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Trading performance and market efficiency: Evidence from algorithmic trading

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  • Syamala, Sudhakara Reddy
  • Wadhwa, Kavita

Abstract

In India, National Stock Exchange directly identifies algorithmic trading participation. Algorithmic traders possess intraday market timing skills. Results are not motivated by extreme short-term signals or transitory price trading. Magnitude of market timing performance in cross-sectional group of traders shows that they earn profit across all the cases, and maximize while providing liquidity. Volume-weighted-average-price decomposition analysis reports algorithmic traders earn profits through intraday market timing performance for five-minute and one-minute intervals, and it is higher compared to short-term market timing performance across all trader groups. Order imbalance and price delay regressions show that algorithmic trading significantly improves price efficiency.

Suggested Citation

  • Syamala, Sudhakara Reddy & Wadhwa, Kavita, 2020. "Trading performance and market efficiency: Evidence from algorithmic trading," Research in International Business and Finance, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:riibaf:v:54:y:2020:i:c:s0275531920304050
    DOI: 10.1016/j.ribaf.2020.101283
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    More about this item

    Keywords

    Algorithmic trading; VWAP; Trading performance; Intraday trading;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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