Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/312957 
Year of Publication: 
2024
Citation: 
[Journal:] Contemporary Economics [ISSN:] 2300-8814 [Volume:] 18 [Issue:] 3 [Year:] 2024 [Pages:] 336-351
Publisher: 
University of Finance and Management in Warsaw, Faculty of Management and Finance, Warsaw
Abstract: 
We study the problem of selecting a sparse, mean reverting portfolio from a universe of assets using simulated annealing (SA). Assuming that assets follow a first order vector autoregressive process (VAR(1)), we make a number of improvements in existing methods. First, we extend the underlying asset dynamics to include a time-independent additive term, thereby enriching the model's applicability. Second, we introduce Extreme Learning Machine (ELM) to decide whether to apply SA or settle for the much faster greedy solution. Finally, we improve the SA method by better calibration of the initial temperature and by determining the exact value of the weights within a selected dimension using the Rayleigh quotient. On real data, these changes result in more than 90% improvement in run time on average and 4.78% improvement in optimized mean reversion in our simulations. We also test the trading performance of our method on both simulated and real data and managed to achieve positive mean trading results in both cases.
Subjects: 
sparse portfolios
mean reverting portfolios
simulated annealing
extreme learning machine
machine learning
JEL: 
C65
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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