We are living in exciting times. The accessibility of stock and cryptocurrency investments has never been greater for the average person; for the first time ever, seemingly anyone with the right set of tools and the right mindset has the ability to take control of their financial future by investing in the markets. The volume of information available to investors has increased exponentially in recent years -- technical indicators, company documents, insider transaction histories, and all the order book data anyone could ever need, now available at the fingertips of anyone with a smartphone or desktop computer. This new information paradigm has been accompanied by an ever-increasing abundance of online investment conversation. Thousands of news articles, ten-thousands of posts on Twitter and Reddit, millions of messages in investing chatrooms on Discord etc. published 24/7, each and every day.
This abundance of financial media has quickly become unmanageable, even the most diligent full-time investor simply does not have the time to read everything. Enter sentiment analysis for finance. The name sentiment analysis describes a large group of methods used to determine whether text is positive, negative, or somewhere in between. The truth is, current sentiment analysis for finance is not that helpful, and our goal is to fix it. Even the best models today (some with 99%+ accuracy) are still lacking, and the main pitfall is that they fail to consider both the time direction and mood of text containing finance-specific language. We are implementing a solution for handling finance text, and the implications are staggering. For more on what we are doing specifically (and what others aren't), read our blog post here.
With sentiment analysis done correctly, we can enable retail investors to comb through much of the noise online without sacrificing value. We can aggregate the mood of the entire online conversation around a particular stock or cryptocurrency, or market or industry. We can determine when siginificant news events are taking place, and paired with some corresponding analysis of ticker financial performance, we can identify when the hype is overpowered, or when a stock may be undervalued by the news. Finance-specific sentiment analysis also enables us to score news publishers in terms of their credibility and insight; with this, we can rank individuals and sources based on how worthwhile they are to get your news from. Overall, these are the things we are passionate about: instead of having to work for your news, we want to make the news work for you. Done correctly, sentiment analysis has the potential to save you time, reduce your biases, and help you gain insight like never before. While we are still a work in progress, this pilot helps us to determine the best ways to present this information so that it can be as impactful for your portfolio as possible.
Babbl was founded by Sam Cartford and Ramsey Shaffer in April 2020 with one straightforward idea: can we automate online conversation for the stock market? With the market in a downward spiral brought on by international COVID-19 pandemic, we were itching to build something to help investors like us make sense of all of the news we were reading. Sam and Ramsey graduated from the University of Minnesota in May 2020 (Sam has a B.S. in Computer Science, Ramsey a B.S. in Industrial and Systems Engineering). Sam currently works as a Technical Analyst for Boom Lab, and Ramsey will complete his M.S. in Data Analytics at the UMN in May 2021. Both Sam and Ramsey have 8-years combined experience in data science, fin-tech, cloud systems, and corpus linguistics, with primary experience in the technical analytics realm with companies like Inspire Brands, Boom Lab, and Wells Fargo. While Sam's past work has involved computer science applications including cybersecurity, cloud platform development, and database management, Ramsey's past work involves process engineering applications such as big data analytics for fin-tech. Together, Sam and Ramsey were involved as undergraduates in the UMN's Carlson School of Management Algorithmic Trading club, and co-authored a research paper in corpus linguistics about the relationship between social media chatter and cryptocurrency price performance (namely Bitcoin), which Ramsey presented at the 2019 International Corpus Linguistics Conference in Cardiff, UK. Since finishing their undergrad, Sam and Ramsey have committed their focus on making Babbl a profitable social-impact start-up. Sam and Ramsey have leveraged lean start-up mentorship from Justin Grammens (co-founder of Lab651) and Matt Geiser (co-founder of Tin Roof Collective), and have received enduring start-up guidance and advice from Marshall Erickson (business accelerator and execution leader with the Red Wing Ignite entrepreneurship program) on a near-weekly basis since October 2020.
In recent months, we have taken Babbl from an idea to a full-fledged pilot MVP website and browser extension. Since then, we have added Jeff Arnold to our team to help us iterate and improve upon our development. Jeff also graduated from the UMN in 2020, and is currently finishing up a graduate degree in Sports and Business Marketing. As group, we have laid the ground work to make 2021 a big year. With suitable funding, Babbl's business model will continue to validate our problem-solution-customer fit by reaching more early adopters. We will pay a UI/UX development firm to complete professional MVP web- and mobile-applications for formal beta launch by July 2021. With our current pricing strategy, we would out-pace our monthly burn rate ($500-$1500/month) with approximately 30 paying users. In parallel to MVP production, funding will allow us to work with a social marketing firm to develop our promotional strategy and reach our goal of 1,000+ early adopters by August 2021. With more user traction, we can affirm initial revenue streams, and begin to add more finance and data science expertise to our team by late 2021. This will prepare Babbl for the release of fully market-ready web and mobile-applications (complete with usage-based subscription capabilities and machine learning models to improve the efficacy/accuracy of our research tools) by early 2022 at the latest, and allow us to engage and capture matching funding. If you made it this far reading through our About page, thanks for being a part of this journey, and we are looking for all the feedback we can get to make this thing truly valuable to retail investors.
595 articles analyzed
572 tickers with data
30 demo users