Reading the room: what Reddit tells us about retail investor sentiment
News originally published by
ECB - European Central Bank12 November 2026
By Fabian Wagner and Julian Metzler
Retail investors are increasingly becoming speculative traders, and many are using Reddit as a platform for discussions that reveal how sentiment evolves over time and across market conditions. Our AI-based indicator turns these discussions into a short-term market signal.
The digital revolution has attracted many new retail investors to financial markets. Among them is a new type of investor: younger, more speculative and comfortable using popular, easy-to-access social media platforms to exchange financial information and investment views, especially Reddit. There, users discuss market or macroeconomic news, individual stocks, earnings, trading strategies and fast-evolving topics such as artificial intelligence (AI) and crypto assets. Reddit discussions offer a high-frequency view into these new retail investors’ potential activities that otherwise cannot be observed.
Using AI, we can convert their collective mood into the Reddit Retail Investor Sentiment Indicator (R-RISI). We can then show how this aligns with broader market trends and how this signals market activity, particularly during periods of stress. As we will demonstrate, the R-RISI proves a useful tool to decompose investor behaviour in equity markets.
The R-RISI is timelier on short-term market dynamics and offers higher frequency information than survey-based indicators. Changes in the R-RISI are also associated with next-day market returns, particularly during market downturns.
Our companion paper[1] provides a breakdown of the indicator and presents the complete empirical results. Our accompanying live dashboard[2] offers an interactive view of sentiment across markets, topics and selected assets.
Extracting sentiment, topics and tickers from Reddit posts
Reddit is organised into communities, known as subreddits, where users post, comment and vote on content. For our indicator, we focus on the most popular investing and macroeconomic-related subreddits, including, among others, r/wallstreetbets, r/stocks, r/StockMarket, r/investing, r/Bitcoin and r/Economics.
Reddit language is often informal and ambiguous, as posts may contain abbreviations, slang and spelling mistakes, or refer to companies by name rather than their stock ticker symbols, which are standardised abbreviations that identify publicly traded companies. Therefore, a large language model can use the context to identify the company or asset more precisely than simple keyword matching.
We use OpenAI’s ChatGPT 5.1 to classify each post by sentiment, topic and stock price ticker or alternative asset class (Chart 1). Sentiment is identified as positive, negative or neutral. Topics include equities, funds, macroeconomics, options, commodities, geopolitics, tariffs or crypto-assets.
Chart 1
Workflow using AI classification to turn Reddit posts into retail investor sentiment indicators
What the indicator shows
The R-RISI shows whether retail investors are optimistic or pessimistic and which topics are driving that mood. As each post is assigned a topic, the indicator can be decomposed into topic-specific contributions. Positive contributions lift the R-RISI, while negative contributions pull it down.
The decomposition of all investment and macroeconomic-related subreddits reveals how quickly the focus of retail investors changes when new risks or narratives emerge. For example, macroeconomic discussions became an important negative driver during the COVID-19 market shock, while tariff-related posts pushed the R-RISI sharply lower during the market turmoil in April 2025.
In calmer periods, movements were driven more by discussions about equities and options. The results also show that the Reddit data provide broad and continuous coverage of individual topics, which allows us to construct dedicated, topic-specific R-RISI indicators (Chart 2).
Chart 2
Decomposing R-RISI sentiment across market narratives
Contributions to the R-RISI over time by topic
Sources: Reddit (Academic Torrents), OpenAI, Bloomberg Finance L.P., R-RISI dashboard and ECB calculations.
Notes: Net retail sentiment is derived from Reddit post titles and bodies using OpenAI’s GPT-5.1 model. Posts are classified by sentiment (positive, negative or neutral) and into market-related topics including equities, funds, macroeconomics, options, commodities, geopolitics, tariffs and crypto, while unrelated “other” content is excluded. Each post is weighted by the logarithm of its comments and Reddit score to reflect its relative importance in the forum. The R-RISI is calculated as the difference between the 365-day z-scores of weighted positive and negative posts. The series are smoothed using a 7-day rolling average and at weekly frequency. Each daily z-score reveals how far positive or negative sentiment deviates from its historical average in standard deviations based on a rolling 365-day window. R-RISI values above zero indicate unusually positive sentiment relative to the past year, while values below zero indicate unusually negative sentiment. Each bar shows a topic’s contribution to the total indicator: positive contributions raise the R-RISI, while negative contributions lower it. The subreddits included in the aggregation include r/wallstreetbets, r/stocks, r/investing, r/StockMarket, r/Economics, r/geopolitical, r/Gold, r/Wallstreetsilver, r/options and r/energy.
By selecting posts linked to a particular topic, stock ticker or asset, the above-described approach can produce indicators for specific themes, individual securities, portfolios or asset baskets. As an example, let’s have a look at the “geopolitics” and “macroeconomics” topic indicators. We have chosen these topics because they capture two closely related but distinct sources of investor concern: geopolitical uncertainty and the macroeconomic environment.
The R-RISI geopolitics indicator captures discussions of conflicts, international tensions, sanctions and political instability. These discussions became more negative around events such as escalating US-Iran tensions, the political transition in Afghanistan, Russia’s invasion of Ukraine and renewed conflict in the Middle East (Chart 3, panel a).
Meanwhile, the R-RISI indicator for macroeconomics captures discussions of inflation, prices, interest rates, unemployment, economic growth and the wider economic effects of shocks and policies. Here, we see pronounced declines in market sentiment around the COVID-19 shock and the subsequent energy price and inflation shock, as well as the 2023 banking turmoil and the announcement of US reciprocal tariffs (Chart 3, panel b).
These indicators show that events can affect investor sentiment differently across topics and at different points in time. Russia’s invasion of Ukraine, for example, led to an immediate decline in geopolitical risk sentiment, while macroeconomic sentiment weakened later as the implications for energy prices and inflation became more apparent.
Conversely, the announcement of US reciprocal tariffs caused a sharp deterioration in macroeconomic sentiment, reflecting concern over prices and economic activity, while the geopolitics indicator reacted much less.
Chart 3
Measuring retail investor sentiment with topical sub-indicators
a) R-RISI Topic: geopolitics |
b) R-RISI Topic: macroeconomics |
|---|---|
(1 Jan. 2019-31 Oct. 2026; sentiment index) |
(1 Jan. 2019-31 Oct. 2026; sentiment index) |
 |
 |
Sources: Reddit (Academic Torrents), OpenAI, Bloomberg Finance L.P., R-RISI dashboard and ECB calculations.
Notes: R-RISI values above zero indicate unusually positive sentiment relative to the past year, while values below zero indicate unusually negative sentiment. Panel a: R-RISI Topic: geopolitics captures sentiment on all posts in the most popular investment-related subreddits filtered for the topic tag “geopolitics” as labelled by ChatGPT. Panel b: R-RISI Topic: macroeconomics captures sentiment on “macroeconomics” labelled posts. The series are smoothed using a 30-day rolling average. Major events relevant to either topic are marked in the graphs.
In addition to topics, the R-RISI can also be tailored to specific assets and investment themes. Interestingly, some sub-indicators closely mirror market dynamics in specific sectors. Following the release of ChatGPT on 30 November 2022, discussions of AI-linked companies became more prominent on Reddit. To examine this theme more closely, we built R-RISIAI based on Reddit discussions about the so-called “Magnificent Seven”, a group of seven major US tech companies closely associated with artificial intelligence.
The indicator broadly correlates with the AI equity theme: it tends to strengthen when the AI basket performs well and weaken when the theme loses momentum (Chart 4, panel a). The two also appear to move more closely together during periods of market stress. However, R-RISIAI is more volatile than market prices, highlighting its ability to capture rapid shifts in retail mood that may not be fully reflected in price movements.
A second sub-indicator broadly correlated with market prices is R-RISIBTC, which measures investor sentiment in Bitcoin-related Reddit debates. If we compare sentiment with Bitcoin price movements, we can see that R-RISIBTC turned markedly negative during the crypto-asset market downturns of 2017-18 and 2022-23 and improved again during stronger market phases (Chart 4, panel b).
However, investor sentiment and market prices diverge more often with respect to Bitcoin than they do in the previous AI example. R-RISIBTC is also more volatile than the AI-related measure, reflecting the rapid shifts in conviction that characterise crypto-asset markets.
Chart 4
Comparing thematic indices of investor sentiment with market prices
a) R-RISI AI versus Magnificent Seven price index |
b) R-RISI BTC versus Bitcoin price |
|---|---|
(1 Jan. 2022-31 Oct. 2026; sentiment index and standardised prices) |
(1 Jan. 2016-31 Oct. 2026; sentiment index and standardised prices) |
 |
 |
Sources: Reddit (Academic Torrents), OpenAI, Bloomberg Finance L.P., R-RISI dashboard and ECB calculations.
Notes: R-RISI values above zero indicate unusually positive sentiment relative to the past year, while values below zero indicate unusually negative sentiment. In both panels, daily prices are standardised using z-scores, which reveal how far positive or negative prices deviate from their historical average in standard deviations, based on a rolling 365-day window. Panel a: R-RISIAI approximates AI-related retail investor sentiment by identifying posts mentioning the Magnificent Seven companies (Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla), given their close association with artificial intelligence. Data are collected from four investment-related subreddits: r/wallstreetbets, r/stocks, r/investing and r/stockmarket. R-RISIAI is compared with a market-capital-weighted Magnificent Seven stock price index. Panel b: R-RISIBTC captures sentiment from all posts referring to Bitcoin, through its asset ticker, its full name or contextual mentions, and is compared with Bitcoin price. Data are collected from the following subreddits: r/wallstreetbets, r/stocks, r/investing, r/StockMarket, r/Economics, r/geopolitical, r/Gold, r/Wallstreetsilver, r/options, r/energy, r/Bitcoin, r/Ethereum, r/CryptoCurrency and r/CryptoMarkets. The series are smoothed using a 30-day rolling average. The lower panel in panel a and b reports the 365-day rolling correlation between net retail investor sentiment and the price z-score data from the same time frame, indicating whether sentiment and market prices tend to move together (positive correlation), in opposite directions (negative correlation), or show little relationship (correlation close to zero).
A high-frequency window into retail investor sentiment
Markets are shaped not only by fundamentals, but also by narratives that spread rapidly and influence behaviour. As demonstrated by the R-RISI indicator presented here, artificial intelligence tools can be used to analyse these narratives at scale. By transforming online Reddit discussions into quantifiable signals, the R-RISI makes an important part of financial-market activity more observable. This gives central banks timely insight into how a more speculative segment of retail investors interprets and responds to market developments.
The views expressed in each blog entry are those of the authors and do not necessarily represent the views of the European Central Bank and the Eurosystem.
Acknowledgement of software use. The API-based data labelling carried out for this research project was performed using the European Central Bank's LLM API. The outputs generated and the subsequent views reflected in the paper are solely those of the authors and do not reflect those of the European Central Bank.
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