nep-mst New Economics Papers
on Market Microstructure
Issue of 2019‒10‒21
three papers chosen by
Thanos Verousis

  1. Limit order books, uninformed traders and commodity derivatives: Insights from the European carbon futures By Yves Rannou
  2. The Value of Intermediation in the Stock Market By Di Maggio, Marco; Egan, Mark; Franzoni, Francesco
  3. Eigen-entropy measure to study phase separation in market behavior By Anirban Chakraborti; Hrishidev; Kiran Sharma; Hirdesh K. Pharasi

  1. By: Yves Rannou (CleRMa - Clermont Recherche Management - Clermont Auvergne - École Supérieure de Commerce (ESC) - Clermont-Ferrand - UCA - Université Clermont Auvergne, Groupe ESC Clermont-Ferrand - Clermont Université)
    Abstract: This paper provides a suitable model for studying the strategic behavior of uninformed investors that trade commodity derivatives via limit order books. Two main testable implications are obtained after solving for the model equilibrium. The adverse selection costs of uninformed traders depend on the inflow of market orders and their risk aversion. Next, the adverse selection costs of uninformed buyers and sellers and the difference of their asset valuations determine the size of their bid-ask spread. An analysis of European carbon futures data confirms the relevance of these implications. Moreover, we detect a diagonal effect that results in a positive correlation of market orders, which is driven by adverse selection, then by order splitting strategies and by imitative strategies of uninformed traders to a lesser extent.
    Keywords: Uninformed traders,Market microstructure,European carbon futures,Bid-ask spread,Limit order book
    Date: 2019–09
  2. By: Di Maggio, Marco; Egan, Mark; Franzoni, Francesco
    Abstract: Brokers continue to play a critical role in intermediating institutional stock market transactions. More than half of all institutional investor order flow is still executed by high-touch (non-electronic) brokers. Despite the continued importance of brokers, we have limited information on what drives investors' choices among them. We develop and estimate an empirical model of broker choice that allows us to quantitatively examine each investor's' responsiveness to execution costs and access to research and order flow information. Studying over 300 million institutional trades, we find that investor demand is relatively inelastic with respect to commissions and that investors are willing to pay a premium for access to top research analysts and order-flow information. There is substantial heterogeneity across investors. Relative to other investors, hedge funds tend to be more price insensitive, place less value on sell-side research, and place more value on order-flow information. Furthermore, using trader-level data, we find that investors are more likely to trade with traders who are located physically closer and are less likely to trade with traders that have misbehaved in the past. Lastly, we use our empirical model to investigate the unbundling of equity research and execution services related to the MiFID II regulations. While under-reporting for the average firm is relatively small (4%), we find that the bundling of execution and research allows some institutional investors to under-report management fees by up to 15%.
    Keywords: Broker Networks; Equity Trading; Financial Intermediation; institutional investors; Research Analysts
    JEL: G14 G23 G24 L11
    Date: 2019–08
  3. By: Anirban Chakraborti; Hrishidev; Kiran Sharma; Hirdesh K. Pharasi
    Abstract: One of the spectacular examples of a complex system is the financial market, which displays rich correlation structures among price returns of different assets. The eigenvalue decomposition of a correlation matrix into partial correlations - market, group and random modes, enables identification of dominant stocks or "influential leaders" and sectors or "communities". The correlation-based network of leaders and communities changes with time, especially during market events like crashes, bubbles, etc. Using a novel entropy measure - eigen-entropy, computed from the eigen-centralities (ranks) of different stocks in the correlation-network, we extract information about the "disorder" (or randomness) in the market and its modes. The relative-entropy measures computed for these modes enable us to construct a "phase space", where the different market events undergo "phase-separation" and display "order-disorder" transitions, as observed in critical phenomena in physics. We choose the US S&P-500 and Japanese Nikkei-225 financial markets, over a 32-year period, and study the evolution of the cross-correlation matrices computed over different short time-intervals or "epochs", and their corresponding eigen-entropies. We compare and contrast the empirical results against the numerical results for Wishart orthogonal ensemble (WOE), which has the maximum disorder (randomness) and hence, the highest eigen-entropy. This new methodology helps us to better understand market dynamics, and characterize the events in different phases as anomalies, bubbles, crashes, etc. This can be easily adapted and broadly applied to the studies of other complex systems such as in brain science or environment.
    Date: 2019–10

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