Can Bobby Axelrod actually win in Billions Season 7?

And what can real life investors learn from it - in conversation with Niall Bolland from ClearMacro

Hi! It’s George from Investorama.

This newsletter is about Billions, although I’m more into Industry these days. If you skip the sex scenes (I’m not a prude, but I think they are pointless from a story perspective, and there are too many), it’s a fantastic show about working in financial markets. I used to do a job similar to Harper Stern’s, but at Deutsche Bank, so it brings back many memories. Here is a playlist.

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If you’re not into the Billions, but you’re into alternatives, you should check it out. It’s one of the only series about the Hedge Fund world, and the 7th and final series is about to be released. Seasons 1-4 were great, but then I felt it was falling apart, and now I am hopeful about Season 7.

I partnered with Bright Data, a web data provider, to devise a winning strategy for the protagonist, Bobby Axelrod, as he returns to Billions after a gap (more info on how hedge funds use alt data here.

My recommendation for his firm, Axe Capital, is based on data, a word that is utterly absent from the first two series and appears elusively in the third. It’s clearly a concept that the writers have missed if they wanted to make the fictional hedge fund more realistic.

But it was not based on thin air. I had to pick up hints from the previous series:

• In Season 1: we see that Axe Capital’s edge is its ability to gain an informational advantage (although not always legally)

• In Season 2: they are using Alternative Data (without calling it like that)

• In Season 3: they explore Quant strategies (but without data)

• In Season 4: they move into Private Markets

• In Season 5: they launch an Impact Fund

• I haven’t watched Season 6!

Alternative Data and Web Data can be applied to all of the above.

The video is hopefully informative and entertaining. (I don’t expect many hedge funds to implement it at face value, although I’d love them to try).

The newsletter is an opportunity to get a more advanced understanding. 👉

A deeper dive with an expert in data for financial markets

I had the opportunity to go deeper in a conversation with Niall Boland, the CEO of ClearMacro and former institutional investor and hedge fund portfolio manager. The company helps institutional investors integrate actionable insights from data into their portfolios for smarter decision-making (especially for medium and longer-term decisions like Asset Allocation), and it was featured in this Billions video which has been watched over 200,000 times.

Niall kindly shared his thoughts on the topics in the video with me. Below are some highlights:

On Public Markets

The quote from Taylor is interesting "Quant – is another word for systemized ordered thinking represented in an algorithm approach to trading. "  

It serves as a good reminder that a lot of the focus on Quant/data/tech is often/all on trading (i.e. short-term holdings, probably seconds to months) and is on individual issuers (i.e. stocks).

As a starting point, it is worth mentioning that many/most traditional asset managers we know (and certainly almost all Wealth Managers and Family offices who don't have the same resources and /or level of sophistication) do not have the ability to source, screen, ingest, clean, calibrate and convert these datasets into actionable signals - let alone aggregate/combine them together to create holistic pictures on the drivers of various investment decisions (one should generally avoid making an investment decision on a single driver).

The analogy here is that even though "Data is the new oil", it is almost irrelevant unless one has the entire chain in place from prospecting to drilling to piping to refining to producing to distribution to the end-user.

Possibly more importantly, it is worth recognizing that trading is only a SMALL part of the investment activity and arguably not the one that drives the material outcomes over time.  So, the terms "investment" and/or "asset allocation" are conspicuous by their absence in Taylor's quote (not surprising though, given Taylor sits in a hedge fund, not an Asset Manager). 

So, we believe there is a huge opportunity to apply the combination of quant/AI/data/data pipeline software, to the broader and arguably more important set of investment questions. There is a large body of academic work which highlights the fact that the majority of investment outcomes are actually driven by the more top-down asset allocation decision (for example: how much equities vs fixed income or cash, which geographies/fx, which sectors etc.) Probably the most visceral recent example of this has been in high-growth sectors (tech, bio), which were decimated in 2022 because they are long-duration assets with a terminal value that is highly geared to interest rates and, by extension, inflation deltas. Any investors who were overly focused on just the stories/bottom-up fundamentals and not allocating sufficient mindspace to the macro overlay ended up nursing severe losses.

On Private Markets

The video referenced sourcing data at scale from Crunchbase because it’s the lowest-hanging fruit, helping with the start of the investment screening process.

Crunchbase is really just the lowest level pre-screening... like a simple valuation filter a public markets person might run on Bloomberg 20 yrs ago. In any case, it's more about earlier stage companies - not really where PE happens but VC. 

For PE, a much more interesting platform is AlphaSense | Market Intelligence and Search Platform; it’s sophisticated and granular, and leverages AI smartly. 

Other than that, and having run a large hybrid team, I would agree with the notion that Private investing is a different discipline than public market investing. Aside from anything else, the investment horizon is different (PE is generally 5-7 years) - which, for data, means that the drivers of short-term are much less relevant in the case of private investments

As Ben Graham said: “In the short-term markets are a voting machine, in the long term a weighing machine”. So, clearly, different approaches (datasets, models, frames are required depending on investing time horizons.

On the ESG/Impact fund 

This section of the video arguablyprobably ages least well because there have been so many problems in this space the ESG space.

Firstly, the labeling and definitions in ESG remain quite questionable a complete messto be honest. There's a lot of BS confusion that still hasn't been cleared up. So much so, that Morningstar eliminated the ESG status from literally hundreds of funds because of perceived "greenwashing" in the last few years..

From a data perspective. I think the lesson here perhaps is that it is hard to create a data-driven strategy on an "asset class"/theme where there data is dubious /  severely compromised. Also, solving just for ESG can create some wonky results – like for example ESG vehicles which were long Tech and short Oil in 2022 – painful!