The empirical relationship between risk and return: evidence from the UK stock market

Previous studies reach no consensus on the relationship between risk and return using data from one market. We argue that the world market factor should not be ignored in assessing the risk-return relationship in a partially integrated market. Applying a bivariate generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) model to the weekly stock index returns from the UK and the world market, we document a significant positive relationship between stock returns and the variance of returns in the UK stock market after controlling for the covariance of the UK and the world market return. In contrast, conventional univariate GARCH-M models typically fail to detect this relationship. Nonnested hypothesis tests supplemented with other commonly used model selection criteria unambiguously demonstrate that our bivariate GARCH-M model is more likely to be the true model for UK stock market returns than univariate GARCH-M models. Our results have implications for empirical assessments of the risk-return relationship, expected return estimation, and international diversification.     

Point and density prediction of intra-day volume using Bayesian linear ACV models: evidence from the Polish stock market

Trading volume is one of the key measures of trading activity intensity and plays a crucial role in the financial market microstructure literature. In this paper, we examine the out-of-sample point and density forecasting performance of Bayesian Autoregressive Conditional Volume (ACV) models for intra-day volume data. Based on 5-min traded volume data for stocks quoted on the Warsaw Stock Exchange, a leading stock market in Central and Eastern Europe, we find that, in terms of point forecasts, the considered linear ACV models significantly outperform benchmarks such as the naïve and Rolling Means methods but not necessarily Autoregressive Moving Average (ARMA) models. Moreover, the point forecasts obtained within the exponential error ACV model are significantly superior to those calculated in other competing structures for which Burr or generalized gamma distributions are specified. The main finding from the analysis of density forecasts is that, in many cases, the linear ACV models with the Burr and generalized gamma distributions provide significantly better density forecasts than the linear ACV model with exponential innovations and the ARMA models in terms of the log-predictive score, calibration and sharpness.     

The dynamics between stock returns and mutual fund flows: empirical evidence from the Greek market

This paper examines the interaction between mutual fund flows and stock returns in Greece. Specifically, we investigate the possibility of a causality mechanism through which mutual funds flows may affect stock returns and vice versa. The statistical evidence derived from the error correction model indicates that there is a bidirectional causality between mutual fund flows and stock returns. Cointegration results show that mutual funds flows cause stock returns to rise or fall. This may be explained by the fact that, in Greece, equity mutual funds are obliged by law to invest a certain percentage of their cash in stocks. Thus, inflows and outflows of cash in equity funds seem to cause higher and lower stock returns in Greek stock market.     

Modeling the cross-section of stock returns using sensible models in a model pool

An increase in the number of asset pricing models intensifies model uncertainties in asset pricing. While a pure “model selection” (singling out a best model) can result in a loss of useful information, a full “model pooling” may increase the risk of including noisy information. We make a trade-off between the two methods and develop a new two-step trimming-then-pooling method to forecast the joint distributions of asset returns using a large pool of asset pricing models. Our method allows investors to focus on certain regions of the distributions. In the first step, we trim the uninformative models from a pool of candidates, and in the second step, we pool the forecasts of the surviving models. We find that our method significantly enhances portfolio performance and predicts downside risk precisely, and the improvements are mainly due to trimming. The pool of sensible models becomes larger when focusing on extreme events, responds rapidly to rising uncertainty, and reflects the magnitude of factor premiums. These findings provide new insights into asset pricing model evaluation.     

Order book characteristics and the volume–volatility relation: Empirical evidence from a limit order market

Using unique data, we address the issue of price formation in a limit order market. A standard volume–volatility relation is documented with the number of trades acting as the important component of volume. The main contribution of the paper is to identify strong evidence that volume, volatility, and the volume–volatility relation are negatively related to the order book slope. These results are robust to the inclusion of several liquidity measures. A significant empirical relationship between the order book slope and the coefficient of variation in earnings forecasts by financial analysts suggests that the slope is proxying for disagreement among investors. Hence, our results support models where investor heterogeneity intensifies the volume–volatility relation.     

Size and book to market effects and the Fama French three factor asset pricing model: evidence from the Australian stockmarket

The present study adds to the sparse published Australian literature on the size effect, the book to market (BM) effect and the ability of the Fama French three factor model to account for these effects and to improve on the asset pricing ability of the Capital Asset Pricing Model (CAPM). The present study extends the 1981–1991 period examined by Halliwell, Heaney and Sawicki (1999) a further 10 years to 2000 and addresses several limitations and findings of that research. In contrast to Halliwell, Heaney and Sawicki the current study finds the three factor model provides significantly improved explanatory power over the CAPM, and evidence that the BM factor plays a role in asset pricing.     

Student satisfaction and commitment towards a blended learning finance course: A new evidence from using the investment model

The present study uses an amended version of a well-known investment model to investigate the levels of satisfaction and commitment of finance students enrolled on a blended e-learning programme. First, it presents new empirical evidence for the validity of each construct and validates the proposed investment model. Second, it examines whether students’ grade point average (GPA) scores influence their levels of satisfaction and commitment the course. A random sample of 100 undergraduate students enrolled at King Khalid University in Saudi Arabia was surveyed using both qualitative and quantitative approaches. The proposed investment model was suitable for predicting the levels of student satisfaction and commitment in a blended learning environment, especially finance courses. However, the levels of satisfaction and commitment among students did not reach the proposed cut-off point for high commitment/satisfaction, which implied that levels of student satisfaction and commitment were only in the middle of the range. Specifically, the results showed a significant negative correlation between the level of satisfaction and GPA score, but a significant positive correlation between student commitment and GPA score. The study also highlights areas in which further research and analysis is recommended.