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Abstract:
This paper deals with a neural network architecture which
establishes a portfolio management system similar to the
Black
/
Litterman
approach. This allocation scheme distributes funds across
various securities or financial markets while simultaneously
complying with specific allocation constraints which meet the
requirements of an investor.
The portfolio optimization algorithm is modeled by a
feedforward neural network. The underlying expected return
forecasts are based on error correction neural networks (ECNN),
which utilize the last model error as an auxiliary input to
evaluate their own misspecification.
The portfolio optimization is implemented such that (i.) the
allocations comply with investor's constraints and that (ii.) the
risk of the portfolio can be controlled. We demonstrate the
profitability of our approach by constructing internationally
diversified portfolios across 21 different financial markets of
the G7 contries. It turns out, that our approach is superior to a
preset benchmark portfolio.
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