• Tactical Asset Allocation using Deep Learning

    In this webinar with Interactive Brokers, our co-founder Gaurav Chakravorty will discuss how Deep Learning can be used in global tactical asset allocation. We will show how this investment strategy compares to a traditional institutional investing benchmark as well as qplum's US investor index. We believe that Deep Learning will provide a huge contribution to asset management in its utility for core tactical asset allocation rather than indirectly predicting returns.

  • 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

  • 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.

  • 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.

  • How to model and trade volatility futures

    How to model and trade volatility futures

  • Boosting and Neural Networks

    Petar Maymounkov is one the most brilliant computer scientists of our age. At qplum's FinTech and Data Science meetup he presents techniques that can be applied to learn from a number of ideas in a scientific manner. He shows how to improve a Deep Learning approach with Boosting

  • FinTech in Investment Management

    The new investing landscape where traders are losing jobs and data scientists rule. Why is this happening? What do I need to do to have a job ten years later?

  • Investment Management using Deep Learning and Big Data

    The new investing landscape where traders are losing jobs and data scientists rule. Why is this happening? What do I need to do to have a job ten years later?

  • Choosing the right machine learning algorithm

    We will look at how representation learning is at the heart of choosing the right machine learning algorithm

  • Using deep learning to trade: Practical lessons for technologists

    What kinds of deep learning have we found useful at qplum? Technical and operational aspects of trading using deep learning. Key bottlenecks in training and inference. Software frameworks and which hardware platforms have proven most useful for those workloads. What does a deployment look like? What are the scaling challenges and key drivers of cost? How does devops work when a lot of the dev is handled by machines.

  • Ten year cycle of innovation in trading

    The next ten years are going to be about deep learning. We show what happened in the past and what were the business drivers, and how the business drivers are converging behind making investing a utility powered by Deep Learning

  • Towards the science of investing - Deep Learning

    The next ten years are going to be about deep learning. We show what happened in the past and what were the business drivers, and how the business drivers are converging behind making investing a utility powered by Deep Learning

  • Applying Deep Learning and High Frequency Alpha to Trading

    The next ten years are going to be about deep learning. We show what happened in the past and what were the business drivers, and how the business drivers are converging behind making investing a utility powered by Deep Learning

  • AI and Deep Learning in Trading

    The next ten years are going to be about deep learning. We show what happened in the past and what were the business drivers, and how the business drivers are converging behind making investing a utility powered by Deep Learning

  • US household wealth index

    The US household wealth index is the right index for individual investors to compare themselves to, if they want to see how well they are investing.

  • How to invest in way to reduce the impact of taxes

    How should an investor go about maximizing their post-tax returns, and how we at qplum are helping them do it

  • Execution Algorithms

    Execution algorithms are a class of intraday trading strategies that can help an investor or a portfolio manager try to get the best available price on that day

  • Investing is a science

    In this talk, I'll present a set of ideas, some results and some quizzes that show that we have all the tools necessary to finally make investing a science and not a game or competition.

  • A.I. and Trading an evolving relationship

    We will talk about past attempts to make investing into a science and where are opportunities of Deep Learning and Artificial Intelligence based disruption in portfolio management today.

  • How to remove overfitting in trading

    The main challenge in some fields of applied data-science is to remove overfitting. We will talk about the approaches taken to reduce overfitting over the years ad the state of the art currently.

  • Top strategies in pension fund investing

    How Artificial-Intelligence, FinTech, Liability-Driven-Investing are reshaping the landscape of institutional investing

  • Overview of Deep Learning Architectures

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

  • Manifold Learning and applications to Trading

  • The evolution of trading and FPGA

    The evolution of trading, the three sources of returns in financial markets, and the state of the art in trading based on information processing. Olivier Baetz will talk about FPGA and its critical role in high speed trading infrastructure.