Mistakes in Stock Prediction: Trying to Predict the Price

November 15, 2021

Hello friends! Curious about how to properly predict stock prices?

 

I’ve now released the third video in this YouTube mini-series.

For those unfamiliar: this is a video series that debunks common mistakes found in nearly all blog articles / Github repos claiming to do “stock price predictions with LSTMs”.

These are typically written by non-experts in the field just looking for clicks, and I have a lot of fun breaking down precisely what they’re doing wrong.

Why is this important?

Beginners are often fooled by such content, wasting money on courses to learn things that don’t work. Even worse, they may end up putting such examples on their own Github accounts or in their portfolios / resumes, worsening their chances of getting a job in the field.

To be clear: the bad part isn’t that they learned something that doesn’t work (although that is pretty bad by itself). The bad part is, they don’t even understand why it doesn’t work. They are confident that it does and will fight me over it! (That is, until I ask them to verify or rebut any of the claims I’ve made).

Thus, not only does this stuff not work (due to all the mistakes I outline in my video series), but it could actually be detrimental to getting a job and working in this area. These are not just harmless mistakes!

 

The first video discussed why min-max scaling over the train set doesn’t work.

The second video discussed why using prices as inputs (i.e. lagged prices to build an autoregressive model) doesn’t work.

This third video (this video) will discuss why using prices as targets does not work.

Enjoy!

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Why you shouldn’t use prices as inputs to predict stock prices in machine learning (YouTube Episode 20)

October 12, 2021

Ever come across a machine learning / data science blog demonstrating how to predict stock prices using an autoregressive model, with past stock prices as input?

It’s been awhile, but I am finally continuing this YouTube mini-series I started awhile back, which goes over common mistakes in popular blogs on predicting stock prices with machine learning. This is the 2nd installment.

It is about why you shouldn’t use prices as inputs.

 

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NEW COURSE: Time Series Analysis, Forecasting, and Machine Learning in Python

June 16, 2021

Time Series Analysis, Forecasting, and Machine Learning in Python

VIP Promotion

The complete Time Series Analysis course has arrived

Hello friends!

2 years ago, I asked the students in my Tensorflow 2.0 course if they’d be interested in a course on time series. The answer was a resounding YES.

Don’t want to read the rest of this little spiel? Just get the coupon:

https://www.udemy.com/course/time-series-analysis/?couponCode=TIMEVIP

(Updated: Expires Dec 18, 2021) https://www.udemy.com/course/time-series-analysis/?couponCode=TIMEVIP6

(note: this VIP coupon expires in 30 days!)

Time series analysis is becoming an increasingly important analytical tool.

  • With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.
  • COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.
  • Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.

Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.

We will cover techniques such as:

  • ETS and Exponential Smoothing
  • Holt’s Linear Trend Model
  • Holt-Winters Model
  • ARIMA, SARIMA, SARIMAX, and Auto ARIMA
  • ACF and PACF
  • Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)
  • Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)
  • Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)
  • GRUs and LSTMs for Time Series Forecasting

We will cover applications such as:

  • Time series forecasting of sales data
  • Time series forecasting of stock prices and stock returns
  • Time series classification of smartphone data to predict user behavior

The VIP version of the course (obtained by purchasing the course NOW during the VIP period) will cover even more exciting topics, such as:

  • AWS Forecast (Amazon’s state-of-the-art low-code forecasting API)
  • GARCH (financial volatility modeling)
  • FB Prophet (Facebook’s time series library)
  • And MORE (it’s a secret!)

As always, please note that the VIP period may not last forever, and if / when the course becomes “non-VIP”, the VIP contents will be removed. If you purchased the VIP version, you will retain permanent access to the VIP content via my website, simply by letting me know via email you’d like access (you only need to email if I announce the VIP period is ending).

Small note:

I wanted to get this course into your hands early. Some sections are still in the editing stages, particularly:

  • Convolutional Neural Networks (done, but more to be added later)
  • Recurrent Neural Networks (done, but more to be added later)
  • Vector Autoregression
  • (VIP) GARCH
  • (VIP) FB Prophet
  • +MORE VIP CONTENT (it’s a surprise!)

UPDATE: The crossed-out items have since been added. There is no timeline for the remaining “surprise” lectures – it’ll be a surprise! 😉

So what are you waiting for? Get the VIP version of Time Series Analysis NOW:

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Data Science Interview Questions: Random Walk Hypothesis and Stock Price Prediction

December 10, 2019

Welcome to another episode of Data Science Interview Questions! In this episode, I discuss the Random Walk Hypothesis and Stock Price Prediction.

Why is stock price data often considered to be a random walk?

If your data is best modeled as a random walk, how can you do a time series forecast into the future?

How can you draw a confidence interval around the forecast?

What does this mean for stock price predictions?

Find out here:

rwhpreview

What you will learn:

  • How to make the best forecast possible if your data is from a random walk model
  • How to find the confidence bounds for your forecast (also called confidence limits or prediction intervals)
  • Why pretty much all the “data science” instructors out there are really just marketers who have been selling you lies for years
  • Hint: No, LSTMs will not help you predict stock prices and in fact perform worse than the simple model described above
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