For institutions to capitalize on fleeting market opportunities, it’s essential that the right decision is made in a split second. Failing to move quickly on Wall Street or global markets can mean broker-dealers lose valuable ground on their rivals. 

Data analytics has entered a boom period, and its global market size is expected to grow from $309.35 billion in 2023 to $845.97 billion by 2031, representing a CAGR of 13.4% during its forecast period. 

This has made the adoption of data analytics technology an essential consideration for broker-dealers seeking to keep ahead of wider market trends. But how exactly will the analytics boom improve institutional performance when it comes to trading stocks and shares? Let’s take a deeper look at the beginning of an investment revolution: 

The Age of Big Data

In the wide world of big data, an estimated 84% of enterprises believe that without an analytics strategy, firms can lose a competitive edge in the market. This is particularly true in the dynamic investment climate, where the failure to capitalize on emerging trends can result in you losing key opportunities. 

Big data analytics help broker-dealers to better inform their investment decisions with more consistency in their returns. With the help of analytical insights and algorithmic tools, institutions can draw on historical data and complex computational models to democratize high volumes of data to convert into actionable insights that can identify market opportunities well before the human eye can identify a pattern. 

Democratizing Unstructured Data

Big data can be both structured and unstructured, and it’s through unlocking the potential of unstructured data that can help broker-dealers outpace their competitors in maximizing their yield prospects.

The more unconventional sources for unstructured big data can come from a variety of places and can help you broaden your market perspectives beyond the confines of technical and fundamental analysis. 

Unstructured data from social media, website traffic, the correct location quotient, and even satellite images can help to inform broker-dealers of emerging trends to gain an advantage in identifying investment opportunities. 

From gauging sentiment with social media analytics to using satellite imagery to analyze parking lot data to track foot traffic across different industries, unstructured data provides a cutting edge for institutions beyond typical analytical tools. 

Accommodating Machine Learning

As a subset of AI, machine learning (ML) utilizes statistics to learn from existing data to improve performance across various fields. This means that in trading, ML algorithms can analyze significant volumes of data to make accurate predictions regarding future market movements. 

The beauty of ML integrations for broker-dealer trading strategies is far greater speeds and stronger levels of accuracy. 

Because ML algorithms have the ability to process large datasets in real-time, actionable insights can be relayed to traders to make rapid decisions or to shape automated, algorithmic trades. 

Machine learning also leans heavily on forming its insights based on historical data and market trends. This means that it can analyze huge volumes of data to identify emerging trends and patterns that would otherwise be invisible to the naked eye. 

The Future of Risk Management

Another key aspect of trading efficiency that broker-dealers are keen to harness is hands-on risk management. 

Data analytics helps to provide investors with more tools than ever before to assess, identify, and mitigate risks through the analysis of big data. This can help institutions to make more focused decisions that conform to their risk tolerance and overall investment strategy. 

While broker-dealers and other institutions like hedge funds are known to thrive in volatile circumstances, traders that are left flat-footed in choppy markets can suffer from taking on more losing positions. 

The utility of data analytics serves as AI-based protection against risk, with broker-dealers provided with more holistic overviews of trading opportunities. 

Crucially, this hands-on risk management can empower more broker-dealers to adopt global outlooks for their trading strategies, with analytics and automation technology capable of offering powerful insights across markets that in-house teams may be less familiar with. 

For institutions that have adopted comprehensive analytics-based risk management, you should get a Tier 1 prime broker that offers wider market access to get the best out of your tools. 

The Hunt for Superior Automation

The future of trading as we know it lies in the constant innovation and evolution of analytics tools powered by AI and ML. The more powerful these tools become, they will be capable of predicting future market movements with greater accuracy, capturing new trends at a faster pace, and more closely aligning trading strategies with institutional goals. 

This will not only empower broker-dealers to capitalize on new trading opportunities, but will also help traders to quickly spot and manage risks before they enter a losing position.  

With the arrival of the analytics boom, AI and algorithmic trading will become an increasingly imperative part of broker-dealer trading strategies, and will ultimately cause more institutions to develop a need for speed to act on brief instances of opportunity before their rivals get there first. 

The battle for precision will become a battle for mitigating slippage and lag. In the highly competitive trading landscape of tomorrow, it will certainly pay for broker-dealers to arm themselves with leading analytical insights today.