During our time in Quant Finance, regression analysis was generally the best tool we had for determining the effectiveness of factors and models. We at SFS, we were especially comfortable performing regressions when the relationship between our variables was clearly linear, our datasets weren’t too big, and we wanted results that were simple to explain.
Machine Learning is now all the rage and it’s the better choice in some situations.
Scalability: ML algorithms can be scaled up to handle both complex problems and large datasets.
Non-linearity: Regression analysis assumes a linear relationship between the independent and dependent variables. In contrast, ML algorithms are very good at dealing with non-linear relationships between variables and discovering less obvious, complex relationships. ML algorithms tend to be more robust to correlated, similar information conditions.
High Dimensionality: Machine learning techniques excel at high dimensional problems where there are many informational “features” to consider. High dimension problems can overwhelm qualitative, subjective methods – particularly in the presence of “novel” features (new information sets) for which analysts have little experience and conventional wisdom to draw upon.
Flexibility: Along with handling large and complex datasets, ML can be used for both supervised and unsupervised learning, and can be trained to detect patterns and relationships that are also not so obvious from looking at the data with traditional tools.
Accuracy: Machine learning models often outperform regression analysis in terms of prediction accuracy, especially when dealing with complex or high-dimensional datasets.
In summary, Machine Learning techniques provide quant stock pickers with a powerful set of tools to analyze and predict stock performance. By leveraging these technologies, quant stock pickers can make more informed investment decisions in a faster and more efficient manner.
To help quants take advantage of this trend, SFS’ Quotient™ Powered by Snowflake has integrated over *50* Machine Learning algorithms into their powerful Forecaster module. Users can easily enter all the characteristics for algorithms and run them against their proprietary models.
Machine Learning models allow us to see further into the data’s underlying relationships than traditional techniques. Looking beyond the domain of traditional methods is where the best investment opportunities are found – particularly for the early adopters. We will discuss this “first mover advantage” of Machine Learning in more detail in our next blog.