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Aditya Nittur Anantha

Publications and source records attributed to Aditya Nittur Anantha.

3 recordsLinked to original sources

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

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.

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Event-Time Anchor Selection for Multi-Contract Quoting

When quoting across multiple contracts, the sequence of execution can be a key driver of implementation shortfall relative to the target spread~\cite{bergault2022multi}. We model the short-horizon execution risk from such quoting as variations in transaction prices between the initiation of the first leg and the completion of the position. Our quoting policy anchors the spread by designating one contract ex ante as a \emph{reference contract}. Reducing execution risk requires a predictive criterion for selecting that contract whose price is most stable over the execution interval. This paper develops a diagnostic framework for reference-contract selection that evaluates this stability by contrasting order-flow Hawkes forecasts with a Composite Liquidity Factor (CLF) of instantaneous limit order book (LOB) shape. We illustrate the framework on tick-by-tick data for a pair of NIFTY futures contracts. The results suggest that event-history and LOB-state signals offer complementary views of short-horizon execution risk for reference-contract selection.

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Forecasting High Frequency Order Flow Imbalance

Market information events are generated intermittently and disseminated at high speeds in real-time. Market participants consume this high-frequency data to build limit order books, representing the current bids and offers for a given asset. The arrival processes, or the order flow of bid and offer events, are asymmetric and possibly dependent on each other. The quantum and direction of this asymmetry are often associated with the direction of the traded price movement. The Order Flow Imbalance (OFI) is an indicator commonly used to estimate this asymmetry. This paper uses Hawkes processes to estimate the OFI while accounting for the lagged dependence in the order flow between bids and offers. Secondly, we develop a method to forecast the near-term distribution of the OFI, which can then be used to compare models for forecasting OFI. Thirdly, we propose a method to compare the forecasts of OFI for an arbitrarily large number of models. We apply the approach developed to tick data from the National Stock Exchange and observe that the Hawkes process modeled with a Sum of Exponential's kernel gives the best forecast among all competing models.

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