To succeed in using sentiment analysis in trading, you’ll need a successful strategy that encompasses real-time data with actionable insights.
Here are our tips on how to succeed with stock sentiment analysis.
What is sentiment analysis in trading?
Sentiment analysis is a use of natural language processing to quantify opinions expressed within text. Within the context of stock trading, this involves analyzing sources such as news reports, social media posts, earnings calls and analyst commentary to evaluate whether the prevailing sentiment is bullish, bearish or neutral.
Unlike technical analysis (which looks at price action) or fundamental analysis (which focuses on financial metrics), sentiment analysis is a method used to understand how the market feels.
The key to trading success is being able to convert an understanding of this emotion into an actionable trading strategy.
How does it work?
Sentiment analysis uses natural language processing tools to process unstructured data at scale.
Many modern systems use a hybrid approach of rule-based filtering and supervised machine learning.
Text parsing and normalization
The first stage is to remove the noise and irrelevant information. The remaining useful textual data will be tokenized into meaningful components that can be analyzed by a trained machine learning algorithm.
Sentiment scoring
Each text token will be assigned a sentiment score, typically on a scale from highly negative to highly positive, sometimes expressed within finance as a scale of bullish to bearish.
Aggregation and contextualization
These individual sentiment scores require context to be of full value to a trader. By aggregating scores over time and across various sources, traders can produce sentiment indexes and alerts.
Signal generation
Contextualised sentiment scores can be used as triggers within algorithmic trading strategies. That is to say, once certain changes in sentiment are detected, automatic bidding strategies to either buy or sell a stock can be initiated.
Why does trading sentiment analysis matter?
Markets do not move on fundamentals alone. As famous economist John Maynard Keynes penned the term “animal spirits” to describe the psychological and emotional factors that influence economic decision-making, especially in terms of investment and consumption.
These non-rational forces, such as confidence, optimism, pessimism and fear can significantly impact price movements of stocks.
When combined with other factors such as liquidity, volatility and momentum, traders and even hedge fund portfolio managers can follow a trading strategy that provides a competitive advantage.
Key sources of sentiment data
To use sentiment analysis in trading to its maximum benefit, traders need real-time access to a diverse dataset of information that typically comprises of:
Mainstream and industry news
Financial news outlets are among the most influential sources of market sentiment. Headlines and interviews with specialist analysts can trigger knee-jerk reactions, especially during earnings season.
Social media
Traders analyse platforms such as Twitter and Reddit to get a better understanding of real-time retail investor psychology. It isn’t just hedge funds and large asset managers that dictate the market movers – the recent viral GameStop and AMC price movements show the power of collective sentiment in moving prices.
Earnings call transcripts
The tone and language used by executives during earnings calls can provide sentiment signals that traders can combine with pricing models to explore potential trades.
SEC Filings and Corporate Disclosures
Changes in language used within official filings such as the American 10-K form can hint at an evolving corporate outlook. This can highlight changes in financial sentiment before traditional methods spot anything out of the ordinary.
Analyst reports
The tone used by brokerage firms and institutional researchers can sometimes be more useful than the actual content of their forecast.
Forums and blogs
Especially prevalent for smaller, emerging assets, retail discussion forums can provide early indications of interest or concern.
Types of trader sentiment strategies
It isn’t as simple as good sentiment means buy shares and negative sentiment means sell. Instead, there are a few different schools of thought of how to use sentiment data.
Momentum confirmation
This trading strategy used sentiment scores to confirm price breakdowns or breakouts. If a bullish breakout price increase is backed by positive rising sentiment, it is believed by adherents to momentum confirmation strategies that the price increases will be more likely to sustain compared to if the price breakout occurred during neutral or negative sentiment.
Contrarian signals
Some traders win big by not following the herd. Extreme sentiment in some situations can be seen to indicate market exhaustion and that a price correction is inbound.
Event-based trading
During financial events such as earnings calls or macroeconomic announcements, sentiment analysis tools can provide an instant insight into market reactions. Traders can use this real-time data to influence their trading decisions.
Risk Management
Risk is a big driver of price volatility. Financial exposure is a measure of to what degree a trader is subject to financial risk from market movements or investment positions. Understanding and managing exposure is key for traders of any level, and sentiment analysis can provide useful insights into the risks associated with certain stocks and markets.
Portfolio rotation
Changes in overall market sentiment can be a driving force in traders adjusting their portfolios. For example, if the overriding sentiment is starting to drop within a sector such as technology, traders might rotate their exposure and look for new market opportunities.
A hybrid approach
Not many traders use sentiment analysis alone to guide their trading decisions.
Instead, sentiment analysis is often layered with other strategies to improve entry and exit timing. Combining sentiment data with fundamental analysis can ensure trades are not purely emotion-driven.
The requirement to integrate stock sentiment analysis with other information is why market sentiment analysis APIs such as StockGeist are so popular with traders.
Whether you are looking to build your own signals or have a custom dashboard built for you, take a look for yourself by signing up here.
NLP Team Lead at Neurotechnology | StockGeist Project Lead – Senior NLP & LLM Developer
Vytas is a figurehead at Neurotechnology – founder and NLP team lead of StockGeist.ai at the age of just 21. With over 7+ years of experience in LLM and NLP development, Vytas’ passion and knowledge for developing AI-powered solutions burns brighter than ever before. He has a vast amount of experience in the field of sentiment analysis for the stock and crypto market, helping traders and investors better understand textual data across social platforms through his innovative platform, StockGeist.ai.
