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Enhancing OHLC Data with Timing Features: A Machine Learning Evaluation

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  • Ruslan Tepelyan

Abstract

OHLC bar data is a widely used format for representing financial asset prices over time due to its balance of simplicity and informativeness. Bloomberg has recently introduced a new bar data product that includes additional timing information-specifically, the timestamps of the open, high, low, and close prices within each bar. In this paper, we investigate the impact of incorporating this timing data into machine learning models for predicting volume-weighted average price (VWAP). Our experiments show that including these features consistently improves predictive performance across multiple ML architectures. We observe gains across several key metrics, including log-likelihood, mean squared error (MSE), $R^2$, conditional variance estimation, and directional accuracy.

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  • Ruslan Tepelyan, 2025. "Enhancing OHLC Data with Timing Features: A Machine Learning Evaluation," Papers 2509.16137, arXiv.org.
    • Handle: RePEc:arx:papers:2509.16137
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    References listed on IDEAS

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    1. Humphery-Jenner, Mark L., 2011. "Optimal VWAP trading under noisy conditions," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2319-2329, September.
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