Zahra Pourahmadi; Dariush Farid; Hamid Reza Mirzaei
Abstract
Stock trading is a significant decision-making problem in asset management. This study introduces a financial trading system (FTS) that leverages artificial intelligence (AI) techniques to automate buy and sell orders specifically in Iran's stock market. Due to limited availability of labeled data in ...
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Stock trading is a significant decision-making problem in asset management. This study introduces a financial trading system (FTS) that leverages artificial intelligence (AI) techniques to automate buy and sell orders specifically in Iran's stock market. Due to limited availability of labeled data in financial markets, the FTS utilizes reinforcement learning (RL), a subset of AI, for training. The model incorporates technical analysis and a constrained policy to enhance decision-making capabilities. The proposed algorithm is applied to the Tehran Securities Exchange, evaluating its efficiency across 45 periods using three different stock market indices. Performance comparisons are made against common strategies such as buy and hold, randomly selected actions, and maintaining the initial stock portfolio, with and without transaction costs. The results indicate that the FTS outperforms these methods, exhibiting excellent performance metrics including Sharp ratio, PP, PF, and MDD. Consequently, the findings suggest that the FTS serves as a valuable asset management tool in the Iranian financial market.
Abbas Shekari Firouzjaie; Navid Sahebjamnia; Hadi Abdollahzade
Abstract
Determining the optimal selling price for different commodities has always been one of the main topics of scientific and industrial research. Perishable products have a short life and due to their deterioration over time, they cause great damage if not managed. Many ...
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Determining the optimal selling price for different commodities has always been one of the main topics of scientific and industrial research. Perishable products have a short life and due to their deterioration over time, they cause great damage if not managed. Many industries, retailers, and service providers have the opportunity to increase their revenue through optimal pricing of perishable products that must be sold within a certain period. In the pricing issue, a seller must determine the price of several units of a perishable or seasonal product to be sold for a limited time. This article examines pricing policies that increase revenue for the sale of a given inventory with an expiration date. Booster learning algorithms are used to analyze how companies can simultaneously learn and optimize pricing strategy in response to buyers. It is also shown that using reinforcement learning we can model a demand-dependent problem. This paper presents an optimization method in a model-independent environment in which demand is learned and pricing decisions are updated at the moment. We compare the performance of learning algorithms using Monte Carlo simulations.