Detecting the Optimal Trading Strategy in the Stock Exchange, with the Application of Dynamic Programming
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.
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.
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.
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