How Machine learning can help in Stock Market Prediction

Machine learning is one of technology’s most valuable blessings to the world. The field has created a lot of scope in several existing areas and paved new paths for people to follow and make their lives more convenient and work faster, smarter and better.

Stock Market is one such facet where the existing trading system can be even more modernized and modified using machine learning techniques. As a result, investors can make more intelligent decisions about investing their money in the stock market with the help of Machine Learning. 

In fact, machine learning or ML can be utilized to play a substantial role in stock trading. It can be used to predict probable market fluctuations, study clients’ behaviour, analyse stock price dynamics, and do even more.

This article explores how Machine Learning can help predict the price of stocks, along with the challenges of using machine learning to do so and the techniques used for stock prediction through machine learning.

Why Should Machine Learning be used for the prediction of Stock Prices?

Machine learning is a competent branch of artificial intelligence used to analyse complicated data sets, identify patterns or discover hidden relationships between various data sets, create forecasts, and become even more precise in performing these tasks.

Therefore, such skills make ML-based tools an ideal choice for finance-related analysis. For example, a trading company can develop ML-based software to anticipate the dynamics of drifts or drops in the value of stocks. 

How can an investor use an ML-based tool to make better decisions while buying stocks? 

  • An investor can use an ML-powered solution that will help them analyse different publications or records related to a specific company and then research the company’s financial history and the behaviour of previous investors in the company. 
  • After doing the research, the ML-powered tool will generate an extensive report on the organisation’s past and prestige economic trends and provide some insightful suggestions based on data and figures. Ultimately, this information will help an investor to make more mindful investment decisions.

What are some top companies that use Machine Learning to predict Stock Prices?

Today’s leading investment companies are applying machine-learning-based algorithms for many daily stock trading activities. Below are some of the most popular examples:


Two Sigma is an investment company based in New York that wields Artificial Intelligence and Machine Learning technologies in most of the company’s investment strategies, such as high-frequency trading or HFT.

This ML-based approach to trading incorporates executing qualitative and quantitative data analysis for numerous markets, enabling the company to make more efficient and profitable trades faster than its competitors in the market. 


Rebellion Research has offered its clients Artificial Intelligence-based investment strategies and solutions since 2007. One such solution is Global Equity, which involves wielding algorithms derived from Machine Learning techniques to adapt according to constantly fluctuating market conditions. 


Bridgewater Associates is an America-based company that manages its client’s assets. They have been utilising various Artificial Intelligence elements to make predictions about the market and increasing the productivity of traders by helping to improve their investment decisions for the past several years.

The company launched a new algorithm based on Artificial Intelligence known as “I Know First” that can be used to evaluate existing market events regularly and create projections for more than 7,000 corporate assets.

What Are the Primary Challenges of applying ML to predict Stock Prices?

Some of the main challenges are listed below: 


The algorithm generated by Machine learning becomes more proficient and accurate over time through practice. Simply, a software tool powered by Machine Learning means you might need to evaluate substantial data first and spend nearly weeks before generating consequential and relevant results.

So, in the beginning, the results might not be so accurate, but over time the tool will gain momentum and start producing more accurate results.


Given that a solution or system based on Machine Learning assesses historical data, it is programmed in a way that makes it consider only existing components along with any precedents that have already transpired in the past. Therefore, even a Machine Learning tool might not be capable of predicting black swan events, such as natural disasters or pandemics which impact the stock market in a major and often drastic way.

Further, the performance of a financial investment or asset in the past can never completely guarantee the same results in the future or even if they will be successful. Various external factors, such as the overall environment of the economy or even its hype on social media platforms, can influence its price. 


While the effect and advantages of using Machine Learning based tools for stock price prediction are massive, the fact that factors such as the expense of developing as well as setting up a machine learning-powered solution are expensive and resource-intensive can’t be ignored.

Moreover, since algorithms generated by machine learning constantly process vast amounts of financial information or data from the past and present, a company using it might be required to administer huge amounts of computing capacity to emanate meaningful results.

How can Machine Learning be Implemented Into Stock Pricing Predictions?

Below are some tips and steps that will help ensure ML’s successful implementation. 


Despite the remarkable data analysing capacity of ML-based tools, the technology isn’t magical and can’t be used to solve all the financial problems of traders. But there’s one way through which the viability of using ML from a business standpoint can be ensured. It can be done by formulating the requirements and objectives precisely, along with analysing the company’s existing resources, before initiating a project.

Corporate chiefs can start the evaluation by examining several factors and having a thorough discussion with the head of the various departments in their company, including IT directors, head data scientists, and CTOs. These meetings and discussions can help the decision-makers achieve a basic awareness and understanding of the requirements and ideals of the undertaking among the main or head employees.

According to the results obtained from the discussions, decision-makers can infer whether the company can start the project and, if they can, what is the best approach to develop an effective ML solution.


Traders or Investors have many options for selecting a specific machine-learning solution or algorithm. Along with this, every viable algorithm has certain pros and cons, so they need to choose one wisely, keeping their company’s distinctive business goals in mind.

Therefore, conventional ML models like the support vector machine, random forest, and ARIMA might be more suitable if a trader intends for a quicker setup or has a specified computing capacity. On the other hand, in an intense learning method comprising models such as long and short-term memory tools or algorithms, or neural graph networks, it might be better for the company to use advanced analytics that operate without much human involvement.


Developing as well as implementing an ML-powered tool or solution is an extremely demanding task, particularly when there is a requirement to implement deep learning models. This is why it is a suitable alternative if traders consult a third-party expert on Machine Learning before starting their project.

Furthermore, if a company has trouble implementing the project independently, it can contemplate assigning the whole development process to ML specialists. Then, based on the organisation’s needs, these consultants can handle the project’s planning, modification or change of management, data mapping, coding, and setting up of the ML models.

What are the machine learning techniques for stock prediction? 

Here are some techniques used for stock prediction.

  • Random Forest: An algorithm developed to achieve highly precise or accurate results with huge data sets. It is generally wielded in predicting stock prices for regression analysis, which involves identifying relationships among numerous variables.
  • Naive Bayesian Classifier: This algorithm is a simple yet productive alternative for analysing comparatively smaller or lesser amounts of financial datasets that help to determine the probability of one event influencing another.
  • Support Vector Machine: It is an algorithm that utilises the administered learning method, which is instructed by procuring tangible instances of inputs and outputs. It is an extremely effective and accurate way of handling large data sets but might have some difficulty with complicated and dynamic strategies.
  • ARIMA: It is a time series method that is excellent at predicting short-term fluctuations in stock prices based on historical trends like seasonality but might not perform so well with non-linear sets of data and make meticulous long-term predictions of stock prices.


Prediction of stock prices is one of the most researched topics in today’s world and is a topic of interest in both academic and business domains.

Since the introduction of artificial intelligence, various algorithms have been used to predict the activity and trends of the stock market. The integrated use of machine learning and statistics has been formulated to make the process more accurate and convenient. As a result, it is a great and effective way of stock price prediction.


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