Predicting Market Movements Using Investor Sentiment Analysis
Sentiment indicators are invaluable tools for forecasting market movements. A high positive sentiment indicator could signal investor complacency and herd mentality that often precede market tops when optimism fades; while extreme bearish sentiment indicates capitulation signalling potential bottoms as pessimism hits its maximum fear level.
Investor sentiment analysis finds its roots in articles related to behavioral finance such as disposition effects. According to co-citation analyses, most investor sentiment literature can be found within clusters such as these journals:
Fundamental Analysis
Fundamental analysis involves studying various factors that affect a security’s true value, such as financial statements, economic indicators, industry reports and management quality assessments. By carefully considering all this data points together, investors can make more informed investment decisions that align with their investment goals and risk tolerance.
Fundamental analysis can be time-consuming and complex; requiring extensive research and interpretation of complex data. Furthermore, fundamental analysis depends on historical performance data that might not predict future performance accurately, as well as subjective judgments that might sway its conclusions.
Fundamental analysis must also take market trends and investor sentiment into account when conducting it, since even companies with impressive revenue growth and profitability could falter in an industry experiencing a decline or unfavorable economic environment. That is why many traders prefer performing both fundamental analysis and technical analysis simultaneously; doing both gives traders a well-rounded understanding of the market which allows them to make intelligent trading decisions based on this insight.
Technical Analysis
Investor sentiment analysis can be done using various techniques, including social media analytics, natural language processing and machine learning algorithms. Sentiment analysis helps identify undervalued or overvalued stocks while also anticipating market movements or turbulence; for example when equity prices decline investors tend to turn toward safer haven assets like gold as it has historically demonstrated strong correlation with equity prices.
This bibliometric analysis of investor sentiment research illustrates its basis in behavioral finance (such as disposition effect and prospect theory). Furthermore, this field is becoming an increasingly fashionable research topic as evidenced by Figure 3, where average publication year results contain keywords related to both traditional and behavioural finance.
Figure 5 from the authors shows a keyword co-occurrence map showing that investor sentiment research tends to overlap with articles in behavioural finance, specifically asset pricing and market efficiency studies.
News Releases
There are various strategies available for predicting stock market trends, but one popular technique is using news releases as an analysis tool. Performing such analysis allows traders to identify possible trends and make more informed trading decisions.
News release analysis involves classifying topics found within financial news releases. A classifier then uses these topics to predict whether markets will rise or fall – this method has proven highly successful among successful traders.
Bollen et al. (2011) conducted one such study that investigated how news can impact stock prices. Their research demonstrated how news events may lead to changes in market optimism which subsequently affect stock prices; more salient news events had greater effects.
Sajid Nazir et al. (2017) conducted research into the impact of political events on stock markets in Pakistan, by analyzing daily news and events affecting KSE 100 index. Their researchers observed that negative sentiment captured from headlines is positively correlated with market returns the next day while negatively correlating with stock market volatility.
Social Media
Social media posts can have a dramatic effect on stock market prices. They can help investors predict stock movements, leading them to make better-informed decisions; but these platforms may be misused by malicious actors to manipulate it.
Many machine learning algorithms are capable of analyzing data and predicting stock price movements using linear regression, naive bayes and decision trees as predictive models. The ML-lib library offers training and deployment services for these models as well as several text analysis and sentimental analysis methods.
Recent research employed naive bayes to forecast stock prices. This model can be applied to Twitter data and its results showed that it accurately predicted stock movements; additionally there was positive information flow between sentiment indexes and stock prices which supported Tetlock’s theory that investors acquire information secondhand and that such second-hand information influences stock markets.