TWITTER SENTIMENT ANALYSIS FOR OPTIMAL PORTFOLIO CONSTRUCTION

Purpose- This research investigates the efficacy of social media sentiment analysis in constructing alpha-generating investment portfolios. Specifically, the study examines whether Twitter-derived sentiment indicators can be leveraged to develop systematic trading strategies that generate risk-adjusted returns exceeding benchmark performance. The research aims to establish quantitative criteria for position initiation and termination based on sentiment metrics, with the ultimate objective of creating a portfolio that demonstrates significant outperformance relative to the reference index. Methodology – The study encompasses 16 companies of the Nasdaq 100 index, selected to represent diverse market sectors while controlling for liquidity and market impact considerations. The dataset comprises 708,080 Twitter posts pertaining to the selected companies throughout the 2022 calendar year, extracted via programmatic data collection methodologies. Sentiment quantification was performed utilizing the Natural Language Toolkit (NLTK) in Python, generating normalized sentiment scores within a [-1, +1] interval. The investigation employed a sophisticated aggregation methodology to compute both daily and weekly sentiment indicators for each security, deliberately excluding neutral sentiment scores (0) to enhance signal clarity. A systematic portfolio construction framework was implemented, whereby securities were hierarchically ranked based on their aggregate sentiment scores on a weekly basis. Multiple portfolio permutations were tested, incorporating various combinations of long positions in top-ranked securities and short positions in bottom-ranked securities. Position entry and exit prices were determined using weekly opening and closing prices, respectively. Portfolio performance was evaluated through the calculation of weekly returns and cumulative performance metrics over the observation period. Findings- The empirical results reveal that portfolios constructed exclusively with short positions demonstrated superior cumulative returns compared to long-only portfolios. This observation can be contextualized within the broader market environment, specifically the Nasdaq 100's negative 33% return in 2022. The research identified statistically significant outperformance in portfolios implementing a combined long-short strategy, with these portfolios generating positive absolute returns despite the challenging market conditions. Conclusion- The empirical evidence substantiates the hypothesis that Twitter sentiment analysis can be effectively utilized as a signal generation mechanism for systematic portfolio construction. The results demonstrate statistically significant alpha generation capabilities, particularly when implementing a long-short strategy, suggesting potential applications for institutional investors and quantitative fund managers. Keywords: Twitter sentiment analysis, algorithmic portfolio construction, market efficiency, natural language processing, behavioral finance JEL Codes: H30, H60, H62