Self-Organizing Maps and Financial Forecasting: an Application

  • Marina Resta
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 78)


Financial markets provide a singular field of analysis and exciting challenges for researchers. During the past decades, strongest assumptions on financial time-series (namely the Random Walk Hypothesis) have been partially discharged, and useful new paradigms (such as chaos, self similarity, self-organized criticality) have been discovered to this field. One of the arguments with respect to random walk is that chaotic time series can be long term random, and still have short term patterns. If, in fact, the marketplace has short term patterns, and some technicians think it does, then there are windows of opportunity that the random walker either denies exist or are unable to be exploited. Self Organizing Maps (SOMs), therefore, offer a powerful tool, suitable to explore financial markets, in order to find significant patterns, and use them as a forecasting tool. Additionally, the possibility to detect features of the market can be employed to find out the statistics of the process under examination, hence to decide whether or not the Random Walk Hypothesis is acceptable. This chapter will explore the use of Self Organizing Maps for such purpose. Both the conditions needed for data to be treated, and the evaluation of results obtained are considered.


Lyapunov Exponent Correlation Dimension Financial Time Series Chaotic Time Series Financial Forecast 
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  • Marina Resta

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