Evaluating Quantitative Metrics: Part 3
Introduction
In the last two articles I wrote, I talked about some common applications of quantitative finance, different usable metrics, and even why they should be used. I went more in-depth into the traditional aspects of quantitative finance as well, so feel free to check out the last two articles here and here.
Common Analysis Techniques
In the complex web of finance, understanding relationships is extremely key. Correlation and regression analyses dissect the connections between different variables. For instance, these methods help investors decide whether a stock goes in harmony with a market index or has its rhythm. These correlation analyses can be done in a variety of ways. All of this is done to help make sure investors are minimizing risk and diversifying investments, maximizing coverage in sectors, and getting the most bang for their buck.
One way is using a multivariate analysis. This can be done by comparing different factors and predicting correlations between sectors, businesses, and other assets. However, a simple version of this involves clustering the stocks (for example hierarchical clustering) and constructing similarity values for these stocks. This can be very important in actually understanding how the market interacts with stocks and not just blindly accepting AI and whatever computers tell us.
Another important point I would like to bring up is the use of NLP (natural language processing) which can be used to analyze sentiment. For just a moment, I’d like to go off on a tangent — remember the Reddit stock craze? Well, it hasn’t exactly ceased to exist, and even now, people use public forums and media outlets to get information about best stocks. Haven’t you ever searched up “best stocks to buy right now,” hoping to strike gold? Using NLP to analyze raw text is very powerful since we’ve added emotion, and although AI isn’t perfect at analyzing this emotion, it’s a step away from traditional limits.
This can be used by analyzing market sentiment within different online forums and websites to make split-second decisions before the masses make them, which is a super-competitive advantage for any investor or investment firm. Now, I chose to talk about this in this article as opposed to just traditional AI analyzing techniques because you are probably already aware of traditional numeric analysis. NLP is the future of financial analysis and it will become one of the fastest growing analyses of stock data — just not in numbers.
Conclusion
AI and even non-quantitative AI (such as market sentiment analysis) will play a huge role in finance in the future. (See below for a list of key technologies to be educated about, and I also agree that these will be extremely important). In the practical world of finance, quantitative metrics aren’t just discrete numbers; they become tools and switches that we can change and tailor to whatever works best for us. Therefore, you shouldn’t think about quantitative finance as something limited but something which will evolve with AI and NLP.
Disclaimer: The information offered by us may not be suitable for all investors. If you have any doubts as to the merits of an investment, you should seek advice from an independent financial advisor.
