Applications of Fintech in Investment Management | CFA Level I Portfolio Management
Welcome back! In this lesson, we’ll explore some of the fintech applications that are relevant to investment management, including:
- Text analytics and natural language processing
- Risk analysis
- Algorithmic trading
- Robo-advisory services
Text Analytics and Natural Language Processing (NLP)
Text analytics involves using computers to analyze unstructured data in text or voice forms, like company filings, financial reports, social media posts, and more. In the finance industry, text analytics may be used to estimate indicators like consumer sentiment by analyzing recent Twitter feeds, for example.
Natural language processing (NLP) takes text analytics a step further by interpreting and making sense of the data using artificial intelligence. Some applications of NLP in the finance industry include:
- Monitoring employee emails and phone conversations for regulatory compliance
- Evaluating research reports to detect subtle changes in sentiment based on language used by analysts
NLP can process large amounts of data much faster than humans, saving time and resources.
As we’ve learned under Risk Management, financial regulators require firms to perform risk assessments and stress testing. Fintech applications, such as machine learning and big data, can model and test risk quickly and comprehensively.
Machine learning for risk analysis allows firms to use real-time data to monitor risk exposures, enabling management to employ risk mitigation measures and hedging practices sooner to preserve asset values.
Algorithmic trading is the automated trading of securities based on a predetermined set of rules. It can be useful for:
- Optimally executing trades based on real-time price and volume data
- High-frequency trading, which identifies and takes advantage of intraday securities mispricings
- Executing large orders by determining the best way to divide them across exchanges
Robo-advisors are online platforms that provide automated investment advice to retail investors. They usually start by asking clients survey questions to determine their financial position, return objectives, risk tolerance, and constraints. Based on these factors, an optimal portfolio allocation is computed for the client.
Robo-advisory services offer several benefits, including low fees, low minimum account sizes, and increased accessibility for a larger number of investors. However, they may lack transparency, and customers may hesitate to trust their recommendations, especially during crisis periods.
As robo-advisors are still relatively new, regulation is still emerging. In many countries, they are subject to the same regulations and registration requirements as traditional investment advisors.
That wraps up this lesson on fintech applications in investment management. Up next, we’ll discuss the biggest buzz in fintech: Blockchain. Stay tuned!