Order Flow Imbalance Research Articles

Asymmetric post earnings announcement drift and order flow imbalance: The impact on stock market returns

We conduct an event study around the earning announcement to examine the asymmetric post earnings announcement drift, and its relationship with the bid-ask bias, order follow imbalance on post earnings announcement period for UK stock market from 2000 2021. The earning drift is significantly asymmetric in the post earnings announcement period, stocks with good news have less drift, and it (at least) partly due to the order flow imbalance during the event period. The earning announcement attracts more sell than buy orders, and an overall negative reaction in the market. The difference between transaction price return and quote price returns shows that the bid-ask bias is also a possible explanation of post earnings announcement drift. Our results are robust across different estimations and robustness tests.

Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book

AbstractWe employ deep learning in forecasting high‐frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state‐of‐the‐art predictive accuracy by training simpler “off‐the‐shelf” artificial neural networks on stationary inputs derived from the order book. Specifically, models trained on order flow significantly outperform most models trained directly on order books. Using cross‐sectional regressions, we link the forecasting performance of a long short‐term memory network to stock characteristics at the market microstructure level, suggesting that “information‐rich” stocks can be predicted more accurately. Finally, we demonstrate that the effective horizon of stock specific forecasts is approximately two average price changes.

Do IOC Orders Improve the Market Quality? Evidence from the KOSPI 200 Futures Market

This study investigates the effect of IOC (immediate-or-cancel) orders on market quality, such as time to execution, volatility, quote spread, and market depth, in the KOSPI 200 index futures market. Analysis of the order flow imbalance indicates that IOC orders, accounting for 50% of trades in the futures market, leads more price impact than non-IOC,. Additionally, the IOC orders, mostly submitted by foreign investors, and simultaneously by multiple traders in the form of herding, incur negative impact on the market quality in the aspect of market volatility, quote spread, and market depth. However, IOC orders have the positive effect of reducing the time to execution.

Information Content of Cross-Sectional and Multilevel Order Flow Imbalances: A Bayesian Approach

Information Content of Cross-Sectional and Multilevel Order Flow Imbalances: A Bayesian Approach

The Effects of Order Flow Imbalance on Stock Prices in Tehran Stock Exchange

در این پژوهش با استفاده از داده‌های دفتر سفارش سهام 30 شرکت بزرگ در بازار سهام تهران در سال2020 میلادی اثر رویدادهای دفتر سفارش بر تغییرات قیمت سهام بررسی می‌شود. هدف از این پژوهش اندازه‌گیری حساسیت قیمت به تغییرات حجم عرضه و تقاضا در سرخط‌های مختلف دفتر سفارش و شناسایی عوامل موثر بر این حساسیت است. برای این منظور ابتدا با استفاده از داده‌های دفتر سفارش، متغیر نابرابری جریان سفارش، که نشان‌دهنده نابرابری حجم سفارش‌ها در دو سمت عرضه و تقاضای سهم است، ساخته می‌شود. سپس مشابه روش پیشنهادی کنت و همکاران (2014)، با انجام حدود سی هزار رگرسیون OLS نشان داده می‌شود که متغیر نابرابری جریان سفارش تغییرات قیمت سهام در بازه‌های کوتاه‌مدت را توضیح می‌دهد [7]. همچنین نشان داده می‌شود که استفاده از متغیر نابرابری جریان سفارش در سه سرخط اول دفتر سفارش می‌تواند توضیح‌دهندگی مدل را برای تغییرات قیمت سهام افزایش دهد که حاکی از آن است که سرخط‌های بالاتر دفتر سفارش نیز بر تغییرات قیمت سهام اثرگذار هستند. همچنین نشان داده می‌شود که بین میزان اثرگذاری سفارش‌ها بر تغییرات قیمت سهام و عمق بازار یک رابطه خطی منفی وجود دارد. بنا بر شواهد ارائه شده، این نتایج به تغییرات در ماه‌ها یا سهم‌ها حساس نیستند.

Investigating the Interaction Between Asset Returns and Order Flow Imbalances

Investigating the Interaction Between Asset Returns and Order Flow Imbalances

Cross-impact of order flow imbalance in equity markets

We investigate the impact of order flow imbalance (OFI) on price movements in equity markets in a multi-asset setting. First, we propose a systematic approach for combining OFIs at the top levels of the limit order book into an integrated OFI variable which better explains price impact, compared to the best-level OFI. We show that once the information from multiple levels is integrated into OFI, multi-asset models with cross-impact do not provide additional explanatory power for contemporaneous impact compared to a sparse model without cross-impact terms. On the other hand, we show that lagged cross-asset OFIs do improve the forecasting of future returns. We also establish that this lagged cross-impact mainly manifests at short-term horizons and decays rapidly in time.

Liquidity fluctuations and the latent dynamics of price impact

Market liquidity is a latent and dynamic variable. We propose a dynamical linear price impact model at high frequency in which the price impact coefficient is a product of a daily, a diurnal, and an auto-regressive stochastic intraday component. We estimate the model using a Kalman filter on order book data for stocks traded on the NASDAQ in 2016. We show that our price changes estimates, conditional on order flow imbalance, on average of real price changes variance. Evidence is also provided on the fact that the conditioning on filtered information improves the estimate of the LOB liquidity with respect to the one obtained from a static estimation of the price impact. In addition, an out-of-sample analysis shows that our model provides a superior out-of-sample forecast of price impact with respect to historical estimates.

Multi-Asset Market Impact and Order Flow Commonality

The empirical finding that market movements in stock prices may be correlated with the order flow of other stocks has led to the notion of and has prompted the development of multivariate models of market impact. These models are parametrized by a matrix of impact coefficients whose off-diagonal elements are meant to capture how trades in one asset influence the price of other assets, leading to a large number of 'cross-impact' parameters which may not be identified solely based on the covariance of returns with order flow. Moreover, empirical evidence suggests that these cross-impact terms are unstable and change sign randomly over time, which poses a problem for their interpretation and use. We reexamine this empirical evidence from a causal standpoint and offer a simpler explanation for the observed correlation between the returns of an asset and the order flow imbalance of other assets, in terms of common components in order flow across stocks which may naturally arise from multi-asset trading strategies. We provide empirical evidence from order flow and price changes of NASDAQ-100 stocks to support this explanation. Our results show the main determinants of impact to be idiosyncratic order flow imbalance as well as a market order flow factor common across stocks. Additional ‘cross-impact’ terms account for less than 1% of market impact. This leads to a parsimonious approach for causal modelling of multi-asset impact, which does not require introducing any concept of cross-impact.

Order book price impact in the Chinese soybean futures market

AbstractWe study the price impact of order flow in the world's largest soybean meal futures markets. Our intraday results indicate that incoming orders can be used to explain and predict future price changes. Our results are shown to be robust to various order flow measures, price aggregation approaches and data frequencies. We find that order flow imbalance (OFI) is a more all‐encompassing measure carrying greater information about price change relative to both trade imbalance (TI) and volume. Moreover, while both OFI and TI are shown to predict future price changes, this predictability diminishes over longer measure and price change frequency horizons.

High-frequency Statistical Arbitrage Strategy Based on Stationarized Order Flow Imbalance

From a high-frequency perspective, the order book data records each market participant’s price expectations for the current underlying assets, and therefore it records the fundamental mechanism of pricing. In this paper, we propose a stationarized indicator based on the classical indicator – Order Flow Imbalance (OFI), and empirically find that there is a stronger linear relationship between the new indicator and the mid-price. Furthermore, we develop a statistical arbitrage strategy based on this new indicator. Compared with the classic indicator, the strategy built with the new indicator has better performance.

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.

Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book

We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy by training simpler off-the-shelf artificial neural networks on stationary inputs derived from the order book. Specifically, models trained on order flow significantly outperform most models trained directly on order books. Using cross-sectional regressions we link the forecasting performance of a long short-term memory network to stock characteristics at the market microstructure level, suggesting that information-rich stocks can be predicted more accurately. Finally, we demonstrate that the effective horizon of stock specific forecasts is approximately two average price changes.

Do fundamentals shape the price response? A critical assessment of linear impact models

We compare the predictions of the stationary Kyle model, a microfounded multi-step linear price impact model in which market prices forecast fundamentals through information encoded in the order flow, with those of the propagator model, a purely data-driven model in which trades mechanically impact prices with a time-decaying kernel. We find that, remarkably, both models predict the exact same price dynamics at high frequency, due to the emergence of universality at small time scales. On the other hand, we find those models to disagree on the overall strength of the impact function by a quantity that we are able to relate to the amount of excess-volatility in the market. We reveal a crossover between a high-frequency regime in which the market reacts sub-linearly to the signed order flow, to a low-frequency regime in which prices respond linearly to order flow imbalances. Overall, we reconcile results from the literature on market microstructure (sub-linearity in the price response to traded volumes) with those relating to macroeconomically relevant timescales (in which a linear relation is typically assumed).

Price Impact of Order Flow Imbalance: Multi-level, Cross-sectional and Forecasting

Price Impact of Order Flow Imbalance: Multi-level, Cross-sectional and Forecasting

Daily investor sentiment, order flow imbalance and stock liquidity: evidence from the Chinese stock market

AbstractWe examine whether and how investor sentiment affects stock liquidity in the Chinese stock market. Utilizing big data mining techniques, we excavate posted texts of a large stock forum and devise daily investor sentiment indicators. While examining the relationship between investor sentiment and stock liquidity, we also test whether order flow imbalance, attributed to investor sentiment, is the channel where investor sentiment delivers its impact on stock liquidity. We find that investor sentiment correlates positively with stock liquidity, and order flow imbalance is proven as an intermediary in this process. This positive correlation is stronger in a bear market than in a bull one. Additionally, liquidity sensitivity is higher for firms with higher book‐to‐market ratio, larger size and lower risk. When the market is less regulated, such as relaxed short‐selling restrictions, liquidity sensitivity also rises.

Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market

PurposeUsing intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).Design/methodology/approachThe authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.FindingsThe empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.Originality/valueThe study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

Clearing price distributions in call auctions

We propose a model for price formation in financial markets based on the clearing of a standard call auction with random orders, and verify its validity for prediction of the daily closing price distribution statistically. The model considers random buy and sell orders, placed employing demand- and supply-side valuation distributions; an equilibrium equation then leads to a distribution for clearing price and transacted volume. Bid and ask volumes are left as free parameters, permitting possibly heavy-tailed or very skewed order flow conditions. In highly liquid auctions, the clearing price distribution converges to an asymptotically normal central limit, with mean and variance in terms of supply/demand-valuation distributions and order flow imbalance. By means of simulations, we illustrate the influence of variations in order flow and valuation distributions on price/volume, noting a distinction between high- and low-volume auction price variance. To verify the validity of the model statistically, we predict a year's worth of daily closing price distributions for five constituents of the Eurostoxx 50 index; Kolmogorov–Smirnov statistics and QQ-plots demonstrate with ample statistical significance that the model predicts closing price distributions accurately, and compares favourably with alternative methods of prediction.

Order Book Price Impact in the Chinese Soybean Futures Market

We study the price impact of order flow in the world’s largest soybean meal futures markets. Our intraday results indicate that incoming orders can be used to explain price changes and to significantly predict future price changes. Our results are shown to be robust to various order flow measures, price aggregation approaches and data frequencies. We compare various order flow measures; finding that Order Flow Imbalance (OFI) is a more all-encompassing measure carrying greater information about price change relative to both Trade Imbalance (TI) and volume. Moreover, while both OFI and TI are shown to predict future price changes, this predictability diminishes over longer measure and price change frequency horizons.

Market Impact in Trader-Agents: Adding Multi-Level Order-Flow Imbalance-Sensitivity to Automated Trading Systems

Financial markets populated by human traders often exhibit "market impact", where the traders' quote-prices move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., "block") buy or sell order in the market: e.g., traders in the market know that a block buy order will push the price up, and so they immediately adjust their quote-prices upwards. Most major financial markets now involve many "robot traders", autonomous adaptive software agents, rather than humans. This paper explores how to give such trader-agents a reliable anticipatory sensitivity to block orders, such that markets populated entirely by robot traders also show market-impact effects. In a 2019 publication Church & Cliff presented initial results from a simple deterministic robot trader, ISHV, which exhibits this market impact effect via monitoring a metric of imbalance between supply and demand in the market. The novel contributions of our paper are: (a) we critique the methods used by Church & Cliff, revealing them to be weak, and argue that a more robust measure of imbalance is required; (b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al., 2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we demonstrate the use of the more robust MLOFI measure in extending ISHV, and also the well-known AA and ZIP trading-agent algorithms (which have both been previously shown to consistently outperform human traders). We demonstrate that the new imbalance-sensitive trader-agents introduced here do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff. The source-code for our work reported here is freely available on GitHub.