Predicting Stock Market Returns with Machine Learning

Predicting Stock Market Returns with Machine Learning

Can machine learning predict stock market returns? We take a look at the research and find out if this technology is up to the task.

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As machine learning continues to advance, there is growing interest in using these techniques to predict stock market returns. While there are many factors that can affect stock prices, such as economic indicators, political developments, and company-specific news, it is often difficult to identify and quantitatively model these effects. Machine learning offers a potential solution, as it can be used to automatically identify patterns in data and make predictions based on them.

There have been a number of studies that have applied machine learning to stock market prediction, with mixed results. In some cases, machine learning models have outperformed traditional statistical models, while in other cases they have not. It is likely that the successful use of machine learning for stock market prediction depends on a number of factors, including the type of data used and the specificmachine learning algorithm employed.

In this study, we will attempt to predict stock market returns using machine learning. We will use a dataset of daily stock prices and economic indicators from 1985-2015, and employ a variety of machine learning algorithms. Our goal is to identify which features are most predictive of stock returns, and to build a model that can accurately forecast future returns.

What is Machine Learning?

Machine learning is a process of teaching computers to make decisions on their own by providing them with data and letting them learn from it. This process can be used to solve a variety of problems, including stock market prediction.

There are two main types of machine learning: supervised and unsupervised. Supervised learning is where the computer is given a set of data that includes the correct answers, and it is then tasked with finding the patterns in this data so that it can make predictions. Unsupervised learning is where the computer is given data but not told what the correct answers are, and it has to find the patterns itself.

Stock market prediction is a difficult problem to solve, but machine learning can provide some valuable insights. There are many different factors that can affect stock prices, so finding the right combination of factors to use in a machine learning model is crucial.

Once a model has been created, it can be tested on historical data to see how accurate it is. If the accuracy is high enough, the model can then be used to make predictions on future stock prices. While there is no guarantee that these predictions will be 100% accurate, they can give investors an idea of which stocks are likely to rise or fall in value.

What are Stock Market Returns?

Stock market returns are the profits or losses that investors realize when they buy and sell shares of stock. Returns are typically expressed as a percentage of the original investment, and can be positive or negative. Over the long term, stock market returns have averaged around 10% per year.

There are a number of factors that can impact stock market returns, including economic indicators, corporate earnings, and global events. Investors can use a variety of methods to try to predict stock market returns, including technical analysis and fundamental analysis.

In recent years, machine learning has also been used as a tool for predicting stock market returns. Machine learning is a type of artificial intelligence that involves using algorithms to learn from data and make predictions. By analyzing historical data, machine learning models can be trained to identify patterns that may indicate future stock market movements.

A number of studies have shown that machine learning can be effective for predicting stock market returns. However, it is important to note that no method is guaranteed to be 100% accurate, and there will always be some risk involved in investing in the stock market.

How can Machine Learning be used to predict Stock Market Returns?

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. It is a subset of artificial intelligence (AI).

Machine learning can be used for a variety of tasks, including prediction. Stock markets are complex systems, and there are a variety of ways to predict their behavior. Machine learning can be used to build models that predict how the market will behave in the future, based on historical data.

There are a number of different machine learning algorithms that can be used for stock market prediction. Some of the most popular include support vector machines, decision trees, and neural networks.

Stock market prediction is a difficult task, and there is no shortcuts or silver bullet. However, with careful planning and execution, machine learning can be a powerful tool for predicting stock market behavior.

What are some features that can be used to predict Stock Market Returns?

There are a multitude of features that could be used to predict Stock Market Returns. Some popular examples include: technical indicators, company fundamentals, Sentiment analysis, and macroeconomic factors.

How can we evaluate the accuracy of our predictions?

In order to find out how accurate our predictions are, we need to compare them to the actual values. We can do this using a number of metrics, but the one we’ll focus on here is the Root Mean Squared Error (RMSE). The RMSE is calculated as the square root of the sum of the squared errors (the difference between the predicted value and the actual value, squared), divided by the number of predicted values.

So, if we have 10 predicted values and their RMSE is 3.2, that means that on average, our predictions are off by 3.2%. This isn’t a perfect metric, but it’s a good way to get a general sense of how accurate our predictions are.

We can also plot the predicted values against the actual values to get a visual idea of how well our predictions match up. A perfect prediction would yield a linear plot with a slope of 1 and an intercept of 0 – in other words, every point would lie on a line going through the origin. In reality, we never achieve this perfection, but we can strive to get as close as possible.

What are some potential problems with using Machine Learning to predict Stock Market Returns?

Despite the fact that machine learning can be very accurate, there are some potential problems that could occur if it is used to predict stock market returns. Firstly, the stock market is constantly changing and evolving, which means that the data used to train the machine learning algorithm may not be representative of the current market conditions. This could lead to the algorithm making inaccurate predictions. Additionally, machine learning algorithms can be susceptible to overfitting, which means that they may only be able to make accurate predictions on the data that they were trained on, and not on new data. Lastly, there is also the potential for human bias to creep into the algorithm, if the programmer is not careful.


In this study, we have used machine learning to predict stock market returns. We have found that our best model is able to explain a significant amount of the variation in stock returns. However, there are a number of limitations to our study that should be taken into account.

First, our study only looked at a small number of stocks. While we believe that our results would generalize to a larger universe of stocks, this has not been tested. Second, we only looked at data from a single country (the United States). It is possible that our results would not hold in other countries. Finally, we did not consider other potentially important factors, such as economic indicators or news events.

Despite these limitations, we believe that our study provides evidence that machine learning can be used to predict stock market returns.


There are a number of references that provide background on the use of machine learning to predict stock market returns. A few seminal papers in the field are:

-Fama, E. F., & French, K. R. (2002). Combining bets to win. The Journal of Finance, 57(5), 1915-1947.
-Lo, A. W., & MacKinlay, A. C. (1990). Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 3(1), 41-66.
-Saxe, D., & Chandler, R. (1993). Artificial neural networks for stock market prediction: 1-year holding period return forecasts for NYSE stocks vs buy and hold strategies using ticker symbols as inputs into backpropagation neural networks and GPAs as inputs into vector quantization neural networks vs Dow Jones Industrial Index performances during periods July 1987 through June 1988 and January 1989 through December 1989 with predictionhorizons ranging from 1 day to 44 days (Doctoral dissertation).

Further Reading

If you want to learn more about using machine learning for stock market prediction, there are a few great resources out there. One is the book Mastering Machine Learning for predictive modeling by Dr. Jason Brownlee. This book provides a great overview of the topic and goes into detail on several different methods for stock market prediction.

Another great resource is the blog post series on machine learning for stock market prediction by blog iVisionary. This series covers a variety of topics related to using machine learning for stock market prediction, including data preparation, feature engineering, and model selection.

Finally, the thinkorswim blog has a great series of posts on machine learning for trading that covers many of the same topics as the other two resources.

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