Generalized Order Flow Imbalance for Short-Term Stock Price Analysis

Summary

This paper develops generalized versions of order flow imbalance indicators to explain short-term stock price changes. Its construction accounts for changes in non-minimum quotation units in actual trades, extending conventional Order Flow Imbalance (OFI) and Stationarized Order Flow Imbalance (log-OFI). The empirical analysis uses high-frequency order-book snapshots for CSI 500 constituent stocks, with ten stocks selected for detailed comparison.

The authors report that the generalized measures explain price changes better than the original indicators. Generalized Stationarized Order Flow Imbalance (log-GOFI), evaluated with linear regression, has higher out-of-sample average R-squared at 30-second, one-minute, and five-minute horizons; the paper also reports more stable interpretability across those scales. These results are specific to the described sample and evaluation. The excerpt does not provide details on the full stock set, data period, model specification, or whether the gains generalize to other markets and trading conditions.

Key ideas

• The proposed imbalance measures account for changes in non-minimum quotation units in executed trades.
• The study compares generalized indicators with OFI and log-OFI using high-frequency order-book data.
• The empirical sample focuses on CSI 500 stocks, with ten selected for comparison.
• Generalized indicators show stronger reported out-of-sample explanatory power across three short horizons.
• The reported evidence does not establish performance outside the study’s sample and setup.

Tags

# The Price Impact of Generalized Order Flow Imbalance


# The Price Impact of Generalized Order Flow Imbalance









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.

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