Skip to content

Séminaires par date

Aot 2017
Lu Ma Me Je Ve Sa Di
31 1 2 3 4 5 6
7 8 9 10 11 12 13
14 15 16 17 18 19 20
21 22 23 24 25 26 27
28 29 30 31 1 2 3

Tous les séminaires

Jeudi 22 Juin

16H15 : Matthias FENGLER (University of St Gallen)

"Textual Sentiment, Option Information and Stock Predictability"


"Dynamical Interaction Between Financial and Business Cycles"



 Matthias FENGLER (University of St Gallen)

Textual Sentiment, Option Information and Stock Predictability

A growing literature shows a predictability of stock returns based on sentiment proxies. More recently, it has been shown that also variables implied from single stock options markets carry predictive content for future equity returns. Where does this predictability stem from? Is it firm-specific information advantage or is it a firm-specific sentiment that is implemented in terms of option-based strategies and thus leads to return predictability?

In this work, we aim at answering this question. We distill sentiment from a huge bulk of NASDAQ news articles and examine the various sources of predictive power. We find that options markets react to sentiment from NASDAQ articles in that higher implied volatility, higher out-of-money put prices and stronger smirk can be observed as more negative articles being posted which constitutes more negative sentiment. Next we inspect return predictability. We find that options variables indeed predict stock returns, yet sentiment variables, in particular, our index sentiment  remains a highly relevant factor for individual stock returns. Firm specific-sentiment becomes weaker after controlling for information implied in options. The strength of predictions appears to subside from high to low attention firms, but still remains for low-attention firms. We conclude that the predictability of options markets cannot exclusively be attributed to information asymmetry but also to sentiment.



We adopt the Dynamical Influence model from computer science and transform it to study the interaction between business and financial cycles. For this purpose, we merge it with Markov-Switching Dynamic Factor Model (MS-DFM) which is frequently used in economic cycle analysis. The model suggested in this paper, the Dynamical Influence Markov- witching Dynamic Factor Model (DI-MS-DFM), allows to reveal the pattern of interaction between business and financial cycles in addition to their individual characteristics. More specifically, with the help of this model we are able to identify and describe quantitatively the existing regimes of interaction in a given economy, and we allow them to switch over time. We are also able to determine the direction of causality between the two cycles for each of the regimes. The model estimated on the US data demonstrates reasonable results, identifying the periods of higher interaction between the cycles in the beginning of 1980s and during the Great Recession, while in-between the cycles evolve almost independently. The output of the model can be useful for policymakers since it provides a timely estimate of the current interaction regime, which allows to adjust the timing and the composition of the policy mix.