A multiple factor model for European stocks

We present an empirical study focusing on the estimation of a fundamental multi-factor model for a universe of European stocks. Following the approach of the BARRA model, we have adopted a cross-sectional methodology. The proportion of explained variance ranges from 7.3% to 66.3% in the weekly regressions with a mean of 32.9%. For the individual factors we give the percentage of the weeks when they yielded statistically significant influence on stock returns. The best explanatory power – apart from the dominant country factors – was found among the statistical constructs „success“ and „variability in markets“.Vorgestellt wird eine empirische Studie, welche die Schätzung eines fundamentalen Multi-Faktor-Modells für ein Universum europäischer Aktien beinhaltet. Als Methode wurde in Anlehnung an die Vorgehensweise im BARRA-Modell der Querschnittsanalyse der Vorzug gegeben. Der Anteil der erklärten Varianz beläuft sich in den wöchentlichen Regressionen auf 7,3% bis 66,3% bei einem Durchschnitt von 32,9%. Für die einzelnen Faktoren wird die Häufigkeit angegeben, mit der sie sich in den Regressionen signifikant erwiesen haben. Den höchsten Erklärungsgehalt im Untersuchungszeitraum hatten Länderfaktoren, aber auch Konstrukte wie „Success“ oder „Variability in Markets“

Correlations, Risk and Crisis: From Physiology to Finance

We study the dynamics of correlation and variance in systems under the load of environmental factors. A universal effect in ensembles of similar systems under the load of similar factors is described: in crisis, typically, even before obvious symptoms of crisis appear, correlation increases, and, at the same time, variance (and volatility) increases too. This effect is supported by many experiments and observations of groups of humans, mice, trees, grassy plants, and on financial time series. A general approach to the explanation of the effect through dynamics of individual adaptation of similar non-interactive individuals to a similar system of external factors is developed. Qualitatively, this approach follows Selye's idea about adaptation energy.Comment: 42 pages, 15 figures, misprints corrections, a proof is added, improved journal versio

Networks of equities in financial markets

We review the recent approach of correlation based networks of financial equities. We investigate portfolio of stocks at different time horizons, financial indices and volatility time series and we show that meaningful economic information can be extracted from noise dressed correlation matrices. We show that the method can be used to falsify widespread market models by directly comparing the topological properties of networks of real and artificial markets.Comment: 9 pages, 8 figures. Accepted for publication in EPJ

Effect of changing data size on eigenvalues in the Korean and Japanese stock markets

In this study, we attempted to determine how eigenvalues change, according to random matrix theory (RMT), in stock market data as the number of stocks comprising the correlation matrix changes. Specifically, we tested for changes in the eigenvalue properties as a function of the number and type of stocks in the correlation matrix. We determined that the value of the eigenvalue increases in proportion with the number of stocks. Furthermore, we noted that the largest eigenvalue maintains its identical properties, regardless of the number and type, whereas other eigenvalues evidence different features