Data Trends for Investment Professionals

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The Data Factory, Part Three: Applying your data

This is part three of a series of posts exploring the Data Factory, the premier approach to data management. If you missed it, catch up on Part Two: Transforming your data here. In our previous post, we explored the second stage of data management: transforming your data into usable, clean information. The third stage in our data factory journey is where we begin to unleash the potential of the data that we’ve worked painstakingly to acquire and transform.  How to apply your data The application stage is where portfolio managers, research heads and quantitative analysts begin to determine how they...

The Data Factory, Part Two: Transforming your data

This is part two of a series of posts exploring the Data Factory, the premier approach to data management. If you missed it, catch up on Part One: Acquiring the right data here. In this post, we’ll tackle the stage that comes after finding the right data for your business’ needs: transforming the data so that it’s usable and reliable in application.  Data quality assurance The truth of the matter is that raw, real-world data can have a multitude of problems—it can be messy, noisy, unstructured and full of gaps, errors, outliers and duplicates.  Before portfolio managers and research heads...

The Data Factory, Part One: Acquiring the right data

This is part one of a series of blog posts exploring the data factory, the premier approach to data management. In the assembly line model that powers the data factory, as with many other things, well begun is half done. The first stage in any data process is acquiring the right data for your  organization’s needs. The process of acquiring data involves a number of smaller steps which include sourcing; contracting; extracting, transforming and loading the data; and business development. These steps are by no means linear. They can and often do happen simultaneously.  This stage is all about ensuring...

The Data Factory

This is an introduction to a series of blog posts exploring the data factory, the premier approach to data management.  The data explosion From the evolving landscape of data privacy regulation to record-breaking acquisitions in data production, distribution and analytics, the world has its eye on data.  Even in the finance industry, data is no longer only the concern of quantitative funds and data scientists. To varying degrees, data is infused in the investment decision-making process for all types of investors. While a quant fund may rigorously test a dataset for months to ensure that it’s up to the task...

COVID-19’s Economic Impact (Thus Far)

Over the past months, we’ve watched the dramatic and unprecedented impact of COVID-19 on society, the economy and markets. Through these waves of uncertainty, our curiosity around how these effects will manifest in the world’s data continues unabated. Based on input from industry insiders and Quandl’s data science team, here are the five categories of data (and the related questions they answer) that we find most interesting and important right now. 1 – Supply Chain Our first category is supply chain data.  In particular, information pertaining to disruptions and shortages, logistical issues, inventory buildup or depletion, factory closures and restarts,...

Finding and using stock fundamentals data

The power of fundamental analysis to ascertain the value of a company is based on the idea that all businesses essentially carry out the same, common activities. These activities can range from salaries and marketing spend to spending on research and capital assets, and receiving investment income.  Because these activities can all be reduced to financial quantities, they can therefore be directly compared across companies. Analysts can use common metrics like revenues, R&D spend, COGS and free cash flow to create an accurate assessment of comparative value between, for example, a software stock versus an auto stock. Between the advent...

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