Analyzing Correlations of Corporation Earning Announcements, Dividend Declarations and Public Sentiment against the Stock Market

Smrkolj Koželj, Nejc (author), Bajec, Marko (mentor)

In our research, we focused on analysing the stock market events and the price movements on market events. We researched the quarterly corporate earning announcement events as well as dividend declaration events and investigated any correlations they might have to the publicly available stock data and stock prices. Furthermore, we analysed how the public sentiment, notably Twitter and Google Trends, can be utilised to help estimate the correlation between event and the stock price movement. To process the data, we utilised the machine learning algorithm logistic regression due to its simplicity and robustness. Our findings were that the earning announcement event outcome could, to some extent, be predicted but the actual movement is not as important as the size of the movement. We also successfully utilised public sentiment to improve our results further.

• Language: English
• Keywords: data mining, data scraping, stock markets, market events, event detection, event analysis, event correlations, earning announcements, dividend declarations, sentiment analysis, social networks, google trends, twitter
• Work type: Master's thesis/paper
• Typology: 2.09 - Master's Thesis
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2020
• PID: 20.500.12556/RUL-121358
• COBISS.SI-ID: 33135107
• Publication date in RUL: 06.10.2020

Secondary language

• Language: Slovenian
• Title: Analiza korelacij korporacijskih zaslužkov, izjav o dividendah in javnega sentimenta z delniškimi trgi

Abstract

V naši raziskavi smo se osredotočili na analizo delniških trgov in njihova gibanja cen ob dogodkih na trgih. Raziskali smo tako dogodke povezane s četrtletnimi poročili o zaslužku kot napovedi o dividendah ter poglobljeno preučili njihovo korelacijo med javno dostopnimi delniškimi podatki in samo ceno delnic. Analizirali smo tudi kako bi lahko javni sentiment z osredotočenjem na Twitter in Google Trends pomagal izboljšati ocene korelacij med samim dogodkom ter gibanjem cene. Za procesiranje podatkov smo uporabili algoritme strojnega učenja logistična regresija, saj je algoritem zelo preprost ter robusten. V raziskavi smo ugotovili, da je možno delno napovedati gibanje cene delnic po dogodku, vendar je velikost gibanja pomembnejša od same smeri gibanja. Uspešno smo tudi uporabili javni sentiment za nadaljnje izboljšanje rezultatov.

• Keywords: podatkovno rudarjenje, borza, analiza dogodkov, delnice, finance, sentiment, analiza prihodkov, google trends, twitter