Financial Market Sentiment Analysis and Investment Strategy Formulation Based on Social Network Data

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Jianhua Liu, Fengyu Xu, Xuchu Liu


The integration of sentiment analysis techniques with social network data has emerged as a promising approach for understanding and predicting financial market trends. This paper presents a comprehensive study on financial market sentiment analysis using data extracted from social networks and explores its implications for investment strategy formulation. The proposed methodology involves the collection and analysis of social media posts, news articles, and other textual data sources to gauge investor sentiment towards various financial assets and markets. Natural language processing (NLP) techniques are employed to extract sentiment-related features and sentiments from the textual data. Furthermore, machine learning algorithms, including sentiment classification models and predictive analytics, are utilized to derive insights and forecasts from the sentiment analysis results. These insights are then integrated into investment strategy formulation processes to guide decision-making and portfolio management. Key aspects of the study include the development of sentiment analysis models tailored to financial markets, the evaluation of sentiment indicators' predictive power, and the formulation of investment strategies based on sentiment-driven signals. Experimental results demonstrate the efficacy of the proposed approach in capturing market sentiment dynamics and its potential for enhancing investment decision-making processes. Moreover, the study explores the impact of social network data characteristics, such as volume, frequency, and sentiment polarity, on market trends and investor behavior. Overall, the findings of this study contribute to advancing the understanding of the role of social network data in financial market sentiment analysis and provide valuable insights for investors and financial professionals seeking to leverage sentiment-driven strategies for better portfolio performance and risk management.

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