Abstract
The basis of portfolio theory is rooted in statistical models based on Brownian motion. These models are surprisingly naïve in their assumptions and resultant application within the trading community. The application of artificial intelligence (AI) to portfolio theory and management have broad and far-reaching consequences. AI techniques allow us to model price movements with much greater accuracy than the random-walk nature of the original Markowitz model. Additionally, the job of optimizing a portfolio can be performed with greater optimality and efficiency using evolutionary computation while still staying true to the original goals and conceptions of portfolio theory. A particular method of price movement modelling is shown that models price movements with only simplistic inputs and still produces useful predictive results. A portfolio rebalancing method is also described, illustrating the use of evolutionary computing for the portfolio rebalancing problem in order to achieve the results demanded by investors within the framework of portfolio theory.
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Marwala, T., Hurwitz, E. (2017). Portfolio Theory. In: Artificial Intelligence and Economic Theory: Skynet in the Market. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-66104-9_11
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DOI: https://doi.org/10.1007/978-3-319-66104-9_11
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