Probabilistic Machine Learning for Finance and Investing


The objective of this session is to make attendees familiar with the reasons why probabilistic machine learning is the next generation of AI in finance and investing.

Here are some of the learning outcomes:
- Why standard ML systems are inherently unreliable and dangerous in finance and investing
- The three types of errors in all financial models and why they are endemic
- The paramount importance of quantifying uncertainty of model inputs and outputs
- The three types of uncertainty and different approaches to quantifying them
- Deep flaws in conventional statistics for quantifying uncertainty in financial models
- The probabilistic ML framework and its various components

In general, all ML models are built on the assumption that patterns discovered in training data will persist in testing and out-of-sample data. However, when non-probabilistic ML models encounter patterns in data that they have never been trained or tested on, they make egregious inferences and predictions. Furthermore, these models do it with complete confidence and without warning decision-makers of their uncertainty.

Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory. These systems treat uncertainties and measurement errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management. These systems are capable of forewarning us when their inferences and predictions are no longer useful in the current market environment.


Deepak Kanungo is the founder and CEO of Hedged Capital LLC, an AI-powered, proprietary trading and analytics firm built around probabilistic machine learning technologies. In 2005, long before machine learning was an industry buzzword, Deepak invented a probabilistic machine learning method and software system for managing the risks and returns of project portfolios. It is a unique framework that has been cited by IBM and Accenture, among others. Previously, Deepak was a financial advisor at Morgan Stanley, a Silicon Valley fintech entrepreneur, and a director in the Global Planning Department at Mastercard International. He was educated at Princeton University (astrophysics) and the London School of Economics (finance and information systems).

Open Data Science




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