Performance Evaluation of Market-Making Methods in the Iranian Stock Market

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Mousavi Kejani, Masoud | 2023

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 56362 (44)
University: Sharif University of Technology
Department: Management and Economics
Advisor(s): Talebian, Masoud; Heidari, Mahdi
Abstract:
Market making is a fundamental trading topic in which an agent creates liquidity in an asset by offering to buy and sell on that security. The challenging problem in market-making is related to inventory risk, which may cause the accumulation of unfavorable positions at the end of the market and create losses. Algorithms are designed for making trades to choose the buying and selling prices and the number of orders by predicting the price to minimize the amount of security in the market maker’s portfolio. In this paper, first, we examine the different market-making algorithms and evaluate their performance in the financial markets of Iran. Then, a model using the reinforcement learning method is designed based on the inputs received from the limit order book with the profitability of the market maker as the reward function. This model performs the learning process on the training data and then conducts high-frequency transactions for market-making on the test data. The results show that the market-maker can learn appropriately in this method, which has led to his profitability
Keywords:
Liquidity ; Limit Order Book ; Reinforcement Learning ; Market Making ; Bid-Ask Spread ; High Frequency Trading (HFT) ; Stock Market

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