Answers.Body: <p>Machine learning based hedge funds are currently the worst performing slice through the industry. Large quant funds also claim to use ML but their performance is also very poor and worsening over time. Mostly what they say they are dong is not really what they are actually doing. I know of cases where funds claimed to be doing AI but actually its just simple technical analysis. Successful applications are more in the area of data collation of sentiment using NLP or feature extraction using dimensional reduction but these are not actually part of the trading strategies themselves. The only ML influenced trading I know is HFT trading of the order book which is a well define problem divorced from the actual price series itself. Apart from that 99% of ML use in the hedge fund industry is solely as snake oil for their marketing departments. Out of 10,000 or so hedge funds world wide only 0.25% generate alpha according to academic studies. That's 25 out of 10,000 ... and many of those will be cheating like SAC. </p>
  
  <p>The fundamental problem is that ML requires data that is high dimensional, highly structured and is low noise and has stationary dynamics and statistical moments. Unfortunately financial price series are low dimensional, unstructured and noise dominated (negative SNR) and exhibit multi-level non-stationarity of the underlying processes and statistical moments in a manner highly related to multi fractals. It is hard to imagine a time series less suitable for machine learning. </p>
  
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Answers.Text: There was a marketwatch article on this re: the techniques not providing utility when everyone is using them--essentially they seem to cancel each other out. [AI has become so popular in picking stocks that it’s become ineffective](https://www.marketwatch.com/story/ai-has-become-so-popular-in-picking-stocks-that-its-become-ineffective-2017-10-06)
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Answers.Text: Correct - its a level 2 chaotic system where realisation of some inefficiency and subsequent exploitation means it will vanish so even if you do find something it wont be there for long unless you have a significant moat of innovation to exclude others. Observation changes the thing being observed
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Answers.Body: <p><a href="https://en.wikipedia.org/wiki/High-frequency_trading" rel="nofollow noreferrer">High-frequency trading</a> is where you see it being used, essentially, decision-making algorithms analyzing and making transactions in microseconds.  It accounts for a significant percentage of market activity, and has been considered a source of greater market volatility (See: <a href="https://en.wikipedia.org/wiki/High-frequency_trading#Flash_trading" rel="nofollow noreferrer">Flash Crashes</a>).</p>
  
  <p>You can bet that hedge funds are evaluating every form of AI for trading and predicting market trends in general, but, unlike academics (and a segment of the tech sector) I doubt they consider it in their interests to publish methods which competitors could then utilize.    </p>
  
  <p>It's difficult to source information on exactly what is being looked into and utilized in the financial sector because there is a lot of marketing noise (exaggerated claims, 
   unreliable sources) but I did find these articles: </p>
  
  <p><a href="https://www.technologyreview.com/s/600695/will-ai-powered-hedge-funds-outsmart-the-market/" rel="nofollow noreferrer">Will AI-Powered Hedge Funds Outsmart the Market? (Tech Review; 2/4/2016)</a><br><br>
  <a href="https://www.bloomberg.com/news/articles/2018-09-07/ai-hedge-fund-sentient-is-said-to-shut-after-less-than-two-years" rel="nofollow noreferrer">"AI Hedge Fund Is Said to Liquidate After Less Than Two Years (Bloomberg; 9/7/2018)"</a></p>
  
  <p>My gut tells me that Machine Learning methods will surpass humans in these kinds of decisions before too long (educated guess), and only increase in utility as the dataset these algorithms draw from grows, because it's ultimately a statistical problem.</p>
  
  <hr>
  
  <p>AI is related to Game Theory, the study of economic decision-making, in the context of <a href="https://en.wikipedia.org/wiki/Rational_agent" rel="nofollow noreferrer">utility</a>.  Game Theory might be said to have had its greatest success in computing in general, via minimax, but has traditionally been much harder to apply in real world economics.  Minimax can be utilized in machine learning, and it's actually hard to think of any economic decision-making that wouldn't utilize it in some form.</p>
  
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Body: <p>Is there any research in this area?</p>
  
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Comments.Text: To OP, I expect there is research on it, but expecting someone to do a literature review on the keywords  "AI" and "hedge fund" and publish the results here is a bit too much work. Any chance you can add some details and some focus to the question so that it is not too broad and could be answered?
Comments.UserDisplayName: Neil Slater
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Comments.Text: Also have a look at the quant forum for more info e.g. https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets/
Comments.UserDisplayName: Z Z
Tags: <machine-learning><research><algorithmic-trading>
Title: Are there profitable hedge funds using AI?
UserDisplayName: bobbyor
ThreadText: # Are there profitable hedge funds using AI?
  
  (Asked by: username_0 on 2019-02-12)
  
  Is there any research in this area?
  
  **Tags:** machine-learning, research, algorithmic-trading
  > **Comments:**
  > **username_1**: To OP, I expect there is research on it, but expecting someone to do a literature review on the keywords  "AI" and "hedge fund" and publish the results here is a bit too much work. Any chance you can add some details and some focus to the question so that it is not too broad and could be answered?  
  > **username_2**: Also have a look at the quant forum for more info e.g. https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets/  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2019-02-15. Score: 2
  
  Machine learning based hedge funds are currently the worst performing slice through the industry. Large quant funds also claim to use ML but their performance is also very poor and worsening over time. Mostly what they say they are dong is not really what they are actually doing. I know of cases where funds claimed to be doing AI but actually its just simple technical analysis. Successful applications are more in the area of data collation of sentiment using NLP or feature extraction using dimensional reduction but these are not actually part of the trading strategies themselves. The only ML influenced trading I know is HFT trading of the order book which is a well define problem divorced from the actual price series itself. Apart from that 99% of ML use in the hedge fund industry is solely as snake oil for their marketing departments. Out of 10,000 or so hedge funds world wide only 0.25% generate alpha according to academic studies. That's 25 out of 10,000 ... and many of those will be cheating like SAC.
  
  The fundamental problem is that ML requires data that is high dimensional, highly structured and is low noise and has stationary dynamics and statistical moments. Unfortunately financial price series are low dimensional, unstructured and noise dominated (negative SNR) and exhibit multi-level non-stationarity of the underlying processes and statistical moments in a manner highly related to multi fractals. It is hard to imagine a time series less suitable for machine learning.
  
  > **Comments:**
  > **username_4**: There was a marketwatch article on this re: the techniques not providing utility when everyone is using them--essentially they seem to cancel each other out. [AI has become so popular in picking stocks that it’s become ineffective](https://www.marketwatch.com/story/ai-has-become-so-popular-in-picking-stocks-that-its-become-ineffective-2017-10-06)  
  > **username_3**: Correct - its a level 2 chaotic system where realisation of some inefficiency and subsequent exploitation means it will vanish so even if you do find something it wont be there for long unless you have a significant moat of innovation to exclude others. Observation changes the thing being observed  
  
  ---
  
  ### Answer by username_4 on 2019-02-13. Score: 2
  
  [High-frequency trading](https://en.wikipedia.org/wiki/High-frequency_trading) is where you see it being used, essentially, decision-making algorithms analyzing and making transactions in microseconds. It accounts for a significant percentage of market activity, and has been considered a source of greater market volatility (See: [Flash Crashes](https://en.wikipedia.org/wiki/High-frequency_trading#Flash_trading)).
  
  You can bet that hedge funds are evaluating every form of AI for trading and predicting market trends in general, but, unlike academics (and a segment of the tech sector) I doubt they consider it in their interests to publish methods which competitors could then utilize.
  
  It's difficult to source information on exactly what is being looked into and utilized in the financial sector because there is a lot of marketing noise (exaggerated claims,
  unreliable sources) but I did find these articles:
  
  [Will AI-Powered Hedge Funds Outsmart the Market? (Tech Review; 2/4/2016)](https://www.technologyreview.com/s/600695/will-ai-powered-hedge-funds-outsmart-the-market/)  
    
  ["AI Hedge Fund Is Said to Liquidate After Less Than Two Years (Bloomberg; 9/7/2018)"](https://www.bloomberg.com/news/articles/2018-09-07/ai-hedge-fund-sentient-is-said-to-shut-after-less-than-two-years)
  
  My gut tells me that Machine Learning methods will surpass humans in these kinds of decisions before too long (educated guess), and only increase in utility as the dataset these algorithms draw from grows, because it's ultimately a statistical problem.
  
  ---
  
  AI is related to Game Theory, the study of economic decision-making, in the context of [utility](https://en.wikipedia.org/wiki/Rational_agent). Game Theory might be said to have had its greatest success in computing in general, via minimax, but has traditionally been much harder to apply in real world economics. Minimax can be utilized in machine learning, and it's actually hard to think of any economic decision-making that wouldn't utilize it in some form.
  
  
  ---
  
  