Fundamental analysis continues to face numerous challenges. The COVID-19 crisis has led to delayed earnings, repeated analyst revisions, suspended dividends, and the absence of earnings guidance. Quantitative analysis has long sought to encroach on this territory with its rock-star math. But quant trading models and strategies that mostly rely on pattern recognition can run afoul of the cardinal sin of overfitting. They may also fail to adequately control for political, structural, and behavioural idiosyncrasies.
Given the recent volatility and relative underperformance of passive investment strategies, both fundamental and quantitative approaches are looking to reassert their active selves. But increasingly, the generation of alpha requires a combination of methods. As its name suggests, quantamental analysis combines the best of both disciplines.
Fundamental shops can be helped by quantitative strategies by reducing bad calls from cognitive and emotional biases. Indeed, many such shops are increasingly looking to develop the option of a rigorous quantitative overlay to their investment process using Auto ML and other big data predictive tools.
Machine Learning (ML) has an astonishing ability to extract signal from both fundamental and technical data. When added to the fundamental understanding of, for example, a management team’s ability, the quantamental insight and prediction is invaluable.
Thus, sophisticated ML can augment the investment process of both fundamental and quantitative shops. With Mind Foundry Horizon, you can undertake the stages of the quantitative journey via an easy to use, transparent, and flexible UI.
Mind Foundry Horizon's machine learning algorithms can be used by analysts, fund managers, and traders to find predictive signal in time-series data.
We have a webinar on August 6th where you can get an in-depth look at Horizon’s forecasting capabilities and learn how to use it to build an end to end the time series prediction model, figure out important factors by performing signal analysis, and then wrap your results into a quantitative trading strategy with an acceptable risk profile. Save a seat.