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.
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An erratum to this article can be found at http://dx.doi.org/10.1007/s10489-007-0104-9
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Bao, D. A generalized model for financial time series representation and prediction. Appl Intell 29, 1–11 (2008). https://doi.org/10.1007/s10489-007-0063-1
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DOI: https://doi.org/10.1007/s10489-007-0063-1