Computer-Assisted Analysis of Qualitative News Dissemination

Abstract

This study delves into the potential of utilizing deep learning (DL) techniques to analyze qualitative news dissemination for trading purposes. DL, renowned for its prowess in handling vast datasets and deciphering intricate patterns, holds promise in aiding investors seeking to enhance their trading strategies. Specifically, Long Short-Term Memory (LSTM) networks, known for their capacity to retain contextual information, are explored in this research. By employing DL models, we aim to forecast market sentiment based on news headlines, focusing on the Dow Jones industrial average from 2008 to 2020. Leveraging 25 daily news headlines, we extend our analysis to develop an algorithmic trading strategy. Through rigorous testing across two distinct cases over five-time steps, our study evaluates the effectiveness of DL-driven approaches in real-world trading scenarios.

Furthermore, this research contributes to the growing body of literature on the intersection of deep learning and financial markets. By examining the application of DL in qualitative news analysis for trading purposes, we provide insights into the potential implications for investors and financial analysts. The findings offer valuable guidance for leveraging advanced computational techniques for decision-making in dynamic market environments. Additionally, this study underscores the importance of incorporating qualitative news data into trading strategies, highlighting the role of DL in extracting meaningful signals from unstructured textual information’s Overall, our findings shed light on the opportunities and challenges associated with harnessing DL for trading on news sentiment.