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Michael Knaus
Former Member
Title
Prof. Ph.D.
Last Name
Knaus
First name
Michael
Email
michael.knaus@unisg.ch
Phone
+41 71 224 23 04
Now showing
1 - 4 of 4
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PublicationHeterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach( 2020)We systematically investigate the effect heterogeneity of job search programmes for unemployed workers. To investigate possibly heterogeneous employment effects, we combine non-experimental causal empirical models with Lasso-type estimators. The empirical analyses are based on rich administrative data from Swiss social security records. We find considerable heterogeneities only during the first six months after the start of training. Consistent with previous results of the literature, unemployed persons with fewer employment opportunities profit more from participating in these programmes. Furthermore, we also document heterogeneous employment effects by residence status. Finally, we show the potential of easy-to-implement programme participation rules for improving average employment effects of these active labour market programmes.Type: journal articleJournal: Journal of Human Resources
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PublicationThe effect of environmental policies on proenvironmental behaviors and intrinsic motivation: evidence from the EU( 2022)Gorkun-Voevoda, LiudmilaType: conference paper
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PublicationType: conference paper
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PublicationHow does post-earnings announcement sentiment affect firms' dynamics? New evidence from causal machine learning( 2020-12-22)
;Chassot, JonathanWe revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms' return, volatility, and trade volume dynamics. To this end we apply causal machine learning on the earnings announcements of a wide cross-section of US companies. This approach allows us to investigate firms' price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic and aggregated investors' moods in a properly defined causal framework. Our empirical results support the presence of (i) investors' overconfidence and mispricing due to biased expectations; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors' underreaction to news. Finally, we show that the difference in the average causal effects of the sentiment's types is larger when the general macroeconomic conditions are worse or the uncertainty in the global financial market is higher.Type: working paper