Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

FAST-SCAN: Forward-looking Analysis System for Stock Trend Anticipation

Version 1 : Received: 10 April 2024 / Approved: 11 April 2024 / Online: 11 April 2024 (10:28:01 CEST)

How to cite: Clarkson, E.; Nasir, W.; Ford, N. FAST-SCAN: Forward-looking Analysis System for Stock Trend Anticipation. Preprints 2024, 2024040780. https://doi.org/10.20944/preprints202404.0780.v1 Clarkson, E.; Nasir, W.; Ford, N. FAST-SCAN: Forward-looking Analysis System for Stock Trend Anticipation. Preprints 2024, 2024040780. https://doi.org/10.20944/preprints202404.0780.v1

Abstract

The rapid expansion of China's A-share market, coupled with the burgeoning volume of financial research reports, presents a unique challenge to traditional stock price prediction methodologies. This study introduces FAST-SCAN, a novel framework designed to leverage natural language processing (NLP) and time series analysis for the efficient and accurate prediction of stock market trends. By integrating advanced sentiment analysis, primarily through the utilization of the RoBERTa model, with dynamic time series forecasting, FAST-SCAN aims to distill actionable insights from the vast corpus of financial research reports. This approach not only enhances the speed and efficiency of financial analysis but also addresses the time-sensitive nature of market-influencing factors, thereby providing a competitive edge in stock price prediction. The framework demonstrates a notable improvement in predictive performance, achieving a 20% annualized return and a 7.19% RankIC, marking a significant advancement over conventional statistical analysis methods. Through its innovative combination of NLP and predictive analytics, FAST-SCAN paves the way for a more informed and strategic approach to investment in the A-share market, emphasizing the importance of timely, data-driven decision-making in the financial industry.

Keywords

Stock price prediction; Natural language processing; Sentiment analysis

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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