Big Data and Investing

The financial services industry has been transformed thanks to the increasing importance Big data and data analytics have in helping financial institutions deliver their products and services, especially in investment management. Big data and investing go hand in hand, and portfolio managers are looking to data analytics to help them make more sound investment decisions on behalf of their clients.

What Is the Role of Big Data and Alternative Data in Investing?

In today’s data-rich world, there’s very little information that investors don’t have access to. Consumers are generating millions of data points every day from their online transactions, and their spending trends are full of insights that can be used by investors.

In the past, investors relied on fundamental data sources like market or competitor research to help them make investment decisions. Over the past 10 years, they’ve gotten more access to an abundance of data sources that enable them to understand consumer trends at a deeper level and make predictions backed by real-time data.

Big data refers to the amount of data collected. In contrast, alternative data refers to where it comes from and counts as information beyond traditional data sources such as SEC filings and financial statements used to inform companies’ business and investment decisions. The leading types of alternative data most companies are already using or plan to start using include web scraping, crowdsourcing, credit cards and POS systems, social media sentiment analysis, search trends, and web traffic. Big data and alternative data in investing have made asset management less prone to human error and human bias in investment decisions and reduces risk. Big data and asset management showcase the power data has in taking the guesswork out of many aspects in investing and replaces it with precise insights portfolio managers can count on.

Big Data Analytics in Investment Banking

Investment banks assist in large, complicated financial transactions, and often provide advice on how much a company is worth and how to structure a deal if a client is considering a merger or acquisition.

Institutional investors may leverage big data analytics in investment banking to research key information about specific markets, companies, customers, historical market trends, through web-scraping, social media sentiment analysis, crowdsourcing, or other strategies to inform their investment strategies. With a rise in the volume of data available to investors, a rising number of investors are using AI, big data, and analytics to gain competitive advantages by enhancing the quality and efficiency of their investment research. Having a strong financial data analytics system in place is key to be able to leverage big data technologies and even discover such insightful information.

How Is Big Data Used in Investment Banking?

Investment banks can use big data sources to improve their pricing accuracy, personalize their investment strategies for every client down to the finest of details, and give clients peace of mind their investments and data are safe and secure.

Whether investors need to quickly value portfolios, approve mortgages, analyze investment returns, make predictions about future market activity, or even assess the best retirement funds for clients, it can all be done faster with data intelligence software that scans through the raw data.

The time saved gathering research to create reports lets them help a greater number of clients in less time, and increases the effectiveness of their investment strategies for every client.

Using Big Data in Investment Research

There’s so much data generated every day; it would be impossible to analyze the data using traditional practices. The modern investor must leverage big data analytics solutions like artificial intelligence (AI) and machine learning technologies to successfully process, manage, and analyze all of the data.

Investment research can be streamlined using these technologies and provide a more comprehensive understanding of market activity.

How Can Investors Leverage Data for Deeper Insights?

Investors use data to improve their investment processes to reveal critical insights that enable them to make faster data-driven decisions.

  • Identifying potential risks informed with the latest market trends lets investors quickly determine whether or not a particular investment is worth their consideration.
  • Revenue signals allow investors to analyze the growing volume of consumer staple companies to forecast performance.
  • Payroll data helps investors uncover employment trends from major employers.
  • Consumer data such as transaction history or outstanding debts help investment managers tailor their recommendations to customers.
  • Near real-time data analytics help investors gain accurate market visibility to manage investments with T+1.

Does Big Data add Alpha?

Big Data and data analytics have the potential to add alpha given enough time and investment to adopt new tools and techniques. Seeing a return on investment may not be immediate, but making the transition to data-driven technologies is crucial for long-term growth and success for all investors.

Envestnet | Yodlee Investor Data Analytics enables investors to gain a competitive edge with timely, unique, and accurate market insights, spending, and income data analytics so they can better manage investments on behalf of their customers.

By turning de-identified data into insights, we enable asset, investment, and portfolio managers to seek out alpha-generating positions, improve investment decisions, monitor current investments, help mitigate risk, and more.

The Future of Big Data in Investing

The future of big data and investments is a lot like the present; an increasing number of investment firms will continue to start leveraging the full powers big data provides in investment advising and management, and additional data sources will start being used to inform algorithms.

Moving forward, machine learning powered technologies will continue to run and refine their processes for better performance and output, and they’ll be more common practice for investment advisors to pick up and start using.

Additionally, investment firms will hire an increased number of data scientists and specialists who will help discover new data sources and develop ways to integrate them into existing systems.

The transformation of investment advising and investment management will continue, and investors not adopting modern methods of big data analytics risk being left behind.

Gain a competitive edge in portfolio management with timely and comprehensive de-identified data analytics to inform investment and risk management decision-making with Envestnet | Yodlee.