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We look at discovering the impact of market microstructure on equitabilityfor market participants at public exchanges such as the New York Stock Exchangeor NASDAQ. Are these environments equitable venues for low-frequencyparticipants (such as retail investors)? In particular, can market makerscontribute to equitability for these agents? We use a simulator to assess theeffect a market marker can have on equality of outcomes for consumer or retailtraders by adjusting its parameters. Upon numerically quantifying marketequitability by the entropy of the price returns distribution of consumeragents, we demonstrate that market makers indeed support equitability and thata negative correlation is observed between the profits of the market maker andequitability. We then use multi objective reinforcement learning toconcurrently optimize for the two objectives of consumer agent equitability andmarket maker profitability, which leads us to learn policies that facilitatelower market volatility and tighter spreads for comparable profit levels.

Profit equitably: An investigation of market maker's impact on equitable outcomes