Fateme Yazdani; Mehdi Khashei; Seyed Reza Hejazi
Abstract
Purpose: This paper aims to propose a model for detecting the most profitable or the optimal Turning Points (TPs) existing in the history of the financial tool's time series. The profitable trading strategy, which is known as a tool for gaining profit in the Stock Exchange, is the strategy formed from ...
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Purpose: This paper aims to propose a model for detecting the most profitable or the optimal Turning Points (TPs) existing in the history of the financial tool's time series. The profitable trading strategy, which is known as a tool for gaining profit in the Stock Exchange, is the strategy formed from the profitable trading points. Trading points, in the corresponding literature, are known as TPs. TPs prediction is a tool for the achievement of a profitable trading strategy. The first step for predicting TPs is to detect TPs existing in the history of the financial tool's time series. The profitability of the detected TPs has a direct effect on the profitability of the predicted TPs. Given this, the literature has always tried to increase the profitability of the detected financial TPs. A complete review of the literature, by researchers, indicates that none of the existing methods can detect the optimal financial TPs.Methodology: This paper implements the problem of detecting TPs from the financial tool's time series, in the context of dynamic programming (DP) and then solves it optimally through a recursive procedure.Findings: Numerical results obtained from the application of the proposed model to four companies listed on the Tehran Stock Exchange indicate that the proposed model can detect the optimal financial TPs.Originality/Value: Originality in research mean what you are doing is from your own perspective although you may draw arguments from other research work to back up your arguments.