Benchmark Invariancy,Seasonality and APM-Free Portfolio Performance Measures

The Jobson-Korkie (1981) Z score and the positive period weighting (PPW) score of Grinblatt and Titman (1989) are applied to various benchmarks of market and mimicking portfolios to study the benchmark invariancy problem. Significantly different portfolio performance inferences are found for a sample of 146 equity mutual funds depending on the mean-variance efficiency of the portfolio benchmarks (mimicking portfolios versus market indices). Portfolio performance inferences are affected significantly by the number of factors, nonsynchronous trading adjustment, and the sizes of the firms used for factor extraction. The returns of the portfolio benchmarks exhibit significant monthly seasonalities, which, in turn, significantly influence mutual fund performance inferences.     

Microstructure and Seasonality in the UK Equity Market

This paper explores seasonality in the UK stock market. It examines the impact of alternative company year-ends on returns as well as seasonality in bid-ask spreads and trading activity variables including volume, number and size of trades. Consistent with the evidence elsewhere, seasonal variation in stock returns and trading activity is established although there is little evidence of a seasonal pattern in relative bid-ask spreads. Trading rules based on the seasonal patterns do not suggest that seasonality can be exploited to earn excess profits.     

Seasonality, Market Timing and Performance Amongst Benchmarks and Mutual Fund Evaluation

Abstract:  Mutual fund performance is normally measured by comparing results of active management with those obtained by one or several benchmarks that should represent the fund's investment. In this context, this paper examines the effect on mutual fund assessment if a relevant benchmark is omitted. This effect is analysed in three elements of active management: stock selection, market timing, and seasonality. The latter is defined as fund management at specific moments of time with the objective of achieving positive abnormal returns to improve performance. For a sample of Spanish mutual funds, we find that the omission of style benchmarks, particularly that corresponding to small-cap stocks, leads to greater evidence of negative market timing and positive seasonality at year beginning. However, the positive abnormal returns of the seasonality at year end, month end and especially at the beginning of July hold regardless of benchmark omission. The paper therefore also analyses the relation between performance and seasonality, finding that positive seasonality at year beginning and at July beginning improves performance; however, at other moments it implies a possible window dressing strategy in mutual fund management.     

期货交易活跃度、波动性与投资者结构关系的实证研究

本文将期货市场上的投资者分为个人与机构两类,基于白糖、优质强筋小麦和PTA三个期货品种的样本数据,探讨了交易结构和持仓结构对期货交易活跃度和期货价格波动性的影响规律。研究结果表明:期货交易活跃度主要受交易结构的影响,而期货价格波动大小主要受持仓结构的影响;根据最优的弹性二次模型结果,两者的弹性均呈现出边际递减的经济学变化规律。     

Seasonality in Fund Performance: An Examination of the Portfolio Holdings and Trades of Investment Managers

Abstract:  This study examines the extent to which seasonal variation arises across calendar months in the performance of active Australian equity managers. While it is well documented that there is seasonality in equity market returns, it is unknown whether calendar month variation in managed fund performance exists. Employing a unique database of monthly stock holdings, we find evidence consistent with systematic variation in the risk-adjusted performance of active investment managers over the calendar year. Specifically, we find fund performance is higher in the months when corporate earnings are announced. We also document that the performance of fund managers is lower in the months preceding the tax year-end. Finally, we report evidence that investment manager performance is greater than normal in December, possibly due to both window dressing and the Christmas holiday effect. These findings have important implications for investors attempting to exploit anomalies in fund returns by timing their entry and exit points from active equity funds.     

A State‐Price Volatility Index for China's Stock Market

This study derives a volatility index for China's stock market with similar properties to the Chicago Board Options Exchange Volatility Index (the ‘VIX’). A long‐term benchmark of historic volatility expectations is here presented for China from 1996 to 2011, called the ‘China‐ State‐Price Volatility (SPV)’. Construction of this index involves the use of SPV methodology, using implied volatility calculated from options on the Hang Seng China Enterprise Index (HSCEI). Historic open–high–low–close volatility on the Shanghai Composite Index (SHCI) is also used to extend the benchmark prior to the availability of HSCEI options data. The China‐SPV successfully forecasts realised volatility for the Shanghai Stock Exchange. It also serves as a ‘fear gauge’ in that it monitors daily movements of the SHCI in the same way that the VIX monitors the S&P 500 index (Whaley, 2009). The China‐SPV evidences an increasing relation with the US market in terms of the dynamic correlation of levels and changes with the VIX since 2004.     

中国股市开、收盘波动性的比较研究

利用EGARCH模型,对2000年1月至2007年4月间沪深两市具有代表性的股票及指数的开收盘收益率的波动性进行实证分析,结果表明收益率序列有明显的ARCH效应,其波动性具有显著的非对称性的冲击的持续性;在样本期内,上交所的个股和指数未能观察到开盘波动性高于收盘波动性的现象,而深交所个股在2006年7月实施收盘集合竞价机制之后比较明显地观察到开盘波动性高于收盘波动性的现象。     

Sequential Learning,Predictability, and Optimal Portfolio Returns

This paper finds statistically and economically significant out‐of‐sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. Investors must account for estimation risk, and incorporate an ensemble of important features, including time‐varying volatility, and time‐varying expected returns driven by payout yield measures that include share repurchase and issuance. Prior research documents a lack of benefits to return predictability, and our results suggest that this is largely due to omitting time‐varying volatility and estimation risk. We also document the sequential process of investors learning about parameters, state variables, and models as new data arrive.     

Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns

This article investigates empirically the comovements of the conditional mean and volatility of stock returns. It extends the results in the literature by demonstrating the role of the commercial paper—Treasury yield spread in predicting time variation in volatility. The conditional mean and volatility exhibit an asymmetric relation, which contrasts with the contemporaneous relation that has been tested previously. The volatility leads the expected return, and this time series relation is documented using offset correlations, short-horizon contemporaneous correlations, and a vector autoregression. These results bring into question the value of modeling expected returns as a constant function of conditional volatility.     

Ambiguous Information,Portfolio Inertia,and Excess Volatility

I study the effects of risk and ambiguity (Knightian uncertainty) on optimal portfolios and equilibrium asset prices when investors receive information that is difficult to link to fundamentals. I show that the desire of investors to hedge ambiguity leads to portfolio inertia and excess volatility. Specifically, when news is surprising, investors may not react to price changes even if there are no transaction costs or other market frictions. Moreover, I show that small shocks to cash flow news, asset betas, or market risk premia may lead to drastic changes in the stock price and hence to excess volatility.     

Stock Returns and the Business Cycle

This article examines whether movements in economic factors dictated by the dividend discount model can explain broad movements in stock returns over the business cycle. As anticipated, stock returns decrease throughout economic expansions and become negative during the first half of recessions. Returns are largest during the second half of recessions, suggesting an important role for expected earnings. These results are consistent with the notion that expected stock returns vary inversely with economic conditions, yet suggest that realized returns are especially poor indicators of expected returns prior to turning points in the business cycle.     

Conditional Volatility of Equity Real Estate Investment Trust Returns: A Pre- and Post-1993 Comparison

We examine the dynamic behavior of Equity Real Estate Investment Trust (EREIT) volatility in a GARCH context 1972–2006 using monthly EREIT returns, and comparing volatility performance for “early” Equity REITs 1972–1992 with that of “modern” EREITs 1993–2006. Consistent with findings for conventional firms, we find that EREIT conditional volatility is time-varying, persistent, and predictable. There is a positive relationship between expected return and expected risk in EREIT stocks pre-1993, but the relationship disappears after 1993. We find no evidence that negative shocks affect EREIT volatility differently from positive ones in either time period. Different from reported results for conventional firms, we find that changes in the conditional volatility of fundamental macroeconomic variables have strong explanatory value for future changes in EREIT volatility. Finally, comparing EREIT volatility performance with volatility in the Russell 2000 Index, a proxy for small stocks, we find that EREIT volatility behaves differently from that of small stocks in many respects, indicating that risks in the small stock index cannot effectively proxy for risks in the EREIT market.

Volatility and market structure

This study examines volatility within three related intra-day series – transaction returns, quote midpoint returns, and limit order book midpoint returns – for a set of NYSE-listed stocks. We document statistically significant GARCH effects both overall and surrounding earnings announcements in all three series for the majority of stocks in the sample. We then compare the extent of volatility clustering among the series. In addition, the relation between volatility and market structure is examined via a set of cross-sectional regressions, and relations among the series over time are studied in a vector autoregressive framework.     

国际资本流入的易变性及其对策研究

国际资本流入的易变性是影响一国金融稳定的主要因素,主要表现在国际资本在短期内过度流入,并在特定环境下迅速逆转。本文采用1980-2008年78个国家和地区的数据,运用变异系数法对国际资本的三种类型(直接投资、组合投资和其他投资)的易变性分别进行了测算和比较,并采用广义的自回归条件异方差(GARCH)模型,对易变性的决定因素进行了面板数据检验。结果表明,组合投资和其他投资的易变性要显著大于直接投资,经济增长率、制度质量对FDI易变性产生影响,而国内金融体系结构、汇率波动和通货膨胀等则造成各国非FDI资本易变性的差异。这一发现对各国资本流动管理具有重要启示。     

Is the Market Portfolio a Dynamic Factor? Evidence from Individual Stock Returns

This paper uses a factor model to test whether the market portfolio is a dynamic factor in the sense that individual stock returns contain a premium linked to the conditional risk of the market portfolio. The market conditional risk is based on a decomposition of the market variance into a time-varying trend component and a transitory component. The evidence shows that the conditional market premium is rising when the permanent trend rises relative to the conditional variance. The evidence for individual stock returns supports the notion that the market portfolio is a dynamic factor. Individual stock return autocorrelations are fully explained by the time variation in the market premium. The risk premia attributed to static factors are statistically insignificant.     

Foreign Ownership and Volatility Dynamics of Indonesian Stocks

This note explores how foreign ownership and participation affect the volatility dynamics of individual stocks in Indonesia. After controlling for size and turnover, we show that stocks with high foreign holdings have greater volatility persistence and lead other stocks in the daily volatility changes. The finding holds during and after the Asian financial crisis, and is consistent with domestic investors mimicking foreign trading.      

The Cross-Section of Volatility and Expected Returns

We examine the pricing of aggregate volatility risk in the cross‐section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book‐to‐market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.     

Idiosyncratic Volatility Covariance and Expected Stock Returns

Given that the idiosyncratic volatility (IDVOL) of individual stocks co‐varies, we develop a model to determine how aggregate idiosyncratic volatility (AIV) may affect the volatility of a portfolio with a finite number of stocks. In portfolio and cross‐sectional tests, we find that stocks whose returns are more correlated with AIV innovations have lower returns than those that are less correlated with AIV innovations. These results are robust to controlling for the stock's own IDVOL and market volatility. We conclude that risk‐averse investors pay a premium for stocks that pay well when AIV is high, consistent with our model.     

Strategic International Diversification and Monthly Seasonality

To the author's knowledge no other studies have dealt with the effect of international diversification on stock market monthly seasonality. The aim of this study is to investigate this effect in various ways: stock market monthly seasonality is analyzed by incorporating exchange rates and trading costs in international portfolio returns. The variance of the world portfolio is decomposed into six components. Stochastic dominance approach is used to show the robustness of the results. Five trading strategies are compared to help international investors be more informed. All the results show that monthly seasonality is clearly present in an economic sense and robust. Particularly, when exchange rates are incorporated into portfolio returns. January has the highest return and the lowest risk in the world portfolio.