Probability of information-based trading and the January effect

We investigate the seasonality in the probability of information-based trading (PIN)–return relationship, the ‘January PIN effect’. We find that on average stock returns decrease with PIN in January, in contrast to other calendar months. This pattern is more apparent for small stocks. We argue that this seasonality is related to the January effect. According to the analysis, the December selling pressure associated with the January effect decreases in PIN, especially for small stocks. This suggests that when the price bounces back in January, low-PIN stocks will exhibit a larger return within a small stock group, leading to the negative PIN–return pattern. Furthermore, this seasonality is not the same as other January anomalies associated with momentum and idiosyncratic risk.     

Contrarian and momentum profitability revisited: Evidence from the London Stock Exchange 1964–2005

We provide evidence relating to contrarian and momentum profits for the LSE, using 64 strategies for all 6531 stocks traded from 1964 to 2005. Thorough analysis demands controlling for key potential (contradictory) explanations of the strategies’ profitability which span psychological characteristics (e.g. overreaction/underreaction), excess risk, seasonality, size, and microstructure induced biases. Results provide a measurement of the miscalculations which occur when ignoring survivorship and microstructure biases. Contrarian/momentum profits cannot be explained by seasonality, size, or a single factor risk model. However, the Fama–French three factor model rationalises all contrarian profits. Important differences are found when examining a truncated sample period demonstrating the need to recognise that financial markets can change markedly through time.     

RISK-RETURN SEASONALITY AND MACROECONOMIC VARIABLES

In this paper I examine seasonality in the pricing of the Chen, Roll, and Ross (1986) macroeconomic variables. January seasonality is observed in the risk premia from 1932 to 1990. Examination of the 1932–57 period indicates nonstationary seasonals in the risk premia. The results cannot be attributed to the previously documented firm size effect. Residual risk is priced, and a strong January seasonality is observed in the residual risk premia. The market portfolio is not priced in the presence of the other variables. Trading volume is not priced in the individual months, but appears significant when all months are considered together.     

Do industries matter in explaining stock returns and asset-pricing anomalies?

Industry returns cannot be explained fully by well-known asset pricing models. This study reveals that common factors extracted from industry returns carry significant risk premiums that go beyond the explanatory power of size, book-to-market (BM) ratios, and momentum. In particular, this study shows that (1) the small-firm effect is significant only for firms whose market capitalization is below their industry average; (2) the BM effect is an intra-industry phenomenon; (3) a one-year momentum effect is significant only for firms whose BM ratio is smaller than the industry average and limited to non-January months; and (4) there is seasonality in all effects that cannot be explained by risk-based asset-pricing models. Neither rational nor behavioral theories alone can explain industry returns, and it is perhaps too hasty to attribute asset pricing anomalies to a single driving force.     

The critical role of conditioning information in determining if value is really riskier than growth

Conditional asset pricing models have been used to determine whether the value premium and other CAPM anomalies are due to risk. We show that the conclusions on whether these anomalies are due to risk are very sensitive to the choice of the information variables used to define good and bad states of the world. We use a conditional CAPM framework allowing for alternative sets of plausible conditioning information and find that value appears to be riskier than growth in only about ten to twenty percent of specifications. We find even less evidence that size, issuance, momentum, and asset growth portfolio returns are due to risk. Overall, our results suggest that common CAPM anomalies are not due to risk.     

Idiosyncratic Cash Flows and Systematic Risk

We show that unpriced cash flow shocks contain information about future priced risk. A positive idiosyncratic shock decreases the sensitivity of firm value to priced risk factors and simultaneously increases firm size and idiosyncratic risk. A simple model can therefore explain book‐to‐market and size anomalies, as well as the negative relation between idiosyncratic volatility and stock returns. Empirically, we find that anomalies are more pronounced for firms with high idiosyncratic cash flow volatility. More generally, our results imply that any economic variable correlated with the history of idiosyncratic shocks can help to explain expected stock returns.     

Seasonality in stock returns and volatility: The Ramadan effect

Calendar anomalies in stock returns are well documented. Less obvious is the existence of seasonality in return volatility associated with moving calendar events such as the Muslim holy month of Ramadan. Using a GARCH specification and data for the Saudi Arabian stock market – now the largest stock market in the Muslim world – this paper documents a systematic pattern of decline in volatility during Ramadan, implying a predictable variation in the market price of risk. An examination of trading data shows that this anomaly appears to be consistent with a decline in trading activity during Ramadan. Evidence of systematic decline in volatility during Ramadan has significant implications for pricing of securities especially option-like products and asset allocation decisions by investors in the Islamic countries.     

January Seasonality in Preferred Stocks

This study documents the existence of January season-ality in preferred stocks. January seasonality is found in all grades of preferred stock while stronger summer returns are also observed in low-grade preferred, which resembles findings reported in common equity. Although January seasonality is also found in high-grade preferred stocks, a size effect-which is used to explain the January seasonality in low-grade bonds-may not be able to account for the same seasonality observed in high-grade preferred.     

Return Seasonalities

A strategy that selects stocks based on their historical same‐calendar‐month returns earns an average return of 13% per year. We document similar return seasonalities in anomalies, commodities, and international stock market indices, as well as at the daily frequency. The seasonalities overwhelm unconditional differences in expected returns. The correlations between different seasonality strategies are modest, suggesting that they emanate from different systematic factors. Our results suggest that seasonalities are not a distinct class of anomalies that requires an explanation of its own, but rather that they are intertwined with other return anomalies through shared systematic factors.     

Market liquidity risk factor and financial market anomalies: Evidence from the Chinese stock market

The Chinese stock market is an order-driven market and hence its characteristics are structurally different from quote-driven markets. There are no studies that consider the role of the market liquidity risk factor in determining cross-sectional stock returns in a model including financial market anomalies for order-driven markets. Our aim is to test whether financial market anomalies such as firm size, the book-to-market ratio, the turnover rate, and momentum both with and without the inclusion of the market liquidity risk factor in the case of the Chinese stock market can explain cross-sectional stock returns. The empirical framework is based on the model proposed by Avramov and Chordia (AC, 2006). Our main finding is that the AC model can capture financial market anomalies except momentum when we include the market liquidity risk factor on the Chinese stock market.     

Can tail risk explain size,book‐to‐market,momentum, and idiosyncratic volatility anomalies?

We examine the impact of tail risk on the return dynamics of size, book‐to‐market ratio, momentum and idiosyncratic volatility sorted portfolios. Our time‐series analyses document significant portfolio return exposures to aggregate tail risk. In particular, portfolios that contain small, value, high idiosyncratic volatility and low momentum stocks exhibit negative and statistically significant tail risk betas. Our cross‐sectional analyses at the individual stock level suggest that tail risk helps in explaining the four pricing anomalies, particularly size and idiosyncratic volatility anomalies.     

Seasonality in the Risk-Return Relationship: Some International Evidence

We report evidence of seasonality in the Fama and MacBeth estimate of the CAPM-based risk premium in four stock exchanges: the NYSE and the London, Paris, and Brussels exchanges. Specifically, we found that, in Belgium and France, risk premia are positive in January and negative the rest of the year. There is no January seasonal in the U.K. risk premium. Instead, we observed in this country a positive April seasonal and a negative average risk premium over the rest of the year. In the U.S., the pattern of risk-premium seasonality coincides with the pattern of stock-return seasonality. Both are positive and significant only in January. We also found that the January risk premium in the U.S. is significantly larger than those observed in the European markets. Interestingly, the reported patterns of risk-premium seasonality in European equity markets do not fully coincide with the observed patterns of stock-return seasonality in these markets. For example, in the U.K., average stock returns are significant and positive in January and April, whereas the market risk premium is significantly positive only in April. A possible interpretation of this phenomenon is presented in the paper.     

Taxes,dividend yields and returns in the UK equity market

The higher rate of taxation on dividend income relative to capital gains has been offered as an explanation for the positive relation between stock returns and dividend yields among US firms. In the UK the relative tax rates are the reverse of those in the US. Thus, UK data provides an independent test of the tax-based approach to explaining the relation between stock returns and dividend yields. We find that high yielding stocks earn positive risk adjusted returns, whereas low yielding stocks earn negative risk adjusted returns. We also detect evidence of non-linearity in the performance of zero-dividend stocks. Controlling for firm size, seasonality and market risk we find a significant positive relation between dividend yields and returns. We conclude that the evidence is inconsistent with a tax-based explanation.     

The influence of intraday seasonality on volatility transmission pattern

Using data on a five-minute interval basis, this article analyses the effects of intraday seasonality on volatility transmission between the spot and futures markets of the CAC40, DAX30 and FTSE100. Remarkable differences in the impulse response analysis and in the dynamic and directional measurement of volatility spillovers are encountered depending on whether the intraday periodic component is considered. Thus, the convenience of removing intraday seasonality seems to be critical to reduce the risk of spurious causality when employing high-frequency data in volatility transmission. Moreover, the impact of market microstructure noise seems negligible when using an optimal frequency of observations.     

INTRAWEEK SEASONALITY IN THE FEDERAL FUNDS MARKET

This paper examines daily Federal funds interest rates for intraweek seasonality over the period 1966 through June 1982. Seasonals are identified using several techniques and are found to vary over time in both intensity and relative size. Furthermore, seasonality is not limited to a Wednesday effect. The seasonals are used to test the effectiveness of active reserve management based on seasonal rate forecasts. The results indicate that extraordinary risk-adjusted profits are not likely.     

Macroeconomic Seasonality and the January Effect

Many financial markets researchers have sought an explanation for the role of January in stock returns. Any explanation of this phenomenon that is consistent with rational pricing must specify a source of seasonality in expected returns. The pervasive seasonality in the macroeconomy is an appealing possibility. A multifactor model that links macroeconomic risk to expected return is found to show substantial seasonality in expected returns. This model accounts for the seasonality in average returns, while the capital asset pricing model cannot.     

Using the credit spread as an option-risk factor: Size and value effects in CAPM

This paper takes an option-theoretic approach to explain why pricing anomalies are observed when traditional CAPM is used. By extending CAPM to incorporate the option-risk factor of stocks, we show that stockholders’ limited liability can explain Fama and French’s size and value effects. We use bonds’ excess credit spread as a proxy for stocks’ default risk to control for the changing non-diversifiable option-risk characteristic of stocks. Because sensitivity to the excess credit spread becomes smaller as size increases and as value decreases, excess credit spread explains the CAPM anomalies in a fashion similar to the Fama–French factors. While the excess credit spread is significant in explaining Fama and French’s size and value effects, adding the Fama–French factors does not improve the performance of our model. Our revised model resembles conditional CAPM, but it offers a more intuitive explanation for the size and value effects.     

Real estate prices: An international study of seasonality's sentiment effect

The current study shows that real estate prices in several countries reveal a significant and persistent seasonality, where the highest rates of return are obtained in the spring and early summer, and the lowest rates of return are obtained in the fall. This seasonality is explained by a joint effect of the change in the number of daylight hours and the latitude of the area zone under consideration. Notably, latitude affects real estate prices above and beyond the effect of the change in the number of daylight hours, which by itself is a function of latitude. This joint effect is robust to the two explanations for seasonality given in the literature: the Matching Theory and the Bargaining Power Hypothesis, as well as to several macroeconomic variables. The effect also conforms to the well-known Seasonal Affective Disorder (SAD), which has been found in other studies to affect people's health, their risk attitude, and consequently their risk perception and investment decisions which, in turn, affect asset prices.     

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.     

Risk and return: Janaury vs. the rest of the year

This paper examines the seasonality in the basic relationship between expected return and risk during 1935–82. The results reveal that the positive relationship between return and risk is unique to January. The risk premiums during the remaining eleven months are not significantly different from zero.