Welcome, everybody, to this session on Jupiter Systematic Equities, and with a particular reference to the launch of the GEARx strategy, and evolution of the product offering within the Jupiter Systematic team that we are super excited to show to you today. I'm joined by Amadeo Alentorn, Head of Systematic Equities at Jupiter. I am Tom Gardner, Global Head of Institutional. Amadeo, you're one of the longest serving multi-factor teams in the market. Perhaps you could take us through the team and how long you've been working together. 

Sure Tom. So, I've been part of this team for over 20 years. The seven of us have been working together for a long time. Some of my team members have been working with me for close to 20 years as well. And over this length of time, what we have done is we have applied a scientific approach to understanding global equity markets and how we can consistently extract alpha with a multi-factor dynamic model. 

And as an asset base, you run about $13.8 billion. Could you take us through the split of that. 

So, of that, around four and a half is in long short equity strategies, and nine and a half in long only strategies. Key to this is that we use the exact same investment process across the entire AUM. So, all of the research that we do, benefits all of the funds. 

Great. And then I think I'm right in saying that you also utilise an external panel. Could you take us through the people on that and just highlighting a few key people? 

A number of us in the team come from PhD and postgraduate research, and thanks to these relationships with these leading academics, we have continued to be able to be at the cutting edge of academic research across a number of different disciplines. This includes accounting, statistics, econometrics, AI, machine learning, econo-physics. Some of these individuals are very senior professors. For example, we've been working with Steve Satchel and Peter Pope for about 20 years. They are a bit like the grandads of the team, experts in econometrics and financial accounting. But then also in more recent years, we have onboarded academics, experts in newer disciplines like AI, machine learning. We have also launched a PhD program where we are fully sponsoring some, full time PhD researchers that are working on projects that we set up for them. So overall, we're able to do a lot more research thanks to these, academic programs.

And the GEAR strategy that the new strategy is based on, that's been going since 2009 with a very strong track record, can you take us through the track record of that. 

GEAR was launched almost 16 years ago as a UCITS, Irish domiciled, daily liquid fund, with single leverage. So, for $100 of your investment, 100 long, 100 short, zero net. And with that, we have been able to deliver approximately cash plus five returns with a realised volatility of five. And that's running with a limit of six. In more recent years, we have achieved net Sharpe Ratio north of two. So very consistent returns and crucially with close to zero correlation with equity markets. So it is a genuine alternative to traditional assets. 

One of the key aspects of your strategy and the team strategy is your proprietary factors. You have some slower-moving signals and you have faster moving signals. Could you just explain the differences between those? 

So over the last 20 years, all the research we have done, has resulted in five factor families. Some of them are more fundamentally inspired. We've got components like valuation, growth, the quality of the management team. I mean, with those you could argue that we have systematised, intuitive, well understood ideas, but using lots of data and very robust models. But then what's also been very important is to recognise that those fundamental strategies may go through environments that are less useful. And that's where sentiment, as a collection of short-term indicators, can be very relevant, particularly in market environments where investors are not focusing so much on fundamental drivers. And then finally, we also recognise that price action or technical indicators can also help us complement analysis of a stock. We are deploying those five factor families across 7,000 stocks every day. Within each family there's a number of indicators. And eventually if you keep drilling down, you end up finding 40 million data points that get consumed every day to refresh those 7,000 stock analyses. 

One of the key aspects that really sets the team apart, I think compared to the competition, is the dynamic weighting of those factor families. And I think it's fair to say that over the past 17 quarters, where you've delivered positive returns in every single one of those quarters, we've seen some sharp reversals, we've seen quite a lot of changing macro picture. Perhaps you can just take us through what that means and how that looks like in terms of the dynamism of the product.

So even if each of the five factor families is capturing an intuitive idea, what we are recognising very explicitly is that, over a full market cycle, some of them will be more or less useful. And that's where a dynamic approach

to weighting or timing comes in. What the model does is it looks at the current environment, it learns from history, assessing which of the five factor families was most useful in similar environments, and automatically those will get overweighted. A good example is what happened in 2022. At the beginning of that year, the models identified that the growth style of investing was under pressure and automatically tilted more towards value. That meant in the long

book, more exposure to value stocks in the short book, more exposure to expensive stocks. And that played out really well as markets were selling off and in particular expensive growth was penalised. But then later that year, in 2022, markets rebounded very strongly and it is in those inflection points that having a dynamic weighting scheme, as well as a very data intensive process to analyse sentiment in markets, help to rotate very quickly away from value that had worked really well into more sentiment-driven trend-growth, quality-type signals: and that resulted in a positive performance towards the end of that year, as well as in 2023. So, over the last three, four years, we've gone through very rapid cycles in markets, a lot of macro uncertainty, and it's been a great environment to highlight the benefits of dynamic weighting of styles, to provide a more consistent alpha generation.

Great. So, what we're doing in the in the Cayman vehicle is we're taking the team's long-standing philosophy process, the dynamic weighting, the factor families, and we're putting that all together into a Cayman vehicle. 

Could you take us through the Cayman vehicle and how that compares to the traditional UCITS strategy that's been going since 2009?

So, it's very simple. With GEARx, everything will be effectively two times. So we're going from running GEAR with a volatility limit of six to GEARx having two times that volatility at 12. Leverage in GEAR to 400. So GEARx will be $200 long, $200 short for $100 invested. With this kind of structure, we are aiming to give you expected returns of cash plus ten, relative to the cash plus five approximately that GEAR had generated: and then realised volatility going from 5% in GEAR to an expected realised volatility of 10%. All the other various portfolio construction parameters will also double in size, so stock level limits going from 1% to 2%. Sector industry net positions from 10% to 20% max. And this is what allows us to scale up both expected returns and expected volatility.

Thank you Amadeo. So, we're super excited to be launching this product.

We're super excited about the evolution of the product set within the systematic team. If you require any more details, please contact me on the details on screen now.

Thank you.