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Predictions, Nonlinearities and Portfolio Choice (Paperback)
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Predictions, Nonlinearities and Portfolio Choice (Paperback)
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Finance researchers and asset management practitioners put a lot of
effort into the question of optimal asset allocation. With this
respect, a lot of research has been conducted on portfolio decision
making as well as quantitative modeling and prediction models. This
study brings together three fields of research, which are usually
analyzed in an isolated manner in the literature: - Predictability
of asset returns and their covariance matrix - Optimal portfolio
decision making - Nonlinear modeling, performed by artificial
neural networks, and their impact on predictions as well as optimal
portfolio construction Including predictability in asset allocation
is the focus of this work and it pays special attention to issues
related to nonlinearities. The contribution of this study to the
portfolio choice literature is twofold. First, motivated by the
evidence of linear predictability, the impact of nonlinear
predictions on portfolio performances is analyzed. Predictions are
empirically performed for an investor who invests in equities
(represented by the DAX index), bonds (represented by the REXP
index) and a risk-free rate. Second, a solution to the dynamic
programming problem for intertemporal portfolio choice is
presented. The method is based on functional approximations of the
investor's value function with artificial neural networks. The
method is easily capable of handling multiple state variables.
Hence, the effect of adding predictive parameters to the state
space is the focus of analysis as well as the impacts of estimation
biases and the view of a Bayesian investor on intertemporal
portfolio choice. One important empirical result shows that
residual correlation among state variables have an impact on
intertemporal portfolio decision making.
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