Order-Flow Filtration and Directional Association with Short-Horizon Returns

ArXiv ID: 2507.22712 “View on arXiv”

Authors: Aditya Nittur Anantha, Shashi Jain, Prithwish Maiti

Abstract

Electronic markets generate dense order flow with many transient orders, which degrade directional signals derived from the limit order book (LOB). We study whether simple structural filters on order lifetime, modification count, and modification timing sharpen the association between order book imbalance (OBI) and short-horizon returns in BankNifty index futures, where unfiltered OBI is already known to be a strong short-horizon directional indicator. The efficacy of each filter is evaluated using a three-step diagnostic ladder: contemporaneous correlations, linear association between discretised regimes, and Hawkes event-time excitation between OBI and return regimes. Our results indicate that filtration of the aggregate order flow produces only modest changes relative to the unfiltered benchmark. By contrast, when filters are applied on the parent orders of executed trades, the resulting OBI series exhibits systematically stronger directional association. Motivated by recent regulatory initiatives to curb noisy order flow, we treat the association between OBI and short-horizon returns as a policy-relevant diagnostic of market quality. We then compare unfiltered and filtered OBI series, using tick-by-tick data from the National Stock Exchange of India, to infer how structural filters on the order flow affect OBI-return dynamics in an emerging market setting.

Keywords: Limit Order Book (LOB), Order Book Imbalance (OBI), Hawkes Processes, High-Frequency Trading, Market Microstructure, Futures

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical frameworks including Hawkes processes and regime-based counting processes, but is highly grounded in real-world implementation using tick-by-tick data from the National Stock Exchange of India with specific, tested filtration methods.

  flowchart TD
    A["Research Goal:<br>Do structural filters on order flow<br>sharpen OBI-Return association?"] --> B["Data: Tick-by-Tick BankNifty Futures<br>Order Flow & Returns"]
    B --> C{"Methodology:<br>Three-Step Diagnostic Ladder"}
    C --> C1["1. Contemporaneous<br>Correlations"]
    C --> C2["2. Linear Association<br>across Discretized Regimes"]
    C --> C3["3. Hawkes Event-Time<br>Excitation"]
    
    C1 --> D["Filtering Approaches"]
    C2 --> D
    C3 --> D
    
    subgraph D ["Computational Processes"]
        D1["Aggregate Order Flow<br>Filtered by Lifetime, Mods, Timing"]
        D2["Parent Orders (Executed Trades)<br>Filtered by Lifetime, Mods, Timing"]
    end
    
    D --> E{"Key Findings"}
    E --> F["Aggregate Flow Filtration:<br>Modest improvement over unfiltered benchmark"]
    E --> G["Parent Order Filtration:<br>Systematically stronger directional association"]
    
    F --> H["Policy Implication:<br>Filters reveal market quality nuances<br>in emerging markets"]
    G --> H