Stock Picking by Probability–Possibility Approaches

This paper presents a performance evaluation of stock picking by merging several technical indicators. Several fusion operators have been proposed either in the probabilistic or in the possibilistic framework. The latter fuzzy framework has been introduced to manage the uncertain information embedded in financial time series due to human biases as studied by behavioral finance. Performances of portfolio resulting from the proposed systems are evaluated according to cumulative returns but also through a risk analysis point of view (Sharpe ratio). Two fusion mechanisms (one probabilistic, one possibilistic) aiming at discriminating common information from merged technical indicators, produce the higher portfolio performances. It also appears that selecting specific technical indicators affects the overall performances of the proposed stock picking systems. Indeed, studying the technical indicators selection through a shared/nonshared information point of view reveals that possibilistic framework is more robust to redundant sources than probabilistic framework. Effects of some parameters used in the fusion algorithms (amount of assets, window length analysis, ...) are also investigated. Results from all these tests clearly show the high potentiality of technical indicators fusion to improve portfolio performances. However, these first promising results have to be further inspected within wider contexts, as discussed at the end of the paper.