Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/258099 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 9 [Issue:] 1 [Article No.:] 9 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-18
Verlag: 
MDPI, Basel
Zusammenfassung: 
Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to select stocks from the global stock market. First, the five important factors of a stock are identified and scored based on its historical performances. Second, HMM is used to predict the regimes of six global economic indicators and find the time periods in the past during which these indicators have a combination of regimes that is similar to those predicted. Then, we analyze the five stock factors of the All country world index (ACWI) in the identified time periods to assign a weighted score for each stock factor and to calculate the composite score of the five factors. Finally, we make a monthly selection of 10% of the global stocks that have the highest composite scores. This strategy is shown to outperform those relying on either ACWI, any single stock factor, or the simple average of the five stock factors.
Schlagwörter: 
economics
economics indicators
global stocks
hidden Markov model
machine learning
regimes
stock ranking
stocks&#x2019
factors
trading
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article
Erscheint in der Sammlung:

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.