Good news, bad news and rating announcements: An empirical investigation

In this paper we employ a new approach to test the contribution of information in rating announcements. This is the first study to test and corroborate how the CDS market responds to rating actions after controlling for the presence of concurrent public and private information. We show that since the clustering of rating announcements characterizes economically significant developments, the common practice of using “uncontaminated” samples underestimates market response. As in previous studies, we find that the market response to bad news is stronger than to good news. Nevertheless, bad news and negative rating announcements tend to cluster. Therefore, the residual contribution of negative rating announcements is small and in some cases insignificant. Positive rating announcements are less frequent and less clustered, though their residual contribution is still significant.     

Applying neutralization theory to fair trade buying behaviour

More and more consumers are willing to pay a premium for fair trade products. However, great potential remains as the market shares of these products are still low. In the present study, neutralization theory was applied to investigate the reasons for consumers hesitating to buy more fair trade products. A postal paper‐and‐pencil survey was sent out using random addresses from the telephone book in the German‐speaking part of Switzerland, resulting in a final sample size of n = 620. The results show that the techniques of neutralization are used to various degrees. A principal component analysis resulted in two strategies of neutralization: an internal strategy (focusing on the consumers themselves) and an external strategy (focusing on the farmers in developing countries). A regression analysis proved that the internal neutralization strategy was an important predictor for fair trade buying behaviour, even when controlling for attitudes towards fair trade.     

P-star in times of crisis – Forecasting inflation for the euro area

In this paper we present three empirically testable versions of the common p-star model and evaluate their forecasting performance using conventional techniques. We try to answer the question if the p-star approach is preferable to achieve a reliable short-run inflation forecast and with regard to the latter we incur the need for a stable demand for money function. Our findings indicate the recurrence of the relevance of the monetary pillar of the ECB's two-pillar framework. In addition, we check for the effects of the current financial and economic crisis that started in 2007 on the forecasting performance, using two sub-sample periods, one excluding and one including the latter, and analyze the impact of the applied filter technique to compute the required equilibrium values.     

Do optimal diversification strategies outperform the 1/N strategy in U.K. stock returns?

This paper examines whether optimal diversification strategies outperform the 1/N strategy in U.K. stock returns. The study focuses on the performance of recent strategies developed by Tu and Zhou (2011) and Kirby and Ostdiek (2010). I find that a number of optimal asset allocation strategies can significantly outperform the 1/N strategy even after adjusting for trading costs. The strategies developed by Kirby and Ostdiek outperform the 1/N strategy, even at higher trading costs, due to the low turnover of these strategies. The strategies of Tu and Zhou have mixed performance after adjusting for trading costs due to the high turnover of these strategies. The results of the paper provide support for the use of optimal diversification strategies.     

Gold and the US dollar: Hedge or haven?

Using a model of dynamic conditional correlations covering 23 years of weekly data for 16 major dollar-paired exchange rates, this paper addresses a practical investment question: Does gold act as a hedge against the US dollar, as a safe haven, or neither? Key findings are as follows. (i) During the past 23 years gold has behaved as a hedge against the US dollar. (ii) Gold has been a poor safe haven. (iii) In recent years gold has acted, increasingly, as an effective hedge against currency risk associated with the US dollar.     

Exchange rate regime choice and currency crises

Exchange rate regime choice is not exogenous, but it depends on the structural, political and financial features of countries. However, it is often the case that the regime actually pursued and the one that is imposed by country features do not match one to one. The existing empirical crisis models do not take fully into account the regime in which the crisis unfolded. The aim of this paper is to incorporate the appropriateness of the regime choice into the standard currency crisis model. The results show that the odds of crisis increase significantly in countries which have chosen regimes inconsistently.     

Measuring price discovery: The variance ratio, the R, and the weighted price contribution

We analyze the statistical properties of three price discovery measures: The variance ratio, the weighted price contribution (WPC), and the R² of unbiasedness regressions. We find that, if the price process is a driftless martingale, only the WPC is an unbiased estimator for the return variance explained during a time interval. For autocorrelated processes with a drift, only the R² of the unbiasedness regression is consistent, but it is biased for small samples.     

Assessing the historical role of credit: Business cycles,financial crises and the legacy of Charles S. Peirce

This paper provides a historical overview of financial crises and their origins. The objective is to discuss a few of the modern statistical methods that can be used to evaluate predictors of these rare events. The problem involves the prediction of binary events, and therefore fits modern statistical learning, signal processing theory, and classification methods. The discussion also emphasizes the need for statistics and computational techniques to be supplemented with economics. The success of a forecast in this environment hinges on the economic consequences of the actions taken as a result of the forecast, rather than on typical statistical metrics of prediction accuracy.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.