Anyone who has watched Bitcoin’s price chart around a Federal Reserve announcement knows the scene: trading volume spikes, prices jolt in one direction or another, and Twitter lights up within seconds. But does that reaction look the same when the market is euphoric as when it is gloomy? A new study suggests the answer is no, and the difference is sizable enough to matter for anyone trading around scheduled economic releases.
Writing in the Journal of Behavioral and Experimental Finance, Nhan Huynh of Griffith Business School at Griffith University set out to examine two linked questions. Do cryptocurrencies systematically respond to scheduled U.S. macroeconomic announcements in the minutes around their release? And does prevailing investor sentiment change how forcefully the market absorbs that information?
A gap between two literatures
Huynh’s research sits at the intersection of two bodies of work that had not been fully connected. On one side, studies of traditional assets have long shown that stocks, bonds, and currencies react sharply to macroeconomic news within minutes of its release. Evidence on cryptocurrencies, however, has been mixed, with some papers finding strong reactions to U.S. economic data and others finding little at all.
On the other side, behavioral finance research has established that investor sentiment plays an outsized role in cryptocurrency prices. But most of that work treats sentiment as a standalone driver of returns, not as something that shapes how investors process other information. Huynh argues that sentiment may instead act as a filter, changing how forcefully macroeconomic signals get incorporated into prices and trading.
That distinction matters because cryptocurrency markets have several features that amplify behavioral influences. They trade continuously, lack the kind of cash-flow fundamentals that anchor stock valuations, and are dominated by retail investors who tend to rely more heavily on heuristics and rules of thumb than institutional traders do.
Mapping the minute-by-minute response
To test these ideas, Huynh assembled high-frequency data on the top 100 cryptocurrencies by market capitalization from January 2014 through December 2021, sampled at five-minute intervals. The list covers more than 90 percent of total crypto market capitalization during the period and includes both active and delisted coins, reducing the risk that results reflect only survivors.
The macroeconomic data came from 32 scheduled U.S. announcements, grouped into eight categories covering real economic activity, consumption, investment, government, trade, prices, monetary policy, and forward-looking indicators. For each release, Huynh calculated a “surprise” — the difference between the actual figure and the median Bloomberg forecast, standardized for comparability across indicator types.
Sentiment was measured using the Thomson Reuters MarketPsych Analytics indices for cryptocurrencies, which use machine learning to score the tone of more than 2,000 global news and social media sources on a scale from negative to positive. Huynh constructed a daily composite sentiment value weighted by each coin’s market cap.
The empirical approach examined how returns and trading turnover moved in a ten-minute window straddling each release, from five minutes before to five minutes after. Turnover was defined as dollar trading volume divided by market capitalization.
Crypto reacts, and not uniformly
The first finding is straightforward: cryptocurrency prices and trading activity do move in response to U.S. macroeconomic news, and the effects show up almost immediately. Across the full set of announcements, a one-standard-deviation macroeconomic surprise was associated with about a 9.8 basis point move in absolute returns and a 17.9 basis point shift in turnover within the ten-minute window.
The reactions were not uniform. GDP releases, nonfarm payrolls, retail sales, industrial production, and ADP employment figures all pushed returns up when the surprise was positive. Unemployment figures moved prices in the opposite direction. Inflation releases and FOMC rate decisions were associated with negative return reactions and sharp increases in trading activity. Forward-looking indicators such as consumer confidence and the ISM manufacturing index also produced consistent responses. In total, 20 of the 32 announcements generated statistically significant return reactions, and 24 produced significant turnover effects.
Sentiment as a volume knob
The more distinctive finding concerns how sentiment changed those reactions. When Huynh added an interaction between each macroeconomic surprise and the sentiment index, the interaction terms came out predominantly negative and statistically significant. In plain terms: the more bullish the prevailing mood, the smaller the market’s response to any given piece of macro news.
The magnitude is substantial. A one-standard-deviation increase in sentiment reduced the return response to macroeconomic surprises by roughly 16 percent and the turnover response by roughly 24 percent. The dampening effect was particularly pronounced for labor-market and real-activity indicators, and it held for both favorable and unfavorable news.
Huynh interprets this pattern through an information-processing lens drawn from prior psychology and finance research: optimistic investors tend to lean more on heuristic shortcuts and pay less attention to fundamentals, while pessimistic or cautious investors engage in more systematic, fundamentals-driven updating. In that framework, high-sentiment periods produce muted reactions not because the news matters less, but because investors are less inclined to re-anchor their beliefs around it.
Bad news hits harder
Huynh also looked at whether good and bad news generated symmetric reactions. They did not. Negative surprises produced larger absolute return responses and sharper turnover reactions than positive ones, consistent with a long-standing pattern in other asset classes. Sentiment also moderated bad-news reactions more strongly than good-news reactions, meaning that optimism went further in blunting negative signals than in dampening positive ones.
Splitting the sample by time period revealed that both the direct news effects and the sentiment interactions grew considerably larger during 2020 and 2021, the COVID-19 period, than in the preceding six years. Larger-cap coins also showed stronger reactions and stronger sentiment effects than smaller ones, with turnover particularly sensitive to sentiment-conditioned information flows among the bigger cryptocurrencies.
Robustness and boundaries
The core results held up across a series of additional tests. Measuring sentiment on the day before the announcement, or averaged over the three prior days, produced similar interaction effects, helping address concerns that sentiment might simply be reacting to the news itself. Swapping in alternative sentiment measures, including the Baker-Wurgler index and the American Association of Individual Investors survey, yielded consistent patterns. Widening the event window to ten or thirty minutes around each release, or adding controls for financial-market volatility, policy uncertainty, business conditions, risk aversion, and investor attention, did not overturn the findings.
Extending the analysis to macroeconomic releases from the European Union, China, Japan, and Germany showed that crypto markets react meaningfully to EU and Chinese data, with sentiment playing a similar moderating role, while responses to Japanese and German announcements were largely muted.
What traders and managers might take from it
Huynh frames the practical implication as a matter of context rather than a trading rule. The same macroeconomic release can carry different weight in crypto markets depending on whether sentiment is running hot or cold, which has implications for short-horizon risk management, timing of rebalancing, and liquidity provision around scheduled events. For portfolio managers holding crypto exposure, ignoring sentiment conditions when gauging news risk may lead to under- or overestimating how prices will move.
The study’s design comes with caveats. The sample focuses on the top 100 cryptocurrencies, which are the most liquid but also the most followed, and the construction rule based on end-of-period market capitalization leaves open some survivorship concerns even with inactive coins retained. Whether the same dynamics extend to smaller tokens, DeFi assets, or on-chain activity is a question Huynh flags for future work.
