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This paper investigates the role of published stock recommendations in print and online media as investor sentiment in the near-term German stock market. In line with extant literature on other sentiment measures, vector autoregressions reveal that past stock returns drive today's sentiment, but not the other way around, and that sentiment is a powerful predictor of itself. In particular, sentiment based on printed analyst recommendations follows reversals, that is, when analysts face a stock market downturn, they see a buying opportunity and become optimistic.

This paper presents an empirical approach that combines competing paradigms of modeling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which the efficient market hypothesis, fundamental data analysis, and behavioral finance contribute to explaining stock market yield. The core of the approach is a dynamic panel model (Arellano-Bond estimator with an MA restriction of the residuals), complemented with an upstream factor analysis to reduce multicollinearity. Due to the complexity of the data set, a great many parameters that influence the yield can be determined. Highly significant parameter estimates are possible even though the information in the data set is interdependent. For the German stock market (the 160 companies listed in DAX, MDAX, SDAX, and TecDAX), the quarterly yield is analyzed for the period between 2004 and 2009. The model has high explanatory power for the entire observation period, even in light of the fact that the period includes the financial crisis of 2008.

In this study, we develop a technique for estimating a firm’s expected cost of equity capital derived from analyst consensus forecasts and stock prices. Building on the work of Gebhardt/Lee/-Swaminathan (2001) and Easton/Taylor/Shroff/Sougiannis (2002), our approach allows daily estimation, using only publicly available information at that date. We then estimate the expected cost of equity capital at the market, industry and individual firm level using historical German data from 1989-2002 and examine firm characteristics which are systematically related to these estimates. Finally, we demonstrate the applicability of the concept in a contemporary case study for DaimlerChrysler and the European automobile industry.

Pricing of cap insurance contracts is considered for political mortgage rates. A simple stochastic process for mortgage rates is proposed. The process is based on renewal processes for modelling the length of periods with downward and upward trend respectively. Prices are calculated by simulation of conditional future sample paths. Future conditional quantiles can be obtained to assess the risk of a contract. The method is illustrated by applying it to observed quarterly mortgage rates of the Swiss Union of Raiffeisenbanks for the years 1970 to 2001.

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“.

Do financial market analysts use structural economic models when forecasting exchange rates? This is the leading question analysed in this apper. In contrast to other studies we use expectations instead of realised data. Therefore we analyse the implicit structural models forecasters have in mind when forming their exchange rate expectations. Using expected short- and long-term interest rates and business expectations as explanatory variables we estimate latent structural models to explain expected exchange rates. A special hypothesis is whether exchange rate expectations are formed according to monetary models. The currencies included in the study are US dollar, British pound, Japanese yen, French franc and Italian lire, each defined against the German mark. A major finding of the analysis is that expected GDP is the most important variable (from the set of our variables) for the determination of exchange rate expectations. For the DM/US dollar expectations a Mundell-Fleming type model is compatible with the data. This means, that increasing interest rates will led to an appreciation of the corresponding currency. The opposite result have been found for French franc and Italian lire where high expected interest rates indicate a weak currency.