Authors:
Zhen Zhang
and
Dave Cliff
Affiliation:
Department of Computer Science, University of Bristol, Bristol BS8 1UB, U.K.
Keyword(s):
Market Impact, Adaptive Trader Agents, Financial Markets, Multi-Agent Systems.
Abstract:
Financial markets populated by human traders often exhibit so-called “market impact”, where the prices quoted by traders move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., “block”) buy or sell order in the market: traders in the market know that a block buy order is likely to push the price up, and that a block sell order is likely to push the price down, and so they immediately adjust their quote-prices accordingly. In most major financial markets nowadays very many of the participants are “robot traders”, autonomous adaptive software agents, rather than humans. This paper addresses the question of how to give such trader-agents a reliable anticipatory sensitivity to block orders, such that markets populated entirely by robot traders also show market-impact effects. This is desirable because impact-sensitive trader-agents will get a better price for their transactions when block orders arrive
, and because such traders can also be used for more accurate simulation models of real-world financial markets. In a 2019 publication Church & Cliff presented initial results from a simple deterministic robot trader, called ISHV, which was the first such trader-agent to exhibit this market impact effect. ISHV does this via monitoring a metric of imbalance between supply and demand in the market. The novel contributions of our paper are: (a) we critique the methods used by Church & Cliff, revealing them to be weak, and argue that a more robust measure of imbalance is required; (b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al., 2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we demonstrate the use of the more robust MLOFI measure in extending ISHV, and also the well-known AA and ZIP trading-agent algorithms (which have both been previously shown to consistently outperform human traders). Our results demonstrate that the new imbalance-sensitive trader-agents introduced in this paper do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff. We have made the source-code for our work reported here freely available on GitHub.
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