Introduction: Who am I, what am I good at, and what I am not good at, what is my mission?
The three sources of alpha: The three main trades by which people make money.
There are no simple trades any more.
What is the new landscape of trading?
An illustration of a new method that is really not so new.
An ideal investment manager today
How to watch out for bad quants?
Suppose there is an island with 32 monks.
Each day in the morning they tell me if it's going to rain or not.
I have to decide whether it will rain or not after hearing their answers.
I am also told that at least one of the monks is always right.
Now my job is to come up with a strategy to answer each day if it will rain or not, in a way that I make the least number of mistakes.
What should be my strategy?
My education in a nutshell: "Financial applications of machine learning"
My work in a nutshell: "Building complex trading systems that traded all over the world interacting with many different providers and without any errors!"
What drives me: "I want to help people with a problem they face using what I know"
What is the hardest part of making a formula-one car?
Short Term
Medium Term
Long Term
Information
Stat-Arb / Options based trade structuring
Prediction or market view
|
Information Speed Short term |
These trades work most of the time. They make a little bit of money, when they do |
|
Structuring Stat-Arb Lead/Lag Medium term |
Works a fair bit of the time. Works particularly well when volatility and risk in the market is low. Reasonable scale, about 3% to 5% of annual alpha. |
|
Prediction View-based Long term |
These trades are typically very few, but when they work, they are very big winners. This is where deep neural networks and combining multiple alphas is most critical. |
Reference: Blackrock fires a 6bn PM. Costs/Returns?
As a trader or an investment manager, we need to understand:
Beta based investing is very tough right now:
Stocks and bonds are both in over-extended uptrends
Macro Trading:
Based on the economic data, will markets end up or down for the year?
Pairs Trading:
If stock X goes up, will stock Y go up too?
Technical Patterns:
Are there visual patterns that repeat in a price series over time?
It's not that simple trades don't work.
They work a small fraction of the time.
Rank these roles in financial services industry by number of job openings (highest to lowest)?
Nationwide job postings of traders
Nationwide job postings of quantitative analysts
Nationwide postings of Machine Learning jobs
Nationwide postings of AI jobs
Nationwide postings of FinTech jobs
Where are people trying new ideas?
Who will pay me to come up with the next goldmine?
Deep Learning is a cool new branch of Artificial Intelligence (AI) with which researchers are breaking previous records in how much we can learn from data.
An upstart trading firm competing with the likes of Goldman Sachs can now run at the same scale without hiring a single in-house person to manage the machines or the network.
Deep Learning refers to using a multi-layered neural network for prediction.
Earlier we used to make models by trying to think of better indicators and improving the correlation.
In a Deep Learning approach, we learn the features of the data in a hierarchical fashion, exactly similar to what humans do.
Imagine being asked to make a single value summary of everything that happened in the stock market on a day.
What would that be?
Now if we ask ourselves that normally when that summary is what it is today then what should have happened to each stock?
This is the direction Stat-Arb is going in.
Deep Learning is about learning the perfect representation of markets on which to make predictive models.
What is the average correlation of hedge funds to the stock market?
When we search on Google, we expect it to search every web-page on the web to find the information we are seeking. We will stop using Google if they don't search thoroughly.
Similarly, an investment manager needs to look into every source of data and every trading strategy to deliver a complete investing experience.
To be low-cost and yet to maintain and increase our commitment to research and data science, we need to find efficiencies in other aspects of our work. We need to use technology to reduce costs for our clients by bringing efficiency in our trading operations and back office processes.
We need to remember that we are just a service provider in an investment stack with the investor taking the real risks.
We need to explain to others above us in the stack the experience they will get by investing with us. Otherwise, it just does not makes sense for the investor or their representatives to consider us.
As investors, we care about future results and not past results.
The portfolio manager only has past data to work with.
Not all aspects of past data re equally applicable to future data.
Summary : make sure we don't overfit
1. Choose Rain on a day if the majority of the currently eligible monks say that it will rain.
Remove the monks that make a mistake from the set of eligible monks for the next day.
2. Brakes, and pressure required for braking.
3. Teller,
Financial Analyst,
Compliance Officer
Note that even in an age of "FinTech",
we need more bank tellers.
4. The average correlation is positive. It is as high as 70%.
It seems like hedge fund returns can be broadly explained as 70% market returns with trend-following contributing the remaining 30%.
Benjamin Graham published an article on 1945 about "Toward a science of security analysis". Somehow we seem to have forgotten it.
The promise of fairness, the promise of progress, the promise of not being short changed for an honest day's work.
Talk to us
www.qplum.co
contact@qplum.co
1-888-QPLUM 4U
| qplum LLC is a registered investment adviser. Information presented is for educational purposes only and does not intend to make an offer or solicitation for the sale or purchase of any specific securities, investments, or investment strategies. Investments involve risk and, unless otherwise stated, are not guaranteed. Be sure to first consult with a qualified financial adviser and/or tax professional before implementing any strategy discussed herein. Past performance is not indicative of future performance. |
By Gaurav Chakravorty
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.
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
