How Machine Learning is Solving the Age-Old Problem of Predicting Stock Prices

Machine learning is increasingly being used in the financial world. The goal is to build algorithms that can take information from past stock market data and predict what will happen in the future. Let's see how this technology helps investors make more informed decisions about how they invest their money.

Machine learning is considered the most prominent part of Artificial Intelligence (AI), which consists of computer algorithms that have the ability to improve with data without being explicitly programmed. Machine learning algorithms can be used to detect patterns in historical stock prices and predict future prices.

Let’s now discuss several machine learning techniques for stock market prediction.

Linear Regression: — It is one of the most basic machine learning algorithms, which aim to fit a linear equation so that it best represents a given set of data points. This technique works very well for many applications such as demand forecasting, stock price forecasting, etc.

Linear regression is applied to historical stock prices in order to predict future trends. The time-series data for a chosen stock price is considered and a linear equation between past and future values is found so that the sum of squared deviations between these points is minimized.

Neural Network: — It is an advanced type of learning machine that mimics our brain’s neuron structure. It consists of a large number of interconnected artificial neurons which initially start randomly by following some basic setup rules. After a training phase, it can be used to make predictions depending upon certain conditions. It can be used as a forecasting tool after being trained with relevant data i.e., price & volume history of stock markets, etc.

Neural Network models are generally used to forecast demand for different products or to estimate sales volume.

Support Vector Machine: — It is a supervised machine learning algorithm, whose aim is to classify data into various categories. The algorithm uses an optimal hyperplane for the classification of different observations and doesn’t make any assumptions about the distribution of data.

Support vector machines can be used to predict future stock price movements by classifying them as either go up or go down.

Random Forest: — It is based on an ensemble learning technique, which generates multiple decision trees and bagging them together in order to classify new observations. The advantage of random forest over other methods is its ability for regression as well as classification.

Random forest uses randomness in order to create several trees and then bagging them together, which makes it a very powerful technique for stock price prediction because of its ability to handle outliers.

K-means: — It is an unsupervised machine learning algorithm that partitions data into K number of clusters in order to describe the data. It starts by randomly assigning K number of observations to a set of clusters which is later found using optimization techniques so that the cluster’s center points are as close as possible to their respective cluster members.

K-means clustering can be used on historical stock prices in order to find different groups, each group is characterized by a distinctive trend by using distance metrics such as Euclidean Distance.

Decision tree:- It is a non-parametric supervised machine learning algorithm that constructs a model of decisions, which are taken to classify data. The aim of this technique is to have an understandable structure for the model so that decision-makers can easily explain their reasoning behind different forecasts.

It is very useful in forecasting as it can be understood by decision-makers and for the same reason, it can be used to forecast future stock prices.

Decision trees are very good at doing predictions, but they tend to overfit especially when there is noise in the data i.e., high variance due to lack of historical data or irregularities in stock behavior.

Boosting:- It is an alternative machine learning technique to decision trees, which reduces overfitting in predictive models by creating multiple copies of an existing model and improving it iteratively. Boosting uses voting for each new iteration i.e., the final prediction is determined by majority vote among all models built in the boosting process.

Boosting also performs well as decision trees when there is a lot of noise in the data or low variance because of having large amounts of historical data but it performs better than decision trees because it reduces overfitting effectively with better accuracy.

The stock market is a complex system that has been studied for decades. The use of machine learning has led to many new insights into how it behaves and what we can expect in the future. Machine learning has been widely studied and its algorithms have been modified to suit the needs of financial institutions in order to help them make smarter decisions about the future.

Originally published at https://protonautoml.com on July 13, 2021.