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Machine learning and asset allocation
Author(s) -
Routledge Bryan R.
Publication year - 2019
Publication title -
financial management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.647
H-Index - 68
eISSN - 1755-053X
pISSN - 0046-3892
DOI - 10.1111/fima.12303
Subject(s) - asset allocation , equity premium puzzle , equity (law) , dividend , preference , simplicity , asset (computer security) , economics , capital asset pricing model , risk aversion (psychology) , risk premium , econometrics , financial market , basis risk , financial economics , computer science , finance , microeconomics , expected utility hypothesis , portfolio , philosophy , computer security , epistemology , political science , law
Investors have access to a large array of structured and unstructured data. We consider how these data can be incorporated into financial decisions through the lens of the canonical asset allocation decision. We characterize investor preference for simplicity in models of the data used in the asset allocation decision. The simplicity parameters then guide asset allocation along with the usual risk aversion parameter. We use three distinct and diverse macroeconomic data sets to implement the model to forecast equity returns (the equity risk premium). The data sets we use are (a) price‐dividend ratios, (b) an array of macroeconomic series, and (c) text data from the Federal Reserve's Federal Open Market Committee (FOMC) meetings.