A stock market crash alarm system based on a Hurst Index Neural Network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Stock market events are commonly modelled using a nor- mal distribution. This approach leaves out big heavy tails, and non- parametric methods such as neural networks can be an appropriate al- ternative approach to the study of non-normal behavior. However, learn- ing devices which are too generic in use may lead to non-ideal decisions, which result in nancial losses. To solve this problem, and de ne a de- fensive investment strategy, we use core method ideas for encoding two functions, moving average and Hurst exponent, which work as indicators over a nancial time series. The multilayer perceptron can then act as a warning system for stock market crashes, avoiding negative asset re- turns, and decreasing the investment risk. Simulation is performed using EURO STOXX 50 nancial index to illustrate the potential of such a strategy.
Original languageUnknown
Title of host publicationNew Trends in Artificial Intelligence, Procedings of the Portuguese Conference on Artificial Intelligence
Publication statusPublished - 1 Jan 2011
EventEPIA -
Duration: 1 Jan 2011 → …


Period1/01/11 → …

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