I am looking for a book, which covers the following topics:

  • stock price prediction using Artificial Neural Network,
  • stock price prediction using LSTM,
  • stock price prediction using linear/non-linear regression methods,
  • other prediction techniques.

I am not interested in Black-Scholes, or other quant stuff. I need book/materials on stock price predictions.



After some decades of absense from the meaningless price chasing (money don't bring happiness), the recent squeeze seasons lured me into exploring some of the literature. Hence, my opinion is not robust.

Anyway, so far my favorites are: Korstanje, 2021, "Advanced Forecasting with Python: With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR" and Brownlee, 2018, "Deep Learning for Time Series Forecasting" because they include the code for some fast implementations and I can easily play with the data. They seem to cover everything you asked.

If I do not lose interest I would like to study some more "science-y" material. The top of my list is Velu et al., 2020, Algorithmic Trading and Quantitative Strategies (which is unrelated to your question, I just mention it).

All in all, I don't think there is predictability, perhaps reactability. There is an enormous web of algorithms already in use and you cannot do anything, and even in the 1 in the million+ that you can, a high-frequency algorithm will be faster and ahead of you. :D


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