This paper examines the determinants of stock returns in a small open economy using an APT framework. The analysis is conducted for the Swiss stock market which has the particularity of including a large proportion of firms that are exposed to foreign economic conditions. Both a statistical and a macroeconomic implementation of the model are performed for the period 1986-2002 with monthly retur...

This paper proposes spectral and asymmetric-volatility based methods for cluster analysis of stock returns. Using the information about both the periodogram of the squared returns and the estimated parameters in the TARCH equation, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coor...

This paper examines the relationship between stock returns and inflation. We focus on various econometric techniques to test this relationship, using monthly values of the Athens Stock Exchange Price index and the Greek Consumer Price index over the period 1988-2002. The results from a simple OLS model show evidence of a positive but not significant relationship, while when we consider a system...

The consequences of regional trade agreements (RTAs) on countries’ welfare are disputed. In this paper, we assess these effects using stock returns from a recent data set that spans over two hundred RTA announcements, eighty economies, and twenty years. We measure the effects of news concerning RTAs on the returns of national stock markets, after adjusting these returns for international stock ...

In this paper we survey the recent research on the fundamental determinants of stock returns. These studies explore how firms’ systematic risk and their investment and production decisions are jointly determined in equilibrium. Models with production provide insights into several types of empirical patterns, including: i) the correlations between firms’ economic characteristics and their risk p...

Several models and techniques have been used to forecast stock returns. Some previous researches have applied Evolutionary Artificial Neural Networks to predict stocks prices. The most of previous researches have been concentrated on either predict stock indexes or trends rather than individual stock returns. In this paper, we use the adaptive EANNs to predict individual stock returns based on ...

This paper provides preliminary evidence that interest groups reduce both the level and the volatility of returns on a national stock market. These findings are robust to model specifications that include traditional growth regression “policy” variables as well as political, economic, and financial institutions variables. The estimated magnitude of the relationship between interest group activi...

In this analysis of the risk and return of stocks in global markets, we apply several applications of robust regression techniques in producing stock selection models and several optimization techniques in portfolio construction in global stock universes.We find that (1) that robust regression applications are appropriate for modeling stock returns in global markets; and (2) mean-variance techn...

It seems plausible that an increase in stock market volatility raises required stock returns, and thus lowers stock prices. We develop a formal model of this volatility feedback effect using a simple model of changing variance (a quadratic generalized autoregressive conditionally heteroskedastic, or QGARCH, model). Our model is asymmetric and helps to explain the negative skewness and excess ku...

This paper investigates the price adjustment and lead-lag relations between returns on five sizebased portfolios in the Taiwan stock market. It finds evidence that the price adjustment of smallstock portfolios is not slower than that of large-stock portfolios. Additionally, limited evidence supports a positive leading role of large-stock portfolio returns over small-stock portfolio returns. The...