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Pierre Ferragu covered tech stocks for Bernstein for a decade, following names such as Nokia (NOK), Ericsson (ERIC) and chip maker Infineon AG (INFN). In a recent interview, he reflects on how the sell-side equity research model is broken. The old model of rich trading margins that support massive numbers of analysts, each focused on a single industry within tech, is out of step with an era of thin trading margins and tech trends that span industries.

In a new role as managing partner for infrastructure technology at New Street Research, a boutique research partnership, Ferragu, 43, aims for something you might call a holistic approach. By having an individual point of view that crosses the boundaries of industries, he believes he can bring unique insights about companies such as Tesla (TSLA) and chip maker Nvidia (NVDA).

Barron’s: Congrats on the new gig, how do you feel?

Pierre Ferragu: Excited and also scared!

B: After a decade covering tech at the old gig, why make the leap now?

PF: Last year, trading volumes were down because volatility was low in the market. That meant pressure on revenues at my former employer. It helped me realize there was something wrong with our model. I didn’t want my commercial success to be tied to volatility or trading volumes, but to the quality of my research and how much value it brings to our clients. Fifty years ago, if you had a strong analytical mind and were good at generating ideas, the best way to get paid for that was to call your clients and get the trade. The fee structure was very rich then. Today, things are different. Good execution is a very material concern for our clients, with the best liquidity, and also with the best costs. Trading is about technology, scale, and liquidity. It requires capital. Research is about none of these. Research is only about intellectual capital. At New Street Research, what we want to do is to be 100%-focused on research. Every dollar our clients pay is directly financing research.

A second difference is company structure. I wanted an ownership structure that would fit well with an intellectual capital business. If you look at places that attract the best talent, they’re all partnerships — the old Goldman Sachs, or consulting firms like BCG and McKinsey, or law firms. You don’t want any other shareholders than those bringing in the intellectual capital. I believe ultimately the leading research houses will all be partnerships and nothing else.

B: Anything else?

PF: There is an important third difference. Historically, research firms like my former employer and others have tried to go for scale on the research side, adding more and more analysts to their coverage, in order to match the scale they needed for trading. This resulted in a lot of analysts covering fairly silo’ed, fairly specific segments. You get a good level of expertise, but it’s extremely inefficient. You get a lot of people spending 80% of their time trying to build a joint perspective on a matter, whereas probably individual perspectives are more valuable.

B: It becomes research by committee.

PF: Yes, and if you do research by committee, that is something investors might value, but If you are a thought leader, is that the best place for you to add value? I think the best place to add value for you is to have as much freedom as possible in your research process.

Let me give you an example. I’ve covered for years Infineon in Germany. I started researching all their end-markets, including the car industry. I looked at the tear-down analysis of a high-end luxury car, and of the Tesla Model S. There’s something called an insulated-gate bipolar transistor, or IGBT. You typically find two or three of them in a high-end traditional car. But in a Tesla Model S, you find 120 of them. Why? You need these components to transfer energy from your super-charger into your batteries, and from your batteries into your motor. And these components are what determines the quality of your super charging, the ability to super charge [an electric vehicle] in 20 minutes. My seven-seater [Tesla] Model X [SUV] accelerates faster than any sub-$1 million Ferrari thanks to these IGBTs. Also, a better IGBT means a better range. I concluded IGBTs are one of the main drivers of performance of an electric car.

I came up with that and wrote one of these beautiful Black Books [a massive Bernstein research report] on these IGBTs. And my only investment conclusion was to rate Infineon a Buy. I could have done much more: push research further, build on the expertise I had developed to understand the market for batteries, implications for the car industry, for Tesla. From a single very important insight, I could have expanded my research not where my coverage is, but where I see I can add the most value for my clients. What other car manufacturers are doing with IGBTs, whether Tesla is going to build a competitive advantage out of a better inverter or charging technologies, etc.

B: It sounds like you see few limits to where you can go in finding connections across different businesses.

PF: Yes, connections between markets in solar panels and electric cars, for example, and I would actually even argue that the fundamentals of business dynamics tend to be very similar. Here’s another example. One can get a unique angle on what’s happening at Tesla today by having covered [chip-equipment maker] ASML (ASML) for the last ten years. ASML has been introducing a game-changing technology [for chip manufacturing], EUV, and working for more than a decade getting the technology right. And then getting the technology to mass-production levels. What is the journey Tesla is going through today? It’s the exact same journey. It started from a single man doing simple math on a Paris subway ticket.

B: That was Elon Musk?

PF: That was Elon Musk.

B: Really, he wrote it on a subway ticket?

PF: I don’t know if he wrote it on a subway ticket, it’s a French expression. But the starting point of Elon’s thinking was figuring out the math, that the equation for an electric vehicle should work over time. And then it took him ten years to first bring that to a prototype, and then to a small series — a petite série, in French, a small run — of almost handcrafted cars. And then an expensive luxury sedan. And now, the mass-market car [the Model 3 sedan.]

B: It sounds like you’re saying some investors may be receptive to research that isn’t just about how the latest earnings report went.

PF: Every time a company reports, investors the next day get in their inbox 40 reports about the earnings. I wouldn’t say this is not a useful mechanism, but it’s a mechanism where I don’t think I can add that much incremental value. After that long night that all these analysts are writing up their reports, they are going to be very tired. But I’ll be in very good shape to publish the last report, which is going to be solely focused on less-researched topics, not so much looking at what metrics the Street is looking at today, but what it will be looking at in six to 12 months.

B: So there’s a thesis at work, let’s say for a company like Tesla, that looks out farther?

PF: Today, most analysts focus on three things: the gross-margin trajectory of the Model 3, the cash burn of the company, and the production ramp of the Model 3.

B: And the production ramp at the moment is in doubt.

PF: At the moment it is in doubt. From my perspective, I think none of these three things matter at all. In six to 12 months, investors may be focusing on the addressable market of the Model 3, the competition, and the economics of battery manufacturing. My idea is to spare the effort of reporting on the quarter, and instead write something about what will matter next.

B: Those three things that seem to matter so much with respect to Tesla — cash burn, margin, and Model 3 production ramp — why don’t they matter as much as people think they do?

PF: Well, for that, you’ll have to wait for my formal comments in due time!

B: Fair enough. Any other teasers you can give us?

PF: Another angle I want to develop is the notion of asymmetry of risks and uncertainty. We often read in research reports about who are the winners, who are the losers. That is rarely enough to make good investment decisions. I think investors need a view on what the trend is, of course, where the disruption is, but it needs to be complemented by quantitative work. How bad is it for the losers, how good is it for the winners? An example: Machine learning is the trend, Nvidia is a winner, Intel (INTC) a loser. Fair enough, but what is there to lose for Intel? Existing revenues, future growth, or a chunk of future growth? What is there to win for Nvidia? The [A.I.] “training" market? The “inference" market? How big are those markets going to be? At the end of the day, what I want to come to is a good quantified idea of what we know, a good understanding of what we don’t know yet, and wrap that into investment recommendations.

B: You said at the start of this you felt excited and also scared. What is the scary part?

PF: The scary part at this point is not, Oh, is this model going to work? I know it works. The question is whether I can be as successful as my New Street partners Iain, James and Jonathan, and many others who have gone down that route, who have been successful already — it’s a fairly long list. Am I entitled to be on that long list? They are star analysts, people who have proved they add value for their clients. I hope I’ll make that bar.

B: We’ll be watching, Pierre!

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