Can Machine Learning Models Capture Correlations in Corporate Distresses?

Benjamin Christoffersen, Rastin Matin, Pia Mølgaard

Publikation: Working paperForskning

Resumé

Nøjagtige konkursmodeller er nødvendige for tilsynsmyndigheder, virksomheder og investorer, som har brug for at evaluere konkursrisikoen i en låneportefølje. En række papirer har vist, at "machine learning"-modeller er bedre til at rangere virksomheder efter deres kreditrisiko end traditionelle statistiske modeller. Men det er stadig et åbent spørgsmål, om de avancerede modeller kan fange
korrelation i konkurser, hvilket traditionelle modeller har svært ved. Vi implementerer en machine learning-model, som generelt har vist sig at være god til at forudsige konkurser, og finder, at konkursestimater på virksomhedsniveau forbedres, mens de estimerede aggregerede konkursrater forbliver næsten uændrede i forhold til de traditionelle konkursmodeller. Vores resultater taler altså for, at komplekse machine learning-modeller ikke eliminerer nødvendigheden af at inkludere en latent variabel, der fanger korrelation i konkurser. Som et alternativ implementerer vi en "frailty"-
model, som direkte introducerer korrelation i konkurser. Modellen er ydermere udvidet med "regression splines", hvilket medfører, at den er god til at rangere virksomheder efter deres kreditrisiko, samtidig med at den vurderer risikoen i en låneportefølje korrekt.
OriginalsprogEngelsk
Udgivelses stedKøbenhavn
UdgiverDanmarks Nationalbank
Antal sider34
StatusUdgivet - 26 okt. 2019
NavnDanmarks Nationalbank. Working Papers
Nummer128
ISSN1602-1193

Emneord

  • Corporate default prediction
  • Discrete hazard models
  • Frailty models
  • Gradient boosting

Citer dette

Christoffersen, B., Matin, R., & Mølgaard, P. (2019). Can Machine Learning Models Capture Correlations in Corporate Distresses? København: Danmarks Nationalbank. Danmarks Nationalbank. Working Papers, Nr. 128
Christoffersen, Benjamin ; Matin, Rastin ; Mølgaard, Pia. / Can Machine Learning Models Capture Correlations in Corporate Distresses?. København : Danmarks Nationalbank, 2019. (Danmarks Nationalbank. Working Papers; Nr. 128).
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Christoffersen, B, Matin, R & Mølgaard, P 2019 'Can Machine Learning Models Capture Correlations in Corporate Distresses?' Danmarks Nationalbank, København.

Can Machine Learning Models Capture Correlations in Corporate Distresses? / Christoffersen, Benjamin; Matin, Rastin; Mølgaard, Pia.

København : Danmarks Nationalbank, 2019.

Publikation: Working paperForskning

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Christoffersen B, Matin R, Mølgaard P. Can Machine Learning Models Capture Correlations in Corporate Distresses? København: Danmarks Nationalbank. 2019 okt 26.