Abstract
The stock market can be considered a nondeterministic and partially observable domain, because investors never know all information that affects prices and the result of an investment is always uncertain. Technical Analysis methods demand only data that are easily available, i.e. the series of prices and trade volumes, and are then very useful to predict current price trends. Analysts have however to deal with the fact that the indications of these methods are uncertain, having different interpretations. In this work, we assume the hypothesis that an investment context can be modeled as a Partially Observable Markov Decision Process (POMDP) and partial observations of price trends can be provided by Technical Analysis methods. A metamodel is proposed to specify POMDP problems, embedding formal interpretations of Technical Analysis methods. Planning algorithms can then try to create investment policies to maximize profits. Due to the complexity for solving POMDPs, algorithms that generate only approximate solutions have to be applied. Nevertheless, the results obtained by an implemented prototype are promising.
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Baffa, A.C.E., Ciarlini, A.E.M. (2010). Planning under the Uncertainty of the Technical Analysis of Stock Markets. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_12
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DOI: https://doi.org/10.1007/978-3-642-16952-6_12
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