Using recommender systems in the Chief Investment Office

Gaurav Chakravorty explains how recommender systems can be utilized for investment management and details how AI and deep learning are used in trading today. Gaurav begins by diving into chief investment offices, which are growing their in-house machine learning teams to fine-tune their allocation, using both traditional and alternative strategies. Gaurav shares a novel approach to deciding asset and strategy allocations, inspired by research in recommender systems. Gaurav then explores the application of deep learning in trading, discussing useful techniques for AI-driven asset managers as well as the blind alleys they’ve gone down. With these cases as context, Gaurav addresses some of the technical and operational aspects of AI, such as key bottlenecks in training and inference, the software frameworks and hardware platforms that are most useful for those workloads, deployments, the scaling challenges, and the key drivers of the cost.

Using data-science to reduce tax impact

In this presentation, we will debunk the myth that buy-and-hold is far superior to data-science based investing. We will show how to build an investment system for taxable accounts that directly optimizes post-tax returns.

Why is machine learning in finance so hard?

Hardik Patel, presents four major pitfalls companies face when using machine learning methods in finance, and his advice on how to solve for them.

AI is the future of asset management

In this talk at AIM Summit Dubai, Gaurav will talk about why AI is the future of asset management, why are we talking about it now, what has changed, and how can investors separate the fakers from the makers

AI-driven multi-asset portfolios

In this talk Gaurav will talk about using AI in the biggest problem in institutional portfolio management, which is tactical asset allocation.

Deep Learning Architectures - An Overview V2

In this presentation at Data Science + FinTech meetup in Jersey City, Sumit Chopra from Facebook Research, presents an overview of Deep Learning architectures

Long Only Portfolios

Long Short

Deep Learning Architectures - An overview

In this presentation at Data Science + FinTech meetup in Jersey City, Sumit Chopra from Facebook Research, presents an overview of Deep Learning architectures

How do you match up against the average U.S. investor?

The U.S. investor doesn't invest only in the S&P 500 Index. Contrary to what real estate agents might tell you, U.S. investors don't just load up on real estate either. In this presentation, we look at publicly available Federal Reserve and BLS data to gauge what investors actually invest in. We will make an index, that is the right benchmark for US investors.

Generalized Linear Models

Linear Models are a powerful class of fully connected models. In trading, however, there are rules of thumb to follow when using GLM ( generalized linear models )

The next ten years: Deep Learning in Trading

In this talk at Univ. of Pennsylvania, Gaurav will talk about some job trends in financial services to start and cover the transformational impact of A.I. and Deep Learning in making trading a scientific process. Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading and investment management fits this paradigm perfectly. It is a social science and not a pure science, and we are generating petabytes of data everyday making it tough to learn from. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or making chatbots. This focus on learning a hierarchical set of concepts is truly making investing a scientific process, a utility.

Bayesian Modeling with Stan