Machine-Learning can be incredibly useful in Finance. BG approach to it is based on two pillars: Resilience and Memory.
|Resilience||We look at within peer-group relative performance resilience: each firm is scored on a case-by-case basis depending on its resilience. BG approach is to focus on identifying stable out/ underperformers from market behaviours in its scoring rather than relying on fundamentals or financial ratios.|
|Memory||We focus on within peer-group representative investor memory: acknowledging the fact that investors adapt their reference point and risk aversion to evolving market conditions.|
Our approach is illustrated in a Position Paper : “What can Machine Learning bring to investing?” Dr. Arnaud de Servigny & Elyes Ben Bouzid, March 2016
The current set of techniques used in the Research Lab.
|Core techniques used||Usage||Reference|
|Variational Inference||Looking at choices under uncertainty as approximated and simplified probability ranges||https://arxiv.org/abs/1601.00670|
|Deep Learning||Building several layers of filtering to strengthen signals contained in the data||www.youtube.com/watch?v=3cSjsTKtN9M|
|Customized learning strategies||