Price pressures

We study price pressures in stock prices—price deviations from fundamental value due to a risk-averse intermediary supplying liquidity to asynchronously arriving investors. Empirically, twelve years of daily New York Stock Exchange intermediary data reveal economically large price pressures. A $100,000 inventory shock causes an average price pressure of 0.28% with a half-life of 0.92 days. Price pressure causes average transitory volatility in daily stock returns of 0.49%. Price pressure effects are substantially larger with longer durations in smaller stocks. Theoretically, in a simple dynamic inventory model the ‘representative’ intermediary uses price pressure to control risk through inventory mean reversion. She trades off the revenue loss due to price pressure against the price risk associated with remaining in a nonzero inventory state. The model’s closed-form solution identifies the intermediary’s relative risk aversion and the distribution of investors’ private values for trading from the observed time series patterns. These allow us to estimate the social costs—deviations from constrained Pareto efficiency—due to price pressure which average 0.35 basis points of the value traded. JEL Classification: G12, G14, D53, D6

Does algorithmic trading improve liquidity?

Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic message traffic (order submissions, cancellations, and executions) as a proxy for algorithmic trading, and we trace the associations between liquidity and message traffic. Based on within-stock variation, we find that algorithmic trading and liquidity are positively related. To sort out causality, we use the start of autoquoting on the NYSE as an exogenous instrument for algorithmic trading. Previously, specialists were responsible for manually disseminating the inside quote. As stocks were phased in gradually during early 2003, the manual quote was replaced by a new automated quote whenever there was a change to the NYSE limit order book. This market structure change provides quicker feedback to traders and algorithms and results in more message traffic. For large-cap stocks in particular, quoted and effective spreads narrow under autoquote and adverse selection declines, indicating that algorithmic trading does causally improve liquidity

Limit order books and trade informativeness

In the microstructure literature, information asymmetry is an important determinant of market liquidity. The classic setting is that uninformed dedicated liquidity suppliers charge price concessions when incoming market orders are likely to be informationally motivated. In limit order book markets, however, this relationship is less clear, as market participants can switch roles, and freely choose to immediately demand or patiently supply liquidity by submitting either market or limit orders. We study the importance of information asymmetry in limit order books based on a recent sample of thirty German DAX stocks. We find that Hasbrouck’s (1991) measure of trade informativeness Granger-causes book liquidity, in particular that required to fill large market orders. Picking-off risk due to public news induced volatility is more important for top-of-the book liquidity supply. In our multivariate analysis we control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. JEL Classification: G14 Keywords: Price Impact of Trades , Trading Intensity , Dynamic Duration Models, Spread Decomposition Models , Adverse Selection Ris

Customer flow, intermediaries, and the discovery of the equilibrium riskfree rate

Macro announcements change the equilibrium riskfree rate. We find that treasury prices reflect part of the impact instantaneously, but intermediaries rely on their customer order flow in the 15 minutes after the announcement to discover the full impact. We show that this customer flow informativeness is strongest at times when analyst forecasts of macro variables are highly dispersed. We study 30 year treasury futures to identify the customer flow. We further show that intermediaries appear to benefit from privately recognizing informed customer flow, as, in the cross-section, their own-account trade profitability correlates with access to customer orders, controlling for volatility, competition, and the announcement surprise. These results suggest that intermediaries learn about equilibrium riskfree rates through customer orders

Competition for order flow and smart order routing systems

In this paper, the authors study the changes in liquidity following the introduction of a new electronic limit order market when, prior to its introduction, trading is centralized in a single limit order market. They also study how automation of routing decisions and trading fees affect the relative liquidity of rival markets.market fragmentation; centralized limit order book; smart routers; trading fees; trade-throughs

Modelling Round-the-Clock Price Discovery for Cross-Listed Stocks using State Space Methods

U.S. trading in non-U.S. stocks has grown dramatically. Round-the-clock, these stocks trade in the home market, in the U.S. market and, potentially, in both markets simultaneously. We develop a general methodology based on a state space model to study 24-hour price discovery in a multiple markets setting. As opposed to the standard variance ratio approach, this model deals naturally with (i) simultaneous quotes in an overlap, (ii) missing observations in a non-overlap, (iii) noise due to transitory microstructure effects, and (iv) contemporaneous correlation in returns due to market-wide factors. We provide an application of our model to Dutch-U.S. stocks. Our findings suggest a minor role for the NYSE in price discovery for Dutch shares, in spite of its non-trivial and growing market share. The results differ significantly from the variance ratio approach

Macro News, Riskfree Rates, and the Intermediary

Signed customer order flow correlates with permanent price changes in equity and nonequity markets. We exploit macro news events in the 30Y treasury futures market to identify causality from customer flow to riskfree rates. We remove the positive feedback trading part and establish that, in the 15 minutes subsequent to the news, intermediaries rely on customer orders to determine a substantial part of the announcement's effect on riskfree rates, i.e. one-third relative to the instantaneous effect. They appear to benefit from privately observing informed customers, as, in the cross-section, their own-account trade profitability correlates with access to customer flow, controlling for volatility, competition, and the macro ``surprise''

Equilibrium bitcoin pricing

We offer an overlapping generations equilibrium model of cryptocurrency pricing and confront it to new data on bitcoin transactional benefits and costs. The model emphasizes that the fundamental value of the cryptocurrency is the stream of net transactional benefits it will provide, which depend on its future prices. The link between future and present prices implies that returns can exhibit large volatility unrelated to fundamentals. We construct an index measuring the ease with which bitcoins can be used to purchase goods and services, and we also measure costs incurred by bitcoin owners. Consistent with the model, estimated transactional net benefits explain a statistically significant fraction of bitcoin returns

Equilibrium bitcoin pricing

We offer an overlapping generations equilibrium model of cryptocurrency pricing and confront it to new data on bitcoin transactional benefits and costs. The model emphasizes that the fundamental value of the cryptocurrency is the stream of net transactional benefits it will provide, which depend on its future prices. The link between future and present prices implies that returns can exhibit large volatility unrelated to fundamentals. We construct an index measuring the ease with which bitcoins can be used to purchase goods and services, and we also measure costs incurred by bitcoin owners. Consistent with the model, estimated transactional net benefits explain a statistically significant fraction of bitcoin returns