Stock market data analytics explained
In the financial sector, data analytics are crucial for gaining a better understanding of the rhythms of the stock market. With good data science, you can build an analysis that allows traders to make informed decisions on whether to buy, sell, or hold a particular security, or determine the right makeup of a trading portfolio to meet specific financial goals or objectives based on a set period of time.
In this article, we'll take a look at ways that data analytics can be used to inform stock trading, and the science behind it.
How do data analytics work in the stock market?
Data analytics refers to the process of analyzing vast quantities of data to identify commonalities, insights, and trends.
Data analytics can be used in all types of industries, including healthcare, politics, retail, banking, and government organizations. In any industry where you have the ability to gather historic data trends and analyze performance compared to current-day data, you’ll be able to get better insights that support your decision-making process.
In healthcare, for example, data science can be used to analyze millions of mammogram scan images, with associated data showing whether or not a malignancy was found. That data can be used to build algorithms that can help doctors assess the likelihood of an abnormality based on commonalities in the screening images – helping to automate much of the work of assessing screenings, and to instantly identify scans that require further assessment from a doctor.
Similarly, in the stock market, data analytics aren’t used to replace human traders or investment advisors – instead, they can be used to provide additional insights and context on likely trends in stock performance, based on a variety of factors and past performance. Traders can use these insights to decide when to buy, sell, or hold a stock, and to determine the best allocation for their portfolios.
While data science in the past relied on algorithms that didn't change, much of today's data science relies on machine learning, particularly using neural networks. With machine learning, you can develop models and supply training data to create an algorithm for solving problems and generating insights – but with machine learning, your algorithm actually continues to learn based on the ongoing data inputs, so that it can optimize results based on new findings over time. That provides a powerful tool for generating market insights based on constantly changing conditions.
How to use data science in finance
There are a number of ways to analyze stock market performance with data science, as well as a variety of ways to use that data – whether your goal is to profit quickly, to protect existing assets as much as possible, or to identify potential fraudulent behavior or other warning signs within a company.
Here's a look at some common use cases.
Get stock market insights in real time
Using streaming data feeds, traders can combine real-time market data with historical data to help them get to-the-second analyses on stock performance. This kind of immediate access to insights is ideal for seasoned day traders, who may only hold a stock for a period of hours. However, even for those planning on longer hold times, integrating the most current possible information will help you make better decisions on which opportunities are most valuable based on your performance criteria.
Use algorithmic trading
Most online platforms now allow investors to put in buy/sell orders which will be automated through a computer program. While the request can be as simple as "buy when price goes below $60 per stock," other requests can be more complex, such as "sell when the 50-day moving average goes below the 200-day moving average." This enables traders to automatically make use of data analytics assessments to understand trends in pricing over time, and enables them to follow their trading strategies without needing to monitor price changes on a day to day basis.
Algorithmic trades can also be used to automatically rebalance investment portfolios: For instance, if a certain portfolio has an 80/20 stock-to-bond target allocation, the computer program will automatically analyze the portfolio allocation and sell stocks to purchase additional bonds if the stock value goes higher than the 80% allocation.
Provide better strategy for your customers
For financial institutions, data science can be used to get better insights into your customer base. By analyzing all of your customer demographics and behaviors, you'll be able to build segmented customer groups that help you understand how much each customer is likely to invest with your organization or remove from their investments each year.
These customer profiles can be used to develop personalized marketing and investment management strategies for each consumer, so that your organization can engage customers to increase their investments with personalized portfolio advice that gives them detailed insights on how much they can expect to earn on their investments. For example, Personal Capital uses predictive data analytics to assess the expected growth of each customer's portfolio over time based on the amount they expect to contribute, so that customers can understand how much they need to save for retirement.
Identify fraudulent behavior
Data analytics in the stock market aren't just important for advisors and investors, but also for regulators, who are able to use data analytics to uncover abnormalities in securities trading that could point to fraudulent behavior.
Regulators are able to use data analytics to set up algorithms that help them monitor the "risk score" of each financial transaction on the market, using a number of variables and setting normal ranges for each, based on historical transactions. In the event that a data set is outside of the normal range, that will trigger further analyst review to determine whether fraud or market manipulation is taking place.
This method can be used to not only identify unnatural price changes, but to identify specific investors who were able to profit from the price change, so that regulators can audit these accounts and investigate further.
Beyond regulators, it's also important that individual investment firms dedicate resources towards internal risk management, to alleviate the risk that internal employees might take part in fraudulent activities. By using a risk management data analytics solution that helps you identify your key risk indicators across a variety of factors, you can instantly get notified when there's a chance that an internal employee might be engaged in an act of malfeasance, and put a mitigation plan in place as quickly as possible.
Why use data analytics in stock trading?
Data analytics isn't set to replace human investment advisors or risk managers in the near future. While data analytics can help you surface patterns and trends, it's still important to rely on a combination of machine and human insights when making investment decisions or monitoring risk.
But data analytics and machine learning, when used effectively, can help automate tasks, help you spot opportunities or red flags, and provide ongoing insights into stock or portfolio performance.
Here are some of the key benefits:
Derive insights from vast quantities of data
Your data analytics solution can integrate data from a wide variety of sources, including ingesting real-time streaming data, to showcase correlations, trends, opportunities, and insights. By building models for analyzing data and feeding your solution a set of training data, it will be able to quickly return results that you can use to analyze stock fundamentals and other data sets with unmatched accuracy, based on historical records.
Automate manual investment trades
By using algorithmic trading, you don't need to watch the market for opportunities or risks – you'll be able to set up detailed calculations for when you want to buy, sell, or hold a security; or set up algorithms to help you automatically rebalance your investment portfolio. This saves time from manually reviewing the data, and ensures that you'll be able to complete the transaction as close as possible to your target price.
Build personalized investment strategies
Rather than putting each customer into a bucket based on a few general questions, you can use the wealth of data insights you can collect on their demographics and their investment priorities and performance to help them meet their goals, with detailed modeling that helps them understand exactly how to make the most of their investment strategy. Hedge fund managers, portfolio managers, and financial advisors can also use data analytics to help them see if past performance supports a specific investment strategy before making changes or opening a new fund.
Get detailed insights into risks within your organization or investment portfolio
Using a data analytics solution for monitoring risk, you'll be able to set up a catalog of key risk indicators, with trigger alerts when data values go outside of your specified thresholds. Whether you're seeking out red flags within your own organization or within your investment holdings, you'll be able to get alerts when manual review or action is required. In cases of potential fraud within your own organization, that can help protect your company’s reputation and protect you from potential liability or financial losses.
Choosing the right data analytics framework
In order to get the best data insights to provide better decision-making power to your investment brokerage or advisory service, it's important to choose a solution that will enable you to integrate all of your organizational data, as well as capital markets data and other streaming data feeds, in one centralized location. You can then use the machine learning solution to provide data analysis based on pre-built models, as well as your own customized models for your own unique needs. As it continually assesses more data, it will be able to change the weightings of different values to improve its analysis over time.
While there are some pre-built data analytics solutions that don't require coding experience to use, you will likely need some technical support with implementation and data integration to set up and onboard the solution.
Or, if you require a custom solution, you'll need programmers to work with your financial analysts to turn your financial models into algorithms that the machine learning solution will be able to work with.
As you get started, take the time to evaluate the different financial data analytics platforms on the market, and understand their capabilities and how well they relate to your needs. You can look at their case studies and customer testimonials to get a better sense of how other organizations have used their tools to optimize their workflows and improve their performance.
If you can't find the right fit, you might also consider developing your own proprietary data analytics solution with the help of a custom development team. While this model may be more expensive upfront than a SaaS solution, it will pay for itself over time. This model gives you complete ownership over your code, and provides the flexibility to come up with custom models and features that will serve your organization well. And because you’re not locked into a SaaS solution, you won’t have to deal with the uncertainty of changing rates or shifts in the product offering that might impact your organization.
Ready to think about a data analytics solution integration or a custom build? Contact our team at iTechArt today for support in seeing your vision through.