Microstructure-Empowered Stock Factor Extraction and Utilization
Proposes a framework for extracting stock factors from order flow data to improve trend prediction and execution.
What it examines
The paper proposes a novel framework to extract and utilize features from high-frequency order flow data for stock trend prediction and order execution tasks, addressing challenges in handling large volumes of data and limitations of traditional factor mining techniques.
What it concludes
The research offers a robust method for extracting and utilizing stock factors from order flow data, with potential applications in stock trend prediction and order execution. Future research could explore further enhancements and broader applications of the proposed framework.
Evidence objects
The research offers a robust method for extracting and utilizing stock factors from order flow data, with potential applications in stock trend prediction and order execution. Future research could explore further enhancements and broader applications of the proposed framework.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: High-frequency quantitative investment is a crucial aspect of stock investment. Notably, order flow data plays a critical role as it provides the most detailed level of information among high-frequency trading data, including comprehensive data from the order book and transaction records at the tick level. The order flow data is extremely valuable for market analysis as it equips traders with esse… ▽ More High-frequency quantitative investment is a crucial aspect of stock investment. Notably, order flow data plays a critical role as it provides the most detailed level of information among high-frequency trading data, including comprehensive data from the order book and transaction records at the tick level. The order flow data is extremely valuable for market analysis as it equips traders with essential insights for making informed decisions. However, extracting and effectively utilizing order flow data present challenges due to the large volume of data involved and the limitations of traditional factor mining techniques, which are primarily designed for coarser-level stock data. To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios. Our method consists of a Context Encoder and an Factor Extractor. The Context Encoder learns an embedding for the current order flow data segment's context by considering both the expected and actual market state. In addition, the Factor Extractor uses unsupervised learning methods to select such important signals that are most distinct from the majority within the given context. The extracted factors are then utilized for downstream tasks. In empirical studies, our proposed framework efficiently handles an entire year of stock order flow data across diverse scenarios, offering a broader range of applications compared to existing tick-level approaches that are limited to only a few days of stock data. We demonstrate that our method extracts superior factors from order flow data, enabling significant improvement for stock trend prediction and order execution tasks at the second and minute level. △ Less
Source row: 1337 · abstract type: unknown