Machine Learning and Market Data Complexity
Financial metrics are different than physical systems
All key metrics in finance – risk, return, correlation – are conditional, multi-layered, and cyclical. It is the nature of the data that there are cycles within cycles. This makes analysis tremendously complicated.
Working with financial data is very different from working with physical systems. The way atoms move can be expressed with fundamental laws that remain stable over time. There are exceptions, of course, but physical systems usually behave in the same way, especially in comparison to financial markets. Newton’s equations for gravity and motion were the foundation that enabled space travel, in spite of the inherent approximations.
Data for financial markets does not have equivalent stable patterns. For example, the negative correlation between stocks and bonds is well established. However, that correlation shifts and becomes positive when there is inflation. The original two-dimensional relationship is made more complex by adding the third dimension of inflation. It only gets more complex from there. What is that relationship for specific sectors or industries? How does foreign exchange, and in particular the US dollar, affect this relationship? What about different regions in the world? How are commodities affecting the relationship? The behavior very quickly becomes overwhelmingly complex.
Financial markets are continuously shifting
Embracing the difference between financial and physical systems is key to recognizing the value that machine learning brings for portfolio management. Financial markets continuously adapt and shift, creating distinct regimes where relationships between variables remain stable for a period of time before changing again, sometimes very abruptly.
Machine Learning provides a powerful perspective
Machine Learning (ML) is built to handle dynamic market environments. It adapts to changing market dynamics to uncover new relationships as they emerge, thanks to capabilities that have only recently become practical with modern computing power.
Traditional statistical methods do give useful insights, and a lot of Machine Learning techniques are based on these methods. However, Machine Learning has two key advantages:
- Machine Learning scales, and can analyze thousands of simultaneous, interacting relationships across huge numbers of data points, securities, and market conditions.
- Machine Learning identifies unexpected characteristics that may be overlooked in traditional analysis, revealing new opportunities and enhancing the investment process.
Financial data requires purpose-built Machine Learning models
Not just any machine learning model can deal with this level of complexity. There is significant research and development needed to effectively use machine learning methods for the unique behavior of financial data. We need purpose-built Machine Learning in order to make effective investment decisions.
If you’d like to learn more about how our machine learning solution could work for you, please contact us.
