Karl Gorman
Predicting Stock Prices Using Machine Learning

Predicting Stock Prices Using Machine Learning

Machine learning is a field of artificial intelligence that involves training algorithms to recognize patterns and make predictions based on data. In finance, machine learning algorithms have been applied to predict stock prices, with varying levels of success.

Previously, stock traders had to study the trends and historical data to make informed decisions on the best stock investments to invest money in. However, these methods have human limitations and can only go so far because human beings have limited processing power for analyzing data. Machine learning helps to take the burden off the stock traders and does the heavy lifting.

So, how does machine learning for stock prediction work, and what algorithms are the best? Can you trust machine learning predictions 100%? Read on to find answers to these questions.

What is the Stock Market?

The stock market is a marketplace of buyers and sellers who only exchange equity shares for publicly traded companies listed on the stock exchange. Stock traders speculate on the prices of these stocks and place buy or sell orders based on those speculations.

Types of stocks that are traded on the stock market include:

  1. Defensive stocks – for essential goods and services
  2. Value stocks – stocks that are foreseen to appreciate over time
  3. Cyclical stocks -for luxury and discretionary stocks
  4. Growth stocks – stocks that bring capital gains, and losses
  5. Speculative stocks – high-risk untested waters stocks with high returns when it succeeds and high losses when it fails.
  6. Penny stocks – new, fresh, and cheap stocks compared to the more costly giants
  7. Income stocks – more stable in terms of volatility but provides high reward

To make accurate or near-accurate predictions, stock traders use trading analysis, such as fundamental analysis and technical analysis, to predict price trends based on historical data. If all goes well, the trader makes profits as predicted. If it doesn’t, they may be left holding the ball of losses.

How Stock Prediction With Machine Learning Works

In machine learning, algorithms learn about the history of price changes from data pools and use these trends to predict future prices.  This is valuable for stock trading, and we’ll show you how.

As mentioned earlier, stock buyers usually study short and long-term data of a single stock or the entire industry it belongs to before they can make a buy or sell move. This process requires intense and long-term brain work. But machine learning models can perform the analysis, draw conclusions, and present results on what the best investment decision for you is.

One way in which machine learning can be used to predict stock prices is through the analysis of historical data. By training a machine learning algorithm on a dataset of historical stock prices and other relevant data, such as economic indicators and company performance, it is possible to predict future stock prices with a certain degree of accuracy. This approach can be beneficial for identifying trends and patterns in the stock market that may not be immediately apparent to human analysts.

Another approach is to use machine learning to analyze real-time data and make predictions based on current market conditions. This can be done by feeding the real-time algorithm data on stock prices, trading volumes, and other relevant factors and using this data to make predictions about the direction of the market

It’s important to remember that the more information the ML statistical model and algorithm have, the higher its accuracy increases. For stock price prediction, it’ll need to consider a wide range of information that could shift the price trajectory or trend, including:

  1. Historical price movement
  2. Industry-wide and company news
  3. Global news, laws, and occurrences
  4. Social media chatter, etc.

Using machine learning with Individual stock trading vs. stock portfolio management

Machine learning works in two significant ways for stock market forecasting. They’re used in stock trading and portfolio management for stock traders and investors. Let’s see how it’s used in both situations.

Stock trading

Machine learning coupled with data mining can be used to create a stock trading software application that can predict price movements, price volatility, and associated risks of a particular stock to recommend the most beneficial stocks for trading. The algorithm by analyzing investor sentiments, company earnings, and global financial trends.

Stock portfolio management

While machine learning can be used to forecast price movements for individual stocks, it’s also helpful in putting together a cocktail of successful stocks for a profitable stock trading portfolio. This is done with machine learning and AI-powered tools and platforms that can absorb big data to help investors make better wealth management decisions.

Predicting Stock Prices Using Machine Learning

What Are Machine Learning Algorithms For Stock Price Prediction?

There are two broad types of machine learning algorithms for stock predictions that have been tested to various degrees by researchers. They are deep learning and traditional machine learning.

Deep learning

Deep learning is designed to mimic the brain’s structure with artificial neurons organized into input and output layers as the first and last nodes. The interconnected nodes in between are called hidden layers. These can either be a few hidden layers or a massive, complex, sprawling structure of layers that are constantly exchanging data.

Examples of deep learning algorithms include the following:

  1. Long short-term memory: Long short-term memory deep learning is another type of recurrent neural network that can analyze non-linear time series data and predict highly volatile stocks. It can do this because it utilizes complex data sequences and single data points.
  2. Recurrent neural networks: Taking its name from the movement of data within its network, the recurrent neural network is a type of artificial neural network that utilizes its processing nodes as memory cells that store essential information and send it back for refinement in previous layers.
  3. Graph neural networks: It is better suited for financial analysts who want to see the relationship between data points visualized at a glance. Graph neural network processes the data and converts them into graphs. However, the data conversion process may result in lower accuracy.

Traditional machine learning

Traditional machine learning is those that don’t fall under deep learning. Traditional algorithms have a higher chance of accuracy over deep learning algorithms, and when combined to form a hybrid machine learning algorithm that utilizes the power of the combined algorithms.

In practice, traditional hybrid algorithms even work better because one algorithm may be better at handling sentimental analysis and data while others work best on historical data and technical analysis. Some examples of traditional machine learning include the following:

  1. Random forest: Random forest algorithm is the go-to algorithm for handling larger datasets during regression analysis for stock price prediction. Its capabilities in handling extensive data for the long-term prediction can ensure higher accuracy.
  2. Naive Bayesian classifier: If you want to work with one event and smaller datasets, try the Naive Bayesian classifier. It’s simple, quick, and efficient for working with small data banks.
  3. Support vector machine: This supervised-learning algorithm handles large datasets but doesn’t pair well for dynamic and complex situations and can’t provide accurate results.
  4. K-nearest neighbor: Uses nearest-to-meaning similar situations or data points referred to as neighbors to predict prices. Most people shy away from it because the distance-based technique consumes time.
  5. ARIMA: Works best for short-term predictions and linear data sets for a time series analysis. If you’re looking for an algorithm to handle non-linear data for long-term prediction, try something else.

Elements of Machine Learning Implementation in Stock Prediction

When using machine learning to predict stock prices, there are several elements you must consider before and during the analysis. They include:

Consider the types of data for technical and fundamental analysis

There are two significant categories of stock price research and analysis in stock prediction. They’re the technical analysis and fundamental analysis.

Technical analysis focuses only on using the stock price and price trends over time to seek patterns, form conclusions concerning the data, and form predictions about future prices. Examples of methods and models under the technical analysis include symmetrical triangles, cup-and-shoulders, etc. With this method, the stockbroker would take a look at the price charts of the stock so far, spot the patterns, and use the information to predict the prices.

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On the other hand, the fundamental analysis doesn’t use technical analysis to try and form the basis of its predictions; instead, it uses factors such as investor feelings, corporate metrics, market parameters, etc. Fundamental analysis doesn’t so much as predict the price using the charts; instead, it uses the human angle of stock trading.

For machine learning, however, it’s always best to have a machine learning stock prediction model that can combine both methods. After all, if one works great alone, combining both in a union would yield more excellent results than if both were used separately. It should combine the corporate and social data with the price charts and trading volumes.

Selecting data sources

In machine learning, many people focus on the algorithms or models when the most crucial element of machine learning and training is the quality of data. The volume and quality of data you have would inform whether or not your ML model or system delivers accurate or ner accurate price predictions.
Accuracy would require reliable, high-quality data before the results can come out well. Fortunately, there are various reliable sources of financial and stock trading data to source from. These include Reuters, NASDAQ, Bloomberg, Financial Times, etc.

Before running your algorithm, ensure you have cross-checked the accuracy and reliability of your data and the data source. Any deviation from the truth can mean far-fetched results that you may not be able to use.

Inclusion of sentimental analysis

We’ve already talked about fundamental and technical analysis as methods for analyzing the actors that affect stock prices. What some stock traders don’t pay attention to is the sentimental nature of stock trading for millions of people. Sentimental trading and analysis refer to traders making decisions based on social media posts, financial articles, and opinion pieces.

These decisions are not based on real analysis but instead on what these media say. For example, if a stock trading influencer on Twitter with hundreds of thousands of followers tweets about a specific stock, what they say has the power to sway those thousands of people to make a decision if they have built a following that has confidence in their words.

When this happens, the ripple effect can sway the general mood toad that specific stock. What most ML enthusiasts are canvassing for is that ML algorithms begin to consider sentimental indicators when making analyses.

Your ML models can include information from social media posts and top financial newspapers and logs to gauge the temperature and general sentiment towards a particular stock or group of stocks.

Complications in the training and modeling phase

Machine learning stock prediction doesn’t end at feeding the system with historical data. For the algorithm to provide accurate outcomes, you’ll need to train and model the system to produce what you need. During this process, there are a number of complications and issues you might encounter.
One of the problems you might encounter during this phase is overfitting. Overfitting occurs when the machine learning algorithm has spent too much time analyzing a particular data set that it can no longer accommodate a new sample. To solve this problem even before it occurs, you should consider categorizing the data sets into different phases and types.

Another issue is that although machine learning models can analyze complex and large data sets, massive data pools take longer to process than smaller ones. A solution would be to categorize the data according to features and select only the most relatable and essential categories to move on with.

Should you Use Machine Learning for Stock Prediction?

There’s no blanket black-and-white answer to this question. Given the information in this article, it’s important that you understand the risks associated with using machine learning to predict stock prices and trade stocks.

First of all, stock trading is an increasingly volatile venture that’s dependent on the whims of those within the market. Except you’re engaged in insider trading, which is illegal, it’s impossible to tell 100% if a stock price will go up or down in a matter of hours.

Secondly, machine learning is still a recent technology and isn’t widely accepted yet. There’s still a dearth of research to be done to determine if this is something people should invest in.

However, risk appetite is a crucial factor in stock trading. Hence, while stock trading with machine learning is risky, you might also have an increased risk appetite to match that level of financial danger. You should only trade what you’re willing to lose and avoid investing money that isn’t yours or that has been earmarked for other important projects.


While machine learning algorithms can be effective at predicting stock prices, it is important to note that the stock market is complex and subject to a wide range of factors that can influence its direction. As a result, the predictions made by machine learning algorithms should be treated with caution and used as one of many tools for making investment decisions.

In conclusion, machine learning has the potential to revolutionize the way in which stock prices are predicted, providing investors with valuable insights and enabling them to make more informed decisions. However, it is vital to understand the limitations of these algorithms and to use them in conjunction with other tools and techniques.

Frequently Asked Questions (FAQs)

What is the definition of the stock market?

The stock market is a physical or virtual place where equity shares of a public company are exchanged between buyers and sellers. Public companies issue parts of their company ownership to the general public in exchange for investment funds which entitle the shareowners to a share of their profits and losses.

What is machine learning?

Machine learning is the development of computer systems and models that utilize statistical models and algorithms to learn and draw inferences from data patterns. Reinforcement, supervised and unsupervised learning are the three main types of machine learning.

What are the best machine learning algorithms for stock price predictions?

The best machine learning algorithm for predicting stock prices are random forest, Naive Bayesian classifier, Support vector machine, K-nearest neighbor, and ARIMA.

How can machine learning techniques predict the stock market?

A machine learning model can use historical records of the company’s stocks, usually over a very long time, and analyze the data, extracting significant markers and trends that triggered the stock price movements. If the model does this job effectively, it can use the information to predict stock prices.

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