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Coronavirus-Related Sentiment and Stock Prices Measuring Sentiment Effects on Swedish Stock Indices

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Coronavirus-Related Sentiment and Stock Prices Measuring Sentiment Effects on Swedish Stock Indices DEGREE PROJECT IN FINANCE PROGRAM: REAL ESTATE AND FINANCE FIRST CYCLE, 15 CREDITS STOCKHOLM, SWEDEN 2020 Coronavirus-Related Sentiment and Stock Prices Measuring Sentiment Effects on Swedish Stock Indices Olga Piksina Patricia Vernholmen KTH INSTITUTIONEN FÖR FASTIGHETER OCH BYGGANDE 1 Bachelor of Science Thesis Title: Coronavirus-Related Sentiment and Stock Prices: Measuring Sentiment Effects on Swedish Stock Indices Authors: Olga Piksina, Patricia Vernholmen Institution: Institution of Real Estate and Construction Management Bachelor Thesis number: TRITA-ABE-MBT-20482 Archive number: Supervisor: Andreas Fili Keywords: market sentiment, behavioural finance, market efficiency, coronavirus, Swedish stock market, text analytics, sentiment analysis, news mining Abstract This thesis examines the effect of coronavirus-related sentiment on Swedish stock market returns during the coronavirus pandemic. We study returns on the large cap and small cap price indices OMXSLCPI and OMXSSCPI during the period January 2, 2020 – April 30, 2020. Coronavirus sentiment proxies are constructed from news articles clustered into topics using latent Dirichlet allocation and scored through sentiment analysis. The impact of the sentiment proxies on the stock indices is then measured using a dynamic multiple regression model. The results show that the proxies representing fundamental changes in our model — Swedish Politics and Economic Policy — have a strongly significant impact on the returns of both indices, which is consistent with financial theory. We also find that sentiment proxies Sport and Coronavirus Spread are statistically significant and impact Swedish stock prices. This implies that coronavirus-related news influenced market sentiment in Sweden during the research period and could be exploited to uncover arbitrage. Finally, the amount of sentiment-inducing news published daily is shown to have an impact on stock price volatility. 2 Examensarbete kandidatnivå Titel: Coronavirus-relaterat sentiment och aktiepriser: En studie av sentimenteffekter på svenska aktieindex Författare: Olga Piksina, Patricia Vernholmen Institution: Fastigheter och Byggande Examensarbete kandidatnivå nummer: TRITA-ABE-MBT-20482 Arkiv nummer: Handledare: Andreas Fili Nyckelord: marknadssentiment, beteendefinans, marknadseffektivitet, coronaviruset, svensk aktiemarknad, textanalys, sentimentanalys, news mining Sammanfattning Denna studie undersöker den effekt coronavirus-relaterat sentiment haft på avkastningen på svenska aktieindex under coronaviruspandemin. Vi studerar avkastningen på large cap- och small cap-prisindexen OMXSLCPI och OMXSSCPI under perioden 2 januari 2020 – 30 april 2020. Proxier för coronavirus- sentiment konstrueras från nyhetsartiklar som klustrats i ämnen genom latent Dirichlet-allokering och poängsatts genom sentimentanalys. Sentimentproxiernas påverkan på aktieindexen mäts sedan med en dynamisk multipel regressionsmodell. Resultaten visar att proxierna som representerar fundamentala förändringar i vår modell — svensk politik och ekonomisk policy — har en starkt signifikant inverkan på avkastningen på båda indexen, vilket är konsekvent med finansiell teori. Vi finner även att sentimentproxierna sport och spridning av coronaviruset är statistiskt signifikanta i sin påverkan på svenska aktiepriser. Detta innebär att coronavirus-relaterade nyheter påverkade marknadssentiment i Sverige under undersökningsperioden och skulle kunna användas för att upptäcka arbitrage. Slutligen visas mängden sentimentframkallande nyheter publicerade per dag ha en inverkan på aktieprisvolatilitet. 3 Acknowledgements We would like to extend our genuine gratitude to our supervisor, Dr. Andreas Fili, whose guidance and support made this work possible. We wish to express our sincere thanks to Dr. Bertram Steininger for sharing meaningful insights into text analytics and recent research in the field. We would also like to thank Dr. Olga Rud for her recommendations on the material used for this study. Our special thanks goes to Stephen Rosewarne who kindly agreed to proofread this thesis. Furthermore, we are thankful to our family members and friends Christoffer Linné, Marina and Laura Vernholmen, Manne Svensson, Helene Törnqvist and Leo P. Thank you all for your unwavering support and inspiration. 4 Table of Contents 1. INTRODUCTION 8 1.1 Research Purpose and Questions 8 1.2 Contribution to the Field 10 1.3 Disposition 10 2. REVIEW OF THE LITERATURE 10 2.1 Fundamental Analysis vs. Technical Analysis 10 2.2 Efficient Market Hypothesis 11 2.3 Behavioural Finance 12 2.4 Event Studies and EMH 16 2.5 Text Analytics in Finance 16 2.5.1. Sentiment Analysis through Computational Linguistics 17 3. METHOD AND MATERIALS 18 3.1 Description 18 3.2 Limitations 19 3.3 Stock Indices 20 3.4 News 20 3.4.1 Collection 20 3.4.2 Preprocessing Textual Data 21 3.4.3 Topic Modelling and Scoring 21 3.4.4 Sentiment Proxies 23 3.4.5 Sentiment Analysis 26 3.4.6 Allocation of News to Dates 27 3.5 Economic Indicators 27 3.6 Multiple Regression 27 3.6.1 Model Specification 27 4. RESULTS 28 4.1 Autocorrelation Analysis 28 4.2 Cross-Correlation Matrices 29 4.3 Specified Regression Model 30 4.3.1 Variance Inflation Factors 30 5 4.4 Regression Outputs 30 4.5 Market Volatility and Coronavirus-Related News 32 5. ANALYSIS 32 5.1 Sustainability Aspects 34 5.2 Further Research 35 6. CONCLUSION 35 REFERENCES 37 6 Terminology list OMXSLCPI Price index of all Large Cap companies listed on Stockholm Stock Exchange (Market value of 1 billion euro or more. Nasdaq, 2020). OMXSSCPI Price index of all Small Cap companies listed on Stockholm Stock Exchange (Market value below 150 million euro. Nasdaq, 2020). Market Sentiment “[...] a belief about future cash flows and investment risks that is not justified by the facts at hand” (Baker and Wurgler, 2007). Market value of an asset = fundamental value + sentiment value. Proxy A proxy is “a variable used instead of the variable of interest when that variable of interest cannot be measured directly” (Oxford University Press, 2009). Proxies used in this study fall into two mutually exclusive categories: 1. Fundamental proxies, representing news which can cause fundamental change to asset values, and 2. Sentiment proxies, which reflect investor sentiment. Proxies belonging to one category have no influence on the other. Web Scraping Automated gathering of data from the internet through any means other than a program interacting with an API (Mitchell, 2018). Latent Dirichlet Allocation Unsupervised machine learning algorithm used to cluster previously (LDA) unlabelled text data according to topics (method known as topic modelling). It finds the most common words appearing in the text and clusters them, thus uncovering themes in text (see Blei, Ng and Jordan, 2003). Text Analytics “[...] large-scale, automated processing of plain text language in digital form to extract data that is converted into useful quantitative or qualitative information” (Das, 2014). Corpus Collection of text documents which can be readily processed in an automated way. Tokenisation Part of data preprocessing identifying basic units, known as tokens, in text corpora. Some methods tokenise by words or entities delimited by blank spaces while others make tokens of more complex entities such as idioms or expressions (Webster and Kit, 1992). 7 1. Introduction On the 11th of March, 2020, the World Health Organisation (WHO) declared the novel coronavirus disease 2019 (COVID-19) outbreak a global pandemic. The outbreak originated in Wuhan, China in December 2019 and has since spread throughout the entire world. In addition to a serious health emergency, the spread of the disease in the majority of the world’s countries has led to a deep economic crisis predicted by many to become a recession similar to the Great Depression. Stock markets all over the globe have reacted with varying degrees of panic, and widespread future uncertainty has resulted in two of the largest single day drops in the Dow Jones Industrial Average. The Swedish stock market has also experienced days of historic decline (see Figure 1). At the time of writing, the pandemic is ongoing and there is no clear outlook on how it is going to develop or when it will end. The coronavirus outbreak has also been remarkable due to its receiving unprecedented, near total media coverage across the globe. With a huge part of the world’s population being isolated or confined to their homes, the current pandemic has become a unique event with news rapidly spreading around the world. Information on the spread of the coronavirus and measures taken by governments in response to the crisis have alternated with news of skyrocketing unemployment rates, industries at risk of collapse, and the total economic impact of the pandemic on the global community. Our primary hypothesis is that non-economic news has made a significant impact on how investors on the Swedish stock market have valued assets during the pandemic. We assume movement on the market is influenced by blanket media coverage of the pandemic as well as various measures the Swedish government and central bank have introduced in response to the crisis. Our hypothesis has its basis in the theory of behavioural finance that indicates investors’ mood, fear and emotions impact the decision making process (Kahneman and Tversky, 1979; Statman, 1995; Shleifer and Vishny, 1997; Donadelli, Kizys and Riedel, 2017; Bukovina, 2016). 1.1 Research Purpose and Questions In this paper, our aim is to analyse how coronavirus-related
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