FinTech Wave Revolutionizes Financial World

[Sponsored article] You have heard the word “FinTech” on various occasions: TV, social media, or even friends’ parties. Is it just a buzzword floating around nowadays? The answer is likely no. More and more people are starting to believe that FinTech is a new wave of digital revolution that could fundamentally change the financial industry and, therefore, our lives in many aspects.

Already, peer-to-peer (P2P) lending websites provide a platform that efficiently matches borrowers with investors from anywhere in the world, a new financing model that has shortened the loan approval process to minutes compared to weeks or months at traditional banks. It also opens up the opportunity for anyone wanting to become an entrepreneur, by lowering financing barriers. According to Morgan Stanley, online loan volume in the US market is expected to reach US$120 billion in 2020, up from US$20 billion in 2015. In investment management, many big companies such as BlackRock Inc. and Vanguard Group Inc. are using computer algorithms called “robo-advisers” to automatically adjust portfolios, according to a customer’s risk preferences. Some investment institutions have used artificial intelligence methods to automate trading decisions, or have even started to make algorithms self-learning. In capital markets, blockchain, the freely available database that underpins the digital currency bitcoin, has prompted much debate as to whether it will replace existing methods of transmitting assets and currencies. Blockchain also has the potential to simplify the way securities are traded, settled and recorded. All these phenomena fall under the umbrella of what we called “FinTech”.

Among others, one important promise of FinTech is that there will be greater reliance on algorithmically-determined financial decisions in areas such as loan, insurance and stock picking. The advancement of artificial intelligence methods has been the propeller facilitating the transition in such a direction. Although humans have the advantage in intuitive and creative thinking, machines are better at weaving through the data and finding hidden connections among different variables. Given the exponential growth in the size of data, the computational advantage of machines begins to outweigh their weakness in sense-making, enabling them to play more roles in business decision-making. Below, two examples are presented to show the power of computational methods in extracting new information from traditional financial documents.

Uncover new info from financial documents

In a collaborative research project by Allen Huang and Amy Zang from the Department of Accounting at HKUST, we use the naïve Bayes machine learning approach to address the challenge of extracting information from a large volume of textual data in a financial analyst report. A typical analyst report contains both quantitative summaries including stock recommendation, earnings forecasts, and target price, and a detailed, mostly textual analysis of the company. Our research found that the textual discussions in analyst reports provide information to investors beyond that contained in those quantitative summaries. We achieved the information extraction by using the naïve Bayes algorithm that can quantify analysts’ sentiment about the covering firms from their written text.