Net Order Flow Research Articles

A Mean Field Game Approach to Equilibrium Pricing with Market Clearing Condition

In this work, we study an equilibrium-based continuous asset pricing problem which seeks to form a price process endogenously by requiring it to balance the flow of sale-and-purchase orders in the exchange market, where a large number of agents $1\leq i\leq N$ are interacting through the market price. Adopting a mean field game approach, we find a special form of forward-backward stochastic differential equations of McKean--Vlasov type with common noise whose solution provides an approximate of the market price. We show the convergence of the net order flow to zero in the large $N$-limit and get the order of convergence in $N$ under some conditions.

Conditional correlation between exchange rates and stock prices

This paper develops a model to explain a time-varying and currency-dependent interaction between stock prices and exchange rates. The linkage is proposed based on portfolio managers’ rebalance between foreign and domestic stocks. Stock prices in two industrialized countries are usually correlated but have different sensitivities to a common risk factor. When a common positive shock occurs, the country with higher sensitivity will attract relatively more investment on their stocks, which generates a net order flow toward that country’s currency in the foreign exchange market and further causes the country’s currency to appreciate. The paper concludes that the correlation between exchange rates and stock prices is determined by the relative sensitivity of two countries’ stock prices to the common stock factor. In specific, rising US stock prices are associated with appreciation(depreciation) of the Dollar when US(foreign) stock prices have higher sensitivity.

News and Intraday Retail Investor Order Flow in Foreign Exchange Markets

This paper examines the trading behavior of individual investors using a proprietary intraday dataset of a large pool of retail investor aggregate (minute by minute) long and short positions in EUR/USD for the period July 2014 to April 2016. Standard event study analysis shows no significant adjustment in trading ahead of scheduled macro news announcements and trading contrary to the announcement surprise after the event. A panel regression analysis shows that the contrarian trading behavior is mostly driven by lagged returns rather than fundamental macro news. Further intraday time series analysis shows that the lagged overall news sentiment also significantly affects investor net order flow. Finally, we show that simple cross-over trading strategies that exploit retail investor order flow imbalance are profitable. Overall our results suggest that retail investors in currency markets follow the news and are influenced by news sentiment and past returns, but do not possess the skills to extract fundamental information from public news. Their trading behavior contributes to the slowdown of the price discovery process. Our findings support the differential abilities of market participants to interpret public information as an explanation for the importance of order flow in price formation in currency markets.

A Mean Field Game Approach to Equilibrium Pricing with Market Clearing Condition

In this work, we study an equilibrium-based continuous asset pricing problem which seeks to form a price process endogenously by requiring it to balance the flow of sales-and-purchase orders in the exchange market, where a large number of agents are interacting through the market price. Adopting a mean field game (MFG) approach, we find a special form of forward-backward stochastic differential equations of McKean-Vlasov type with common noise whose solution provides a good approximate of the market price. We show the convergence of the net order flow to zero in the large N-limit and get the order of convergence in N under some conditions. We also extend the model to a setup with multiple populations where the agents within each population share the same cost and coefficient functions but they can be different population by population.

Who trades in competing firms around earnings announcements

This study investigates individual and institutional trading activities around earnings announcements. We find evidence of individual and institutional informed trading in competing firms before earnings announcements. We also observe that institutional trading exhibits quicker incorporation of new information, which suggests that institutional investors are more likely to use private information in trading in competing firms. The individual net order flow transmits information cross-stock when a competitor is a small firm, while institutional net order flow conveys information cross-stock irrespective of firm size. Finally, we find that institutional trading in competing firms exhibits cross-stock price impact before earnings announcements.

Co-impact: crowding effects in institutional trading activity

This paper is devoted to the important yet unexplored subject of crowding effects on market impact, that we call ‘co-impact’. Our analysis is based on a large database of metaorders by institutional investors in the U.S. equity market. We find that the market chiefly reacts to the net order flow of ongoing metaorders, without individually distinguishing them. The joint co-impact of multiple contemporaneous metaorders depends on the total number of metaorders and their mutual sign correlation. Using a simple heuristic model calibrated on data, we reproduce very well the different regimes of the empirical market impact curves as a function of volume fraction φ: square-root for large φ, linear for intermediate φ, and a finite intercept when . The value of grows with the sign correlation coefficient. Our study sheds light on an apparent paradox: How can a non-linear impact law survive in the presence of a large number of simultaneously executed metaorders?

Modelling Transaction Costs When Trades May Be Crowded: A Bayesian Network Using Partially Observable Orders Imbalance

Using a large database of US institutional investors’ trades in the equity market, this paper explores the effect of simultaneous executions on trading cost. We design a Bayesian network modelling the inter-dependencies between investors’ transaction costs, stock characteristics (bid-ask spread, turnover and volatility), meta-order attributes (side and size of the trade) and market pressure during execution, measured by the net order flow imbalance of investors meta-orders. Unlike standard machine learning algorithms, Bayesian networks are able to account for explicit inter-dependencies between variables. They also prove to be robust to missing values, as they are able to restore their most probable value given the state of the world. Order flow imbalance being only partially observable (on a subset of trades or with a delay), we show how to design a Bayesian network to infer its distribution and how to use this information to estimate transaction costs. Our model provides better predictions than standard (OLS) models. The forecasting error is smaller and decreases with the investors' order size, as large orders are more informative on the aggregate order flow imbalance (R2 increases out-of-sample from -0.17% to 2.39% for the smallest to the largest decile of order size). Finally, we show that the accuracy of transaction costs forecasts depends heavily on stock volatility, with a coefficient of 0.78.

The impact of the US stock market opening on price discovery of government bond futures

AbstractWe examine price discovery in sequential markets for the 10‐year US Treasury note, German bund, and UK gilt futures over the period 2010–2017. We find that price discovery increases after the opening of the US stock market. Order flows in the bond futures markets are more informative for permanent price changes in the 30‐min period after the US stock market opens. A placebo test using US statutory holidays confirms our findings. A cross‐market analysis suggests that the increased price discovery in the bond futures is related to returns and net order flows of the US stock market.

Co-Impact: Crowding Effects in Institutional Trading Activity

This paper is devoted to the important yet unexplored subject of crowding effects on market impact, that we call co-impact. Our analysis is based on a large database of metaorders by institutional investors in the U.S. equity market. We find that the market chiefly reacts to the net order flow of ongoing metaorders, without individually distinguishing them. The joint co-impact of multiple contemporaneous metaorders depends on the total number of metaorders and their mutual sign correlation. Using a simple heuristic model calibrated on data, we reproduce very well the different regimes of the empirical market impact curves as a function of volume fraction Φ: square-root for large Φ, linear for intermediate Φ, and a finite intercept I_0 when Φ to 0. The value of I_0 grows with the sign correlation coefficient. Our study sheds light on an apparent paradox: How can a non-linear impact law survive in the presence of a large number of simultaneously executed metaorders?

Individual and Institutional Informed Trading in Competing Firms Around Earnings Announcements

This study investigates individual and institutional trading in competing firms around earnings announcements. We find individual and institutional informed trading in competing firms, which is dominant prior to earnings announcements. Magnitude of institutional (individual) net order flow coefficient decreases (increases) with lag length, suggesting that institutional trading captures information faster than individual trading. Individual net order flow transmit information cross-stock when competitor is a small firm while institutional net order flow conveys information cross-stock irrespective of firm size. Our results are informative for regulators regarding insider trading laws and provide insights for market participants in understanding individual and institutional trading impact on cross-stock price discovery process.

Individual and Institutional Informed Trading in Competing Firms Around Earnings Announcements

This study investigates individual and institutional trading in competing firms around earnings announcements. We find individual and institutional informed trading in competing firms, which is dominant prior to earnings announcements. Magnitude of institutional (individual) net order flow coefficient decreases (increases) with lag length, suggesting that institutional trading captures information faster than individual trading. Individual net order flow transmit information cross-stock when competitor is a small firm while institutional net order flow conveys information cross-stock irrespective of firm size. Our results are informative for regulators regarding insider trading laws and provide insights for market participants in understanding individual and institutional trading impact on cross-stock price discovery process.

Who Is Informed in Options Market?

This paper studies the information content of options net order flows and its effect on stock returns. The order flows are separated in customer-initiated (retail or institution) and firm initiated (proprietary desks of institutions) orders. These two types of order flows are negatively correlated and predict stock returns with opposite signs. Customer-initiated net trading volume is largely driven by positive information about underlying and positively predicts stock returns. Firm order flows recognize both future positive and negative news, supply liquidity to overoptimistic customer order flows, largely short-sell underlying stocks in the options market, and negatively predict stock returns. There is also a clientele effect: customer order flows prefer long options positions to short positions, and trade more in call options; firm order flows prefer short options positions and trade in both, calls and puts.

Order-Flow and Liquidity Provision

We show how to optimally take positions in the limit order book by placing limit orders at-the-touch when the midprice of the asset is affected by the trading activity of the market. The midprice dynamics have a short-term-alpha component which reflects how instantaneous net order-flow, the difference between the number of market buy and market sell orders, affects the asset's drift. If net-order flow is positive (negative), so short-term-alpha is positive (negative), the strategy may even withdraw from the sell (buy) side of the limit order book to take advantage of inventory appreciation (depreciation) and to protect the trading strategy from adverse selection costs.

Asymmetric effect of market liquidity demand shocks on price shocks

Purpose – With the launch of CSI 300 Index Futures trading on April 16, 2010, China's stock market presents a more diversified trend, such as arbitrage, trends strategy entering the market rapidly. Therefore, the liquidity demand also presents a higher frequency, and the change is more complex than the original situation. In recent years, many literatures are engaged in high-frequency trading (HFT) related research, and an important concern is the impact of HFT on market volatility and liquidity. Is it playing the role of stabilizing the market, or bringing more noise and turmoil? Based on this, the purpose of this study is trying to study what kind of impact the HFT have on market liquidity before and after the launch of the CSI 300 Index Futures. Design/methodology/approach – The paper uses the simultaneous equations model of price and net order flow proposed by Deuskar and Johnson and for the first time introduces an asymmetric identification through heteroskedasticity (ITH) method. The paper applies the method to the high-frequency data of CSI 300 Index and the Futures and classifies the buying and selling orders through volume clock. The price risks are decomposed into a component driven by the impact of liquidity demand shocks (flow-driven risks (FDRs)) and a component driven by external information (information-driven risks (IDRs)). Findings – The empirical results show that the flow-driven risk of CSI 300 Index Futures is about 20 percent. In addition, before the introduction of the Index Futures, there is no asymmetric effect between liquidity demand shocks and price shocks existing in either CSI 300 Index or CSI 300 Index Futures. While after the introduction of stock Index Futures, the asymmetric effect in the both two markets emerges. The impact of the buying net order flows on the price is less than the impact of the selling net order flows on CSI 300 Index, whereas the impact of the buying net order flows on the price is larger than the impact of the selling net order flows on CSI 300 Index Futures. The paper further analyzes the relationship between liquidity and FDR and gets the conclusion that the reasons for the deterioration of the liquidity level are caused by the impact of the external information shocks, rather than the liquidity demand shocks. And entries of HFTs like arbitrage traders and hedge traders play a positive role in improving the liquidity level in the market. Originality/value – The paper introduces an asymmetric ITH method for the first time and finds asymmetric effect of the net order flow on the return in both CSI 300 Index market and the corresponding Index Futures market.

The Impact of Visible and Dark Orders

Using publicly-available market data we construct a three component order flow imbalance vector that represents executions against displayed quotes, executions against dark liquidity and the net imbalance in the flow of limit orders. The main accomplishment of this paper is to provide empirical evidence on how these different components of order flow affect returns. We find that market order executions against dark liquidity have the smallest impact on stock returns. In contrast, market executions against displayed liquidity provide the highest pressure on market returns, followed by the price impact from the net limit order flow. Drawing from these results, we define a scalar imbalance that correlates to stock returns more closely than the traditional Lee-Ready imbalance.

Cognitive Dissonance, Sentiment, and Momentum

AbstractWe consider whether sentiment affects the profitability of momentum strategies. We hypothesize that news that contradicts investors’ sentiment causes cognitive dissonance, slowing the diffusion of such news. Thus, losers (winners) become underpriced under optimism (pessimism). Short-selling constraints may impede arbitraging of losers and thus strengthen momentum during optimistic periods. Supporting this notion, we empirically show that momentum profits arise only under optimism. An analysis of net order flows from small and large trades indicates that small investors are slow to sell losers during optimistic periods. Momentum-based hedge portfolios formed during optimistic periods experience long-run reversals.

Analysts’ sale and distribution of non fundamental information

We examine an analyst’s sale and distribution of information related to short-term price movements but unrelated to underlying firm value. By selling non fundamental information, the analyst increases competition on the signal, but prices become more sensitive to net order flow, creating an offsetting increase in the non fundamental signal’s value. More precise non fundamental information is more widely distributed. In the limit, a perfect non fundamental signal will be publicly disclosed for an arbitrarily small fee, and the analyst earns profits as if he possessed fundamental information. Consistent with empirical findings, analysts’ recommendations can be profitable, even when widely distributed or seemingly inconsistent with detailed forecasts. Analysis based on non fundamental information does not contribute to greater price efficiency but reduces liquidity costs. In a multi-period setting, traders with non fundamental information do not front-run, preferring to transact only in the period in which uninformed demand is executed.

An investigation of customer order flow in the foreign exchange market

This paper examines the effect that heterogeneous customer orders flows have on exchange rates by using a new, and the largest, proprietary dataset of weekly net order flow segmented by customer type across nine of the most liquid currency pairs. We make several contributions. Firstly, we investigate the extent to which customer order flow can help to explain exchange rate movements over and above the influence of macro-economic variables. Secondly, we address the issue of whether order flows contain (private) information which explain exchange rates changes. Thirdly, we look at the usefulness of order flow in forecasting exchange rate movements at longer horizons than those generally considered in the micro-structure literature. Finally we address the question of whether the out-of-sample exchange rate forecasts generated by order flows can be employed profitably in the foreign exchange markets.

Trading Volume in Dealer Markets

AbstractWe develop a financial market trading model in the tradition of Glosten and Milgrom (1985) that allows us to incorporate nontrivial volume. We observe that in this model price volatility is positively related to the trading volume and to the absolute value of the net order flow (i.e., the order imbalance). Moreover, higher volume leads to higher order imbalances. These findings are consistent with well-established empirical findings. Our model further predicts that higher trader participation and systematic improvements in the quality of traders’ information lead to higher volume, larger order imbalances, lower market depth, shorter duration, and higher price volatility.

Sentiment and Momentum

This paper sheds empirical light on whether sentiment affects the profitability of price momentum strategies. We hypothesize that news that contradicts investors’ sentiment causes cognitive dissonance, which slows the diffusion of signals that oppose the direction of sentiment. This phenomenon tends to cause underpricing of losers under optimism and underpricing of winners under pessimism. While the latter phenomenon can be corrected by arbitrage buying, short-selling constraints impede arbitraging of losers under optimism, causing momentum to be stronger in optimistic periods. Our empirical analysis supports this argument by showing that momentum profits arise only under optimism, and are driven principally by strong momentum in losing stocks. This result survives a host of robustness checks including controls for market returns, firm size and analyst following. An analysis of net order flows from small and large trades indicates that small (but not large) investors are slow to sell losers during optimistic periods. Momentum-based hedge portfolios formed during optimistic periods experience long-run reversals.