MAXIMUM-SHARPE-RATIO ESTIMATED AND SPARSE REGRESSION (MAXSER): PORTFOLIO OPTIMIZATION FOR LARGE STOCK UNIVERSES WITH FACTOR INVESTING
CHANG WEI CHING
CHANG WEI CHING
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Abstract
The traditional mean-variance portfolio conceptualised by Markowitz (1952) is a constrained
optimization problem that does not perform well with large asset universes due to the curse of
dimensionality. Ao et al. (2019) proposed a novel unconstrained regression representation of the
mean-variance portfolio problem and devised a model, maximum-Sharpe-ratio estimated and sparse
regression (MAXSER), that utilizes penalized regression to obtain the optimal portfolio. MAXSER
also has a specification to allow for factor investing. This thesis (1) evaluates the performance of
the MAXSER model using the Least Absolute Shrinkage and Selection Operator (LASSO); and (2)
extends the factor set in the MAXSER with factor investing model and the data set to December
2022. We conclude that the MAXSER model does not consistently outperform the market index
and the naïve 1/N-rule in a 20-year investment horizon, and the MAXSER with factor investing
model is highly sensitive to the moments of factors.
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2023-11-06
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