---
title: Machine Learning Execution Time in Asset Pricing
url: https://www.ml-quant.com/papers/ssrn/4587923/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4587923
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4587923
featured: 2023-10-04
citations: 0
topic: Trading, Microstructure & Execution
---


# Machine Learning Execution Time in Asset Pricing

The XGBoost machine learning model is found to be highly accurate and efficient in empirical asset pricing, with improved performance through feature reduction and shorter time observations.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4587923
- Identifier: SSRN 4587923
- Released: 2023-09-29
- First featured: Quant Letter No. 18 (2023-10-04): https://www.ml-quant.com/issues/2023-10-04/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [Machine Learning Execution Time in Asset Pricing](https://www.ml-quant.com/papers/ssrn/4623947/): The research analyzes the execution time of machine learning models in empirical asset pricing, finding that XGBoost is the fastest and most accurate, and that reducing features and time observations can significantly cut execution time.
- [Improving Project Cost Estimates](https://www.ml-quant.com/papers/repec/taf-tprsxx-v-62-y-2024-i-12-p-4372-4388/): A machine learning model using XGBoost has been developed to enhance the accuracy of project cost forecasting, providing consistent and accurate estimates throughout project execution.
- [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://www.ml-quant.com/papers/arxiv/2501.09747/): A new tokenization scheme, Frequency-space Action Sequence Tokenization (FAST), has been proposed for robot actions, facilitating the training of vision-language action policies for complex and high-frequency tasks.
- [Deep Reinforcement Learning for Active High Frequency Trading](https://www.ml-quant.com/papers/arxiv/2101.07107/): A new Deep Reinforcement Learning framework has been developed for high frequency stock trading, showing potential for profitable long-term strategies.
- [LIGHT Benchmark - Comprehensive Backtesting Framework for Market Risk Models Comparison](https://www.ml-quant.com/papers/ssrn/4586897/): Market Risk Backtesting: The article presents LIGHT Benchmark, a tool for comparing market risk models, including a scoring system for evaluating Value at Risk and Expected Shortfall models.
- [Efficiency Metrics in Investments and Trading: A Comprehensive Examination](https://www.ml-quant.com/papers/ssrn/4584524/): A study emphasizes the need to measure risk-adjusted returns in investments and trading, highlighting metrics like the Sortino Ratio, Calmar Ratio, and Pareschi Ratio.