Wealth Data Solutions team
Dr. Richard Peterson
In today’s hyper-connected markets, the challenge isn’t finding information - it’s filtering out the noise. With AI-generated headlines, social media buzz, and robo-commentary flooding every trading day, wealth managers and advisers need tools that can distill meaningful signals from the noise. LSEG MarketPsych Analytics transforms unstructured global chatter into structured, predictive insights - helping wealth professionals stay ahead of market-moving themes and sentiment shifts. In this insight we explore how to:
- Spot the signal early: Monitor emerging macro and thematic catalysts—like tariffs or AI developments—and identify which stocks are likely to benefit or suffer before the market fully reacts.
- Enhance portfolio precision: Use sentiment-driven rankings to tilt toward high-conviction names and away from underperformers, improving risk-adjusted returns with data-backed confidence.
- Strengthen risk and client experience: Detect sentiment drops as early warning signs and turn complex analytics into intuitive visuals that resonate with customers.
Every trading day brings an avalanche of “information”: AI-generated news releases, auto-summarised earnings blurbs, eyeball-chasing social posts and an ever-growing stream of robo-commentary. For wealth managers and financial advisers, the problem is no longer a shortage of data but a glut of low-value noise masquerading as useful insight. The challenge is two-fold: first, to identify the handful of macro and thematic catalysts that truly sway asset prices; and second, to connect those themes to the individual stocks that are most likely to benefit - or slide - in the weeks ahead.
Themes are driving markets
Market history shows that narratives periodically dominate investor psychology. Investor sentiment in the first half of 2025 was shaped by escalating trade-war rhetoric, new tariff measures, notable progress in Chinese AI capabilities, and a weakening US dollar. In each case, share-price reactions were swift, but not uniform: companies perceived as winners surged, while those cast as casualties lagged. For portfolio stewards, the goal is to spot theme-related sentiment shifts early enough to rebalance exposure before the price trend is exhausted.
From unstructured text to structured signal
To dial down the noise and boost decision-making quality from AI, LSEG MarketPsych Analytics and Models uses robust news and social media analytics, coupled with strong data governance, symbology frameworks and data transparency. LSEG MarketPsych Analytics software ingests millions of articles, posts and transcripts each day, classifying them by company or asset class and more than 200 economic and behavioural themes (see MarketPsych: NLP & Predictive Analytics in Finance | LSEG). Each mention is scored for intensity and direction, producing minute-level sentiment and thematic indices across 100,000-plus global equities. The resulting data feed can be streamed directly into dashboards or systematic models, giving advisors and clients an at-a-glance view of how a headline is resonating across the market, as seen in Figure 1 below.
Evidence that sentiment leads prices
Quantitative tests run on these sentiment scores demonstrate that media and social tone is not just descriptive - it is predictive. In a 20-year back-test covering more than 4,000 U.S. stocks, the decile of shares with the most positive one-month media sentiment outperformed the most negative decile by a meaningful margin over the subsequent three months (see Figure 2). This result is seen globally, and it appears strongest in India. This signal has strengthened in the past five years, demonstrating the increased power of media in driving stock prices.
The above finding is the foundation of a stock price prediction model that has been successfully producing meaningful predictions for several years. By combining sentiment with other media signals, such as ‘innovation’, ‘layoffs’, and ‘management sentiment’, the StarMine MarketPsych Model, converts raw sentiment and thematic indicators into a simple 1-to-100 daily ranking for each stock. The StarMine MarketPsych Model has shown an average 10% annual spread between the top-ranked and bottom-ranked deciles of U.S. stocks since it launched.
Practical applications for wealth advisors and clients:
- Theme monitoring: Set alerts for spikes in tariff-related or AI-related sentiment at the market, sector or company level. Quickly assess which companies are most at risk in a portfolio or watchlist when a theme re-emerges.
- Idea generation & portfolio construction: Tilt toward high-sentiment names and underweight low-sentiment peers within a sector. Even slight sentiment bias can meaningfully improve risk-adjusted returns.
- Risk management: Sudden drops in aggregate sentiment - often invisible amid headline noise - can act as an early-warning system for profit-taking or earnings disappointments.
- Client communication: Visual aids such as the sentiment-price chart and thematic heat-map turn complex Natural Language Processing outputs into intuitive storytelling devices that resonate with end-investors.
A disciplined lens, not a crystal ball
Sentiment and thematic data should complement - never replace - fundamental analysis and valuation work. Moreover, past outperformance of high-sentiment stocks does not guarantee future results. But in a world where the signal-to-noise ratio is deteriorating by the hour, tools that transform unstructured text into structured, empirically validated indicators give advisers a measurable edge.
Wealth managers, traders, and investors worldwide rely on LSEG MarketPsych Analytics and Models to cut through the noise and uncover actionable insights—even during periods of heightened market volatility. By integrating sentiment and thematic scores into your research and risk workflows, you can spend less time parsing headlines and more time acting on the signals that truly move markets.
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