Erschienen in:Big Data and Machine Learning in Quantitative Investment, Wiley finance series. 2018
Umfang:
1 Online-Ressource (3 p)
Sprache:
Englisch
Entstehung:
Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments February 28, 2018 erstellt
Beschreibung:
In this chapter, we apply a popular Machine Learning approach (extreme gradient boosted trees) to build enhanced diversified equity portfolios. A simple naïve equally-weighted portfolio of US stocks based on a boosted tree-based signal generates on average an excess return of 3.1% per annum, compared to a simple multifactor portfolio. We demonstrate that using boosted trees on a large number of features give an average error rate of 20% for predicting the 12-month sector neutral outperformance of a stock. In addition, enhancing a simple multi-factor signal with an ML-boosted signal proves to add value on a risk-return basis without altering the factor exposure of the traditional multi-factor portfolio