Jakub Kolodziej joined the European Quantitative research team in London in 2014 prior to which he worked as an investment analyst at a quantitative hedge fund. He holds a Master’s degree in Finance & Private Equity from the London School of Economics and a Bachelor’s degree in Finance & Accounting from Warsaw School of Economics.
Big is beautiful: How data from email receipts can help predict company sales
Macquarie analyse a large dataset of email receipts that covers the purchases of more than two million US customers. The data, sourced from QUANDL, contains weekly information on all the items purchased by each individual consumer from a large set of companies including Amazon, Walmart and Apple. In particular, for each product Macquarie gives a description, its likely classification in terms of broad goods categories, price paid, number of units, shipping costs, any discounts received and many more fields. Consumers opt in to share information available from their email accounts with a data vendor. The data is anonymised but each consumer is assigned a unique identifier which allows them to follow individual purchase histories over time and infer a profile.Using Amazon.com as a case study, they show that the data can generate real-time forecasts of quarterly sales that are at least as accurate as consensus. It is, however, in combining analyst insights and big data that they find the most significant improvement in predictive power. They also highlight the possibilities opened by this kind of large-scale database for a truly quantamental approach to equity valuation. Finally, they describe the technological solutions adopted to overcome the challenges posed by a dataset that can reach hundreds of millions of rows for a single firm.