The Price Impact of Generalized Order Flow Imbalance

TL;DR

This paper introduces a generalized method for constructing order flow imbalance indicators, significantly improving their ability to explain short-term stock price changes and demonstrating enhanced stability and interpretability across multiple time scales.

Contribution

The paper proposes a novel generalized order flow imbalance construction method that outperforms traditional OFI in explaining stock price movements and offers greater stability and interpretability.

Findings

Generalized OFI improves R-squared by over 30% across time scales.

Log-GOFI shows stronger stability and interpretability.

Empirical analysis on CSI 500 stocks confirms effectiveness.

Abstract

Order flow imbalance can explain short-term changes in stock price. This paper considers the change of non-minimum quotation units in real transactions, and proposes a generalized order flow imbalance construction method to improve Order Flow Imbalance (OFI) and Stationarized Order Flow Imbalance (log-OFI). Based on the high-frequency order book snapshot data, we conducted an empirical analysis of the CSI 500 constituent stocks. In order to facilitate the presentation, we selected 10 stocks for comparison. The two indicators after the improvement of the generalized order flow imbalance construction method both show a better ability to explain changes in stock prices. Especially Generalized Stationarized Order Flow Imbalance (log-GOFI), using a linear regression model, on the time scales of 30 seconds, 1 minute, and 5 minutes, the average R-squared out of sample compared with Order Flow Imbalance (OFI) 32.89%, 38.13% and 42.57%, respectively increased to 83.57%, 85.37% and 86.01%. In addition, we found that the interpretability of Generalized Stationarized Order Flow Imbalance (log-GOFI) showed stronger stability on all three time scales.