How Machine Learning Helps Predict Stock Prices – Built In

Machine learning (ML) is playing an increasingly significant role in stock trading. Predicting market fluctuations, studying consumer behavior, and analyzing stock price dynamics are examples of how investment companies can use machine learning for stock trading.
This article examines the use of machine learning for stock price prediction and explains how ML enables more intelligent investment decisions. Here, I cover the main challenges of ML adoption and argue that starting with an ML-based software project is a good strategy.
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Machine learning is a branch of artificial intelligence that analyzes complex sets of historical data, discovers hidden relationships between data sets, makes forecasts, and learns along the way to become even more accurate. Such capabilities make ML-based tools well-suited for financial analysis. In particular, a trading company may develop and use an ML-based software solution to predict the dynamics of rises or falls in stock prices. 
How can an ML-based tool help an investor considering buying a stock? An ML-powered solution may analyze publications related to a particular company and research its financial history, including past investors behavior. Then it may generate a comprehensive report on the organizations economic trends and provide some data-based recommendations. Eventually, this information enables an investor to make more informed investment decisions.
Multiple investment companies are successfully applying machine-learning algorithms for day-to-day stock trading activities. Here are three examples from the field.
This New York City-based investment company uses AI and ML technologies in its strategies, which includes high-frequency trading (HFT). This approach to trading involves performing quantitative and qualitative data analysis for multiple markets, which helps a company make profitable trades faster than its competitors.
Since 2007, Rebellion Research has been offering its clients AI-powered investment strategies. One of these, called Global Equity, involves using ML algorithms to adapt to constantly changing market conditions. 
Bridgewater Associates, an American asset management company, has been using various forms of AI to make market predictions and improve traders’ productivity for several years. In 2022, the company launched its new AI-based algorithm called “I Know First” to analyze current market events daily and generate forecasts for more than 7,000 corporate assets.
Now that we’ve covered the basics, let’s move on to the challenges of ML implementation.
Machine learning algorithms become more competent and accurate over time. This means that an ML-powered software tool may need to analyze vast amounts of data and spend weeks before it can generate relevant and meaningful results.
Given that an ML-based system analyzes historical data, it can only consider existing factors and any precedents that have already occurred. Thus, ML may not be able to predict black swan events like pandemics and natural disasters. Moreover, the past performance of a financial asset never guarantees its future results, as many external factors like the broader economic environment or even social media hype can affect its price. 
Developing and setting up a machine learning solution is costly and resource-intensive. Further, since machine learning algorithms continuously process large amounts of data, a company may need to allocate large amounts of computing power to derive meaningful insights.
Here are some tips to help ensure the success of ML implementation.
Despite ML’s impressive data analysis capabilities, the technology isn’t magic and can’t solve all traders’ problems. One way to ensure the viability of ML from a business perspective is to formulate precise requirements and goals, analyze the company’s existing resources, and only then initiate a project.
Corporate decision-makers may start by discussing several questions with their company’s department heads, including CTOs, IT directors, and chief data scientists. These discussions can help achieve at least a basic understanding of the goals and requirements of the project among the key company employees.
Depending on the results of the discussions, decision-makers can determine whether they should proceed with the project and how it is best to approach ML development. If the project goals, requirements, and ML viability still need to be clarified, it may be worth consulting with third-party machine learning experts.
Traders have a wide range of options when choosing particular machine learning algorithms. In addition, each of these algorithms has its specific pros and cons, so choose wisely in consideration of the company’s unique business goals.
Thus, traditional ML models such as random forest, support vector machine, and ARIMA may be more relevant if a trader aims for a faster setup or has limited computing powers. In turn, a deep learning technique, including such models as long short-term memory algorithms or graph neural networks, may be better if an organization requires advanced analytics operating without human involvement.
Developing and implementing an ML-based solution is highly demanding, especially when discussing the implementation of deep learning models. So, traders may decide to consult with third-party ML experts before starting their projects.
Also, if a company cannot implement the project itself, it may consider entrusting the development to ML consultants. Depending on the organization’s needs, experts can take up project planning, change management, data mapping, coding and setting up ML models.
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Increasingly more trading companies build machine learning software tools to perform stock market analysis. In particular, traders utilize ML capabilities to predict stock prices, improving the quality of investment decisions and reducing financial risks.
Despite the benefits of ML for predicting stock prices, implementing machine learning technologies is challenging. Clear business goals and requirements, suitable algorithms and ML models, and the involvement of third-party ML experts boost the chances of the project’s success.
Built In’s expert contributor network publishes thoughtful, solutions-oriented stories written by innovative tech professionals. It is the tech industry’s definitive destination for sharing compelling, first-person accounts of problem-solving on the road to innovation.


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