Automated Market Maker Strategies

This article of QuantVan is a brief of a paper from the University of Nebraska, Omaha.

Market Making strategies:

In the past, several market-maker strategies have been proposed and there have been a few studies on the market-maker‘s effect on the market. most of these past studies focus on a market with a single market-maker or a market with multiple market-makers of the same strategy. However, there does not exist a study comparing the effect of different market-maker strategies in a market with multiple market-makers.

below, we have the analysis of the effect of each market-making strategy and the combinations of strategies on the market price dynamics.

The fundamental role of a market-maker is to bring buyers and sellers together so that trading can occur in an efficient and fair manner.

Automated Market Makers:

The main advantage of automated market-makers is its ability to maintain liquidity in the market.

Additionally, market-makers can help to smooth price fluctuations due to spurious supplies or demands.

As the role of the market-makers grows, the need for a better understanding of the impact of the market-makers in the market increases as well.

Comparison of Automated strategies:

Four different strategies for automated market-making had investigated:

In financial markets:

  • a myopically optimizing strategy,
  • a reinforcement learning strategy,
  • a market scoring rule,
  • and a utility-maximizing strategy.

The goal was to test and find out the weakness and strengths point of strategies

The myopically optimizing and market scoring rule-based strategies perform well in maintaining low spread and smooth market price. They , however,fall short in maximizing utilities as compared to other strategies.

On the other hand, the utility-maximizing strategy with different risk attributes performs very well in obtaining high utilities, although it fails in maintaining low and consistent spread.

Consequently, the market price tends to fluctuate significantly.

Finally, the reinforcement learning strategy fulfills its tasks of both controlling the spread and maximizing utility.

A model with a single, monopolistic market-maker, who sets prices, receives orders and clears trades, and tries to maximize expected profit per unit time. Such a market-maker fails when it runs out of inventory or cash.

The optimal behavior of a single market-maker who gets a stochastic demand and tries to maximize its expected utility of final wealth, which depends on the profit it receives from trading. A heuristic strategy that adds a random value to zero-profit market-makers improves the profits in the markets.

The results show that a profit-maximizing market-maker’s objectives may not align with price variance minimization.                                   This can be one of the qualities of an orderly market.

Market Maker Goals:

The market-maker price adjustment reactions differ depending on the current inventory position along with current excess demands. The market-maker is assumed to make greater price adjustments.

Market-making can also be as a test-bed for machine learning techniques.

it can be with a goal to demonstrate the general effectiveness of a learning algorithm.

Also, empirical work has demonstrated the limitations of hard-coding market-making rules into an algorithm.

The primary goal is to optimally change the spread over the next iteration instead of finding the best model for past transactions.