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We estimate implied cost of equity capital for a sample of firms from 1984 to 1998 using the Ohlson and Juettner (2000) model that does not make restrictive assumptions about clean surplus and payout policies. We find that cost of equity capital is strongly positively associated with conventional risk factors such as earnings variability, systematic and unsystematic return volatility, and leverage, and is negatively associated with analyst following. These associations are robust to controls for industry membership and to running the regression in changes instead of levels. Our results support the Ohlson-Juettner metric as a robust and appealing measure of cost of equity capital.

In this paper, we analyze the trading records of a major discount brokerage house to investigate the disposition effect, the tendency to sell winners too quickly than losers. In contrast to previous research that has demonstrated the disposition effect by aggregating across investors (Odean, 1998), our main objective is to identify individual differences in the disposition bias and explain this in terms of underlying investor characteristics. Building on the findings in experimental economics and self-correction in psychology, we hypothesize that investors' sophistication about financial markets and trading experience is responsible in part for the variation in individual disposition effect. Using demographic and socio-economic data as proxies for investors' sophistication, we find empirical evidence that wealthier and individual investors in professional occupations exhibit less disposition effect. Consistent with experimental economics, trading experience also tends to reduce the disposition effect. We provide guidelines for investment advisors, regulators and investment communities to utilize our findings and help investors make better decisions.

We examine a comprehensive sample of going-dark deregistrations where companies cease SEC reporting, but continue to trade publicly. We document a spike in going dark that is largely attributable to the Sarbanes-Oxley Act. Firms experience large negative abnormal returns when going dark. We find that many firms go dark due to poor future prospects, distress and increased compliance costs after SOX. But we also find evidence suggesting that controlling insiders take their firms dark to protect private control benefits and decrease outside scrutiny, particularly when governance and investor protection are weak. Finally, we show that going dark and going private are distinct economic events.

Evaluating Constant Maturity Swap (CMS) derivatives is a lot more complex than plain vanilla interest rate swaps, because of the unnatural schedule of their payments. Their pricing requires either a convexity adjustment or the use of a model. Hence multiple approaches have been proposed. We selected the most common ones in the marketplace, to analyse and compare them by examining their characteristics. We found that the most accurate prices are generated by Monte Carlo simulations with BGM model for forward rates, while the continuous swaption replication method offers prices consistent with the other instruments of the trading book.

Usually in financial textbooks and courses the theory of portfolio selection is taught in a strictly theoretical way. There is a model (Markowitz) that stipulates that an investor has preferences and that she will choose the best portfolio, given her preference curves and an efficient frontier. On the other hand, the Capital Asset Pricing Model (CAPM) is presented as it is: a genial idea that served to simplify and to make operative the Markowitz setup. Most students and practitioners conclude that those models are just inapplicable theory. This is the most rational behavior one can expect. What can an investor do with the textbook recipes to configure an optimal portfolio? Very little. My purpose with this note is to rescue a simple procedure presented by Black (1972), Merton (1973) and later by Levy and Sarnat (1982), Elton and Gruber (1995) and Benninga (1997). They just propose that the optimal portfolio can be found maximizing the slope of the line that joins the point of risk-free return and the efficient frontier. When this maximum tangent is reached, that line is the capital market line (CML) (it is tangent to the efficient frontier). This is a simple procedure that does not require one to calculate the efficient frontier and is an easy task with Excel Solver. It is just one point of the efficient frontier. An example is presented. Keywords CAPM, efficient frontier, porfolio selection, capital market line, optimal portfolio

This paper analyzes the risk characteristics for various hedge fund strategies specializing in fixed income instruments. Because fixed income hedge fund strategies have exceptionally high autocorrelations in reported returns and this is taken as evidence of return smoothing, we first develop a method to completely eliminate any order of autocorrelation process across a wide array of time series processes. Once this is complete, we determine the underlying risk factors to the "true" hedge fund returns and examine the incremental benefit attained from using nonlinear payoffs relative to the more traditional linear factors. For a great many of the hedge fund indices we find the strongest risk factor to be equivalent to a short put position on high-yield debt. In general, we find a moderate benefit to using the nonlinear risk factors in terms of the ability to explain reported returns. However, in some cases this fit is not stable even over the in-sample period. Finally, we examine the benefit to using various factor structures for estimating the value-at-risk of the hedge funds. We find, in general, that using nonlinear factors slightly increases the estimated downside risk levels of the hedge funds due to their option-like payoff structures.

This paper discusses the empirical literature on the economic consequences of disclosure and financial reporting regulation (including IFRS adoption), drawing on U.S. and international evidence. Given the policy relevance of research on regulation, we highlight the challenges with: (i) quantifying regulatory costs and benefits, (ii) measuring disclosure and reporting outcomes, and (iii) drawing causal inferences from regulatory studies. Next, we discuss empirical studies that link disclosure and reporting activities to firm-specific and market-wide economic outcomes. Understanding these links is important when evaluating regulation. We then synthesize the empirical evidence on the economic effects of disclosure regulation and reporting standards, including the evidence on IFRS adoption. Several important conclusions emerge. We generally lack evidence on market-wide effects and externalities from regulation, yet such evidence is central to the economic justification of regulation. Moreover, evidence on causal effects of disclosure and reporting regulation is still relatively rare. We also lack evidence on the real effects of such regulation. These limitations provide many research opportunities. We conclude with several specific suggestions for future research.

Recent empirical work shows that a better legal environment leads to lower expected rates of return in an international cross-section of countries. This paper investigates whether differences in firm-specific corporate governance also help to explain expected returns in a cross-section of firms within a single jurisdiction. Constructing a corporate governance rating (CGR) for German firms, we document a positive relationship between the CGR and firm value. In addition, there is strong evidence that expected returns are negatively correlated with the CGR, if dividend yields and price-earnings ratios are used as proxies for the cost of capital. Most results are robust for endogeneity, with causation running from corporate governance practices to firm fundamentals. Finally, an investment strategy that bought high-CGR firms and shorted low-CGR firms would have earned abnormal returns of around 12 percent on an annual basis during the sample period. We rationalize the empirical evidence with lower agency costs and/or the removal of certain governance malfunctions for the high-CGR firms.

This paper examines expected option returns in the context of mainstream asset pricing theory. Under mild assumptions, call options have expected returns which exceed those of their underlying security and which are increasing in their strike prices. Likewise, put options have expected returns which are below the risk-free rate and which are also increasing in their strike prices. Across a variety of time periods and return frequencies, S&P 500 and 100 index option returns strongly exhibit these characteristics. Under stronger assumptions, expected option returns are a linear function of option betas. Fama-MacBeth-style option return regressions produce risk premia close to the expected market return. However, the regression intercepts are significantly below zero. As a result, zero-beta, at-the-money straddle positions produce average losses of approximately three percent per week. Zero-beta straddles in other markets also lose money consistently. These findings suggest that some additional factor, such as systematic stochastic volatility, is priced in option returns.

In the last decade, neural networks have drawn noticeable attention from many computer and operations researchers. While some previous studies have found encouraging results with using this artificial intelligence technique to predict the movements of established financial markets, it is interesting to verify the persistence of this performance in the emerging markets. These rapid growing financial markets are usually characterized by high volatility, relatively smaller capitalization, and less price efficiency, features which may hinder the effectiveness of those forecasting models developed for established markets. In this study, we attempt to model and predict the direction of return on the Taiwan Stock Exchange Index, one of the fastest growing financial exchanges in developing Asian countries. Our approach is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The Probabilistic Neural Network (PNN) is used to forecast the direction of index return after it is trained by historical data. The forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy and hold strategy, and the investment strategies guided by the forecasts estimated by the random walk model and the parametric Generalized Methods of Moments (GMM) with Kalman filter. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. The influences of the length of investment horizon and the commission rate are also considered.

We propose a measure for systemic risk: CoVaR, the value at risk (VaR) of the financial system conditional on institutions being in distress. We define an institution’s contribution to systemic risk as the difference between CoVaR conditional on the institution being in distress and CoVaR in the median state of the institution. From our estimates of CoVaR for the universe of publicly traded financial institutions, we quantify the extent to which characteristics such as leverage, size, and maturity mismatch predict systemic risk contribution. We also provide out-of-sample forecasts of a countercyclical, forwardlooking measure of systemic risk and show that the 2006:Q4 value of this measure would have predicted more than half of realized covariances during the financial crisis.

This paper analyzes the impact of various assumptions about the association between aggregate default probabilities and the loss given default on bank loans and corporate bonds, and seeks to empirically explain this critical relationship. Moreover, it simulates the effects on mandatory capital requirements like those proposed in 2001 by the Basel Committee on Banking Supervision. We present the analysis and results in four distinct sections. The first section examines the literature of the last three decades of the various structural-form, closed-form and other credit risk and portfolio credit value-at-risk (VaR) models and the way they explicitly or implicitly treat the recovery rate variable. Section 2 presents simulation results under three different recovery rate scenarios and examines the impact of these scenarios on the resulting risk measures: our results show a significant increase in both expected and unexpected losses when recovery rates are stochastic and negatively correlated with default probabilities. In Section 3, we empirically examine the recovery rates on corporate bond defaults, over the period 1982-2000. We attempt to explain recovery rates by specifying a rather straightforward statistical least squares regression model. The central thesis is that aggregate recovery rates are basically a function of supply and demand for the securities. Our econometric univariate and multivariate time series models explain a significant portion of the variance in bond recovery rates aggregated across all seniority and collateral levels. Finally, in Section 4 we analyze how the link between default probability and recovery risk would affect the procyclicality effects of the New Basel Capital Accord, due to be released in 2002. We see that, if banks use their own estimates of LGD (as in the "advanced" IRB approach), an increase in the sensitivity of banks' LGD due to the variation in PD over economic cycles is likely to follow. Our results have important implications for just about all portfolio credit risk models, for markets which depend on recovery rates as a key variable (e.g., securitizations, credit derivatives, etc.), for the current debate on the revised BIS guidelines for capital requirements on bank credit assets, and for investors in corporate bonds of all credit qualities.

Twenty years ago we published a paper, "The Mechanisms of Market Efficiency," that sought to describe the institutional underpinnings of price formation in the securities market. Since that time, financial economics has moved forward on many fronts. The sub-discipline of behavioral finance has struggled to bring yet more descriptive realism to the study of financial markets. Two important questions are (1) how much has this new discipline changed our understanding of the efficiency and nature of the institutional mechanisms that set price in financial markets; and (2) how far does this discipline carry novel implications for the regulation of financial markets or corporate behavior more generally? We argue that, despite its heavy reliance on the psychology of cognitive bias, the principal contribution of behavioral finance is to enrich our understanding of market institutions rather than to present us with a fundamentally new paradigm of market behavior. In particular, the cognitive limitations of individual investors or noise traders are likely to matter to pricing behavior to the extent that they interact with - and are not offset by - the arbitrage mechanism in the market. The most important contribution of behavioral finance lies in sharpening our understanding of the limitations of the arbitrage mechanism. Even when cognitive bias does not have clear implications for securities prices, however, it may have important implications for policy. These implications are unlikely to arise in the area of corporate takeovers, as some have claimed, but they do arise in areas akin to consumer protection, as where cognitive bias might lead unsophisticated investors to construct dangerously undiversified retirement portfolios.

A simple and fundamental question in derivatives pricing is the way (contingent) cash-flows should be discounted. As cash can not be invested at Libor the curve is probably not the right discounting curve, even for Libor derivatives. The impact on derivative pricing of changing the discounting curve is discussed. The pricing formulas for vanilla products are revisited in the funding framework described.

The paper examines the relation between the probability of manipulation, accruals, and future returns. We show that firms that have a high likelihood of earnings manipulation (as measured by the Beneish (1999)'s M-Score) experience lower future earnings, but that investors expect these firms to have higher future earnings. Indeed, we find that investors overestimate next-period return on assets by 490 to 690 basis points (this is significant as the median ROA in the sample 4.6%). We also show that the probability of manipulation is a correlated omitted variable for the earnings forecasting models used in prior research on accrual mispricing and that including the probability of manipulation greatly attenuates the mispricing of accrual persistence. Finally, we show that the probability of earnings manipulation predicts economically significant abnormal returns of approximately 15% per year after controlling for accruals and various controls for risk factors, including a factor compensating for earnings quality differences (Easley and O'Hara (2004), Francis et al. (2005)). We interpret our results that the predictive ability of accruals for returns is greatly diminished in the presence of the M-Score as indicating that accrual mispricing arises because investors are misled by managers' opportunistic management of earnings.

This paper reviews the theoretical and empirical literature on the channels through which blockholders (large shareholders) engage in corporate governance. In classical models, blockholders exert governance through direct intervention in a firm’s operations, otherwise known as “voice.” These theories have motivated empirical research on the determinants and consequences of activism. More recent models show that blockholders can govern through an alternative mechanism known as “exit”—selling their shares if the manager underperforms. These theories give rise to new empirical studies on the two-way relationship between blockholders and financial markets, linking corporate finance with asset pricing. Blockholders may also worsen governance by extracting private benefits of control or pursuing objectives other than firm value maximization. I highlight the empirical challenges in identifying causal effects of and on blockholders as well as the typical strategies attempted to achieve identification. I close with directions for future research.

This article studies the relation between the skewness of commodity futures returns and expected returns. A trading strategy that takes long positions in commodity futures with the most negative skew and shorts those with the most positive skew generates significant excess returns that remain after controlling for exposure to well-known risk factors. A tradeable skewness factor explains the cross-section of commodity futures returns beyond exposures to standard risk premia. The impact that skewness has on future returns is explained by investors’ preferences for skewness under cumulative prospect theory and selective hedging practices.

A common view of retail finance is that conflicts of interest contribute to the high cost of advice. Within a large sample of Canadian financial advisors and their clients, however, we show that advisors typically invest personally just as they advise their clients. Advisors trade frequently, chase returns, prefer expensive, actively managed funds, and underdiversify. Advisors' net returns of −3% per year are similar to their clients' net returns. Advisors do not strategically hold expensive portfolios only to convince clients to do the same; they continue to do so after they leave the industry.

We provide new out-of-sample evidence on trend-following investing by studying its performance for 82 securities not previously examined and 16 long-short equity factors. Specifically, we study the performance of time series momentum for emerging market equity index futures, fixed income swaps, emerging market currencies, exotic commodity futures, credit default swap indices, volatility futures, and long-short equity factors. We find that time series momentum has worked across these asset classes and across several trend horizons. We examine the co-movement of trends across asset classes and factors, the performance during different market environments, and discuss the implications for investors.

Are endogenous liquidity providers (ELPs) reliable in times of market stress? We examine the activity of a common ELP type – high frequency traders (HFTs) – around extreme price movements (EPMs). We find that on average HFTs provide liquidity during EPMs by absorbing imbalances created by non-high frequency traders (nHFTs). Yet HFT liquidity provision is limited to EPMs in single stocks. When several stocks experience simultaneous EPMs, HFT liquidity demand dominates their supply. There is little evidence of HFTs causing EPMs.

This paper develops a tractable, dynamic, no-arbitrage model for the pricing of bonds and stocks that are subject to default risk. The model produces the bond pricing equations of the Duffie and Singleton (1999) framework. It is then shown that a particular choice of dividend process, characterized by affine dividend yields, along with the Duffie and Singleton (1999) default specification, produces stock prices that are exponential affine in the model's state variables. Importantly, the model allows for quite general interdependence between the prices of risky debt and equity. This, along with the model's tractability, makes it a natural platform for empirical investigations into the pricing of a firm's capital structure.

The provision and use of financial services and products that conform to Islamic religious principles pose special challenges for the identification, measurement, monitoring, and control of underlying risks. Effective and efficient risk management in Islamic financial institutions has assumed particular importance as they endeavor to cope with the challenges of globalization. This requires the development of not only a more suitable regulatory framework, but also new financial instruments and institutional arrangements to provide an enabling operational environment for Islamic finance. The recent establishment of the Islamic Financial Services Board, facilitated by the IMF, addresses these needs.

We train an AI analyst that digests corporate disclosures, industry trends, and macroeconomicindicators to the extent it beats most analysts. Human wins the “Man vs. Machine”contest when a firm is complex with intangible assets, and AI wins when information istransparent but voluminous. Analysts catch up with machines over time, especially afterfirms are covered by alternative data and their institutions build AI capabilities. AI powerand human wisdom are complementary in generating accurate forecasts and mitigating extremeerrors, portraying a future of “Man + Machine” (instead of human displacement) infinancial analyses, and likely other high-skill professions.

In this paper we present a tree model for defaultable bond prices which can be used for the pricing of credit derivatives. The model is based upon the two-factor Hull-White (1994) model for default-free interest rates, where one of the factors is taken to be the credit spread of the defaultable bond prices. As opposed to the tree model of Jarrow and Turnbull (1992), the dynamics of default-free interest rates and credit spreads in this model can have any desired degree of correlation, and the model can be fitted to any given term structures of default-free and defaultable bond prices, and to the term structures of the respective volatilities. Furthermore the model can accommodate several alternative models of default recovery, including the fractional recovery model of Duffie and Singleton (1994) and recovery in terms of equivalent default-free bonds (see e.g. Lando (1998)). Although based on a Gaussian setup, the approach can easily be extended to non-Gaussian processes that avoid negative interest-rates or credit spreads.

An investor in the stock market would be interested in analysing the stock price movements. Prices in the stock market fluctuate due to continuous buying and selling in the market. There are basically two approaches used in analysing the share price movements. They are fundamental approach and technical approach. Both these approaches have the same objective of buying at lower price and selling at a higher price to gain good return on investment. It can be said that the end goal of these two methods are one and the same. However, there exists vast difference between the fundamental concepts of these two methods. In fundamental analysis the analyst would be concerned with the fundamental factors. He would be interested in determining the true worth or intrinsic value of a share based on its current and future earning capacity. They would buy the share when its market price is below its intrinsic value. The term "Technical Analysis'' is a general heading for myriad of trading techniques. Technical analysis attempts to forecast future prices by the study of past prices and a few other related summary statistics about security trading. A technical analyst is always concerned with the direction of price movements. The aim of this study is to evaluate technical analysis from Indian perspective and to find out its usefulness in Indian stock market.