From the time of Benjamin Graham to Jeremy Grantham, using macroeconomic data in asset allocation has been the most universally accepted way of tactical asset allocation. However, without a machine learning foundation, global macro has often been considered an art and not a science. In this webinar, we will give a preview to use of macro-economic data for asset allocation in a deep learning framework. Think of this as a "RoboWarren" which is able to look at over 100 macro-economic indicators to predict if a stock market correction is imminent.
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.
In this presentation, we will talk about the machine learning and system design challenges of building chatbots
Over the last decade deep learning has had tremendous success in pushing state of the art in numerous domains such as computer vision, natural language processing, machine translation and speech recognition. All of these domains are characterized by large quantities of data. In order to leverage deep learning research to the fullest, many progressive asset managers are experimenting with different approaches to generate and use hypothetical data so that the models can learn what to do in scenarios that the markets haven't seen yet.
Over the last decade deep learning has had tremendous success in pushing state of the art in numerous domains such as computer vision, natural language processing, machine translation and speech recognition. All of these domains are characterized by large quantities of data. In finance, however, even 20 years of end-of-day data is merely 5,000 points and any data-driven trading strategy is only as good as the data itself. In order to leverage deep learning research to the fullest, many progressive asset managers are experimenting with different approaches to generate and use hypothetical data so that the models can learn what to do in scenarios that the markets haven't seen yet. In this webinar, we will discuss use of synthetic/hypothetical data that can potentially solve this problem.
We will talk about the machine learning infrastructure needed in high frequency trading. We will show the algorithmic and infrastructure innovations that helped us derive alpha from market information faster than others.
Our co-founder Mansi Singhal will discuss the common myths that surround active investing, among them: the high trading costs and used only for outright alpha. We will challenge these common fallacies and discuss how active investing is being severely misunderstood by many investors. Active investing can play a crucial role in portfolio management. We will demonstrate how some of the biggest market players use active investing to: 1- Target constant risk. 2-Defend against market crashes. 3- Reduce costs and improve performance via methods like rebalancing, tax-loss harvesting and algorithmic execution.
Gaurav Chakravorty, co-founder and Chief Investment Officer at qplum, 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.
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.
Gaurav Chakravorty, co-founder and Chief Investment Officer at qplum, will discuss how market dynamics have changed in favor of ETFs versus stocks in the last five years. There is a lot more opportunity to realize returns in ETF trading than stocks now.
Gaurav Chakravorty, co-founder and Chief Investment Officer at qplum, 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.
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
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.
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
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
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?
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?
We will look at how representation learning is at the heart of choosing the right machine learning algorithm