Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

Stock Predictions

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Stock traders look at many variables to decide on what to do with a stock,
whether they want to buy or sell or wait it out. They look at certain
characteristics of a stock, and trends in the market environment to make an
educated guess on what they should do. This is the way it has been done for
years. Brokers and traders had to do research manually in order to make the
best guess.
Machine learning can now be used to do the same thing, except that
machine learning can do it much faster and more efficiently. In order to be
an effective trader, you must be able to analyze trends in real-time so that
you don’t miss out on opportunities. Machine learning can help traders with
finding similarities between stock to make financial decisions using
statistical data.
Traders can use linear regression models to study data about past trends in
stock market prices, and what variables cause a stock price to go up and
down. They can use these regressions to decide on what to do with a stock.
Often, traders who want to analyze the performance of stock do so by
utilizing what’s called a support vector machine. A support vector machine
is a classification model where data points are separated by a boundary line,
with one category on a side and a different category or another. Traders will
use support vector machines to classify which stocks to buy and which
stocks to sell. Using certain variables that should be indicative of the
performance of a given stock, that stock is placed on the side of the
boundary line that denotes whether the price is likely to go up or go down.
Deep learning is also commonly applied in making stock models. The
hidden layers of a neural network may be useful in identifying unseen

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trends or characteristics of a stock that could cause them to rise or fall in
price.
There is no such thing as a sure bet or a risk-free investment. This was true
when people made decisions, and it’s still true now when we use data
science to make financial predictions. It's important to remember that
investing in the stock market will always be risky. It’s impossible to create a
model that will predict anything reliable about the stock market. It's wild
and unpredictable. But we’ve already learned that machine learning can
find patterns that humans may not be capable of finding on their own.
If you understand that trends in the stock market may be totally random and
unpredictable, then it’s useful to have another model that will help you
estimate a stocks predictability. Knowing how accurate your predictions are
for a given stock, is just as important as the predictions itself. Create a
separate model to measure the predictability of a given stock, so you know
how reliable your predictions are. Different stocks will have varying levels
of predictability. It's important to illustrate that with your model so that you
can choose from the most reliable predictions.
Traders will continue to make the final call on whether or not a stock will
go up or down in value. But data science and machine learning can
streamline the process of analyzing information that will help the decision
process. Which is why you will see more and more examples of machine
learning models used in predicting stock, and why you should at least make
yourself familiar with the idea.