grow longer, perhaps surpassing the home’s value before it is used, providing a bigger base to continue retirement spending after the portfolio is depleted. Use of tenure payments or one of the coordinated spending strategies can also be justified as providing a middle ground which balances the upside potential of using home equity first and the downside protection of using home equity last. A key theme is that there is great value for clients to open a reverse mortgage line of credit at the earliest possible age.

We propose a new definition of skill as a general cognitive ability to either pick stocks or time the market at different times. We find evidence for stock picking in booms and for market timing in recessions. Moreover, the same fund managers that pick stocks well in expansions also time the market well in recessions. These fund managers significantly outperform other funds and passive benchmarks. Our results suggest a new measure of managerial ability that gives more weight to a fund’s market timing in recessions and to a fund’s stock picking in booms. The measure displays far more persistence than either market timing or stock picking alone and can predict fund performance.

We survey the literature on private equity performance, focusing on venture capital and buyout funds rather than portfolio companies. We describe recent findings on performance measures, average fund returns, risk adjustments, cyclicality and liquidity, persistence, interim returns and self-reported net asset values, the performance of different types of investors in funds, and the links between management contracts and fund returns. Buyout funds have outperformed the S&P 500 net of fees on average by about 20% over the life of the fund. Venture capital funds raised in the 1990s outperformed the S&P 500 while those raised in the 2000s underperformed. The results are consistent across a number of datasets and papers. Before the 2000s, buyout and venture capital fund performance showed strong evidence of persistence. Since 2000, buyout fund persistence has declined, while venture capital fund persistence has remained equally strong.

Using an hourly dataset on retail investor individual security positions from Robinhood Markets, we find no evidence that ESG disclosures inform retail investors’ buy and sell decisions. The response on ESG press release days by retail investors is indistinguishable from non-event days. In contrast, these same investors make economically meaningful changes to their portfolios in response to non-ESG press releases, especially those that pertain to earnings announcements. We use stock return tests to show that there is economic content in ESG press releases, and we conduct subsample analyses showing that retail investors do not respond to the most salient and economically transparent ESG disclosures. Collectively, these tests suggest that a lack of economic content, a lack of visibility, and difficulty with investment integration are unlikely to explain our findings.

The study of transparency is increasingly a more topical, broadly relevant, but also more under-researched enterprise. The Asian financial crisis has highlighted not only the welfare consequences of financial sector transparency, sparking a series of yet unresolved debates, but has also linked this relatively narrow problem to the broader context of transparency in governance. Its significance has broadened geographically as well as across different sectors. It has been observed that curtailment of transparency, often on scanty pretexts, is commonplace even in the highly developed countries. This suggests a broad and possibly radical reform agenda. Departing from the urgency of these observations, this paper reviews the existing literature on transparency in finance and governance, indicates remaining knowledge gaps, and offers some hypothesis on the mutual significance of the two issues.

Blanket exclusion of "brown" stocks is seen as the best way to reduce their negative externalities by starving them of capital. We show that a more effective strategy may be tilting -- holding a brown stock if the firm has taken a corrective action. While such holdings allow the firm to expand, they also encourage the action. We derive conditions under which tilting dominates exclusion for externality reduction. If the action is not publicly observable, the investor might not tilt even if she can gather private information on the action -- tilting would lead to accusations of greenwashing. The presence of an arbitrageur who buys underpriced stocks increases the relative effectiveness of tilting. A responsible investor who is partially profit-motivated may be more likely to tilt than one whose sole objective is minimizing externalities.

This paper discusses differences in countries’ approaches to reporting regulation and explores the reasons why they exist in the first place as well as why they are likely to persist. I first delineate various regulatory choices and discuss the tradeoffs associated with these choices. I also provide a framework that can explain differences in corporate reporting regulation. Next, I present descriptive and stylized evidence on regulatory and institutional differences across countries. There are robust institutional clusters around the world. I discuss that these clusters are likely to persist given the complementarities among countries’ institutions. An important implication of this finding is that reporting practices are unlikely to converge globally, despite efforts to harmonize reporting standards. Convergence of reporting practices is also unlikely due to persistent enforcement differences around the world. Given an ostensibly strong demand for convergence in reporting practices for globally operating firms, I propose a different way forward that does not require convergence of reporting regulation and enforcement across countries. The idea is to create a “Global Player Segment” (GPS), in which member firms play by the same reporting rules and face the same enforcement. Such a segment could be created and administered by a supra-national body like IOSCO.

We extend the widely used SABR model (Hagan et al (2002)) to include a general volatility function and a CEV power on the stochastic volatility process itself. Using a short time expansion we derive results for the Dupire local volatility which in turn is inserted into a single time step finite difference scheme to generate arbitrage free option prices. Our approach has a number of advantages over the standard SABR model: a. it eliminates arbitrage for low and high strikes, b. it allows for an exact fit to a set of discrete option quotes, and c. it gives more explicit control over the wings, both for low (and potentially negative) strikes and for very high strikes. All of this without sacrificing speed in the implementation.

Inspired by visualization techniques à la Feynman, we introduce Stochastic Flow Diagrams (SFDs), a new mathematical approach to represent complex dynamic systems into a single weighted digraph. This topological representation provides a way to visualize what otherwise would be a morass of equations in differences. SFDs model the propagation and reverberation that follows a shock. For example, reverberation explains how a shock to a financial system can initiate a sequence of events that lead to a crash long after the occurrence of the shock. SFDs can simulate systems in stable, steady or explosive state. SFDs add Topology to the Statistical and Econometric toolkit. We believe that SFDs will help policy makers, investors and researchers communicate and discuss better the complexity of dynamic systems.

In this paper, we derive a general asymptotic implied volatility at the first-order for any stochastic volatility model using the heat kernel expansion on a Riemann manifold endowed with an Abelian connection. This formula is particularly useful for the calibration procedure. As an application, we obtain an asymptotic smile for a SABR model with a mean-reversion term, called lambda-SABR, corresponding in our geometric framework to the Poincare hyperbolic plane. When the lambda-SABR model degenerates into the SABR-model, we show that our asymptotic implied volatility is a better approximation than the classical Hagan-al expression. Furthermore, in order to show the strength of this geometric framework, we give an exact solution of the SABR model with beta=0 or 1. In a next paper, we will show how our method can be applied in other contexts such as the derivation of an asymptotic implied volatility for a Libor market model with a stochastic volatility.

The integration of Datastream and Worldscope databases at the Wharton Research Data Services (WRDS) offers researchers a simple solution to bypass the cumbersome data collection and preparation process for global empirical research. This study guide proposes a clean approach to link these two databases through Refinitiv's Quantitative Mapping facilities. We then discuss various details about constructing stock returns through Datastream Return Index (RI). Finally, we demonstrate through an empirical case study on how to combine Datastream's pricing data and Worldscope's fundamental data to compare market characteristics across countries.

Statement of Financial Accounting Standards No. 157 (FAS 157), Fair Value Measurements, prioritizes the source of information used in fair value measurements into three levels: (1) Level 1 (observable inputs from quoted prices in active markets), (2) Level 2 (indirectly observable inputs from quoted prices of comparable items in active markets, identical items in inactive markets, or other market-related information), and (3) Level 3 (unobservable, firm-generated inputs). Using quarterly reports of banking firms in 2008, we find that the value relevance of Level 1 and Level 2 fair values is greater than the value relevance of Level 3 fair values. In addition, we find evidence that the value relevance of fair values (especially Level 3 fair values) is greater for firms with strong corporate governance. Overall, our results support the relevance of fair value measurements under FAS 157, but weaker corporate governance mechanisms may reduce the relevance of these measures.

Despite the recent market upheavals, the OTC derivatives markets continue to comprise one of the largest components of the financial markets, with an overall outstanding notional of $547 trillion in December 2008, 70% of which are in interest rate derivatives. As of June 2009, this grew to $605 trillion. And in spite of market contractions, gross values in the OTC markets are up. From June 2008 to December 2008, OTC gross market value increased 60%, from $20 trillion to $32 trillion (Bank for International Settlements, June 2009). Interest rate derivatives’ gross market value doubled from $9 trillion to $18 trillion. Prompted by the desire to weather or even reduce market turmoil, regulations, accounting practices and investment practices have been under reevaluation. In particular, approaches for analyzing and mitigating counterparty risk have garnered renewed interest. Regulators have been advocating greater usage of clearing houses. Accounting boards have been refining and codifying fair market valuation, placing additional emphasis on careful consideration of counterparty risk. The IASB has even issued a request for comment on counterparty risk calculation methodologies. And investors and traders have been trying to better factor some notion of counterparty risk into their trading and risk management practices. Here we will investigate the notion of counterparty risk and the associated counterparty valuation adjustment (CVA) in the fixed income markets. We will outline the CVA calculation, detail the underlying model assumptions, give examples of the calculation and discuss the impact the CVA has in the value of these instruments.

We explore the possibility that overnight returns can serve as a measure of firm-specific investor sentiment by analyzing whether they exhibit characteristics expected of a sentiment measure. First, we document short-term persistence in overnight returns, consistent with existing evidence of short-term persistence in share demand of sentiment-influenced retail investors. Second, we find that short-term persistence is stronger for harder-to-value firms, consistent with evidence that sentiment plays a larger role when there is less objective data available for valuation. Third, we show that stocks with high (low) overnight returns underperform (outperform) over the longer-term, consistent with evidence of temporary sentiment-driven mispricing.

The Black-Scholes formula is often used in the backward direction to invert the implied volatility, usually with some solver method. Solver methods, being aesthetically unappealing, are also slower than closed-form approximations. However, closed-form approximations in previous works lack accuracy, often providing option pricing errors well exceeding the bid-ask spreads. We develop a new closed-form method based on the rational approximation. By exploiting the homogeneity in the Black-Scholes formula, we are able to show explicitly our domain of approximation and investigate thoroughly the accuracy of our method. The rational approximation is much faster than typical solver methods and very accurate for both at-the-money and away-from-the-money options. Its accuracy can be further improved by one or two steps of Newton-Raphson iterations.

This paper investigates the determinants of hedge fund portfolio performance - whether hedge funds exhibit security selection skill and market-timing skill. We examine a sample of 157 long-short equity hedge funds over the 10-year period from January, 1996 through December, 2005. To account for nonlinearities we employ the Treynor and Mazuy (1966) quadratic model. To account for illiquidity we incorporate the Scholes and Williams (1977) nonsynchronous data model. Before and after adjusting for illiquidity, we find strong evidence of security selection skill and limited evidence of market-timing skill.

This research is an update of the study that we published last year (Bennani et al., 2018) and that explored the impact of ESG investing on asset pricing in the stock market. It extends the original period 2010-2017 by adding eighteen months from January 2018 to June 2019. These new results confirm the previous results as we reach the same essential conclusions once again. ESG investing tended to penalize both passive and active ESG investors between 2010 and 2013. Contrastingly, ESG investing was a source of outperformance from 2014 to 2019 in Europe and North America. Moreover, ESG can be considered as a risk factor in the Eurozone, while it continues to be an alpha strategy in North America. However, the last 18 months exhibit new interesting patterns. First, we observe a transatlantic divide since the results for North America and the Eurozone are different for the recent period. Second, we document a partial ordering between ESG ratings and performance that can be explained by a shift from a static to a dynamic approach to ESG investing. Third, we note some discrepancies between active and passive management. Fourth, the social pillar seems to have gained traction these last years, and is no longer the laggard pillar. Fifth, factor investing and ESG investing are more and more connected. In what follows, we develop and explain these five key findings.

Macroeconomic variables (e.g. economic output, unemployment and employment, and inflation) play a vital role in the economic performance of any country. For the past three decades, evidence of key macroeconomic variables helping predict the time series of stock returns has accumulated in direct contradiction to the conclusions drawn by the Efficient Market Theory. The majority of research concentrates on the financial markets of the developed countries, which are efficient enough and do not suffer from the inefficiency problems found in less developed countries. Considering this matter, the subject of financial markets in developing countries still needs lengthy analysis and more research attention. This research studies the pattern of CPI, WPI, GDP, GNI and Rate of interest in India and Sri Lanka for the year 2002-2009 while also analyzing the impact of macro-economic variable on GDP growth in India vis-à-vis Sri Lanka. The econometrics tools (e.g. unit root test, Granger Causality Test, cointegration test, vector auto regression, Variance decomposition, and Variance Decomposition Analysis) have been used for the analysis purpose.

We examine transaction-level data from Intrade's 2012 presidential winner market for the entire two-year period for which trading occurred. The data allow us to compute key statistics, including volume, transactions, aggression, directional exposure, holding duration, margin, and profit for each of 6,300 unique trader accounts. We identify a diverse set of trading strategies that constitute a rich market ecology. These range from arbitrage-based strategies with low and fleeting directional exposure to strategies involving large accumulated positions in one of the two major party candidates. Most traders who make directional bets do so consistently in a single direction, unlike the information traders in some canonical models of market microstructure. We present evidence suggestive of manipulation by a single large trader, and consider the possible motives for such behavior. Broader implications for the interpretation of prices in financial markets and the theory of market microstructure are drawn.

This paper incorporates investor preferences for return distributions' higher moments into a Polynomial Goal Programming (PGP) optimisation model. This allows us to solve for multiple competing hedge fund allocation objectives within a mean -variance - skewness - kurtosis framework. Our empirical analysis underlines the existence of significant differences in the return behaviour of different hedge fund strategies. Irrespective of investor preferences, the PGP optimal portfolios contain hardly any allocation to long/short equity, distressed securities, and emerging markets funds. Equity market neutral and global macro funds on the other hand tend to receive very high allocations, primarily due to their low co-variance, high co-skewness and low co-kurtosis properties. More specifically, equity market neutral funds act as volatility and kurtosis reducers, while global macro funds act as portfolio skewness enhancers. In PGP optimal portfolios of stocks, bonds, and hedge funds, where equity exposure tends to be traded off for hedge fund exposure, we observe a similar preference for equity market neutral and global macro funds.

This paper develops a new top-down valuation framework that links the pricing of an option investment to its daily profit and loss attribution. The framework uses the Black-Merton-Scholes option pricing formula to attribute the short-term option investment risk to variations in the underlying security price and the option's implied volatility. Taking risk-neutral expectation and demanding no dynamic arbitrage results in a pricing relation that links an option's fair implied volatility level to the underlying's volatility level with corrections for the implied volatility's own expected direction of movement, its variance, and its covariance with the underlying security return.

Traditionally, asset allocation is performed via an optimization problem in the mean-variance framework. Mean-variance analysis assumes that either the investor's utility function is quadratic or the returns are normally distributed. However, it is well known that a quadratic utility function is inconsistent with the behavior of a rational investor. In addition, assets returns may not be normally distributed. This is typically the case for hedge fund returns which distributions usually exhibit negative skewness and excess kurtosis. Alternative methods capturing these statistical properties have been introduced but they still reduce the dimensionality to a few characteristics and do not take into account moments of higher order than skewness and kurtosis. Omega is a new measure that reflects all the statistical properties of a returns distribution. The measure incorporates all the moments of the distribution and requires no assumptions on its shape or on the investor's utility function. As a measure of attractiveness, omega can be used for performance measurement but also for optimal asset allocation. This paper sets first the theoretical foundations for using omega in the investment decision problem. Then, the measure is applied to optimal asset allocation for portfolios containing hedge fund indices. It shows that the portfolios derived in the omega framework can markedly depart from those obtained with other conventional techniques, which emphasizes the importance of including higher moments than skewness and kurtosis in the analysis. It further suggests that omega provides enhanced capabilities for risk diversification and performance enhancement when returns are not normally distributed. Finally, it shows that even if returns are normally distributed, omega still contributes to the analysis by considering investor?s specific perception of gain and loss.

This paper comprehensively reviews the Accounting for Income Taxes (AFIT). It begins by identifying four distinctive aspects of AFIT and briefly covering the rules surrounding AFIT. It then reviews the existing studies in detail and offers suggestions for future research. We emphasize the research questions that have been addressed (most of which relate to whether the tax accounts are used to manage earnings and whether the tax accounts are priced by equity market participants). We also highlight areas that have not received as much research attention or warrant future analysis.