A quant’s guide to finding alpha in commodities trading

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Eloise Goulder: Hi, and welcome to J.P. Morgan's Making Sense. I'm Eloise Goulder, and today I'm delighted to be sitting down here in New York with Kranthi Gade, who is Head of Global Macro QIS product structuring, to discuss the commodity space and  macro overall. So Kranthi, thank you so much for joining us here today.

Kranthi Gade: Thanks for having me. It’s a pleasure.

Eloise Goulder: So Kranthi, could you start by introducing yourself and your background and your mandate here at J.P. Morgan?

Kranthi Gade: Yes, I joined J.P. Morgan in October 2025 to head the global macro QIS product structuring. Before that, I spent 17 years in this space, focused more on commodities, but also FX. So right now, my mandate is broader, which includes commodities, FX and rates.

Eloise Goulder: And it's worth noting QIS standing for Quantitative Investment Strategies is really about extracting systematic alphas from a given asset class and we’ve spoken to many of your colleagues Kranthi including Rui Fernandes and Arnaud Jobert.

So Kranthi, could we dive into the commodity space? Because there's clearly been a lot of volatility in this asset class this year, with precious metals, both gold and silver, rallying hard into late January this year, and then the energy space rallying into, and in fact, since the geopolitical conflict in March. So Kranthi, how do you go about extracting alpha in this asset class? And why do you find the commodity space so compelling?

Kranthi Gade: Yeah, so I would say one thing that is very different about commodities is that it's a physical asset class. So you have a lot of price insensitive consumers, producers who need to hedge their production or consumption, irrespective of the price level. And commodities is also very uncorrelated to equities and bonds. So it provides diversification and potential for uncorrelated returns. And even within commodities, energy, metals, agricultures behave very differently. So within like 20, 25 different commodities, you get a lot of diversification.

So quantitative strategies can exploit the inefficiencies in futures curves, seasonality, and also cross market relationships, which are often overlooked by discretionary investors. So just to stress the importance of commodities in QIS context, in a typical long only portfolio, investment managers can have 5 to 10% of commodities, you know, mostly to hedge inflation, but in a long short portfolio, that can be as high as 30 to 40%, which is second only to equities.

Eloise Goulder: And why is it that quant investment managers so often have that higher weighting towards commodities? Is it a function of that diversification benefit?

Kranthi Gade: So the diversification benefit is one, but also the richness of the asset class when it comes to these kinds of strategies. As I mentioned, there are a lot of producers and consumers who are tend to be price insensitive, there's a lot of index flows that you can monetize.

Eloise Goulder: That makes sense. And can you give an example of one of these strategies, perhaps focusing on trend?

Kranthi Gade: Sure. Trend is like a very well known strategy across asset classes and specifically for commodities. CTAs actually stands for commodity trading advisors. It's named after commodities. So these trend strategies took hold in commodities and why does trend work? Because commodities is a physical market. So the supply and demand, they have to balance.

So a small dislocation can lead to prolonged trends. Like we saw more recently, it's been a bigger disruption. But if you take out, for example, an oil market, which is 100 million barrels a day per market. And if you take out like 1 million barrels a day, the market has to balance. So to force that, prices have to go up. And so that leads to these prolonged trends, which can be captured by systematic strategies.

Eloise Goulder: And coming back to what we've seen this year, I mentioned earlier the rally in precious metals into late Jan and then the rally in the energy segment. These have obviously been very powerful trends, but on the other hand, they have had sharp reversals. So how have commodities trend strategies performed this year?

Kranthi Gade: Yes, so trend strategies generally performed well during these moves that you mentioned capturing the momentum from macro events. But there are trade-offs in how you implement this particular strategy. So you can implement it as faster reactive signal, but at the risk of higher bleed in mean reverting markets. Or you can implement it as a strategy to capture long lasting trends, but at the risk of missing out on some short term trends.

Personally, I prefer the second implementation in commodities as they can be strong trends, but also sharp mean reversions, as we are seeing now in oil. So overall, I would say, yes, you know, trend strategies have done well, but there are trade-offs in how you implement trend in commodities.

Eloise Goulder: So you've explained trend, which is obviously a very well known strategy within the commodity complex. After all, it's what CTAs were originally named after. But outside of trend, what are the other key systematic strategies that you attempt to harvest?

Kranthi Gade: Yeah, in commodities, I mean, I would say two very popular strategies in the last two decades have been index congestion and curve carry. So if we start with curve carry, this is a strategy that monetizes the shape of the curve. The shape reflects storage costs and roll yields. So in a contango shape, the roll yields tend to be more negative in the front of the curve compared to the back. By taking a position in the back of the curve versus the front, you're capturing that difference in roll yields.

So one of the reasons that this strategy works is also because you're taking the other side of price insensitive flows. For example, producers who have the commodity, but they need to hedge the production of that commodity, they tend to sell in the back of the curve and the consumers, because they need to get hold of the commodity right now, they tend to hedge in the front. So this strategy by not taking the other side of the flows can monetize those.

A second strategy is what we call index congestion. And this is based on index flows. So this congestion strategy is similar to index rebalancing strategy in equities. So we have standard benchmark indices in commodities, like Bloomberg Commodity Index and S&P GSCI Index, which roll their exposure in a public five to nine business days of each month. So by avoiding that window, and by pre-rolling some of the exposure, the strategy captures alpha.

And if we move on from curve carry and congestion, commodities is a rich asset class when it comes to alpha strategies in general. So a few other examples that I can think of is what we call relative value strategy, which can be a relative value between two very closely linked commodities like WTI and Brent, or fundamental strategy, which is using fundamental data to guide investment decisions, or skewness, which is a more recent strategy, which uses third moment, and also volatility, which is a whole other episode in itself. So if you see commodities is quite a rich asset class when it comes to these alpha opportunities.

Eloise Goulder: Absolutely. And it's really helpful to hear you articulate the rationale behind the systematic alpha that you can extract, whether it's across commodity trend, or curve carry or index congestion, which, as you say, is very similar to an equities index rebalance strategy, or indeed value, skewness, volatility, etc. I guess my question is, to the extent that these strategies have become quite well known, do you believe this alpha could be eroded or arbitraged away? And to what extent do you need to continue iterating and enhancing these strategies in order to continue to capture some alpha?

Kranthi Gade: That's a great question. And it's a question that comes up quite often. So what I would say on that is, these strategies are what we call risk premium strategies. So they get paid a premium for taking on a particular risk. For example, the flows from producers and consumers, who generally tend to be quite price insensitive. And, you know, sometimes the strategies may get hurt, like we are seeing now. So that is the risk in those strategies.

So over time, as the strategies become well known, some of the alpha decays. But if you look at, for example, curve carry, what used to be a sharp two strategy in mid 2000s, has become more a one sharp strategy, but it has not gone down to zero. That said, the operational platform aspect of the strategy has become more and more important. You can always continue to improve how the strategies are implemented. What we're seeing more now is clients requiring flexibility to change the portfolio, especially during volatile times. And we've also developed the platform in such a way that in addition to changing strategies within a portfolio, the clients have also the flexibility to change the parameters within an index.

So the index is fully customized for the particular client, and they can also come in and tweak some of the parameters within an index. And the other aspect is the ability to observe and trade signals with minimal lag. And especially during more volatile times, this can add incremental alpha on top of what you normally get with, let's say, a one day lag. And so these are some of the things that, yes, the risk premium doesn't completely decay, but there are better and more efficient ways of capturing the same risk premium. And that's where a platform aspect becomes a key point.

Eloise Goulder: And I can see that the strength of the platform is completely key because it allows you to run all of these different strategies across all of these different assets and all of these different commodities, which creates these diversification or sharp enhancing qualities, but also to the extent that the platform can allow intraday trading, it can shorten those time horizons and further improve alphas or at least offset some of the alpha decay that we might be seeing otherwise. Although I do take your point, this is a fundamental risk premium at the end of the day that I think has been proven to work over multi decades.

I'd also love to hear about how the clients using these strategies have evolved over time, because on the hedge fund side, we're certainly seeing some forms of convergence between strategies. To what extent is this platform enabling investors who might not have been significant in commodities previously, enabling them to enter this space?

Kranthi Gade: Sure, traditionally, these strategies have been used by institutions like asset managers, pension funds, endowments, sovereign wealth funds, etc. More recently, we've seen an increase in total portfolio allocation approach, TPA, which emphasizes allocation to factors, as opposed to traditional asset classes. So that led to an increase in the use of QIS, which essentially is factor investing. More recently, though, hedge funds have been a fast growing segment for us. And there's a few ways that hedge funds use QIS. One is as complimentary exposure, and the second is, even in these multi-strat funds, even the commodity parts that they have generally tend to focus on individual sectors like energy, metals, etc. And they generally tend to be discretionary portfolio managers. So for them, the alpha that is generated by commodity QIS can be quite complimentary. And we've seen hedge funds use this at a central risk book level.  And the third way they could use these strategies is also as a way to benchmark. QIS can be a way to access and benchmark, for example, the performance of their portfolio managers. So that can be another way for hedge funds to use QIS.

Eloise Goulder: Interesting. So you have really seen this broadening in the types of investors utilizing the QIS strategies. Turning to other assets outside of commodities, I know you're also responsible for FX and rates. And I know that those assets have also moved, most notably bonds selling off amid the inflation pressures this year. So Kranthi, are there any trends worth mentioning across those assets?

Kranthi Gade: Sure. Both of these asset classes are quite rich when it comes to QIS. One thing I want to emphasize is the use of JPMaQs data, which is point in time macro data.  So just to give an example of one strategy within that basket, which we call real carry, which again is a rates carry strategy, but adjusted for inflation. And so we use inflation estimates from JPMaQs. And that strategy has performed significantly better than a nominal carry strategy. And that goes back to the point that you raised about inflation, because the bonds sold off in those countries where inflation has been a persistent concern, for example, UK. And by adjusting for inflation, you're picking up more carry compared to just looking at it from a nominal perspective.

Another theme that has been quite topical in rates is what we call rates nexus, which is essentially clients trading their models or strategy, but using our platform. And we've seen a few use cases of this either because of operational considerations in that they can get one line item of an index versus having to trade hundreds of these underlyings, especially in the OTC space. So that's been a theme that we got quite a bit of interest this year.

Moving on to FX. In FX, I would highlight a few topical themes. The first one is in wall carry, especially in emerging markets. So the performance has been quite strong since launch. It's been a bit more challenging recently on the back of Iran war.

The second theme in FX is what we call systematic hedging, where we use systematic signals like carry and value to systematically decide how to and how much to hedge. For example, clients who are looking at hedging weak dollar, they can use some of the signals to decide whether to or how much to hedge in a systematic fashion.  So this has become more topical recently on the back of a weak dollar and the whole de-dollarization theme. And then the third theme I would say is, again, similar to rates, is the macro strategies using JPMaQs data. And one example uses terms of trade data from JPMaQs to trade the different currencies. And this has become more topical recently because of the disruption that we've seen in oil markets. So we saw countries which are commodity exporters do very well, those currencies do very well relative to countries which import oil. And that is captured by these terms of trade strategy. So this is another example of using point in time micro data from JPMaQs to implement QIS strategies.

Eloise Goulder: It's fascinating to hear how you're combining macro fundamental data, for example, inflation in the rates carry strategy, plus, of course, more traditional price volatility type metrics in order to come up with your systematic strategies. And to hear that you're enhancing the alpha, enhancing the returns by combining fundamentals with price and volatility. It's worth noting that JPMaQs, that stands for the J.P. Morgan Macro Quantumental System created by the macro synergy team in collaboration with J.P. Morgan. And we did hear from Ralph Sueppel from the macro synergy team on this podcast series a while ago.

So before we close, Kranthi, could we touch on what's next? What are the future opportunities you're looking towards?

Kranthi Gade: For us, just expanding the universe of underlying has been a key theme, for example, in commodities, what we call off benchmark commodities, for example, emissions and frontier currencies in FX. And then on the rate side, emerging market rates, invoice spread, which is a relative value between futures and interest rate swaps, TBAs, which is market mortgage exposure, inflation swaps, corporate bonds. So these are all new underlyings that we onboarded recently or looking to onboard, which can lend very well to QIS, new QIS development.

And then the other theme that has become more topical is also alternative data, which we are looking to leverage internal research data or even some external vendors, for example, natural language processing to parse central bank statements or options flow data in FX or weather data for commodities. So these kind of alternative data can have good alpha opportunities and alpha signals, but it can also be used for hedging or adjusting a client portfolio.

Eloise Goulder: So clearly further expansion of the underlying assets, including these more idiosyncratic or alternative assets is a great way of expanding the reach of the product and of course, improving the diversification benefits of everything that you offer. But is there also further expansion into the solutions you can offer for our clients?

Kranthi Gade: It's a great point. As we mentioned before, there's a lot of interest from hedge fund clients or generally more active clients in these kinds of strategies. So for them, these strategies are generally not buy and hold. They tend to be quite active with respect to holding these strategies or taking the position off or looking at like opportune times to enter these strategies. So for them, building signals or like dashboards on how they can monitor the strategies or making the platform in such a way they can tweak the strategy on the go instead of having to just buy and hold the strategy. So those are some of the platform aspects that can be quite critical as we look to grow beyond the traditional client base in these strategies.

Eloise Goulder: Being able to tactically tap into these strategies is almost combining the systematic approach of those strategies with a discretionary overlay. And this combination of discretionary plus systematic can be so powerful in terms of timing the strategies potentially and/or adding diversification to a pre-existing portfolio that an asset manager might hold.

Kranthi Gade: Yes, 100%.

Eloise Goulder: Fantastic. Well, this has been a truly fascinating conversation covering an asset class commodities and other macro asset classes that have very much been in vogue this year. Lots of volatility, but great to see how you're rolling out the full breadth of systematic alpha generating strategies across this space and how the evolution of that space has been unfolding. So thank you very much Kranthi for taking the time to speak with us today.

Kranthi Gade: Thanks for having me. It was a pleasure.

Eloise Goulder: Thank you also to our listeners for tuning into this Making Sense podcast. If you have any questions, do go to our website at jpmorgan.com / market - data -intelligence. And there you can always reach out. And with that, we'll close. Thank you.

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