EL HANAFI, Imad (2016) Testing optimal detection of support and resistance levels against market data PRE - Research Project, ENSTA.
Available under License Creative Commons Attribution Non-commercial.
Recently the theory of optimal stopping was used to introduce a mathematical model to predict the formation of support and resistance levels under the assumption that these can be described as unobservable random variables independent of the initial value of the asset . Such assumption is consistent with the aspiration level hypothesis often used in financial economics. The main aim of this project is to test these results against real market data. This requires a detailed analysis of the model assumptions and of its fundamental theoretical aspects. Statistical tools and technical analysis are used to calibrate the parameters of the model according to the observation of past stock prices and (observed) support/resistance levels. Once the input parameters are set the support and resistance predicted by the model will be found by evaluating numerically suitable non-linear equations of Volterra type. Finally, we test the performance of the proposed model against market data.
|Item Type:||Thesis (PRE - Research Project)|
|Uncontrolled Keywords:||Optimal stopping theory, resistance and support levels, technical analysis, geometric brownian motion|
|Subjects:||Mathematics and Applications|
|Deposited By:||Imad El Hanafi|
|Deposited On:||13 oct. 2016 11:26|
|Dernière modification:||13 oct. 2016 11:26|
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