Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage

Carta S. M.;Podda A. S.;Reforgiato Recupero D. A. G.;Stanciu M. M.
2021-01-01

Abstract

In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial market behavior and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two key elements: an ensemble of regression algorithms for asset return prediction, followed by a dynamic asset selection. More specifically, we construct an extremely heterogeneous ensemble ensuring model diversity by using state-of-the-art machine learning algorithms, data diversity by using a feature selection process, and method diversity by using individual models for each asset, as well models that learn cross-sectional across multiple assets. Then, their predictive results are fed into a quality assurance mechanism that prunes assets with poor forecasting performance in the previous periods. We evaluate the approach on historical data of component stocks of the SP500 index. By performing an in-depth risk-return analysis, we show that this setup outperforms highly competitive trading strategies considered as baselines. Experimentally, we show that the dynamic asset selection enhances overall trading performance both in terms of return and risk. Moreover, the proposed approach proved to yield superior results during both financial turmoil and massive market growth periods, and it showed to have general application for any risk-balanced trading strategy aiming to exploit different asset classes.
2021
Inglese
9
9353479
29942
29959
18
Esperti anonimi
internazionale
scientifica
Ensemble learning; Machine learning; Statistical arbitrage; Stock market forecast
no
Carta, S. M.; Consoli, S.; Podda, A. S.; Reforgiato Recupero, D. A. G.; Stanciu, M. M.
1.1 Articolo in rivista
info:eu-repo/semantics/article
1 Contributo su Rivista::1.1 Articolo in rivista
262
5
open
Files in This Item:
File Size Format  
ensembling_and_dynamic_asset_selection.pdf

open access

Type: versione editoriale
Size 2.24 MB
Format Adobe PDF
2.24 MB Adobe PDF View/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Questionnaire and social

Share on:
Impostazioni cookie