Practical analysis for investment professionals
03 February 2022

The US Investment Olympics: Smart Money, Crowd Intelligence, and AI


Welcome to the qualifying round of the 2022 US Investment Olympics.

The goal of the games is simple: beat the S&P 500, either by generating higher returns or playing dirty and going for higher risk-adjusted returns. 

Let the games begin!

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Like the 2022 Winter Olympics in Beijing, the US Investment Olympics are not easy to qualify for. Mutual funds are automatically barred from participation: Their fees are just too high for them to have a realistic shot against the S&P 500. Hedge funds have even higher fees and theoretically should be hedged, so they can’t compete with the stock market either. In fact, the only securities capable of matching the index are exchange-traded funds (ETFs).

So far, there are eight ETF contestants representing three themes:

  • Smart Money (GVIP, GURU, GFGF, and ALFA): These ETFs mimic the trades of famous investors and mutual and hedge fund managers. Their pitch is high alpha at low fees.
  • Crowd Intelligence (BUZZ and SFYF): Stocks are selected based on the wisdom and sentiment of the crowd.
  • Artificial Intelligence (AI, AIEQ and QFRT): The equities in these ETFs are chosen by AI programs. In the case of AIEQ, IBM’s famous Big Watson makes the picks.

Although less expensive than the average mutual or hedge fund, the ETFs have fees of 64 basis points (bps) and are not cheap compared to low-cost index trackers. But then again, top-notch performance isn’t free.

Financial Analysts Journal Current Issue Tile

Despite their contemporary themes, our ETFs have yet to resonate much with the investment community. Their cumulative assets under management (AUM) are only $700 million, even though some have track records going back to 2012. But then again, who doesn’t love cheering for the underdog?

Smart Money, Crowd Intelligence, and AI ETFs AUM, in US Millions

Chart showing Smart Money, Crowd Intelligence, and AI ETFs: Assets under Management, Millions
Source: FactorResearch

Smart Money, Crowd Intelligence, and AI ETFs: Performance

So how did our eight ETFs fare against the S&P 500? We created equal-weighted indices for the three groups, with Smart Money’s track record going back to 2012, AI’s to 2016, and Crowd Intelligence’s to 2019.

Since all invest in US stocks, they all performed in line with the S&P 500. Some have beaten the benchmark on occasion but not consistently. The judges are not especially impressed.

Outperforming the S&P 500: Smart Money, Crowd Intelligence, and AI ETFs

Chart showing Outperforming the S&P 500: Smart Money, Crowd Intelligence, and AI ETFs
Source: FactorResearch

Of course, the Olympics, like finance, is all about data and details. Eyeballing an investment’s chart is not a particularly scientific approach to performance evaluation. The judges want to know what sort of alpha our competitors have generated since their inception. Smart Money yielded a negative alpha of -3.0% per annum since 2012, Crowd Intelligence -7.2% per year since 2019, and AI -0.9% since 2017. 

A cynic might say the smart money isn’t that smart, the crowd not that wise, and AI not that intelligent.

Alpha Generation: Smart Money, Crowd Intelligence, and AI ETFs

Chart showing Alpha Generation: Smart Money, Crowd Intelligence, and AI ETFs
Source: FactorResearch

Better at Risk Management?

But before eliminating all these contestants from medal contention, our judges examine their risk-management characteristics. Our ETFs may not have the longest track records, but they all experienced the last severe stock market shock: the COVID-19 crisis. So how did they do?

Smart Money and Crowd Intelligence fell further than the S&P 500 in March 2020, while AI did marginally better. Perhaps humans are overrated and AI is better at risk management?

Less Downside? Maximum Drawdowns during 2020 COVID-19 Crisis

Chart showing Less Downside? Maximum Drawdowns during 2020 COVID-19 Crisis
Source: FactorResearch

Although lower drawdowns may help investors stick to an investment strategy, on a stand-alone basis, they are not especially helpful metrics. After all, cash would outperform in a down market too, but it is unlikely to beat the benchmark over time. So the judges turn to risk-adjusted returns and the Sharpe ratio.

AI beat Smart Money and Crowd Intelligence, but none of our contenders generated higher Sharpe ratios than the S&P 500. That means none of them qualify to advance.

Better Risk-Adjusted Returns? Sharpe Ratios, 2019–2021

chart showing Better Risk-Adjusted Returns? Sharpe Ratios, 2019–2021
Source: FactorResearch

Further Thoughts

Although these ETFs had distinct flavors, they exhibited similar behavior: In fact, they all outperformed the S&P 500 in 2020. The question is why.

A factor exposure analysis reveals that they have almost identical exposures: negative exposure to value and positive exposure to the size and momentum factors. Our competitors were all overweight outperforming small-cap growth stocks.

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Smart money investors like hedge funds may not appreciate that the crowd is picking up the same risk exposure as they are. And they all might be surprised that the AI ETFs are too. 

The right factor exposure can help outperform the S&P 500 over time, but it does not resemble alpha. In fact, it is the investment world equivalent of doping. Especially when hidden within thematic products.

Though it wouldn’t have mattered in this round, it would have been cause for disqualification.

Thus far, the S&P 500 is beating the field.

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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.

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About the Author(s)
Nicolas Rabener

Nicolas Rabener is the managing director of Finominal, which provides quantitative solutions for factor investing. Previously he founded Jackdaw Capital, a quantitative investment manager focused on equity market neutral strategies. Previously, Rabener worked at GIC (Government of Singapore Investment Corporation) focused on real estate across asset classes. He started his career working for Citigroup in investment banking in London and New York. Rabener holds an MS in management from HHL Leipzig Graduate School of Management, is a CAIA charter holder, and enjoys endurance sports (100km Ultramarathon, Mont Blanc, Mount Kilimanjaro).

1 thought on “The US Investment Olympics: Smart Money, Crowd Intelligence, and AI”

  1. Very interesting and to the point results. I would suggest a couple of things. Do any of the competitors significantly improve the S&P500 Sharpe ratio if they were included in the optimal allocation (or a simpler fixed weight allocation)? See Jobson and Korkie (1984) Journal of Finance, and Jobson and Korkie (1982) Journal of FInancial Economics. Also, you might want to check for statistical significance of the Sharpe ratios, see Jobson and Korkie (1981), Journal of Finance.

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