The Determinants of Stock Returns in a Small Open Economy

Abstract

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 returns on industrial sector indices. The results show that the statistically determined factors yield a better representation of the determinants of stock returns than the macroeconomic variables and that stock returns are influenced by both global and local economic conditions. This suggests that the Swiss stock market is an internationally imperfectly integrated market.

Estimation of the underlying structure of systematic risk with the use of principal component analysis and factor analysis

“…There is no clear supremacy of one approach over the others. Among the theoretical and empirical comparative studies made, Maringer (2004) presents a good summary of the advantages, disadvantages and recommended uses of macroeconomic, fundamental and statistical models; Connor (1995) shows that statistical and fundamental models outperform macroeconomic models in terms of explanatory power, and that fundamental models slightly outperform statistical ones for the USA market; Chan et al (1998) found evidence that fundamental factors perform better than macroeconomic, technical, statistical and market factors in the UK and japanese markets; on the other hand, Teker et al (1998) showed that the statistical model outperforms the macroeconomic one for the US market; and Cauchie et al (2004) demonstrated that statistical factors yield a better representation of the determinants of the swiss market stock returns than the macroeconomic ones. In addition, Miller (2006a) makes a new comparison, complementing that of Connor's classic study.…”

Risk Factors for the Swiss Stock Market

“…Table 1 shows the average number of companies included in the sample for each year, after all exclusions. This sample is larger than those analysed by Rouwenhorst (1998), Fama and French (1998), , Arshanapalli, Coggin, Doukas, and Shea (1998), Grünen-felder (1999), Liew and Vassalou (2000), and Cauchie, Hoesli, and Isakov (2004). As Vaihekoski (2004) shows, this is a very important prerequisite, as the number of companies in Switzerland is small in comparison to the US and therefore a larger dataset significantly increases the reliability of the results.…”

“…There are studies that include some Switzerland-specific aspects of the Carhart factor model in the context of other research, for example Rouwenhorst (1998) in the context of momentum strategies, Capaul, Rowley, and Sharpe (1993), Fama and French (1998), and Arshanapalli, Coggin, Doukas and Shea (1998) in the context of value and growth strategies and Grünenfelder (1999), Liew and Vassalou (2000) and Cauchie, Hoesli, and Isakov (2004) in the context of macroeconomic studies. However, to our best knowledge, there exists no exhaustive study for a Switzerland-specific four-factor Carhart model.…”

Changes in the risk structure of stock returns: Consumer Confidence and the dotcom bubble

“…Since only unexpected changes in the variables are of importance (‘news’), innovations were derived in every case by applying the Kalman Filter to the raw series — a technique that has seen increasing use in recent years (Priestley, 1996; Cauchie, Hoesli, & Isakov, 2004). Using STAMP, white noise residuals (the innovations) were generated by either an unobserved components model or (if this produced autocorrelated residuals) an autoregressive model with time‐varying parameters.…”