Dear Colleagues,

it is my pleasure to bring to your attention the following two seminars in Quantitative Finance, organised by LTI@UniTO. The seminars can be attended online via Zoom at the links accessible via the buttons below.

Best regards,

Luca Regis

-- 
Luca Regis
Associate Professor
Department of Economics and Statistics (ESOMAS)
University of Torino
sites.google.com/view/lucaregis
Office: +39 011 670 6065
www.carloalberto.org/lti
 
 

 

 
 
LTI@UniTO and Fondazione Collegio Carlo Alberto are pleased to invite you to the following seminars:
  
 
 
May 24, 2022 | 12:00-13:15
Predicting Corporate Bond Returns: Merton Meets Machine Learning
Amit Goyal (University of Lausanne)
 
 
Abstract. We investigate the return predictability of corporate bonds using big data and machine learning. We find that machine learning models substantially improve the out-of-sample performance of stock and bond characteristics in predicting future bond returns. We also find a significant improvement in the performance of machine learning models when imposing a theoretically motivated economic structure from the Merton model, compared to the reduced-form approach without restrictions. Overall, our work highlights the importance of explicitly imposing the dependence between expected bond and stock returns via machine learning and Merton model when investigating expected bond returns.
 
 
May 25, 2022 | 12:00 - 13:15
JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality
Marco Pagano (University of Naples Federico II)
 
 
Abstract. Does the matching between workers and jobs help explain productivity differentials across firms? To address this question we develop a job-worker allocation quality measure JAQ by combining employer-employee administrative data with machine learning techniques. The proposed measure is positively and significantly associated with labor earnings over workers’ careers. At firm level, it features a robust positive correlation with firm productivity, and with managerial turnover leading to an improvement in the quality and experience of management. JAQ can be constructed for any employer-employee data including workers’ occupations, and used to explore the effect of corporate restructuring on workers’ allocation and careers.
 
 
 

 

 
 
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