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By tying quantum bits into voting blocks, scientists can create significant protection against decoherence.
We develop the idea of using Monte Carlo sampling of random portfolios to solve portfolio investment problems. In this first paper we explore the need for more general optimization tools, and conside...
In this paper, we consider a financial market with assets exposed to some risks inducing jumps in the asset prices, and which can still be traded after default times. We use a default-intensity model...
In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evol...
In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evol...
Classical mean-variance portfolio theory12 tells us how to construct a portfo-lio of assets which has the greatest expected return for a given level of return volatility. Utility theory then allows an...
The optimization of large portfolios displays an inherent instability to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look ...
It is widely recognized that when classical optimal strategies are applied with parameters estimated from data, the resulting portfolio weights are remarkably volatile and unstable over time.The predo...
We consider the problem of dynamic buying and selling of shares from a collection of N stocks with random price fluctuations. To limit investment risk, we place an upper bound on the total number of s...

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