AIEQ the AI Powered Equity ETF: Artificial Intelligence is Still Losing to a Natural Stupidity

A year ago the Business Insider reported about the "the stocks market's robot revolution". Whereas the title was crying, a summary was more reserved: The fund has outperformed the S&P 500 so far, but a much longer trading period is needed to assess whether it can truly offer market-beating returns. I scheduled in my calendar to have a look at this fund in a year, telling a colleague, who pointed me on AIEQ that I would bet a bottle of whisky (bot not a farm!) that this ETF will perform worse than its benchmark. I turned out to be right.

Usually we analyze in detail the performance of investment strategies that we review. This time we will spare the effort, finally we have little data on AIEQ. The original publication by BusinessInsider reports that AIEQ "uses IBM's Watson supercomputing technology to analyze more data than humanly possible, all in the pursuit of building the perfect portfolio of 30 to 70 stocks". Anyway, we can see a strong correlation with SPY, which lets us assume that the algorithm likely chooses from the heaviest components of SP500. Since AIEQ uses Watson, its strategy may be based on the news analysis... but finally it all does not matter much as long as the fund does not beat it benchmark!

Two years ago we published a post Big Data and Deep Learning, a technology revolution in trading or yet another hype?: a good non-technical introduction to a problem.
Later we considered a more technical (counter)-example, showing that (and explaining why) the glorified LSTM neural networks may fail even on simulated time series (let alone the real data, often noisy and incomplete).



The morality (which we repeat to our readers over and over again): don't get fooled by the buzz-words (even if they are AI, BigData or Blockchain). And even if current AI tools can learn a lot (they really can), the market is an ever changing essence, meaning that an AI algorithm shall not only learn but also forget quickly. But this is exactly what the AIEQ strategy failed to do: it (again) started to lose to SPY as soon as the volatility regime has switched. Volatility clustering and regime switching is a well-known stylized fact and likely the creators of AIEQ has taught their algo w.r.t. this phenomenon. But in the market one does not necessarily need to be (artificially) intelligent, rather one shall be smart and not to follow the natural stupidity of the crowd. I mean, if a guess that AIEQ tries to assess the news is correct, then the drop is actually obvious: how the market evaluates essentially the same news strongly depends on current market sentiment.

#R-code used to create the charts (called on 27.10.2018):
library(alphavantager)
library(quantmod)
av_api_key(<set_your_key_here>)
ai = av_get(symbol = "AIEQ", av_fun = "TIME_SERIES_DAILY_ADJUSTED", outputsize = "full")
sp = av_get(symbol = "SPY", av_fun = "TIME_SERIES_DAILY_ADJUSTED", outputsize = "full")
AIEQ = xts(ai$adjusted_close, order.by=ai$timestamp)
SPY = xts(sp$adjusted_close, order.by=sp$timestamp) 
commonDates = as.Date(intersect(index(AIEQ), index(SPY)))
plot(AIEQ[commonDates]/as.numeric((AIEQ[commonDates])[1]), main="AIEQ (black) vs. SPY (red): normalized daily_adjusted prices")
lines(SPY[commonDates]/as.numeric((SPY[commonDates])[1]), col="red")
#
par(mfrow=c(2,1))
plot(dailyReturn(AIEQ[commonDates]))
plot(AIEQ[commonDates]/as.numeric((AIEQ[commonDates])[1]), main="AIEQ (black) vs. SPY (red): normalized daily_adjusted prices")
lines(SPY[commonDates]/as.numeric((SPY[commonDates])[1]), col="red")
plot(dailyReturn(AIEQ[commonDates]))

P.S. One year is, however, not a big time span to evaluate the strategy. As I publish this post on Facebook, the FB will friendly remind me about it after a year. So we will have a look at AIEQ once again... unless it becomes a victim of funds survivorship bias 😉

Like this post and wanna learn more? Have a look at Knowledge rather than Hope: A Book for Retail Investors and Mathematical Finance Students

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5 thoughts on “AIEQ the AI Powered Equity ETF: Artificial Intelligence is Still Losing to a Natural Stupidity”

  1. As a Computer Science undergrad who wants to be a “quant” which would be the best specialisation???
    AI and BigData don’t seem very useful in the Asset Management industry. What would you suggest then? Programming in general?

    1. Hi Chris,

      first of all we would suggest to read this 🙂
      https://letyourmoneygrow.com/2018/05/13/wanna-be-quant-probably-not-anymore-after-reading-this/

      If after reading this you still want to be a quant then our suggestion would be to gather some hands-on experience with financial markets.
      You can do it by trading the stocks but you need relatively big capital, otherwise your wealth will be eaten-up by broker fees.
      Some brokers like DeGiro (s. https://letyourmoneygrow.com/category/juniordepot/ ) offer quasi-free trades with ETFs, but this is generally suitable for long-only and long-term strategies (ETFs are usually low volatile).
      Another option is FX-trading, which is hard but possible ( s. https://letyourmoneygrow.com/2018/05/07/12-consistentently-profitable-automatic-fx-strategies/ ) as well as CFDs (we are going to publish a story on CFD soon).
      But you shall avoid a scam like binary options https://letyourmoneygrow.com/2016/09/03/binary-options-you-may-earn-in-a-short-term-but-eventually-you-will-lose/

      1. Well in fact im interested in the whole Asset Management industry, not only quant stuff. Sorry for not mentioning it. Some more questions:

        1. Should i aim for firms with passive or active investment strategies and why? Index Funds, ETFs etc are getting more AUM every year, but is this a trend/bubble or the new norm?
        2. Im from EU and as far as i know Luxembourg is very big on Asset Management (2nd biggest investment hub in the world after USA) with many UCITS etc. Very big on Wealth Management too. Do you think that this small country is promising or its just regulation-tax-back office stuff going on there? Is Switzerland a better choice?

        Sorry for all those questions, im on my 2nd year of CS. Do you have any advice on what my specialisation should be? BigData and DeepLearning seem “cool” but are not so useful for financial uses. Any ideas?

        Thanks a lot for the reply!!!

        1. >im interested in the whole Asset Management industry
          This is too broad and thus too unspecific 🙂
          Well, if you want to work in Asset Management industry (why, actually?) as a Computer Scientist, the bitter truth is that there is much more IT in backoffice/reporting than in frontoffice/trading. But if you are in back office, do not expect a luxury wage (if it is your primary motivation).

          A good compensation (with a big weight of bonuses) is not uncommon in front offices of active asset managers. But the competition for jobs there is extremely high and, as we already said, besides knowing the purely technical side (IT), you need to have at least basic understanding of the markets and trading strategies.

          1. I dont think that the type of the degree matters. I see engineering, political science and economics majors in the finance sector at various roles. Also, comp is not my biggest concern, but it certainly is a factor. I dont mind starting from middle office, i have no experience so i would be better than nothing (it would great actually).

            Well, to be more specific, im more interested in passive asset management (buy and hold etc), but not Private Equity stuff. How is Luxembourg for a career in that sector? It would never be UK but many funds are domiciled there. Is it just portfololio delegation and tax stuff going on there? Is Switzerland a better move?

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