About Bullishbeat
Welcome to Bullishbeat! I’m Gustav Andersson, a network engineer with a side passion for data analytics. Although I had some background in C++ courses through university, it wasn’t until years later that I rediscovered my passion for programming. Meanwhile, I knew that finance was also an interest of mine, so I combined this with my data analytics interest, which I believe falls under the term “fintech.”
I’ve been exploring numerous sites to learn on my own, but I realized that most data analytics resources are hidden behind paywalls and subscriptions. That’s when I grew the idea of why not learn myself and share the knowledge with people for free as a project? And here I am. I hope you’re willing to join me on this endeavor.
The Journey Begins: From Programming to MarketInsights.
Over the years, I have become fascinated by the subjective factors influencing a company’s valuation. If there is no word for it, I will brand it as “Market Insights.” In my opinion, I have witnessed legitimate and robust companies, beyond just the “meme stock” phenomena, which have had such extensive media coverage that I believe it has a significant impact on the company’s valuation, at least in the short to mid-term. Based on this so-called “hype,” I have decided to collect data and follow the media coverage, as well as track the flow of money surrounding these companies
As I see it, we have fundamental technical analysis, but we need to add media coverage and sentiment analysis to our toolbox.
There are already studies that correlate media coverage and sentiment analysis with changes to a stock’s price in the short to medium term.
In my opinion, media sentiment analysis should be part of every technical analyst’s toolbox.
Furthermore, to prove that we are on the right track with this blog, I have posted research articles to back up my claims about media coverage and sentiment analysis driving a company’s valuation metrics, including stock prices and overall enterprise value, which you can find at the bottom of the page.
Empowering Curious Learners with Accessible Insights
So in our arsenal we have quantitative analysis, machine learning techniques, and market insights. Together we will create thorough case studies on both industries/sectors as well as the leading companies inside each industry/sector to finally land on companies that could be of interest.
At this blog we will break down these complex techniques into easy understandable information using visualizations like graphs and interactive dashboards to provide clear and easy to understand analysis.
Transparency and Objectivity at the Core
I am committed to presenting my findings and analyses openly, supported by credible sources whether they originate from academic studies, official data sources, or proven analytical methodologies. This approach provides you with the ability to fact-check, explore deeper, and verify the validity of the concepts discussed, ensuring you have a well-rounded understanding of the market landscape.
Join me on this exciting journey as we explore the intersection of technology and finance, unlocking opportunities and uncovering hidden insights within the stock market using the powerful tools of Python and Pandas. For a detailed explanation of the quantitative methodology leveraging APIs and data manipulation libraries, please visit the dedicated “Methodology” page.
References
- Kelly, S., & Ahmad, K. (2015). The Impact of News Media and Affect in Financial Markets. In K. Jackowski et al. (Eds.), IDEAL 2015, LNCS 9375 (pp. 535-540). Springer International Publishing Switzerland. https://doi.org/10.1007/978-3-319-24834-9_62
- Takale, D. G. (2024). Enhancing Financial Sentiment Analysis: A Deep Dive into Natural Language Processing for Market Prediction Industries. Journal of Computer Networks and Virtualization, 2(2), 1-5. https://doi.org/10.48001/JoCNV.2024.221-5
- Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2020). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 13, 3433-3456. https://doi.org/10.1007/s12652-020-01839-w