Testing stock market convergence: a non-linear factor approach

This paper applies the Phillips and Sul (Econometrica 75(6):1771–1855, 2007) method to test for convergence in stock returns to an extensive dataset including monthly stock price indices for five EU countries (Germany, France, the Netherlands, Ireland and the UK) as well as the US between 1973 and 2008. We carry out the analysis on both sectors and individual industries within sectors. As a first step, we use the Stock and Watson (J Am Stat Assoc 93(441):349–358, 1998) procedure to filter the data in order to extract the long-run component of the series; then, following Phillips and Sul (Econometrica 75(6):1771–1855, 2007), we estimate the relative transition parameters. In the case of sectoral indices we find convergence in the middle of the sample period, followed by divergence, and detect four (two large and two small) clusters. The analysis at a disaggregate, industry level again points to convergence in the middle of the sample, and subsequent divergence, but a much larger number of clusters is now found. Splitting the cross-section into two subgroups including euro area countries, the UK and the US respectively, provides evidence of a global convergence/divergence process not obviously influenced by EU policies

Mood and the Market: Can Press Reports of Investors’ Mood Predict Stock Prices?

We examined whether press reports on the collective mood of investors can predict changes in stock prices. We collected data on the use of emotion words in newspaper reports on traders’ affect, coded these emotion words according to their location on an affective circumplex in terms of pleasantness and activation level, and created indices of collective mood for each trading day. Then, by using time series analyses, we examined whether these mood indices, depicting investors’ emotion on a given trading day, could predict the next day’s opening price of the stock market. The strongest findings showed that activated pleasant mood predicted increases in NASDAQ prices, while activated unpleasant mood predicted decreases in NASDAQ prices. We conclude that both valence and activation levels of collective mood are important in predicting trend continuation in stock prices

Tiebreaker: Certification and Multiple Credit Ratings

Bongaerts is with Finance Group, RSM Erasmus University Rotterdam, and Cremers and Goetzmann are with the International Center for Finance, Yale University. We would like to thank Patrick Behr; Michael Brennan; Mark Carlson; Erwin Charlier; Long Chen; Frank de Jong; Joost Driessen; Frank Fabozzi; Rik Frehen; Gary Gorton; Jean Helwege; Mark Huson; Ron Jongen; Pieter Klaassen; David Lesmond; Hamid Mehran; Catherine Nolan; Frank Packer; Ludovic Phalippou; Paolo Porchia; Jörg Rocholl; Joao Santos; Joel Shapiro; Chester Spatt; Walter Stortelder; Dan Swanson; Anjan Thakor; Laura Veldkamp; Evert de Vries; Jacqueline Yen; Weina Zhang; as well as conference participants at the Financial Crisis conference at Pompeu Fabra University, the European Finance Association annual meetings in Bergen (Norway, 2010), the Texas Finance Festival at UT Austin, the RMI conference at National University of Singapore, the NBER meeting on Credit Ratings in Cambridge, the Conference on Credit Rating Agencies at Humboldt University, the American Finance Association annual meetings in Denver (2011); and seminar participants at the University of Amsterdam, Rotterdam School of Management, and the Dutch National Bank for helpful comments and information. We especially thank Campbell Harvey (the Editor), an anonymous associate editor, and an anonymous referee for many helpful comments and advice

Transmission of returns and volatility in art markets: a multivariate GARCH analysis

This study examines the transmission of returns and volatility among eight major art markets. The art indices included in the analysis are Contemporary Masters (CM), 20th Century English (TE), 19th Century European (NE), French Impressionist (FI), Modern European (ME), Modern US Paintings (US), Old Masters (OM) and Surrealists (SR). A multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model is used to identify the source and magnitude of spillovers. The results indicate the presence of large and predominantly positive mean return and volatility spillovers, though the spillovers between art markets are not homogeneous.