An Examination of Alternative Factor Models in UK Stock Returns

This paper examines the mean-variance efficiency of a number offactor models in UK stock returns. The paper also explores, using theapproach of MacKinlay (1995), whether missing risk factors ornonrisk-based explanations best explain the pricing errors of thedifferent factor models. The evidence in the paper suggests that themean-variance efficiency of each factor model is rejected and missing riskfactors are unable to explain the pricing errors of any of the models.Some nonrisk-based explanations, which posit a wide spread in abnormalreturns, may be a more plausible source of explaining the pricing errorsof the factor models.     

Size,time-varying beta,and conditional heteroscedasticity in UK stock returns

The purpose of this study is to examine the relationship between firm size and time-varying betas of UK stocks. We extend the Schwert and Seguin (1990)(Journal of Finance 45, 1120–1155) methodology by explicitly modeling conditional heteroscedasticity in the market model residual returns. Our results show that the time-varying coefficient is not statistically significant for both small and large firm stock indexes. We also find that accounting for GARCH effects in the Schwert-Seguin market model yields beta estimates that are markedly differently from those when conditional heteroscedasticity is ignored. Event studies that ignore conditional heteroscedasticity may bias the abnormal returns of small and large firms, thereby leading to a different conclusion regarding the significance of an information event.     

Evaluating density forecasts from models of stock market returns

Density forecasts have become important in finance and play a key role in modern risk management. Using a flexible density forecast evaluation framework that extends the Berkowitz likelihood ratio test this paper evaluates in- and out-of-sample density forecasts of daily returns on the DAX, ATX and S&P 500 stock market indices from models of financial returns that are currently widely used in the financial industry. The results indicate that GARCH-t models produce good in-sample forecasts. No model considered in this study delivers fully acceptable out-of-sample forecasts. The empirical findings emphasize that proper distributional assumptions combined with an adequate specification of relevant conditional higher moments are necessary to obtain good density forecasts.     

An analysis of inflation and stock returns for the UK

Whether common stocks provide a hedge against inflation has been long debated. This paper focuses on this question by investigating the relationship between inflation and stock returns in the short term and medium term and under different inflationary regimes using the UK data. Empirical evidence suggests that the UK stock market fails to hedge against inflation in the short term. However, in the medium term there is mixed results. Results from different inflationary regimes show that the relationship between inflation and returns varies in different regimes.     

Persistence and mean reversion in UK stock returns

This paper re-examines the issue of persistence and mean reversion in UK stock returns in the light of new developments published in Chow and Denning (1993) the random walk hypothesis is tested using multiple variance ratios for returns on the Financial Times All Share Index and 330 individual stocks for the period January 1965 to June 1994. There is no evidence of reversion in the UK stock market. Persistence only exists in high frequency data and is less strong in more recent times. Moreover, it is a portfolio phenomenon and is related to firm size. There is a possibility that persistence/reversion is also industry-related.     

Transitions in the stock markets of the US,UK and Germany

In an analysis of the US, the UK and German stock market, we find a change in the behaviour based on the stocks’ beta values. In the years 1995–2006, trades of stocks with high beta and large volume were concentrated in the IT and technology sector, whereas in 2006–2012 those trades are dominated by stocks from the financial sector. We show that an agent-based model can reproduce such a transition. We further show that the initial impulse for the transition might stem from the increase of high-frequency trading at that time.     

World capital markets and Finnish stock returns

The paper explores issues related to time-varying global equity market integration from a Finnish perspective. Finland is an interesting market since profound economic changes and financial deregulation have taken place since the mid-1980s. Using Finnish firm size ranked portfolios and a conditional four-factor asset pricing model, several restrictions on asset behaviour are examined. It is found that a proxy for changing market integration — lagged foreign equity ownership — has a significant impact on the relative importance of local and global risk factors. Significant differences are found between the pricing of shares that were freely-available to all (unrestricted shares) and domestic investors only (restricted shares). Results also suggest that major capital market reforms profoundly affect the degree of market integration, but local risk factors do not become redundant.     

An examination of the relation between asymmetric risk measures,prior returns and expected daily stock returns

We use a sample of individual firm stock returns over the 1988–2009 time period to determine whether: (1) expected daily returns are related to asymmetric risk measures, (2) expected daily returns are related to the directional change of the prior day's price, and (3) our results are robust to the addition of firm size, book-to-market equity and liquidity. We find that investors are compensated for asymmetric risk; however, the positive risk–return relation is present only for our smallest firm quintile. We find a short-term return reversal present in all subgroups, except for the largest firms in our sample. We also document that the low volatility anomaly may be related to firm size and liquidity.     

A best choice among asset pricing models? The Conditional Capital Asset Pricing Model in Australia

We use Australian data to test the Conditional Capital Asset Pricing Model (Jagannathan and Wang, 1996). Our results are generally supportive: the model performs well compared with a number of competing asset pricing models. In contrast to the study by Jagannathan and Wang, however, we find that the inclusion of the market for human capital does not save the concept of the time‐independent market beta (it remains insignificant). We find support for the role of a small‐minus‐big factor in pricing the cross‐section of returns and find grounds to disagree with Jagannathan and Wang's argument that this factor proxies for misspecified market risk.     

Estimation of conditional asset pricing models with integrated variables in the beta specification

We introduce a methodology which deals with possibly integrated variables in the specification of the betas of conditional asset pricing models. In such a case, any model which is directly derived by a polynomial approximation of the functional form of the conditional beta will inherit a nonstationary right hand side. Our approach uses the cointegrating relationships between the integrated variables in order to maintain the stationarity of the right hand side of the estimated model, thus, avoiding the issues that arise in the case of an unbalanced regression. We present an example where our methodology is applied to the returns of funds-of-funds which are based on the Morningstar mutual fund ranking system. The results provide evidence that the residuals of possible cointegrating relationships between integrated variables in the specification of the conditional betas may reveal significant information concerning the dynamics of the betas.     

Idiosyncratic momentum and the cross‐section of stock returns: Further evidence

In this article, we evaluate the profitability and economic source of the predictive power of the idiosyncratic momentum effect, by using five popular asset pricing models to construct the idiosyncratic momentum. We show that all five idiosyncratic momentum strategies produce similar return predictability and consistently outperform the conventional momentum strategy in the cross‐sectional pricing of equity portfolios and individual stocks. This positive effect of idiosyncratic momentum on returns is consistent with the investment capital asset pricing model (CAPM). Further analysis reveals that the firm‐level idiosyncratic momentum effect cannot extend to the aggregate stock market.     

Risk and the cross section of stock returns

This paper mathematically transforms unobservable rational expectation equilibrium model parameters (information precision and supply uncertainty) into a single variable that is correlated with expected returns and that can be estimated with recently observed data. Our variable can be used to explain the cross section of returns in theoretical, numerical, and empirical analyses. Using Center for Research in Security Prices data, we show that a −1σ$-1\sigma$ to +1σ$+1\sigma$ change in our variable is associated with a 0.31% difference in average returns the following month (equaling 3.78% per annum). The results are statistically significant at the 1% level. Our results remain economically and statistically significant after controlling for stocks' market capitalizations, book-to-market ratios, liquidities, and the probabilities of information-based trading.     

Forecasting stock returns with large dimensional factor models

We study equity premium out-of-sample predictability by extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the well-known factor model with a static representation of the common components with the Generalized Dynamic Factor Model, which accounts for time series dependence in the common components. Using statistical and economic evaluation criteria, we empirically show that the Generalized Dynamic Factor Model helps predicting the equity premium. Exploiting the link between business cycle and return predictability, we find accurate predictions also by combining rolling and recursive forecasts in real-time.     

Modelling long-term investment returns via Bayesian infinite mixture time series models

This paper introduces the class of Bayesian infinite mixture time series models first proposed in Lau & So (2004) for modelling long-term investment returns. It is a flexible class of time series models and provides a flexible way to incorporate full information contained in all autoregressive components with various orders by utilizing the idea of Bayesian averaging or mixing. We adopt a Bayesian sampling scheme based on a weighted Chinese restaurant process for generating partitions of investment returns to estimate the Bayesian infinite mixture time series models. Instead of using the point estimates, as in the classical or non-Bayesian approach, the estimation in this paper is performed by the full Bayesian approach, utilizing the idea of Bayesian averaging to incorporate all information contained in the posterior distributions of the random parameters. This provides a natural way to incorporate model risk or uncertainty. The proposed models can also be used to perform clustering of investment returns and detect outliers of returns. We employ the monthly data from the Toronto Stock Exchange 300 (TSE 300) indices to illustrate the implementation of our models and compare the simulated results from the estimated models with the empirical characteristics of the TSE 300 data. We apply the Bayesian predictive distribution of the logarithmic returns obtained by the Bayesian averaging or mixing to evaluate the quantile-based and conditional tail expectation risk measures for segregated fund contracts via stochastic simulation. We compare the risk measures evaluated from our models with those from some well-known and important models in the literature, and highlight some features that can be obtained from our models.     

UK stock returns and robust tests of mean variance efficiency

We test both the unconditional and conditional Mean Variance Efficiency of the UK stockmarket, paying particular attention to choosing a suitable set of instruments for the conditional version of the model. By considering more carefully than previous authors the pricing of economic risk within the mean-variance framework we show that certain instruments can enhance the basic model structure. Given the tendency for financial market data to display non-constancy in variance and non-normality we employ the GMM procedure described in Hansen (1982), which requires much weaker distributional assumptions than the more traditional OLS techniques. We discuss forming portfolios of stocks using both size and dividend yield as a criterion to achieve a suitable spread of risk and return, and find that our conclusions are sensitive both to the method of portfolio formation and to the choice of estimator. This is an important finding given the problem of thin trading associated with the size ordering of UK stocks. We find some support for both the unconditional and conditional version of the CAPM, though we are cautious about our conclusions given the instability of the parameter estimates.     

Intraday volatility and periodicity in the Malaysian stock returns

Many empirical studies using high-frequency intraday data from a variety of markets indicate that PGARCH models give superior return volatility forecasts than those produced from standard GARCH models. This paper investigates into modelling approaches of four versions of PGARCH models of high-frequency data of Bursa Malaysia, in particular where the intraday volatility of double U-shaped pattern. It is examined through half-hourly dummy variables, quarterly-hourly dummy variables, Fourier Functional Form (FFF) based variables and spline-based variables. The non-periodic GARCH models, i.e., GARCH, EGARCH and TARCH are used for comparison of performance of best fit. The analysis show that among the four versions of PGARCH models, the half-dummy and the spline-based versions perform the best. EGARCH produced consistently superior results to other GARCH specifications.     

Short selling and stock returns: Evidence from the UK

The practice of shorting stocks was put forward as one of the causes of the recent financial crisis whereas Shiller (2003), for example, considers shorting an essential element of an efficient market. Shorting involves selling borrowed stocks and subsequently closing the position by purchasing and returning the stock to the lender. A profit will be realised if the stock's price decreases. Shorting enables investors who do not own a perceived overvalued stock to sell. Using a high-frequency UK dataset for the period between September 2003 and April 2010, our findings suggest shorting indicates evidence of overvalued stocks as significantly negative abnormal stock returns appear to follow an increase in shorting. These results do not hold, however, for shorting which occurs around the ex-dividend date. We further find that these results hold during the recent financial crisis.     

The structure of information release and the factor structure of returns

We model how firms releasing information on different dates causes the CAPM to fail, requiring an additional factor based on the information structure to price assets. We exemplify this mechanism’s empirical relevance using quarterly earnings announcements, which cluster across months along size and book-to-market. Seventy percent of the alpha reduction from including SMB and HML occurs in the four main earnings announcement months. The information structure factor accounts for all of SMB and HML’s seasonal alpha reduction and one third of their overall alpha reduction. Controlling for size and book-to-market, exposures to SMB and HML vary with firms’ earnings announcement month.     

An application of a random level shifts model to the volatility of Peruvian stock and exchange rate returns

The literature has shown that the volatility of stock and forex rate market returns shows the characteristic of long memory. Another fact that is shown in the literature is that this feature may be spurious and volatility actually consists of a short memory process contaminated with random level shifts (RLS). In this paper, we follow recent econometric approaches estimating an RLS model to the logarithm of the absolute value of stock and forex returns. The model consists of the sum of a short-term memory component and a component of level shifts. The second component is specified as the cumulative sum of a process that is zero with probability ‘1-alpha’ and is a random variable with probability ‘alpha’. The results show that there are level shifts that are rare, but once they are taken into account, the characteristic or property of long memory disappears. Also, the presence of General Autoregressive Conditional Heteroscedasticity (GARCH) effects is eliminated when included or deducted level shifts. An exercise of out-of-sample forecasting shows that the RLS model has better performance than traditional models for modelling long memory such as the models ARFIMA (p,d,q).