Stock price prediction using sentic API

This study investigates the potential of sentiment analysis derived from textual data across platforms like Reddit, StockTwits, Benzinga, and Twitter to enhance stock price prediction and develop trading strategies. Leveraging SenticNet for sentiment analysis, we explore the relationship between inv...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Phoa, Justyn Zairen
مؤلفون آخرون: Erik Cambria
التنسيق: Final Year Project
اللغة:English
منشور في: Nanyang Technological University 2024
الموضوعات:
NLP
الوصول للمادة أونلاين:https://hdl.handle.net/10356/175063
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
الوصف
الملخص:This study investigates the potential of sentiment analysis derived from textual data across platforms like Reddit, StockTwits, Benzinga, and Twitter to enhance stock price prediction and develop trading strategies. Leveraging SenticNet for sentiment analysis, we explore the relationship between investor sentiments and stock price movements. While some trading strategies show abnormal excess returns over 8 years, outperforming the market with higher Sharpe and CAGR ratios, Fama-Macbeth regressions reveal a lack of systemic alpha. We acknowledge limitations in using news headlines as sentiment proxies and suggest further research into the interplay between sentiment analysis and established financial factors to refine predictive models and understand stock price movements better.