Cost-sensitive estimation of ARMA models for financial asset return data
type: Item
instance of: scholarly article
title: Cost-sensitive estimation of ARMA models for financial asset return data
zbMATH Open document ID: 1395.91513
DOI: 10.1155/2015/232184
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: The autoregressive moving average (ARMA) model is a simple but powerful model in financial engineering to represent time-series with long-range statistical dependency. However, the traditional maximum likelihood (ML) estimator aims to minimize a loss function that is inherently symmetric due to Gaussianity. The consequence is that when the data of interest are asset returns, and the main goal is to maximize profit by accurate forecasting, the ML objective may be less appropriate potentially leading to a suboptimal solution. Rather, it is more reasonable to adopt an asymmetric loss where the model's prediction, as long as it is in the same direction as the true return, is penalized less than the prediction in the opposite direction. We propose a quite sensible asymmetric cost-sensitive loss function and incorporate it into the ARMA model estimation. On the online portfolio selection problem with real stock return data, we demonstrate that the investment strategy based on predictions by the proposed estimator can be significantly more profitable than the traditional ML estimator.
Mathematics Subject Classification ID: 91G70
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 6925810
author: Min-Young Kim
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/232184
OpenAlex ID: W2116015908
Wikidata QID: Q59117663
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description: scientific article; zbMATH DE number 6925810