Top Takes of Giuseppe Paleologo's Interview - Part 1
Top Takes of Giuseppe Paleologo's Interview - Part 1
In-depth summary of an interview with a quantitative researcher
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As traders and researchers, we all feel lost in our roles as participants in the markets at times.
Some of the questions ask ourselves:
Why am I losing money?
Why am I making money?
Am I a good trader, or am I just correctly exposed, to a driver of returns, that happens to be hot in the market right now?
Does making money even make me a good trader?
These questions, and others, make us feel lost, because we don’t truly understand the nature of what drives the returns of our portfolios.
But they are essential as you move towards a more professional approach to your trading.
There’s no perfect solutions, but there’s better estimates.
I found this interview extremely interesting when it comes to the practical side of to these questions.
Giuseppe, also known as Gappy, has been in the financial industry for 14 years.
Before then, he was an applied mathematician and worked for IBM research.
He then transitioned to Axioma, a factor model and optimization provider.
Later on he went to work at Citadel, Millennium and HRT.
Here’s the full 1 hour and 32 minutes episode:
In this article, I’ll summarize the key and most important lessons I gathered from this episode, as they were extremely insightful to how I look at portfolio management.
I hope you enjoy it!
TLDR
The main idea behind a quantitative researcher is to maximize P&L for the firm.
If you don’t know why your portfolio is in a drawdown, it’s hard to pinpoint if it’s your problem, or just the factor you’re exposed to, not being there.
When portfolio managers consider their divergent performance in idiosyncratic space, they become more aware of what they add to the table in terms of returns that can’t be explained by known factors.
Every portfolio manager believes that they have amazing sizing positions.
But the reality is, when you look at the data, that the vast majority of P&L, is coming from being on the right side of the a bet.
Hedging doesn’t directly increase profits but improves the strategy's risk-adjusted performance, potentially boosting it anywhere from 10% to 50%.
John Cochrane points out the perspective that says that there's no true alpha—only factors we understand and those we don’t.
If a PM successfully trades momentum, should they be paid for that? Maybe. But if they’re just following momentum trends, should they be paid for that? Maybe not.
Scaling is a similar problem to a common life example, finding a career:
First, you focus on getting good at something,
Then you work on scaling that skill to achieve the most success possible.
Some trading firms can have incredibly high Sharpe ratios, like 10 or even 30, but only make $50 million a year. The real difficulty is making billions, like $15 billion a year, with a Sharpe ratio of 2.
Index
Introduction
TLDR
Index
What is a Quantitative Researcher (QR)
What is a Portfolio Management Advisory/Coverage Role?
The Growth of a Portfolio Manager
False Belief About Sizing Capabilities
Hedging
Why is a Factor Model Important?
What Does a Factor Model Do?
What should a portfolio manager be paid for?
The Scaling Problem
Internal Alpha Capture
Factor Research
Conclusion
What is a Quantitative Researcher (QR)
The main idea behind a quantitative researcher is to maximize P&L for the firm.
They do this by:
Helping monetize their ideas better, through repeatable methods
Help the individual portfolio managers improve.
Having worked as a quantitative researcher in some of these funds, he mentions that QR on the buy side, is mainly about doing central services.
A central service include:
Hedging
Internal alpha capture
Factor models
Custom factor models
Portfolio management
Basically the quantitative researcher helps the portfolio managers in their daily job, of understanding their performance, and monetizing their ideas.
We will touch on each of these topics over the course of this article.
What is a Portfolio Management Advisory/Coverage Role?
First thing, let’s get a few requirements out of the way.
A Portfolio Management Advisor needs to be a good communicator and have sound quantitative training, so that he can have a disciplined research process.
This is especially important since a lot of the projects are very short-term , and you don’t want to provide inaccurate solutions, or data, to the portfolio managers.
Essentially they do 2 things:
They monetize
They advise
Most market neutral hedge funds, employ risk models and factor models, to find performance attribution and risk decompositions.
In essence, breaking down how their performance is affected and different sources of risk.
Here’s an example of why this role is important:
Think of a talented stock picker, that is used to being long only for an institutional asset manager, or a family office.
They come into a pod shop, and now the environment is completely different.
Before they only thought about their active positions or portfolio, but now they have to think at the portfolio level of the firm.
And let’s not forget that his positions, are probably 10x in size, what they used to be.
That can really mess with the manager’s head.
The task of explaining the model, and training the PM’s, is the initial task of a portfolio management advisory role.
Then there’s some other tasks that involve advanced risk analysis.
An example might be making decisions between using country level factor models, or global factor models, that might cover US and Europe together, or even the entire world.
But every time you go up a level, you are going to lose something in the resolution. There’s always tradeoffs between these that need to be carefully thought through.
There’s other problems like having large drawdowns.
The advisory role needs to find what are the sources of these drawdowns.
If you don’t know why your portfolio is in a drawdown, it’s hard to pinpoint the problem.
But even then, we can’t always explain all of the drawdowns, in a factor model.
Most of the drawdown causes are idiosyncratic P&L, which is not systematic P&L.
In simpler terms, not all drawdowns are derived from broad market phenomena, but from specific factors we are not aware of.
In essence, the job is to come in, improve a little bit, help PM’s by telling them what not to do, and make sure that they are not doing obvious mistakes.
But the benefit of coverage is that it's an ongoing learning process. There is not a maximum ROI for coverage.
Nobody has solved the investment problem, and nobody has solved the coverage problem.
There are always a lot of improvement that compounds over time.
If we think about the example of the stock picker, who was completely oblivious to factors, coverage as a whole, can add like another 50% in risk adjusted performance, and it will keep going for the indefinite future.
The Growth of a Portfolio Manager
As portfolio managers progress towards more sophisticated analysis, and understanding of their performance, they will get surprised, a lot.
Here’s an example of a surprise to a portfolio manager:
“Hey, today I’ve made $1 million trading NVDA, but my idiosyncratic P&L in NVDA is negative.”
What is idiosyncratic P&L you may be wondering?
Idiosyncratic P&L, refers to the profit or loss, that comes from factors that cant be explained (alpha), rather than the P&L from known factors.
I’ll simplify this further as we go through our example.
The PM is concerned because he made a lot of money, but his idiosyncratic P&L in NVDA is negative.
Perhaps even the idiosyncratic P&L of his portfolio is negative. How can it be?
After all, he did make money.
Let’s add to the surprise, that the overall market is flat in returns today. How is that possible?
This is where the portfolio manager advisor steps in.
And he has to show that, let’s say the sector in which NVDA is at, made a lot of money today, or it has super strong long momentum and momentum had an amazing day.
If you put all of these together, they might explain more than the P&L that the portfolio manager generated.
Which means he should have made more, given how strong the factors he’s exposed to, were today.
Thinking in so called idiosyncratic space is hard in the beginning for every PM.
It’s unintuitive, but in the long run, is very empowering.
They realize they had confounding variables in their mind. They stop caring for the market as a first order, and now they can look at the divergent performance in idiosyncratic space.
When portfolio managers consider their divergent performance in idiosyncratic space, they become more aware of what they add to the table, in terms of returns that can’t be explained by known factors.
False Belief About Sizing Capabilities
Every portfolio manager believes that they have amazing position sizing skills.
But the reality is, when you look at the data, the vast majority of P&L, is coming from being on the right side of the a bet.
Not by being too large or too small.
When we look at the yearly P&L of a PM, typically it tends to be equally distributed across names.
Except, when it is either very large positive or very large negative. Then you see a distribution that is very skewed towards a handful of names.
But when you try to explain this, people tend to associate it to sizing, to those names.
If you look at a name, and it has made 30%, 40%, or more of your P&L, clearly it has to be that name.
So traders might want to oversize on it going forward.
But that is a behavioral bias.
And it’s conditional on that name, to continue to perform as it did in the past year.
And in most times they are not.
Hedging
99% of people hedge against market risks, which is pretty straightforward.
Market hedging is well understood; you calculate your hedge ratio, and the transaction costs are low, so it's a simple process.
However, there are some details to consider.
For example, should you hedge using futures, SPY, or something else?
Those are decisions to make.
But when it comes to using a factor model for hedging, things get more complicated.
Trading factors is more complex, expensive, and messier than trading the overall market. Factor portfolios are just rough estimates, of the true systematic risk in the market, which is a mathematical challenge.
Organizationally, some firms don’t hedge at all. Even large firms might rely on what we could call "internal hedging," where they tell a portfolio manager (PM) to stay within certain risk limits, leaving the PM, to manage their portfolio accordingly.
This approach pushes the responsibility onto the PMs.
The problem with this is that many PMs, even when hedging on their own, might end up with similar risk exposures.
This means the risk adds up linearly, while individual risks add up more slowly.
If you have enough PMs, a common risk, like momentum exposure, can become so significant that you have to hedge at the firm level.
So, what can you do? One option is to create a firm-wide hedge that offsets the common risk, like momentum.
If momentum loses money, the hedge gains, and vice versa. But this means the firm takes on the risk of that hedge.
Another option is to push the responsibility back to the PMs, letting them manage the hedge themselves. You could also choose a middle ground, allowing PMs to offload some of their hedge responsibilities to the firm.
In general, hedging doesn’t directly increase profits but improves the strategy's risk-adjusted performance, potentially boosting the Sharpe ratio by anywhere from 10% to 50%.
This higher Sharpe ratio allows the firm to take on more risk or scale up, which can lead to increased profits.
So, while hedging doesn’t add to P&L directly, it’s still very valuable.
What Does a Factor Model Do?
A factor model does two main things: pricing and identifying anomalies.
First, it helps with pricing, by accounting for factors that influence returns across your investment universe, even if those factors themselves don’t generate returns.
You need this pricing aspect to effectively model anomalies.
What are anomalies?
In simpler terms, anomalies are opportunities for profit, that deviate from normal market behavior.
You can’t focus on anomalies, without first getting the pricing right.
Additionally, a factor model predicts risk, by creating a covariance matrix, which helps you understand how different returns move together.
I’ve described in simpler terms the covariance matrix in the article below:
When it comes to "alpha," it’s a bit more complex. Mathematically, alpha is well-defined, but commercially, it's trickier.
Essentially, alpha is whatever your investors are willing to pay for.
As economist John Cochrane points out, there’s a perspective that says there's no true alpha—only factors we understand and those we don’t.
From this view, everything could be considered a factor, especially in strategies that trade on widespread market influences.
However, there are still pure alpha opportunities, such as individual stocks or very specific investment themes.
What should a portfolio manager be paid for?
So, what should a factor model include, and what should a portfolio manager (PM) be paid for?
This is a very interesting question.
If a PM successfully trades momentum, should they be paid for that?
Maybe.
But if they’re just following momentum trends, should they be paid for that?
Maybe not.
There’s no definitive answer—it’s negotiable and depends on how a good quantitative research (QR) team sets up the right incentives for PMs to build their portfolios.
One key takeaway is that both PMs and quantitative researchers should have the humility to recognize that models are never perfect.
Sometimes, models miss certain losses that are widespread, indicating the model might need improvement.
Other times, profits (P&L) are due to factors that the model doesn’t account for.
The Scaling Problem
"Once you solve the performance problem, the ongoing problem is how to deploy capital"?
Let me explain what that means.
Scaling is a similar problem to a common life example, finding a career:
First, you focus on getting good at something,
Then you work on scaling that skill to achieve the most success possible.
Hedge funds face a similar challenge.
When they start, it’s unclear if they’re good or not.
Over time, they refine their processes and, if they’re good, achieve strong performance, known as a high Sharpe ratio.
Many firms can make money if they're good enough.
The real challenge isn’t just making some money with a high Sharpe ratio; it’s making a lot of money while still maintaining that strong performance.
If you’ve been following twitter, there has been some controversy around this statement , in the last few days:
For example, some trading firms can have incredibly high Sharpe ratios, like 10 or even 30, but only make $50 million a year.
The real difficulty is making billions, like $15 billion a year, with a Sharpe ratio of 2.
That’s much harder and is where the real opportunity lies.
As a hedge fund, the goal is to make the most money possible for your investors.
Internal Alpha Capture
Sometimes, when a PM manages too much money, it can negatively impact their returns.
We’re all humans, and individual portfolio managers can only manage so much, until it starts to affect them.
Some have higher tolerances, while other have a lower tolerance for risk.
So this is where internal alpha capture comes in.
It is a process that involves adding a layer, to the original portfolios, managed by portfolio managers.
This overlay is designed to allocate risk more efficiently, to individual sources of alphas, without relying on PM’s, and remove behavioral biases in the trading of the original sources of alpha.
In essence, trying to achieve optimal allocation, considering the alphas of individual portfolio managers.
This is a way that generates extra profit (P&L) for the firm.
The process is called "internal" alpha capture because it strictly uses the firm’s own strategies (internal alphas).
If you mix these internal strategies with external signals, things can get more complicated and less clear.
Generally, it’s best to keep internal and external strategies separate. If you have external signals, you might start a separate system to capture those.
Internal alpha capture can be extremely valuable. In some cases, it can nearly double the profits of a fundamental business.
Even a 30% increase is significant.
Overall, it provides a tremendous return on investment (ROI) compared to other options, though it’s also a challenging task.
Conclusion
The conversation is about 1h30 long, and it goes quite deep into these topics.
This is just part 1 of this conversation, and I’ll make another article if there’s demand for it, with the remaining topics.
It’s already pretty dense material, and putting it all together in just 1 article might be ineffective and also hard for me to do justice.
It takes me about 3-4h to write these articles, in order to be careful about what I say and also truly understand what I’m writing.
Sure, I could copy and paste and just give a superficial review of what has been said, but I don’t think that that is the way of building a great article.
Also, trading is what I do, and anything that can potentially add to my P&L is welcome, and I want to understand it to the core.
I hope you’ve enjoyed today’s article!
If you want to have my own custom assistance, with your systematic trading business, I help individuals and enterprises, implement and deploy strategy portfolios, tailored to their specific needs.
If you want to discuss the possibility of working with me:
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