Key Details
The European Central Bank (ECB) working paper, When the Crowd Speaks: AI-based Retail Investor Sentiment Indicator with Reddit Data, develops the Reddit Retail Investor Sentiment Indicator (R-RISI) in four stages:
Source: More than eight million posts published between 2015 and April 2026 across 14 investment, economics, commodities and cryptocurrency communities.
Classification: ChatGPT-5.1 categorises posts by sentiment and investment topic. The authors find it handles slang, humour and irony better than FinBERT, which frequently classifies informal posts as neutral.
Construction: Positive and negative posts are weighted by user engagement and compared with their respective patterns over the previous year.
Market test: Changes in the indicator are compared with the S&P 500, AI-related stocks and Bitcoin, as well as established measures of US retail-investor sentiment and activity.
Online Sentiment Becomes More Informative in Falling Markets
R-RISI broadly tracks major financial events and established retail-sentiment measures while providing a daily, more granular view of the assets and subjects attracting investor attention.
For the S&P 500, a change in sentiment is associated with a statistically significant movement in prices on the following day. The relationship is 3.4 times stronger during the initial phase of a market drawdown than outside such periods, although the amplification is concentrated largely in the first day.
Positive sentiment also contains next-day information for Bitcoin prices. The corresponding result for the paper’s portfolio of AI-related stocks is positive but not statistically significant at conventional thresholds.
These results make R-RISI potentially useful for identifying when speculative retail sentiment is becoming relevant to market dynamics. They do not establish a reliable longer-term forecasting strategy.
A Signal from Speculative Investors, Not the Entire Market
Reddit offers unusually timely information, but its users are not representative of all retail investors. They tend to be younger, male, technologically inclined, largely US-based and more interested in speculative trading.
The indicator may therefore capture the section of the market most likely to become active during volatility, rather than the quieter and more diversified household investment flows covered by conventional surveys or brokerage data. Posts may also contain coordinated narratives, while images and screenshots — often important in online investment discussions — were not analysed.
The paper further cautions that generative AI models operate as “black boxes” and may produce slightly different classifications when prompts or model settings change.
Policy Relevance
The paper contains no India-specific sample or market estimate. Its relevance for India lies in the monitoring method: SEBI, exchanges and research institutions could examine whether a locally validated indicator adds an early signal during sharp market moves. Such a tool should supplement and not replace complaint data, order-level surveillance and evidence of actual trading behaviour.
What the paper demonstrates | What India would need to test |
|---|---|
Social-media sentiment can complement surveys and trading data, particularly during market stress. | An Indian indicator would require domestic platforms, market-linked datasets and validation against Indian securities and trading activity. |
Large language models can interpret informal financial language better than tools trained mainly on formal financial text. | Coverage would need to extend across Indian languages, transliterated text and locally used market terminology. |
Asset- and topic-specific indicators can show where speculative attention is concentrating. | Regulators would need safeguards against bots, coordinated promotion, duplicated content and manipulation of the indicator itself. |
Follow the Full Working Paper Here: When the Crowd Speaks: AI-based Retail Investor Sentiment Indicator with Reddit Data