My guess is that RNNs appear to have lower absolute error for extreme values than AR/-MA models at 13:27 because financial data, like daily stock returns, aren't normally distributed, but rather they follow heavy-tail distributions, like Cauchy. Since NNs are universal approximators, they can be used to capture the parameters of any distribution, whereas using AR/-MA models implies that the data are generated by a stationary gaussian process; thus they miss out on the extreme values of the true distribution.

the vast majority of content available on youtube is related to the application of ML models to price prediction when those of us who are dedicated to this know perfectly well that the area where it is most successfully applied is in portfolio rebalancing and the classification of market regimes. It would be great to make a comparison of different models such as RNN, GMM, HMM, RF, DBSCAN and KNN + Lorentzian distance, to see which of them is able to better classify market regimes using as features the first n PCA extracted from inputs such as volatility, LR slopes, rsi, stochastic, etc.

All the predictions are positives. Both models have simply learned that stocks tend to go up. This is a common results when you train your model on stocks listed in the SP500, which introduces survivorship bias (you are implicitly introduction lookahead bias, because those are stock that are still listed and ignored those which disappeared).

Videos related to trading are quite informative and really helpful. Would like to see more of such content.

Best video on these two models I've ever seen. Simple, clear, concise, with calculus and theory

I would've liked to see the base line you usually use. The return that you would've made if you simply just invested X$ every day over the course of the experiment. Overall great video though, love your work

Only recently discovered your channel and enjoy following your work. You show the distribution of predicted returns for the three models. I would have liked seeing another plot showing the actual realized returns. I expect those realized returns would look very much like the AR returns - highly leptokurtic. It appears to me that the RNN models are hugely overestimating the market volatility. To me, it seems an effective predictive model would have to match the distributional properties of the returns for one to have any confidence in its application to individual stocks. A possibly interesting experiment would be to generate some artificial stock data with an AR process and see how well those dynamics are captured by the RNN model. Keep up the good work.

This is highly informative. More of such videos!

Nice video, love your content! I liked how you aligned the x axis on the video, but I think you forgot to align the y-axis on density.

Four things: 1. Would a weighted average of the two models have the same outcome? It looks like GEHC is the only overlap. 2. Would stacking the two models improve the result? 3. Would adding another year or two of training data improve either model? That would probably dampen the response, but could help pick up seasonality. 4. It would be interesting to run a naive regressor (like next value = previous value) on the data to see if there's a general population trend.

Great video idea!. BUT I'd love to have a comparison with the index (How much would a direct investment in the S&P have made) and maybe some metrics for preformance (sharpe, sortino, ...). Keep up the good work.

Wow, first video i have seen from you and i really liked the way you explained the concepts in a easy to understand yet detailed way!

There was no mention of the vanishing/exploding gradient in your RNN model. That is, because there was a difference in prediction results between the AR model and the RNN model, it would have helped to see how close or far the largest eigenvalue is in the activation function is from 1. Intuitively, it would have helped to isolate the two models to one stock's time series to see if the RNN model has either an exploding or vanishing gradient. Specifically, the AR model can be iterated in discrete steps to model the time series. This, then, should be able to be replicated by the RNN model. If the discrete steps veer towards a sink or an attractor, that is, towards infinity or zero, then the problem is inherent with the RNN model and not with your data.

Great video! I'm going to college next year for data science + machine learning and your whole channel so informative!

Would love a video on the actual maths behind estimating the AR model parameters (e.g. Yule-Walker etc.). The model form is easy to grasp but how it actually works - not so much

If you want to make these much more realistic, get the returns per period (instead of one single avg) and calculate some measure of risk for them, especially maximum drawdown. From my data analysis, my models may not improve my returns over buy and hold strategy that much but they do decrease the max drawdown significantly.

So what I would love to see is your thought process behind the the setup. How did you choose the particular AR(IMA) model that you ran against the RNN models. Why did you choose a regularised RNN model? While I'm not interested in stocks, I do find time series stuff really interesting, so thanks for the entire playlist you've been working on. I found that it really supplemented the course content that I was working with.

Hey, it's just marvellous...please upload more! If possible can you show the above concept by doing it on Python etc It will be really wonderful. Thanks a lot, I really learnt a lot from your videos they are amazingg 💯

Can you please make a video on prediction for cryptos??

Great video, very interesting and informative! I was wondering if you would mind sharing the code by chance? I want to try something similar but need some inspiration on the coding front.

Very nice, i wonder how well this performs in a falling market though. Since from march 2023 - march 2024 the sp500 increased quite a bit! (And more than .4%!)

How long did you run the system on real money for? Many models are positive at the start becuse the training data resembles the current market - then as soon as dynamics or regime changes they fall off a cliff. So I would be skeptical to conclude anything from the fact it was briefly positive - but good vid. Where did you get those models from btw? (the equations) Also regarding overfit, have you heard of k fold cross validation? Its super simple - you can do basic cross validation in 15 minute in py.

Excellent video! Does the equation on the right apply to LSTMs as well?

What was the time horizon used to forecast? As someone else already mentioned, I think it would also be nice if you could explore using tree based methods (XGB, LGBM, RF) for time series forecasting and compare te results to these kinds of methdods (AR, RNN).

The models traded for a single day. I suppose that's not a good way to evaluate their capabilities, right?

Seems that AR predicts pretty well stocks with low-volatility. Then the question is why to even predict, if a basic quant analysis can do it ?

Very nice! Im not at all adequately equipped to critique or question the RNN model strengths and weaknesses so I’ll just pose this as a question instead. Wouldn’t another weakness be that it inputs previous predictions? Meaning if previous predictions were poor, this will negatively impact future predictions?

Love it!

Well done.

I'm curious what kind of training/validation loss you were training to? I'm a physics major that has been playing around with different model architectures for my own stock prediction model. Mine looks at 120 day time steps and trys to predict the next two weeks of closing prices. Ive seen some good results in training my transformer model, I've also found that the more training data I've added the better my model performs. Takes about 2hrs per epoch on my laptop though 😵‍💫

What are the features that you considered? Is it only the stock price itself.?

Would it be possible to share the code?

could you possibly do this again for a VAR model or ARIMA model if you haven't already? I would be interested in seeing a comparison to some more model analysis

Is there a mistake in the way you wrote the AR model sum at the start, as the derivative w.r.t x_i wouldn't quite be \beta_i ?

Great video - I've been using LSTMs for over 2 years to try and create a robust model capable of predicting movements on the market, trained on the SP500 stocks, not dissimilar to your video! Would be interested to see your setup for pre-processing, feature enhancement, model architecture and lookback parameters. Also subscribed!

at 12:00 you said you used regularization but sound like you gave description of normalization, can you clarify? thanks.

13:14 Aren't you conditioning on the outcome variable here? The actual return the next day is unknown at the time of prediction. I think it would be more useful to condition on whether the *predicted* return was >1% to know ex-ante as an investor whether the prediction from AR or RNN is more reliable. Informative video though, thank you!

.4 is 40%, .04 is 4%, 4% of $1000 is $40. --To only make $4, the result would be .004 not .4…. Did this do better than you thought or was there a mistake in the graphic for the video? Because the result of .4 means the you made $400.

Also can you share the code you used to make these models?

great vid :)

I wonder the performance of RNN in forecast weekly return

What would the error look like if you only used the picks where both models concur? I wonder if that would help or hurt your returns in the end 🤔

The real take-away is that in the US, $4 buys you only half a cup of coffee.

Is there any way to message you directly?

could you please do a video on bayesian structural time series!!

Why not marry both and use the deepAR architecture? :)

Github code share? 😊 please

Have you tried transformers?

Can you tell us which tech stack you used for the automatic trading ?

Gotta pay taxes on that $4 though 😅😅

Or maybe, y’know… everything makes money when the market goes up 🤷🏻‍♂️