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Strategic Learning and Trading in Broker-Mediated Markets

Strategic Learning and Trading in Broker-Mediated Markets ArXiv ID: 2412.20847 “View on arXiv” Authors: Unknown Abstract We study strategic interactions in a broker-mediated market. A broker provides liquidity to an informed trader and to noise traders while managing inventory in the lit market. The broker and the informed trader maximise their trading performance while filtering each other’s private information; the trader estimates the broker’s trading activity in the lit market while the broker estimates the informed trader’s private signal. Brokers hold a strategic advantage over traders who rely solely on prices to filter information. We find that information leakage in the client’s trading flow yields an economic value to the broker that is comparable to transaction costs; she speculates profitably and mitigates risk effectively, which, in turn, adversely impacts the informed trader’s performance. In contrast, low signal-to-noise sources, such as prices, result in the broker’s trading performance being indistinguishable from that of a naive strategy that internalises noise flow, externalises informed flow, and offloads inventory at a constant rate. ...

December 30, 2024 · 2 min · Research Team

How Competitive is the Stock Market? Theory, Evidence from Portfolios, and Implications for the Rise of Passive Investing

How Competitive is the Stock Market? Theory, Evidence from Portfolios, and Implications for the Rise of Passive Investing ArXiv ID: ssrn-3821263 “View on arXiv” Authors: Unknown Abstract The conventional wisdom in finance is that competition is fierce among investors: if a group changes its behavior, others adjust their strategies such that noth Keywords: Market Efficiency, Investor Behavior, Game Theory, Strategic Interaction, Equities Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs a semi-structural economic model with equilibrium conditions, endogenous elasticities, and formal estimation challenges (reflection problem, endogeneity), requiring advanced mathematics. It is empirically rigorous, using detailed institutional portfolio data and a novel identification strategy with instruments to estimate the demand system and the strategic response of investors. flowchart TD A["Research Goal: Quantify investor competition<br>and its implications for passive investing"] --> B["Methodology: Game-theoretic model<br>of strategic portfolio choice"] B --> C["Data: US equity market portfolios<br>1980-2015 (CRSP)"] C --> D["Computational Process:<br>Simulate competitive equilibria<br>under varying investor assumptions"] D --> E["Key Findings:<br>1. Competition is strong but incomplete<br>2. Passive investing reduces competition<br>3. Market efficiency varies with investor structure"]

April 7, 2021 · 1 min · Research Team