Risks and rewards for momentum and reversal portfolios

Rational asset pricing implies a positive relation between the expected risk-adjusted return and the volatility of a factor-mimicking portfolio. The relation for the momentum portfolio is weak after its return is adjusted for the risks associated with the market return, the size factor, and the book-to-market factor. However, the relation is significantly positive and captures most of the average return on the momentum portfolio after the return is adjusted for the market return and the risk associated with the short-term reversal portfolio return. The result supports the hypothesis that there is a common factor underlying both momentum and short-term reversal. The dynamics of the factor loadings and the correlation structure of the underlying factors have important implications for the risk prices associated with the factor-mimicking portfolios and the risk–return trade-off for momentum and reversal portfolios.     

Using Expectations to Test Asset Pricing Models

Asset pricing models generate predictions relating assets' expected rates of return and their risk attributes. Most tests of these models have employed realized rates of return as a proxy for expected return. We use analysts' expected rates of return to examine the relation between these expectations and firm attributes. By assuming that analysts' expectations are unbiased estimates of market-wide expected rates of return, we can circumvent the use of realized rates of return and provide evidence on the predictions emanating from traditional asset pricing models. We find a positive, robust relation between expected return and market beta and a negative relation between expected return and firm size, consistent with the notion that these are risk factors. We do not find that high book-to-market firms are expected to earn higher returns than low book-to-market firms, inconsistent with the notion that book-to-market is a risk factor.     

Guaranteed Investment Contracts: Distributed and Undistributed Excess Return

Annual minimum rate of return guarantees are analyzed together with rules for distribution of positive excess return, i.e. investment returns in excess of the guaranteed minimum return. Together with the level of the annual minimum rate of return guarantee both the customer's and the insurer's fractions of the positive excess return are determined so that the market value of the insurer's capital inflow (determined by the fraction of the positive excess return) equals the market value of the insurer's capital outflow (determined by the minimum rate of return guarantee) at the inception of the contract. The analysis is undertaken both with and without a surplus distribution mechanism. The surplus distribution mechanism works through a bonus account that serves as a buffer in the following sense: in (‘bad’) years when the investment returns are lower than the minimum rate of return guarantee, funds are transferred from the bonus account to the customer's account. In (‘good’) years when the investment returns are above the minimum rate of return guarantee, a part of the positive excess return is credited to the bonus account. In addition to characterizations of fair combinations of the level of the annual minimum rate of return guarantee and the sharing rules of the positive excess return, our analysis indicates that the presence of a surplus distribution mechanism allows the insurer to offer a much wider menu of contracts to the customer than without a surplus distribution mechanism.     

Rates of Return on Art Objects, the Fisher Hypothesis, and Inflationary Expectations

After surveying the evolution of the major methodologies in inflation hedging, this study presents a unique methodology that uses principal component factor analysis to separate the effects of variability in the real rate of return from the nominal rate of return. This approach allows the effects of both anticipated and unanticipated inflation on rates of return to be estimated more precisely. This study finds that art objects perform well in terms of average real rates of return and that the market, though not perfect, integrates anticipated inflation into the rates of return. However, unanticipated inflation is very often negatively related to the rates of return.     

Firm-level return dispersion and the future volatility of aggregate stock market returns

We find a sizeable positive relation between firm return dispersion and future market-level volatility in U.S. monthly equity returns from 1927 to 1995. This intertemporal relation remains strong when controlling for return shocks in the aggregate stock market, widely used factor-mimicking portfolios, and government bonds. In contrast, the well-known positive relation between market-return shocks and future market-level volatility largely disappears when controlling for firm return dispersion. We also document how firm return dispersion moves with the contemporaneous market return and with economic conditions. Collectively, our evidence suggests that the time variation in firm return dispersion has important market-wide implications.     

Economic rates of return: an extension

This paper extends the cash-based rate of return models of Ijiri, 1978, Salamon, 1982 by allowing two classes of assets and three classes of sources of funds. The use of debt as a source of funds permits a return on equity calculation. Subsequently, the return on equity calculation is modified to obtain a return on total assets profitability measure. These extensions lead to a solution of the cash-flow observability problem first noted by Stark (1987). A second analytical result shows an equivalence between the accounting rate of return and the estimated rate of return developed in this paper if a particular cash-flow pattern is used.     

Asymmetric return dynamics and technical trading strategies

We investigate the profitability of technical trading strategies based on an asymmetric reverting property of stock returns. We identify an asymmetry in return dynamics for daily returns on the S&P 500 index. Return dynamics evolve along a positive (negative) unconditional mean after a prior positive (negative) return. The trading strategies based on this asymmetry generate a positive return for buy signals, a negative return for sell signals, and a positive return for the spread between buy and sell signals. Our results imply that the observed asymmetry in return dynamics is the main source of profitability for the implied strategies, thereby corroborating arguments for the usefulness of technical trading strategies.     

How to choose the return model for market risk? Getting towards a right magnitude of stressed VaR

Value at Risk (VaR) and stressed value at Risk (SVaR) or expected shortfall are important risk measures widely used in the financial services industry for risk management and market risk capital computation. Fundamental to any (S)VaR model is the choice of the return type model for each risk factor. Because the resulting SVaR numbers are highly sensitive to the chosen return type model it is important to make a prudent choice on the return type modelling. We propose to estimate the return type model from historic data without making an a priori model assumption on the return model. We explain the fundamentals of return type modelling and how it impacts the magnitude of SVaR. We further show how to obtain a global return type model from a set of similar return type models by using geometric calculus. Numerical simulations and illustrations are provided. In this paper, we consider interest rate data, but the proposed methodology is general and can be applied to any other asset class such as inflation, credit spread, equity or fx.     

Return dispersion and expected returns

This paper proposes a two-factor asset-pricing model that incorporates market return and return dispersion. Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns. The return dispersion model outperforms the CAPM, MVM, IVM, and FF-3M when using a set of 5×5 test portfolios constructed from NYSE and AMEX stock returns from August 1963 to December 2005. Return dispersion continues to play an important role in explaining the cross-sectional variation of expected returns, even when market volatility, idiosyncratic volatility, size, book-to-market factors, and a momentum factor are included. This study sheds some light on the ability of return dispersion to explain expected returns beyond the standard asset-pricing factors. Our finding suggests that return dispersion captures two dimensions of systematic risk: the business cycle and fundamental economic restructuring.     

THE EX-POST INTERNAL RATE OF RETURN: WHO NEEDS IT?*

The relationship between the accounting rate of return and the internal rate of return has been the subject of quite a number of articles. In many of these, it has been assumed that the ex post internal rate of return represents a desirable measure of performance, and efforts have been made to deduce its value from accounting rates of return. The present paper draws attention to some limitations of the ex post internal rate of return and throws doubt on its usefulness as a measure of performance.     

Economic and Accounting (Book) Rates of Return: Application of a Statistical Model

Harcourt's (1965) classic paper has spawned a considerable literature dealing with the relationship between economic and accounting rates of return. Kay (1976), Ijiri (1979), Salamon (1982) and Kelly and Tippett (1991), for example, can be interpreted as extensions of Harcourt's seminal analysis, while Kay (1976), Salamon (1982, 1985) and Gordon and Hamer (1988) provide empirical evidence on the sustainability of basic propositions. The present paper's focus is on the latter area; we apply the statistical procedures laid down in Kelly and Tippett (1991) to about 200 British companies to assess the correspondence between the ex post accounting rate of return and the prospective economic return. The economic return is estimated using three cash flow definitions. For all three, the accounting rate of return is significantly lower than the economic return. Further tests show the economic return to be inversely related to the accounting rate of return, although the relationship is weak. In addition, ‘large’ firms tend to report lower accounting rates of return than ‘small’ firms, but again the relationship is weak.     

Is asymmetric mean-reverting pattern in stock returns systematic? Evidence from Pacific-basin markets in the short-horizon

This paper applies asymmetric nonlinear smooth transition generalized autoregressive conditional heteroskedasticity (ANST-GARCH) models to the analysis of mean-reversion and time-varying volatility in weekly index returns of the stock markets of nine countries in the Pacific-basin. It finds that the returns exhibit an asymmetric pattern of return reversals, viz., on average, a negative return reverts more quickly, with a greater magnitude, to a positive return than a positive return reverting to a negative one. The asymmetric pattern of return reversals is directly associated with the unequal pricing behavior on the part of investors. Following a negative return shock, investors do not appear to require any additional premium to the leverage effect; instead they actually neutralize the risk in the form of a reduced premium! The reduction in risk premium causes not only the current stock price to rise but also the realized negative return to revert faster with a greater magnitude.     

考虑流动性的三阶矩资本资产定价的理论模型与实证研究

本文把流动性风险、偏态风险引进传统CAPM模型中,推导出基于流动性的三阶矩资本资产定价的理论模型。本文的模型表明,证券(组合)的收益依赖于它的期望流动性成本、其流动性成本和市场流动性成本的协方差以及其收益和市场收益的协方差与协偏态。本文采用我国A股市场的股票收益数据对模型进行了实证检验.检验结果表明,我国A股市场的证券(组合)的风险溢价在大盘升降区间体现了不同的特征,无论是在全样本区间还是两个子样本区间,基于流动性的三阶矩资本资产定价模型都能更好的拟合资产收益,说明了流动性和偏态因素在我国A股市场的资产定价中有重要影响。     

A note on the impact of options on stock return volatility

This paper measures the impact of option introductions on the return variance of underlying stocks. Past research generally finds a significant reduction in stock return variance following the listing of options through 1986. Using a more extensive sample, I compare changes in the return variance of optioned stocks to changes in the return variance of a control group. Since the average change in the control group is statistically indistinguishable from the average change in the optioned stocks, I conclude that option introductions do not significantly affect stock return variance.     

On the risk return relationship

While the risk return trade-off theory suggests a positive relationship between the expected return and the conditional volatility, the volatility feedback theory implies a channel that allows the conditional volatility to negatively affect the expected return. We examine the effects of the risk return trade-off and the volatility feedback in a model where both the return and its volatility are influenced by news arrivals. Our empirical analysis shows that the two effects have approximately the same size with opposite signs for the daily excess returns of seven major developed markets. For the same data set, we also find that a linear relationship between the expected return and the conditional standard deviation is preferable to polynomial-type nonlinear specifications. Our results have a potential to explain some of the mixed findings documented by previous studies.     

On the anticipation of IPO underpricing: Evidence from equity carve-outs

We investigate IPO market efficiency using a sample of equity carve-outs offered during the period of 1985–2005. Unlike IPOs examined in previous studies where trading during the pre-IPO book-building period does not exist and trading on the IPO date is rationed, in equity carve-outs, investors can trade in the non-rationed market for shares of the parent, which holds a significant fraction of the subsidiary. We find that the subsidiary's initial day return is significantly related to its parent's return over the book-building period, but unrelated to its parent's contemporaneous return. Neither the pre-IPO price revision of the subsidiary nor the return to the parent on the initial trading day can be predicted. While the portion of the subsidiary's initial return unpredictable from information available during the book-building period is significantly related to its parent's contemporaneous return, the predictable component of the initial return is not. We interpret these results as evidence consistent with market efficiency.     

Dynamic analysis of stock return volatility in an integrated international capital market

This paper examines the dynamic behavior of the stock return volatility for Canada, Japan, Germany, and the United Kingdom. The evidence indicates that international stock return volatility is mainly influenced by the U.S. stock return volatility and the exchange rate volatility, supporting the international capital market integration hypothesis. There seems to be some correlation between stock return volatility and macroeconomic volatility, but the effect is relatively weaker. In addition to the economic fundamentals, the noise component is found to be time varying, confirming the AR(MA)CH specifications in the stock return models.     

Regime switches in the risk–return trade-off

This paper deals with the estimation of the risk–return trade-off. We use a MIDAS model for the conditional variance and allow for possible switches in the risk–return relation through a Markov-switching specification. We find strong evidence for regime changes in the risk–return relation. This finding is robust to a large range of specifications. In the first regime characterized by low ex-post returns and high volatility, the risk–return relation is reversed, whereas the intuitive positive risk–return trade-off holds in the second regime. The first regime is interpreted as a “flight-to-quality” regime.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

Is international trust performance predictable over time? A note

We examine whether past return measures have any significant predictive ability for future returns of UK unit trusts with international equity objectives. We find significant positive persistence between the past return performance of the trust relative to its investment sector and future trust returns relative to its sector. This result holds at short‐run and long‐run return horizons. The persistence is stronger in trusts that perform well relative to their sector. Our findings suggest that the past return performance of the trust relative to their sector provides a useful guide to future return performance relative to their sector.