A generalized model for financial time series representation and prediction |
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Authors: | Depei Bao |
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Affiliation: | (1) Tsinghua University, Beijing, 100084, China |
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Abstract: | Traditional financial analysis systems utilize low-level price data as their analytical basis. For example, a decision-making
system for stock predictions regards raw price data as the training set for classifications or rule inductions. However, the
financial market is a complex and dynamic system with noisy, non-stationary and chaotic data series. Raw price data are too
random to characterize determinants in the market, preventing us from reliable predictions. On the other hand, high-level
representation models which represent data on the basis of human knowledge of the problem domain can reduce the randomness
in the raw data. In this paper, we present a high-level representation model easy to translate from low-level data into the
machine representation. It is a generalized model in that it can accommodate multiple financial analytical techniques and
intelligent trading systems. To demonstrate this, we further combine the representation with a probabilistic model for automatic
stock trades and provide promising results.
An erratum to this article can be found at |
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Keywords: | Financial market Intelligent trading system Financial series representation Generalized model Critical point model |
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