Dynamic Multi-Factor Bid-Offer Adjustment Model: A Feedback Mechanism for Dealers (Market Makers) to Deal (Grapple) with the Uncertainty Principle of the Social Sciences

TL;DR

This paper introduces a dynamic, feedback-based model for adjusting bid-ask spreads in financial markets, incorporating market volatility and trade activity to adapt to changing conditions in real-time.

Contribution

It presents a novel adaptive bid-offer adjustment model that integrates multiple market factors and feedback mechanisms, applicable across various financial instruments.

Findings

Model effectively captures market volatility and trade volume effects.

Simulations demonstrate the model's adaptability in currency markets.

Potential for extension to other electronically traded products.

Abstract

The author seeks to develop a model to alter the bid-offer spread, currently quoted by market makers, that varies with the market and trading conditions. The dynamic nature of financial markets and trading, as with the rest of social sciences, where changes can be observed and decisions can be made by participants to influence the system, means that this model has to be adaptive and include a feedback loop that alters the bid-offer adjustment based on the modifications observed in the market and trading conditions, without a significant time delay. The factors used to adjust the spread are price volatility, which is publicly observable, and trade count and volume, which are generally known only to the market maker, in various instruments over different historical durations in time. The contributions of each factor to the bid-offer adjustment are computed separately and then consolidated to produce a very adaptive bid-offer quotation. The author uses the currency markets to build the sample model because they are extremely liquid and trading in them is not as transparent as other financial instruments, such as equities. Simulating the number of trades and the average size of trades from a lognormal distribution, the parameters of the lognormal distributions are chosen such that the total volume in a certain interval matches the volume publicly mentioned by currency trading firms. This methodology can easily be extended to other financial instruments and possibly to any product with the ability to make electronic price quotations, or can even be used to periodically perform manual price updates on products that are traded non-electronically.