Data Trends for Investment Professionals

GO TO QUANDL.COM ⟶

Data Monetization: Compliance and Privacy

In our previous post, we completed our discussion of data productization, focusing on the importance of robust delivery systems and packaging. Next, we will discuss the data compliance and privacy concerns most often expressed by data providers. The media has made much of datasets used by Wall Street that are not “anonymized”, but the reality is that professional investors do not care one bit about an individual’s information. They only care about data that moves markets. That Lucy Morgan, 37, of Louisville, Kentucky just bought six family-sized bars of Hershey’s chocolate using her MasterCard at Costco does not interest them in...

Data Monetization: Delivering Your Data

As discussed in part one of our Data Monetization series, Building a Data Product, in order to sell data to finance professionals, your product must be clean and predictive. Data hygiene and predictiveness, however, are just one of many prerequisites on the path to productization. Though you are not expected to provide trading insights or make stock predictions, analysts and investors don’t want to have to corral your data. They want to be able to consume it easily without doing too much legwork. In this post we cover two key components to minimize data wrangling: How to properly package your...

Data Monetization: Building a Data Product

One thing we are often asked here at Quandl is how to go from raw data to a salable data product. We're the first to admit that the process of data monetization is complex and tedious, but ultimately worthwhile for your company. Whether you're a start-up or a publicly traded corporation, taking the time and upfront investment to transform your existing data into a market-ready product can pay handsome dividends in the long-run. Our specialty lies in developing data products and marketing them to a Wall Street audience. By this, we mean any institutional investor who is interested in consuming data to...

API for Stock Data

Quandl offers a simple API for stock market data downloads. Our daily data feeds deliver end-of-day prices, historical stock fundamental data, harmonized fundamentals, financial ratios, indexes, options and volatility, earnings estimates, analyst ratings, investor sentiment and more. This post describes how our stock market data is organized, and explains how to access it. Data Organization: Time-series vs. Tables Quandl's data products come in many forms and contain various objects, including time-series and tables. Through our APIs and various tools (R, Python, Excel, etc.), users can access/call the premium data to which they have subscribed. (Our free data can be accessed...

API for Zacks Earnings Data

US Earnings Data Quandl is the largest, most comprehensive, most accurate source of Zacks earnings data on the internet. Data coverage includes consensus earnings estimates, actual earnings, earnings estimates trends and earnings surprises for thousands of North American companies. Investors assess a company's stock performance based on its estimated future earnings. A company's earnings forecasts are based on analysts' expectation of its future growth and profitability. Earnings estimates are usually reported as a "consensus estimate", which is the average of all estimates made by analysts who follow the company's finances. The most important factor in influencing a stock's value in...

The Cost of Free Data: Why Use Premium Stock Prices?

Quandl has always prided itself on making financial and economic data accessible to all investors. This is why a number of our datasets are free resources, like the Federal Reserve Economic Data (FRED) and US Energy Information Administration Data (EIA). While we’re happy to support these products for the foreseeable future, we nonetheless advocate for the use of premium sources for professional investors. Understandably, one of the most common questions we get is "Why should users pay for premium stock price data, when stock quotes are available for free from many different sources?" Free sources may be appropriate for casual...

1 2 3 7
Fix This