Data Trends for Investment Professionals

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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...

Wall Street Wants Your Data Webinar [VIDEO]

We recently hosted a webinar, “Wall Street Wants Your Data: Steps to Creating a New Revenue Stream,” presented by Quandl’s Chief Data Officer, Abraham Thomas. During the presentation, Abraham focused his session on three parts: Part 1 — Wall Street’s hunger for data and how those not typically viewed as data companies can benefit from this phenomenon Part 2 — Best practices for building, selling and delivering a data product that generates additional revenue for your business Part 3 — A Q&A session where Abraham answered attendee’s questions If you couldn’t make the webinar or would simply like to revisit the presentation again,...

Stock Market Data: The Ultimate Guide [Part 2]

Continuing with our guide to stock market data, in this post we will detail the various databases available for analyst ratings and targets, options, futures and indexes, and alternative data. If you haven't yet read Part One then you may do so by visiting here or else continue reading for Part Two. Analyst Ratings and Targets Understanding what other investors and analysts expect from a stock is a key component of forecasting that stock accurately. In certain cases, rosy analyst ratings may reflect a healthy underlying company; in other cases, they may reflect a consensus that is ripe to be...

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...

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