In the long run, valuations may drive stock prices, but in the short term it is market sentiment that moves prices. This can create investment opportunities for long term investors to find attractive entry points, and for active traders to both enter and exit positions.
Market sentiment analysis is an evolving technique which can be effectively used to compliment fundamental, quantitative and technical analysis. Sentiment analysis is also one of the more successful methods of including the effects of market psychology in a trading strategy. Empirical evidence suggests that investor sentiment is one of the most reliable indicators of future price movements.
- What is market sentiment?
- How emotions affect the stock market
- Market sentiment indicators and how market sentiment can be tracked
- New developments in sentiment analysis
- How to utilize sentiment analysis for trading
- How Catana Capital uses sentiment analysis for asset management
What is market sentiment?
Market sentiment is a qualitative measure of the attitude and mood of investors to financial markets in general, and specific sectors or assets in particular. Positive and negative sentiment drive price action, and also create trading and investment opportunities for active traders and long-term investors.
It could be described as the aggregated public opinions, views, feelings, mood, or outlook that make up the market psychology at any point in time. Because market sentiment cannot be exactly defined or measured, there is no specific correct or incorrect way to conduct sentiment analysis. Nevertheless, there are ways to use and combine other indicators that reflect market sentiment.
How emotions affect the stock market
In the short-term markets are driven by emotion – fear and greed in particular. Traders and investors are often driven by one form of psychological need or another. The fear of missing out, FOMO, can cause investors to pay prices for an asset that have no basis in reality. In that case they are not buying because the asset is a good investment, but because they need to do something to avoid the feeling of missing out. During bear markets, investors will often sell stocks at prices well below their value because they need to stop feeling the pain of losing money.
These are both examples of how emotions can force investors to make decisions that aren’t rational. It also shows why major market highs and lows are usually accompanied by extreme levels of positivity and negativity. Sentiment is highest just before major market tops, and lowest just before major market bottoms. By using sentiment analysis, investors can attempt to determine when the market is being driven by emotion rather than by rational decision making. They can pick up changes in sentiment before there is any news to explain the behaviour of stock prices.
Market sentiment indicators and how market sentiment can be tracked
As mentioned, there is no one specific way to measure market sentiment. However, there are quite a few indicators and metrics that can be used to give us a good idea of how participants view the outlook for markets.
- VIX Index
- Put Call Ratio
- Safe haven assets
- “Risk on” assets
- High / Low Index
- Stock price breadth
- CNN’s Fear and Greed Index
One of the most well-known is the VIX Index, or CBOE Volatility Index, which is an index that records the implied volatility of S&P 500 index options. Investors buy options to hedge and protect their portfolios. When they expect volatility to rise, they bid the options higher, and the index rises. The VIX is known as the “Fear Index” as it gives a good indication of the amount of fear in the market.
Put Call Ratio
A similar measure which is also widely followed is the Put Call Ratio, which measures the ratio of put options versus call options being bought in the market. Because investors buy puts to protect their investments, a high reading means investors are fearful of a market decline, while a low reading indicates increased appetite for risk.
Safe haven assets
The strength of safe haven assets is also a good indication of the level of fear in market. These include risk free assets like US treasuries, the currencies of the US and Switzerland, and precious metals. When sentiment is negative, these assets often appreciate in price as investors seek safe vehicles to store wealth.
“Risk on” assets
Investors move to safe haven assets when they are risk averse, and to riskier assets when sentiment is positive. This is known as the “risk on / risk off trade”. “Risk on” assets include emerging market currencies, debt and equities, high yield and junk bonds and small cap stocks.
High / Low Index
The High / Low index is a ratio of stocks making new 52-week highs vs those making new 52-week lows. A reading below 30 implies bearish sentiment, while a reading above 70 implies bullish market sentiment.
Stock price breadth
Stock price breadth is a similar measure which compares the traded volumes of rising stocks with that of declining stocks. The rationale is that this will show whether money is really flowing into the stock market or not, regardless of the number of different stocks rising.
CNN’s Fear and Greed Index
CNN’s Fear and Greed Index combines 7 different sentiment indicators to produce a reading between 1 and 100, with 1 indicating extreme fear and 100 indicating extreme greed. The indicators it uses include all those listed above, as well as market momentum.
New developments in sentiment analysis
Advances in technology and online media platforms over the past few decades are opening up new possibilities for sentiment analysis. This area is still relatively new, but several very promising techniques have been developed using among other things social media content, crowd sourcing platforms and Google search trends.
Data from these platforms add a new dimension to sentiment analysis by making thoughts, opinions and activity of millions of people available in real time. Artificial intelligence can also be used to find patterns and correlations between sentiment and price history from the stock market. This new area of sentiment analysis represents the convergence of online media, big data and artificial intelligence and is resulting in sentiment analysis becoming an increasingly important tool for traders and fund managers.
How to utilize sentiment analysis for trading
For the most part, sentiment should be combined with other forms of analysis to be most useful. Often the best opportunities occur when sentiment and fundamentals do not agree. Empirical evidence also shows that extreme sentiment readings very often occur at turning points. Ultimately what moves prices is what people in the market think, regardless of whether its rights agree with the fundamentals or not. Sentiment drives supply and demand, which in turn drives the price. It can also move the price in the same direction as the fundamentals, or in the opposite direction – and in the short term, sentiment often overrides fundamentals.
There are two opposing factors to consider when using sentiment to make trading decisions. Firstly, as long as sentiment continues to improve, prices will rise or stop falling. Likewise, deteriorating sentiment will cause prices to fall or stop rising. At the same time, rising sentiment can create overbought or bubble-like conditions, which will almost always result in a sharp reversal at some point. Negative sentiment can result in oversold conditions where stock prices become undervalued.
Using market sentiment to trade is therefore a case of being aware of how sentiment is changing, as well as the broader context, fundamentals and trends. As a trader you need to be aware of what might happen if sentiment begins to change in one way or another. The largest price moves happen when sentiment changes quickly, and when a large group of market participants switch from bullish to bearish or vice versa. The most profitable opportunities therefore exist when the conditions for rapidly changing sentiment are in place.
Buy the rumour, sell the fact
When an idea is already widely agreed or known by the market, the impact will be limited. News events are often priced into the market long before they occur, at which point much of the price action reverses as profits are taken. This is known as the buy the rumour, sell the fact trade, where sentiment causes prices to anticipate a best- or worst-case scenario. When the event occurs, only a substantial surprise can keep the momentum going – in most cases a move in the opposite direction will occur.
When there is little agreement within the market, and no news flow to change the opinions of market participants, prices will become rangebound and move sideways. This will continue until something happens that changes the outlook of enough participants to change the sentiment of the overall crowd.
Analyzing sentiment is easiest when a regular process is established using a variety of inputs so that anomalies in one or two indicators doesn’t distort the results. A rules-based strategy will also help you deal with all the ambiguity that can occur when studying sentiment, fundamentals and price action.
A sentiment score should also be considered within the context of a trend on higher timeframes. Apart from major market tops and bottoms, extreme readings may well signal the end of a counter trend move on a higher timeframe. Market sentiment on each time frame can be rated as either positive, negative or neutral. This rating can then be combined with other forms of analysis to make decisions or time entries and exits.
When using sentiment to make decisions you are interested in changes in sentiment, and in extreme sentiment readings. When sentiment switches from positive to negative or visa versa, you can look for supporting evidence, or for trading opportunities to trade with the momentum created by rising or falling sentiment.
Extreme readings may give you an opportunity to look for mean reversion trades, or trades in the direction of the longer-term trend. However, extreme readings in sentiment alone should not be used to predict market turning points. Rather, investors should look for other evidence that a top or bottom may be in place, by analysing volume, support and resistance levels or momentum.
How Catana Capital uses sentiment analysis for asset management
Catana Capital leverages the power of sentiment analysis with big data, artificial intelligence and the emotion free process of automated trading to manage its Data Intelligence Fund. The fund employs a long / short equity strategy with no leverage and is based on European equities.
Big data describes large datasets that are often gathered automatically by computer networks and can be analysed to reveal pattern and correlations. Catana Capital uses big data and proprietary software to analyze information that can be used to conduct and use advanced market sentiment analysis to inform its decisions.
The algorithm uses information from social media platforms, news articles and other forms of crowd sourced data to analyze over 2 million user generated messages and news articles a day. Sentiment classification signals are generated with text analysis via natural language processing software. This information is then used for coming up with investment decisions by Catana Capital.
These signals are further combined with price action data and deep learning algorithms are used to find patterns and relationships between sentiment and price movements. Combining substantial computer processing power with machine learning techniques allows tradable patterns to be identified that go well beyond the way sentiment analysis is traditionally used.
In addition, artificial intelligence (A.I.) and big data is used to manage exposure and risk. By automating the entire process, the effects of emotions are eliminated, allowing innovative investment funds by Catana Capital to take advantage of opportunities created by the emotion and sentiment that drives markets.
Outlook: Investment decisions based on sentiment analysis
Sentiment analysis has been used successfully by traders for some time. However, new advances in data science, A.I. and text analytics is now taking the use of market sentiment to a new level, and its importance in the investment industry will continue to grow. We can expect it to become a field of analysis as important as fundamental, quantitative or technical analysis. It will also compliment the rapidly growing use of A.I. in the investment decision.
However, sentiment models have a limited lifespan. The edge enjoyed by any one model will only exist as long as few other market participants are unaware of it. Individual traders may be able to maintain an edge if their model identifies edges that are not viable for larger players. But, when it comes to professional funds, as the field becomes more competitive only those with a real competitive advantage will prosper.
This will mean that only companies like Catana Capital that conduct ongoing research to find new ways to use and employ sentiment data will be able to maintain their edge.