Answers.Body: <p>Your list is complete for what is considered 'popular' by most practitioners who apply AI for stock trading.  Supervised learning and rule learning are at the top for accuracy.  There are more academic papers published on classifiers than on regression approaches;
  classifiers are typically more accurate than regressors.</p>
  
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Answers.CreationDate: 2020-08-04T15:56:29.663Z
Answers.LastActivityDate: 2020-08-04T15:56:29.663Z
Answers.UserDisplayName: Brian O'Donnell
Answers.Body: <p>One technique I've used in the past (I don't know how mainstream this is) is to use unsupervised learning (self-organizing maps) to identify commonly occuring features in stock charts, and then use those features as input to a traditional back propagation network.  I found I was able to get equivalent results with slightly smaller network size having done this, but it wasn't a huge gain.</p>
  
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Answers.CreationDate: 2023-02-21T12:42:22.653Z
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Answers.UserDisplayName: occipita
Answers.Body: <p><em><strong>Usecases I came across:</strong></em></p>
  <ol>
  <li>As mentioned by Saurav, the use of NLP is definitely a use case. Adding to his source, you can check out the Two Sigma's Kaggle competition. Going through their careers page it is evident that use of NLP is prominent. Cofounder of Kavout, Alex Lu reconfirms that in
  <a href="https://emerj.com/ai-podcast-interviews/artificial-intelligence-in-stock-trading-future-trends-and-applications/" rel="nofollow noreferrer">https://emerj.com/ai-podcast-interviews/artificial-intelligence-in-stock-trading-future-trends-and-applications/</a></li>
  </ol>
  <p>Two Sigma Challenge Kaggle: <a href="https://www.kaggle.com/c/two-sigma-financial-news" rel="nofollow noreferrer">https://www.kaggle.com/c/two-sigma-financial-news</a></p>
  <ol start="2">
  <li>ML to determine whether some large market player is rebalancing or liquidating his or her portfolio.
  Source: <a href="https://qr.ae/pGnoGx" rel="nofollow noreferrer">https://qr.ae/pGnoGx</a></li>
  <li>I have heard about ML being used in Statistical Arbitrage(Pairs trading) also.
  Example case: <a href="http://cs229.stanford.edu/proj2019spr/report/26.pdf" rel="nofollow noreferrer">http://cs229.stanford.edu/proj2019spr/report/26.pdf</a></li>
  <li>Further, I am aware of use of ML for hedging purposes in a couple of banks. It's called deep hedging. Source: <a href="https://www.maths.ox.ac.uk/system/files/attachments/2019%2004%2024%20Deep%20Hedging%20Frontiers%20Imperial%202.1.pdf" rel="nofollow noreferrer">https://www.maths.ox.ac.uk/system/files/attachments/2019%2004%2024%20Deep%20Hedging%20Frontiers%20Imperial%202.1.pdf</a></li>
  </ol>
  
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Answers.LastActivityDate: 2021-05-13T02:53:52.883Z
Answers.LastEditDate: 2021-05-13T02:53:52.883Z
Answers.UserDisplayName: Ashutosh Bhushan
Answers.Body: <p>NLP is one of the tools you may use for stock prediction. Here, are a couple of articles to help you get started:</p>
  <ul>
  <li><a href="https://towardsdatascience.com/using-nlp-and-deep-learning-to-predict-the-stock-market-64eb9229e102" rel="nofollow noreferrer">Using NLP and deep learning to predict the stock market</a></li>
  <li><a href="https://towardsdatascience.com/heres-how-i-predicted-apple-s-stock-price-using-natural-language-processing-13a578c41b8e" rel="nofollow noreferrer">Here's how I predicted apple's stock price using NLP</a></li>
  </ul>
  
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Answers.CreationDate: 2021-05-25T07:56:25.377Z
Answers.Text: Do you know any company that has published the way they are using NLP to predict the price of a stock (in the short team I guess)?
Answers.UserDisplayName: Raul Alvarez
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Answers.CreationDate: 2021-05-01T20:01:59.703Z
Answers.LastActivityDate: 2021-05-10T00:01:05.900Z
Answers.LastEditDate: 2021-05-10T00:01:05.900Z
Answers.UserDisplayName: Raul Alvarez
Body: <p>Currently, what are the most popular and effective approaches to leveraging AI for stock price prediction?</p>
  <p>It seems like there could be several approaches and problem formulations:</p>
  <ul>
  <li>Supervised learning:</li>
  <li>Regression: predict the stock price directly</li>
  <li>Classification: predict whether the stock price goes up or down</li>
  <li>Unsupervised learning: find clusters of stocks that move together</li>
  <li>Reinforcement learning: let the agent directly maximize its stock market return</li>
  <li>Other AI methods: rules, symbolic systems, etc.</li>
  </ul>
  <p>Which are most popular/performant? Are there other ways that people are using machine learning in stock trading (sentiment analysis on financial statements, news, etc.)?</p>
  
Tags: <machine-learning><reference-request><applications><algorithm-request><algorithmic-trading>
Title: What are the most popular and effective approaches to leveraging AI for stock price prediction?
UserDisplayName: information_interchange
ThreadText: # What are the most popular and effective approaches to leveraging AI for stock price prediction?
  
  (Asked by: username_0 on 2020-04-29)
  
  Currently, what are the most popular and effective approaches to leveraging AI for stock price prediction?
  
  It seems like there could be several approaches and problem formulations:
  
  * Supervised learning:
  * Regression: predict the stock price directly
  * Classification: predict whether the stock price goes up or down
  * Unsupervised learning: find clusters of stocks that move together
  * Reinforcement learning: let the agent directly maximize its stock market return
  * Other AI methods: rules, symbolic systems, etc.
  
  Which are most popular/performant? Are there other ways that people are using machine learning in stock trading (sentiment analysis on financial statements, news, etc.)?
  
  **Tags:** machine-learning, reference-request, applications, algorithm-request, algorithmic-trading
  
  ---
  
  ## 4 answers
  
  ### Answer by username_1 on 2020-08-04. Score: 1
  
  Your list is complete for what is considered 'popular' by most practitioners who apply AI for stock trading. Supervised learning and rule learning are at the top for accuracy. There are more academic papers published on classifiers than on regression approaches;
  classifiers are typically more accurate than regressors.
  
  
  ---
  
  ### Answer by username_2 on 2023-02-21. Score: 1
  
  One technique I've used in the past (I don't know how mainstream this is) is to use unsupervised learning (self-organizing maps) to identify commonly occuring features in stock charts, and then use those features as input to a traditional back propagation network. I found I was able to get equivalent results with slightly smaller network size having done this, but it wasn't a huge gain.
  
  
  ---
  
  ### Answer by username_3 on 2021-05-10. Score: 1
  
  ***Usecases I came across:***
  
  1. As mentioned by Saurav, the use of NLP is definitely a use case. Adding to his source, you can check out the Two Sigma's Kaggle competition. Going through their careers page it is evident that use of NLP is prominent. Cofounder of Kavout, Alex Lu reconfirms that in
     <https://emerj.com/ai-podcast-interviews/artificial-intelligence-in-stock-trading-future-trends-and-applications/>
  
  Two Sigma Challenge Kaggle: <https://www.kaggle.com/c/two-sigma-financial-news>
  
  2. ML to determine whether some large market player is rebalancing or liquidating his or her portfolio.
     Source: <https://qr.ae/pGnoGx>
  3. I have heard about ML being used in Statistical Arbitrage(Pairs trading) also.
     Example case: <http://cs229.stanford.edu/proj2019spr/report/26.pdf>
  4. Further, I am aware of use of ML for hedging purposes in a couple of banks. It's called deep hedging. Source: <https://www.maths.ox.ac.uk/system/files/attachments/2019%2004%2024%20Deep%20Hedging%20Frontiers%20Imperial%202.1.pdf>
  
  
  ---
  
  ### Answer by username_4 on 2021-05-01. Score: 1
  
  NLP is one of the tools you may use for stock prediction. Here, are a couple of articles to help you get started:
  
  * [Using NLP and deep learning to predict the stock market](https://towardsdatascience.com/using-nlp-and-deep-learning-to-predict-the-stock-market-64eb9229e102)
  * [Here's how I predicted apple's stock price using NLP](https://towardsdatascience.com/heres-how-i-predicted-apple-s-stock-price-using-natural-language-processing-13a578c41b8e)
  
  > **Comments:**
  > **username_4**: Do you know any company that has published the way they are using NLP to predict the price of a stock (in the short team I guess)?  
  
  ---
  
  