Michael Knaus
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Prof. Ph.D.
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Knaus
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Michael
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michael.knaus@unisg.ch
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+41 71 224 23 04
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  • Publication
    Type: journal article
    Journal: Labour Economics
    Volume: 67
    Issue: 101904
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/112902
  • Publication
    Type: journal article
    Journal: Econometrics Journal
    DOI: https://doi.org/10.1093/ectj/utaa014
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/112903
  • Publication
    Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach
    ( 2020)
    Lechner, Michael
    ;
    Strittmatter, Anthony
    ;
    Knaus, Michael
    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 article
    Journal: Journal of Human Resources
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/112927
  • Publication
    Predicting Match Outcomes in Football by an Ordered Forest Estimator
    (Edward Elgar Publishing, 2021-10-26)
    Goller, Daniel
    ;
    Knaus, Michael
    ;
    Lechner, Michael
    ;
    Okasa, Gabriel
    Predicting the outcome of football (i.e. soccer) games based on past information is a non-standard predictive task because of the nature of the game outcome, as well as because of the importance of uncertainty (luck and unobservables). The game outcome consists of the scores of the two teams that are usually either collapsed into a goal-difference or further aggregated to reflect whether the game ended as a win for the home or away team, or as a draw. From a statistical perspective, such outcomes have bounded support and, thus, standard linear modelling can be expected to perform poorly. The large amount of uncertainty in the game outcomes due to just luck or due to game- or team-specific unobservables (e.g. hidden injuries of players, etc.) makes it imperative to use prediction methods that fully exploit the potential of the available information, as well as to uncover the uncertainty of a match outcome. The latter is also relevant when interest is not only in single games but also in a league table at the end of the season. Obviously, such league tables should capture the uncertainty for the single games accumulated over a season to be useful guides on what to expect. Recently, machine learning methods have shown their power in all sorts of prediction problems, in particular in situations where the relation of the variables capturing the information used to predict with the target of the prediction, i.e. here the outcome of the game, is non-linear. However, so far there has been only little development in gearing these methods explicitly towards the estimation of the probabilities of ordered outcomes, such as score differences and points, or just wins, draws, and losses. Lechner and Okasa (2019) propose adapting classical random forest estimation, which is known to have excellent predictive performance (e.g. Biau and Scornet (2016), Fernández-Delgado et al. (2014)) to the problem of predicting probabilities of ordered categorical outcomes, such as the win-draw-loss problem of a football game. In this chapter, we use their approach to predict game outcomes of the German Bundesliga 1 (BL1) based on more than ten years' data on game outcomes as well as extensive information about teams, their players, and their environment. These predictions are then used to obtain the final season rankings in a way that reflects and shows the magnitude of the inherent uncertainty of football games.
    Type: book section
    DOI: https://doi.org/10.4337/9781789906530
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/109796
  • Publication
    How does post-earnings announcement sentiment affect firms' dynamics? New evidence from causal machine learning
    ( 2020-12-22)
    Audrino, Francesco
    ;
    Chassot, Jonathan
    ;
    Huang, Chen
    ;
    Knaus, Michael
    ;
    Lechner, Michael
    ;
    Ortega Lahuerta, Juan-Pablo
    We 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
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/111461