“Buy on Rumors, Sell on Facts”: How to Utilize Information Drivers in Trading
In trading, information drivers often hold more weight than fundamental factors. The adage "buy on rumors, sell on facts" is based on the idea that market expectations are reflected in prices even before the official data is released, with corrections or reversals occurring after the publication of the facts. To effectively apply this strategy, one must comprehend the mechanics of informational momentum, analyze sources, account for behavioral aspects, technical signals, and manage risks.
1. Fundamentals of the “Rumors-Facts” Strategy
1.1 Mechanics of Informational Momentum
The market reflects expectations in advance: rumors about significant events—such as reports, central bank decisions, or geopolitical developments—ignite buying or selling momentum even before the official data is made available. This momentum is driven by mass orders based on rumors.
1.2 Principle of Correction Post-Fact
When actual data is published, the majority of positions are already opened. If the actual result falls short of expectations, traders close positions at a loss, leading to a downward correction; conversely, if the actual results exceed expectations, there is a brief upward surge followed by a rebound from the extremes.
1.3 Timing Lags in Reactions
The delay between rumors and facts can range from minutes to days. On shorter timeframes (M15–H1), the moments of informational momentum are expressed quickly, while on longer timeframes (D1), expectations are smoothed out.
2. Information Gathering and Analysis
2.1 Sources and Rumor Filtering
– Social media (Twitter, Reddit, Telegram) and specialized insider chat rooms.
– News feeds from Bloomberg, Reuters, CNBC featuring a “Pre-Market” section.
Reliability is checked through cross-verification of multiple independent sources.
2.2 News Sentiment Analysis
Platforms like RavenPack and Bloomberg News Analytics evaluate the sentiment of mentions. NLP tools enable automatic analysis of tweets and headlines, generating a “tension” indicator.
2.3 Fundamental Factor Assessment
– Macroeconomic data (GDP, unemployment, CPI).
– Corporate earnings reports.
– Statements from regulators.
Comparing facts to analysts' forecasts determines the “excess” effect on price.
3. Behavioral Aspects and Emotional Traps
3.1 FOMO and Mass Psychology
The fear of missing out (FOMO) leads to early entries on rumors, while panic selling results in profit-taking post-fact.
3.2 Self-Fulfilling Prophecy
The more participants place orders at a specific level, the stronger the demand/supply zone is created, making the performance of mirror levels more reliable.
3.3 Avoiding Emotional Traps
– Strict rules for profit-taking.
– Automated emergency exit algorithms.
– Diversification of signals.
4. Technical Signals on Rumors and Facts
4.1 Pre-event Spikes and Gaps
Strategies such as gap fade or momentum continuation allow traders to capture sharp movements ahead of news, but only with confirmation from volume and candlestick patterns.
4.2 Volume and Momentum
– A surge in volume during rumors signals mass entry.
– Oscillators (RSI, MACD) indicate overbought conditions before facts and oversold conditions afterward.
4.3 False Breakouts (Fakeouts)
Fakeouts are filtered based on volume and retests of levels, rather than solely by candlestick breakouts.
5. Algorithmic and News Trading
5.1 News-Driven Algos and HFT
Algorithms analyze RSS feeds and API data in milliseconds, executing orders based on rumors before manual reactions can take place.
5.2 Latency Arbitrage
Utilizing direct channels to exchanges and APIs to minimize delays allows for arbitraging the differences in reactions to news among participants.
5.3 Automation Tools
– News trading plugins for MT5 and TradingView.
– Python bots for Telegram.
– Custom scripts on exchange APIs.
6. Risk Management and Execution
6.1 Stop-Loss and Slippage
Stop-losses are set wider than the extreme plus ATR. In cases of high slippage, part of the position is manually closed after the facts are known.
6.2 Position Sizing
Formula: allowable risk (%) × capital / distance to stop-loss. The risk per trade should not exceed 2% of the deposit, and can be increased to 3% with multiple confirmations.
6.3 Execution and Liquidity
– Trading during active sessions (London, New York).
– Splitting orders into parts.
7. Timing and Strategic Scenarios
7.1 Pre-Fact (Pre-News)
Entries on shorter timeframes (M15–H1) based on volume spikes and sentiment signals.
7.2 Moment of Fact
Partial profit-taking, moving the remaining position to a breakeven stop.
7.3 Post-Fact (Fade the News)
Opening contrarian positions when confirming that the fact has already been priced in.
8. Examples and Case Studies
8.1 Federal Reserve Decisions
Expectations of a Federal Reserve rate hike lead to a rise in the dollar, followed by a correction of 0.5% after the actual decision is made.
8.2 Apple Reports
Stocks often rise on revenue rumors, then decline if the results do not meet forecasts.
8.3 Geopolitics
Rumors of escalating conflicts raise oil prices, which then revert back down following official statements.
Conclusion
The strategy of “buying on rumors, selling on facts” requires a comprehensive approach: from analyzing sources and news sentiment to technical confirmations and stringent risk management. In an era of instant messaging and algorithmic strategies, the ability to filter rumors from facts provides a significant competitive advantage.
