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The needs of Market Making Strategies
According to a research in Nebraska, Over the past few years, the rapid growth and success of automated techniques for e-commerce have resulted in their wide adoption in various domains beyond traditional B2B and B2C commodity markets.
As the role of the market-makers grows, the need for better understanding of the impact of the market-makers in the market increases as well.
Finally, the reinforcement learning strategy fulfills its tasks of both controlling the spread and maximizing utility.
History of market making
The automation of a market-makers’s functions was suggested more than three decades ago. Previously, several theoretical approaches, albeit with certainsimplifying assumptions, have been proposed to understand the effects of market makers on financial markets.
Designing a strategy based on :
This is the first step in performing a comparison of multiple market-maker strategies. In the future, we wish to explore different extensions of this work. First of all, we would like to propose and perform comparisons of other market-maker strategies such as using a minimax regret algorithm for price adjustments by the market-maker. Secondly, we would like to study the performance of the market makers with a more complex behavior, such as dynamically switching strategies based on past performance. This way, a better balance of maintaining marketquality and maximizing market-maker utilities may be obtained. And lastly, we would like to add various behavioral attributes to the market-maker model such as different risk attributes and making untruthful price revelations through bluffing for improving profits.
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