Sentiment Analysis and Stock Volatility : Evidence from Financial News headlines Published related to Finland and NASDAQ Helsinki

Abstract

The challenge of forecasting the stock market fascinates researchers. Studies that use innovative prediction approaches continue to emerge, despite the overwhelming body of data suggesting that the dynamics of the financial market cannot be foreseen. The widely accepted theory for predicting stocks is the Efficient Market Hypothesis. However, numerous studies have still been conducted in the field of stock price prediction. With the use of news sentiment, this thesis examines non-quantifiable financial data, such as financial news headlines, through machine learning techniques in Python to quantify the news headlines. Predicting financial market trends using time series analysis and natural language processing is a challenging and complex task given the magnitude of factors that might affect stock prices, such as political and economic events. Nevertheless, due to the rapid development of digital plat-forms, the clarity of forecasting future financial trends has greatly improved. A variety of financial data can be freely accessed and evaluated for insights, and people's opinions are widely shared, which may set the sentiment of the market. Numerous studies have examined the link between public opinion and market volatility and suggest that sentiment ex-pressed by individuals may influence market trends due to causes such as the ripple effect and the herd effect. The variations in opinion and irrational decision-making may depend on factors such as personal risk appetite, financial literacy level, and other social factors like age, gender, and religion. Therefore, it is interesting to examine these relationships for a smaller yet economically important nation in Europe.

This master’s thesis investigates the relationship between News Sentiment (NS) and NASDAQ Helsinki (NH) as well as their effect on a sample of companies with different volatility profiles. It also looks at the lagged effects of variables and the impact of inflation, interest rates, and indexes on each variable. The study concludes that there was no statistically significant relationship between NS and NH and that NS could not account for the variation in NH. The study applies vector auto-regressive and auto-regressive lag models to examine the relationship between variables with an optimal lag length selected according to AIC. The first lag of NH on its own showed a strong correlation with the results of the ARDL test. confirming the finding from Engle in 1982, who reveals that prior error terms have predictive power over current error terms. However, the tests did not show a lagged effect of NS on NH, nor was there a noticeable correlation between other companies. The combined effect of NH and NS was found to be less significant for high-volatile companies in the selected sample than for low-volatile companies. Additionally, some variables' statistical significance suggests that inflation, interest rates, and indexes had a significant impact on them.