The maturity structure of term premia with time-varying expected returns

This paper analyzes the maturity structure of term premia using McCulloch’s US Treasury yield curve data from 1953–91, allowing expected returns to vary across time. One, 3, 6, and 12 month holding period returns on maturities up to 5 years are projected on 3 ex ante variables to compute time-varying expected returns, and simulations are employed to generate distributions of conditionally expected return premia. The likelihood of expected returns monotonically increasing in maturity (as implied by the liquidity preference hypothesis) is relatively high when the yield curve is steep and interest rates are high, and with longer holding periods, but low in other cases. The hypothesis that intermediate maturity bonds have the highest expected returns (a “hump-shaped” maturity-return pattern) around the onset of recessions does not receive much support.     

The day of the week effect on stock market volatility

This study tests the presence of the day of the week effect on stock market volatility by using the S&P 500 market index during the period of January 1973 and October 1997. The findings shown that the day of the week effect is present in both volatility and return equations. While the highest and lowest returns are observed on Wednesday and Monday, the highest and the lowest volatility are observed on Friday and Wednesday, respectively. Further investigation of sub-periods reinforces our findings that the volatility pattern across the days of the week is statistically different.(JEL G10, G12, C22)     

Day-of-the-week effect on the VIX. A parsimonious representation

The study of significant deterministic seasonal patterns in financial asset returns is of high importance to academia and investors. This paper analyzes the presence of seasonal daily patterns in the VIX and S&P 500 returns series using a trigonometric specification. First, we show that, given the isomorphism between the trigonometrical and alternative seasonality representations (i.e., daily dummies), it is possible to test daily seasonal patterns by employing a trigonometrical representation based on a finite sum of weighted sines and cosines. We find a potential evolutive seasonal pattern in the daily VIX that is not in the daily S&P 500 log-returns series. In particular, we find an inverted Monday effect in the VIX level and changes in the VIX, and a U-shaped seasonal pattern in the changes in the VIX when we control for outliers. The trigonometrical representation is more robust to outliers than the one commonly used by literature, but it is not immune to them. Finally, we do not find a day-of-the-week effect in S&P 500 returns series, which suggests the presence of a deterministic seasonal pattern in the relation between VIX and S&P 500 returns.     

Monday returns and asset pricing

The seasonal patterns observed on Monday stock returns are still unexplained by different asset pricing models. We attempt to fill this gap in the finance literature by using the Fama-French (Journal of Financial Economics 33:3–56, 1993) risk factors to explain the Monday seasonal. The results in the study show that Monday returns are explained by risk factors such as the market return, the size of the firms, and the book-to-market ratios of firms.     

Return and Risk Premium Seasonalities in three Emerging Asian Markets: Hong Kong,Korea and Taiwan*

In this study, we find that seasonal return patterns differ from that implied by risk premiums in three emerging Asian markets; namely, Hong Kong, Korea and Taiwan. Positive January seasonal returns are found in the Hong Kong and Taiwan markets, while positive February seasonal returns are also found in Taiwan. These findings suggest that investors should place their money in these markets during January but not for the months of June and December in Korea, and for the months of May and November in Taiwan. Corporate managers should also be aware of the need to adjust for such seasonal variations when they use market data to evaluate the risk premium or required rate of return for projects in these markets. The results also show that the size effect may also be priced in some of these markets.     

The yearend effect in stock returns over business cycles: A technical note

This paper provides evidence in support of the claim that the well-knownJanuary effect is influenced by the stage of the business cycle. Using monthly data for the S&P Composite Index for the period from November 1948 through December 1988 and the standard methodology for seasonal anomalies, the authors show that theJanuary effect is present during the entire period examined as well as in the expansionary phases of that period. However, its existence was not detected during the contractionary phases of that period.     

Top performing banks: the benefits to investors

In this study, we examine whether superior accounting performance as reported in the annual ABA Banking Journal Top Performing Banks survey translates into higher investor returns. We observe that the announcement effect is more pronounced during the early years of the survey. For the entire survey period and for later sub-periods in which bank holding companies (BHCs) are ranked based on return on equity (ROE), we observe statistically-significant superior holding period returns against both the S&P 500 index and in some cases a matched sample. These results include raw and risk-adjusted returns as well as buy and hold abnormal returns (BHARs). We obtain similar results after controlling for the market return, size, book-to-market ratio, and momentum factors.     

Jumps in cross‐sectional rank and expected returns: a mixture model

We propose a new nonlinear time series model of expected returns based on the dynamics of the cross‐sectional rank of realized returns. We model the joint dynamics of a sharp jump in the cross‐sectional rank and the asset return by analyzing (1) the marginal probability distribution of a jump in the cross‐sectional rank within the context of a duration model, and (2) the probability distribution of the asset return conditional on a jump, for which we specify different dynamics depending upon whether or not a jump has taken place. As a result, the expected returns are generated by a mixture of normal distributions weighted by the probability of jumping. The model is estimated for the weekly returns of the constituents of the SP500 index from 1990 to 2000, and its performance is assessed in an out‐of‐sample exercise from 2001 to 2005. Based on the one‐step‐ahead forecast of the mixture model we propose a trading rule, which is evaluated according to several forecast evaluation criteria and compared to 18 alternative trading rules. We find that the proposed trading strategy is the dominant rule by providing superior risk‐adjusted mean trading returns and accurate value‐at‐risk forecasts. Copyright © 2008 John Wiley & Sons, Ltd.     

Weekly pattern in higher moments: An empirical test in Hong Kong stock market

This paper provides a direct test on the day-of-the-week effect on higher moments of stock returns and compares across different industrial sectors of the Hong Kong market. Empirical results show that daily returns of six different industrial sectors on all weekdays are non-normally distributed. The hypothesis of equal higher moments is rejected by most pairs of weekdays, particularly the Monday-Tuesday pair, for all indices, supporting the existence of the day-of-the-week effect on higher moments. The results also show that the weekly pattern on volatility and higher moments cannot help explain the weekly pattern on mean returns through the concept of risk premium. Further analysis shows that Rogalski’s effect exists on the higher moments because the day-of-the-week effect exists only in non-January months.     

Stable seasonal pattern models for forecast revision: A comparative study

Many firms prepare forecasts at the beginning of each financial quarter that predict total sales over the upcoming quarter. Such forecasts may be used to make financial projections, or to plan manufacturing capacity and materials purchases. As weekly sales are recorded during the quarter, these quarterly forecasts are often revised, allowing plans and projections to be adjusted appropriately. A formal basis for these forecast revisions may be found in so-called stable seasonal pattern models, which are based on the observation that in many instances, the sales that accrue during a given period of a quarter follow a regular pattern. This paper discusses a number of stable seasonal pattern models – several from the literature, two that are novel – which have been evaluated for making forecast revisions at Sun Microsystems, Inc. Commonalities between the models are elucidated using a general theoretical framework, and a straightforward sample-based mechanism is described that affords great flexibility in the design and use of stable seasonal pattern models. The paper culminates in a detailed comparison of the performance of new and existing stable seasonal pattern models with respect to Sun's sales data.     

The impact of the Financial Institutions Reform, Recovery, and Enforcement Act (FIRREA) on the value of S&L stocks

This study examines the effects of the Financial Reform, Recovery, and Enforcement Act of 1989 on the stock returns to shareholders of publicly traded savings and loans (S&Ls). Abnormal returns to stockholders are measured in response to each new piece of information concerning the passage of the Act. Using weekly data to have the largest possible sample, we found two significant time periods: a) the initial announcement of the Act and b) the time of passage and signing of the Act into law. We also provide evidence that stock return behavior differed between large and small S&Ls.     

Load and no-load mutual fund dynamics during the 1987 market crash: A stochastic dominance analysis

Using a large sample of equity mutual fund returns, we compare performance of load and no-load funds during the 1987 crash. Differences in return distributions, particularly in the higher moments when the market was under stress, suggest a greater use of portfolio insurance by no-load fund managers. Using stochastic dominance, we find that load and no-load funds performed equally well before the crash. No-load returns dominated load fund returns during the crash. Load fund returns dominate after the crash. Over the entire month, no-load funds dominate. We attribute this to investor behavior motivated by the lack of a front-end load.     

Idiosyncratic risk,market risk and correlation dynamics in the US real estate investment trusts

This study examines total, market and idiosyncratic risk and correlation dynamics using weekly return data on two US REIT firm samples from 1988 to 2008. We find that both market and idiosyncratic variance are time-varying and that idiosyncratic variance represents a dominant component of a REIT firm’s total variance. We find a decline in idiosyncratic risk as well as a rise in average REIT correlation during the new REIT era, from 1993 to 2008. This recent downward trend of idiosyncratic risk among REITs is different to the stylized upward trend of idiosyncratic risk among stocks. There is bi-lateral Granger causality between the market and idiosyncratic risks. Finally, we detect a positive relationship between the idiosyncratic risk and expected returns, implying that the risk premium of REITs is positively related to the idiosyncratic risk during the period new REIT era, 1993–2008. Our results have important asset-pricing implications for under-diversified investors.     

Autocorrelation,return horizons,and momentum in stock returns

In this study we examine Lewellen’s (Rev Financ Stud 15:533–563 2002) claim that momentum in stock returns is not due to positive autocorrelation as behavioral models suggest. Using portfolio-specific data, we find the autocovariance component of the momentum profit to be negative, suggesting no return continuations. However, we also find that the autocorrelations calculated from short-term (e.g., monthly) returns are quite different from long-horizon (e.g., annual) autocorrelations. While the first-order autocorrelations of 6– and 12-month returns tend to be negative, the autocorrelations across twelve lags in monthly returns of the industry, size, and B/M portfolios are in general positive. Our results show that these portfolios exhibit return continuations when returns are measured on a monthly basis. Therefore, our finding appears to be consistent with the behavioral models, which suggest positive autocorrelation in stock returns.     

A reexamination of the market efficiency hypothesis: Evidence from an electronic intra-day,inter-dealer FX market

Researchers acknowledge that the evidence of autocorrelation (price dependency) in daily/weekly asset returns provides no conclusive evidence against the market efficiency hypothesis since the holding period of actual speculative positions may be less than a day. Using a high frequency (up to one hundredth of a second), transaction-based, electronic foreign exchange (FX) brokerage data set, we show that dealers in this market tend to close their speculative positions in less than a minute. We provide evidence that there is a significant negative autocorrelation in the rate of return on DM/USD exchange rate. However, when we sample data at frequencies shorter than a minute, profits are infeasible for two reasons: (1) the structure of the autocorrelation pattern is not consistent enough; (2) the largest potential speculative profit derived from the autocorrelation pattern is smaller than the regulated tick size. Our results support the market efficiency hypothesis as dealers have evidently engaged potentially profitable speculation based on price dependency.     

How far can changes in general business activity be forecasted?

An F test [Nelson (1976)] of Parzen's prediction variance horizon [Parzen (1982)] of an ARMA model yields the number of steps ahead that forecasts contain information (short memory). A special 10 year pattern in Finnish GDP is introduced as a ‘seasonal’ in an ARMA-model. Forecasts three years ahead are statistically informative but exploiting the complete 10 year pattern raises doubts both about model memory and model validity.     

The expected real return to equity

The expected return to equity – typically measured as a historical average – is a key variable in the decision making of investors. A recent literature uses analysts' forecasts, investor surveys or present-value relationships and finds estimates of expected returns that are sometimes much lower than historical averages. This study extends the present-value approach to a dynamic optimizing framework. Given a model that captures this relationship, one can use data on dividends, earnings and valuations to infer the model-implied expected return. Using this method, the estimated expected real return to equity ranges from 4.9% to 5.6% . Furthermore, the analysis indicates that expected returns have declined by about 3 percentage points over the past 40 years. These results indicate that future returns to equity may be lower than past realized returns.     

Positive IVOL-MAX effect: A study on the Singapore Stock Market

This paper demonstrates a positive and significant IVOL effect in the Singapore Stock Market meaning that the highly volatile stocks are showing better returns in the subsequent month. More explicitly, there is a strong positive relationship between stock’s idiosyncratic volatility (IVOL) and its subsequent month’s return in the Singapore equity market. This positive IVOL effect is stronger only for small market-statistic firms. But for the Large capital firms, the positive IVOL effect is insignificant. In addition, this paper shows that the relationship between maximum daily return over a month (MAX) and the subsequent month’s return is positive and significant in this market. However, IVOL is the true effect of this market rather than MAX.     

Loss from the chasing of MAX stocks: Evidence from China

We evaluate the implications of the MAX effect in the Chinese financial market. First, the MAX effect prevails in China: A zero-cost MAX strategy, which goes long (short) stocks with the highest (lowest) maximum daily return in the prior month, generates significant losses over the full sample period. Second, further analysis on firm characteristics confirms that the MAX stocks exhibit lottery-like features, and the (negative) performance of the MAX strategy varies over time and is related to investor sentiment. Third, the MAX effect gets weaker after the introduction of short-selling in 2010. Finally, we document that there exists a reversed MAX effect among mutual funds, because a similarly implemented MAX strategy generates significant positive risk-adjusted returns among equity funds in China.     

Market efficiency, discount-rate changes, and stock returns: A long-term perspective

This paper examines the market efficiency issue by analyzing stock returns surrounding Fed announcements of discount-rate changes. Based on an analysis ofex post returns over a 58-year period, the results provide evidence of long-term market efficiency. Consistent with recent literature, the findings also reveal some predictability in return patterns where an active trading strategy based on directional reversals in the pattern of discount rate changes outperforms a passive buy-and-hold approach. The results indicate that the proposed active trading produces substantially higher risk-adjusted returns than the buy-and-hold strategy.