We’re living in unprecedented times, and everyone is adjusting to a new, heavily virtual way of working – from startups to enterprises to academic institutions.
This shift has been happening for several years, but learning and research institutions are especially under pressure now, as they are forced to move classes and other educational opportunities fully online.
It is because of these new circumstances that we’re excited to introduce Intrinio Academic – our new data solution for learning and research institutions. Intrinio Academic is not only a web-based platform that makes it easy for students, faculty, researchers, and administrators to access data remotely, it’s also a suite of tools that helps finance students learn computer science skills and prepare for some of the most in-demand jobs on the market.
(Watch our Intrinio Academic webinar here to see a demo and hear from Intrinio’s Director of Research & Development, Ken Miller.)
Major banks, hedge funds, and other institutions are culling traditional analyst jobs in favor of hybrid roles that combine finance and computer science. Why is that? It’s no secret that finance is becoming increasingly automated. Take investing – analysts are no longer manually completing trades based on instinct. They use advanced computer programs, algorithms, and analytical tools to make trades based on ever-increasing amounts of data. Building, operating, and maintaining those programs requires a melding of financial and technology skills.
These hybrid jobs are in high demand, they pay well, and they’re more accessible than ever before. Programming technologies are becoming easier to learn for professionals in other fields, like finance. This growth in accessibility also means that employers expect employees in all departments to provide analytic insights. Businesses have access to massive amounts of data, and they need people who can analyze that information efficiently and make decisions that produce results.
There’s a demand for experts who can meet the needs of financial firms with custom applications such as customer-facing investment management portals, financial advisor software solutions that help create portfolios and manage trades, or automated investment systems that utilize machine learning to maximize the value of a portfolio.
Quantitative analysts, or “quants,” are most well-known within the investment industry, but quantitative finance can be used in other industries as well. Quants can manage financial assets and risks for firms, model and control uncertainty in financial markets and financial arrangements, manage pension funds, work with insurance companies, control operational risks for manufacturing and consumer products companies, and much more.
That’s not even mentioning the large number of fintech companies taking over sections of the banking, investment, and payment industries, among others. These companies need to build financial platforms that are easy for individuals and businesses to use. All companies, regardless of size, are under pressure to continually innovate and develop new features that win customers over.
As is the case with many tech jobs, most of the necessary skills didn’t exist ten or even five years ago. If your curriculum hasn’t changed much in the past five years, that’s a sure sign that you’re not covering some crucial practical skills. Here are some of the skills that are in high demand among employers:
The big names here are Python and R. They're versatile, well supported with numerous scientific computing packages, and popular in the financial industry.
MySQL, Postgres, SQL Server, AWS RedShift, and Snowflake are popular relational database options. Today, knowledge of standard query language is quickly becoming a must. Non-relational data-stores – or, NoSQL databases – such as MongoDB and ElasticSearch are increasingly popular storage options for various kinds of transactional data which often find their way into financial algorithms.
Big data processing requires loads to be distributed. Knowledge of MapReduce architectures are required for success at scale. Hadoop and Hive are popular package options.
A variety of popular tools are frequently encountered including Tableau, Microsoft Power BI, and SAS.
Knowledge of Supervised, Unsupervised, and Reinforcement learning algorithms is quickly becoming necessary to innovate in the financial sector.
From a traditional perspective, the role of higher learning institutions is to lay an intellectual foundation with knowledge that doesn’t shift based on short-term trends or the whim of the market – double-entry bookkeeping has been around for 2000 years and the Gaussian distribution will be relevant 2000 years from now. That said, there’s no better way to inculcate true understanding than connecting theory directly to present-day practice – and that’s what Intrinio Academic is all about.
Some universities and colleges are keeping up with these changes, typically through cross-disciplinary programs in subjects like quantitative finance and fintech. Some institutions also offer financial analytics labs, student-managed investment funds, and other practical education. Unfortunately, many of these opportunities are only available via graduate programs, which means undergraduates are missing out on crucial skills that will make a significant difference in their careers.
There are many schools with no collaboration between their finance and computer science departments at all. Historically, academic institutions have not taught these skills together, which creates a noticeable skill-gap in the financial workforce. The demand is there. It’s higher education that’s lagging.
Intrinio Academic is a financial data portal for academic institutions that offers access to financial data for every student, professor, administrator, and researcher. We built Intrinio Academic for two simple reasons: we see the skill shortage in the incoming workforce, and we understand the tools students will need to use to have a successful career in finance. We use them every day.
Intrinio Academic provides access to the financial data and tools professors need to create engaging curriculums around finance and give students the skills they’ll need in today’s financial workforce. It also provides university researchers unfettered access to numerous financial datasets which, when combined with state-of-the-art methods, yields publishable results.
Intrinio Academic is available via a web interface, which means users can access the data on their personal computers anytime, anywhere. If you’ve ever waited in line to use a Bloomberg terminal, you know this is a game-changer.
Users can access data in three ways: via our powerful API and SDKs, our Excel add-in for Mac and Windows, or bulk download CSV files. Our primary access method is the Intrinio Financial Data API, which stands for application programming interface. The Intrinio Financial Data API runs on internet servers and provides functionality you can interact with through the code you write. In this case, the functionality provided is financial data retrieval. Data is returned in JSON, which is supported by nearly every programming language.
We also provide an Excel plug-in for both Mac and Windows. Our Excel plug-in can be used to pull data – including stock prices, standardized and reported company financials, plus a variety of data points such as P/E ratios and returns on equity – from our API into Excel spreadsheets. You can copy formulas, change out the tickers, and automatically update information across cells. This eliminates huge amounts of manual data entry.
Finally, Intrinio Academic includes bulk downloads of historical data that correspond with the data feeds available through our API. This includes historical data for US stock prices, standardized company financials, company news, and more. Bulk downloads of data are particularly useful for building quantitative models that utilize machine learning or artificial intelligence algorithms. This bulk data is provided as large files in CSV format, which is convenient whenever volumes of financial data spanning several years or multiple companies is called for, since it saves you the hassle of running through page after page of API data to gather what you need.
A vast amount of data is included with Intrinio Academic. This includes basic US company information, such as addresses and overview descriptions. There are over ten years of US company fundamental financials, including standardized financials generated by our XBRL machine learning algorithms – this enables apples-to-apples comparison of all US company financials. You also get over 400 standard company calculations and metrics, company news (which is perfect for sentiment analysis), stock prices, 30+ calculated technical indicators for EOD stock prices, and a security master.
Intrinio Academic comes with extensive documentation for every financial data API endpoint in all of our SDK languages, including full documentation for parameters and returned objects. You can also use the API Explorer to experiment with data retrieval without writing any code.
Also included to support our Excel plug-in is an Excel Formula Builder, which helps users construct the correct Excel formulas for the data they want to retrieve.
Since Intrinio Academic is an extension of our existing Intrinio Financial Data API, your Intrinio Academic portal will automatically update any time we make changes to our platform – for example, by updating API endpoints or our SDKs.
One of the highlights of Intrinio Academic is the Labs. Labs are simple introductions to high-level financial data concepts, like:
We also have a Lab for our security screener, which is an API endpoint that allows you to return a list of securities that fall within parameters that you specify.
In the Labs, we run students through these tasks step-by-step with code examples in Python and R. Each code example builds on the previous one. We also provide the ability to run these code samples directly in the browser using Jupyter notebooks. Alternately, the code samples are cut and paste runnable, so a student can paste them directly into any development environment they already have set up on a local machine.
These labs help eliminate any friction for new Intrinio Academic users and serve as engaging elements when included within any financial or computer science course syllabus.
While terminals are the industry standard, they isolate students and make it hard to work together. With a widely accessible financial data platform, you pave the way for code collaboration, seminars, workshops, case studies, demos, and other activities that weren’t possible before.
Don’t worry, we haven’t forgotten about the administrators. Managing data licenses can be exhausting and complicated. Intrinio Academic gets up and running with minimal setup and maintenance. Everyone is under one license, and access to the portal for all users only requires knowing the URL and six-digit pin-code. We handle all the vetting and onboarding for data partners, so you’re always getting high-quality data without the hassle of searching, testing, negotiating, and integrating. Plus, we offer live chat support for portal administrators seven days a week to resolve problems and answer questions quickly.
We’ve already had the privilege of working with some of the country’s top schools, like Harvard Business School, Stanford, and Caltech. We also work closely with several universities in Florida, where Intrinio is headquartered. Here’s how some schools are using Intrinio data:
Quant Modeling Course
Students leverage Intrinio data to pull key metrics like price to earnings ratios and historical prices for several hundred tickers into a spreadsheet and build a quantitative model.
Student Managed Investment Fund/Applied Security Analysis Course & Discounted Cash Flow Analysis
Students pull financial statement and stock price data from Intrinio into an Excel template to analyze equities and choose stocks for real investment into the USF portfolio. Students learning about discounted cash flow analysis also use Intrinio functions to pull financial statement data into a spreadsheet and manually build their own DCF models.
A team of professors leveraged historical equity pricing data for backtesting, research, and model construction.
Intrinio has been quoted as a data source in quite a bit of academic research – here are a few examples from schools around the world.
Pricing will depend on your institution. Visit our website to get started, and a member of our sales team will reach out to discuss how we can help
There are no limits on the number of users from your school that can access Intrinio Academic. One license covers your entire institution. There’s also no limit to the amount or breadth of data you can pull. Every individual (student, faculty, administrator, researcher) gets a unique access code, and there are rate limits for each individual API key that dictate how many API calls you can make within a specific timeframe.
Bloomberg terminals have served an important purpose for 30 years, but the world has changed a lot in that time. The biggest issue with Bloomberg terminals is that while they’re great for looking information up, you can’t build anything with them. Intrinio Academic allows students to collaborate with each other or with professors, since they all have simultaneous, unfettered access to financial data.
Although Bloomberg occasionally offers API access, it doesn’t offer the tools that Intrinio Academic provides, like SDKs in six different languages and simple financial data labs to help students and researchers get up and running quickly.
Our data is intended for use in research and learning, so you can’t resell the data or present it in a commercially viable way – for example, pulling financials for US companies and creating a website that displays them publicly. However, our data can be referenced in research papers, capstone projects, and other academic contexts.
Ready to get started? Talk to our team.