My dream is to make investing a science ... not a competition, but a truly inclusive process. The way I think we can come together to achieve that is to make investment management more (1) Thorough (2) Efficient (3) Transparent.
Means: A.I and Tech
Challenges and opportunities of using machine learning in asset management
Apr 01, 20191,4712
A Machine Learning Approach to Systematic Global Macro Strategies
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.
Oct 10, 20183,0440
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.
Oct 09, 20181,6610
Building Chatbots
In this presentation, we will talk about the machine learning and system design challenges of building chatbots
Oct 02, 20181,4761
Building data set pipelines for deep learning strategies
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.
Sep 07, 20181,6990
Use of Hypothetical Data in Machine Learning Trading Strategies
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.
Aug 31, 20181,6520
The confluence of high frequency trading and machine learning infrastructure
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.
Aug 23, 20181,5930
Active Investing vs Buy and Hold
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.
Jul 10, 20181,3110
Using Artificial Intelligence in the Chief Investment Office
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.
Apr 23, 20181,4230
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.
Apr 10, 20181,2350
ETFs versus single stocks
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.
Feb 15, 20182,3340
Tactical Asset Allocation using Deep Learning
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.
Dec 08, 20173,1231
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
Nov 08, 20171,5740
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.
Sep 30, 20171,5420
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.
Sep 21, 20172,0650
How to model and trade volatility futures
How to model and trade volatility futures
Sep 08, 20171,9360
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
Aug 11, 20172,2240
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?
Aug 10, 20171,4780
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?
Aug 03, 20171,9300
Choosing the right machine learning algorithm
We will look at how representation learning is at the heart of choosing the right machine learning algorithm
Jul 25, 20171,5750
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.
Jun 01, 20171,7090
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
May 18, 20172,5800
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
May 13, 20172,6780
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
May 13, 20175,0800
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
May 04, 20177,3682
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.
Apr 13, 20171,3900
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
Mar 18, 20171,5870
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
Mar 14, 20175,5652
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.
Mar 09, 20174,7772
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.
Feb 02, 20172,0010
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.
Jan 26, 201712,2370
Top strategies in pension fund investing
How Artificial-Intelligence, FinTech, Liability-Driven-Investing are reshaping the landscape of institutional investing
Jan 07, 20173,7430
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
Dec 24, 20161,6352
Manifold Learning and applications to Trading
Dec 24, 20161,8492
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.