Behavioural Finance & Sentiment Analysis

Classical finance assumes investors are rational actors, but behavioural finance recognises that emotions and cognitive biases often drive decision‑making. Herd behaviour, overconfidence, fear and greed can lead to bubbles and crashes. AI offers tools to quantify and exploit these biases through sentiment analysis and behavioural modelling. By processing news articles, earnings calls, social media posts and even search trends, algorithms gauge market mood and anticipate how sentiment shifts may affect asset prices.

Sentiment analysis uses natural language processing to classify text as positive, negative or neutral. More advanced models detect specific emotions such as excitement, fear or uncertainty. Time‑series analyses track sentiment over days or hours, revealing correlations with market returns and volatility. Machine learning models integrate sentiment scores with other features—like trading volume or options positioning—to forecast price movements. Behavioural clustering identifies groups of traders who respond similarly to news, enabling strategies that capitalise on overreactions or corrections.

Applications abound. Hedge funds incorporate social media sentiment into trading signals, betting that bullish chatter will boost stocks. Retail platforms monitor sentiment to recommend content or caution users about hype‑driven assets. Regulators watch for coordinated misinformation campaigns or pump‑and‑dump schemes. Academic research uses sentiment data to explain anomalies like momentum or reversals. Predictive analytics can even anticipate macroeconomic indicators by analysing how people talk about jobs, housing or inflation.

However, sentiment‑driven strategies carry risks. Online discussions can be manipulated by bots or coordinated groups, leading to false signals. Language models may misinterpret sarcasm or slang, especially across cultures. Privacy concerns arise when scraping personal communications. Ethical use requires respecting platform terms of service and anonymising data. Furthermore, sentiment should be one component in a broader analytical framework; relying solely on mood can backfire when fundamentals diverge from hype. By combining behavioural insights with rigorous analysis, AI can enhance our understanding of markets without succumbing to noise.