Articles by Apurv Jain on CNBCTV18
The health of a country’s economy is hiding in plain tweets
Bloomberg News July 31, 2019 This article was written by Jeff Kearns and Saijel Kishan. It appeared first on the Bloomberg Terminal. Apurv Jain has used some unconventional data to predict the course of U.S. employment: 1.2 billion tweets and 830 million web searches. As a Microsoft Corp. researcher in 2014, he was inspired by a […]
Know Faster, Invest Better: Machine Learning and Alternative Data for Nowcasting
CFA Society San Francisco
Improvised Marketing Interventions in Social Media.
Does quick marketing wit on social media matter for firm value? We investigate the online phenomenon of improvised marketing interventions (IMI) – social media actions that are composed and executed in real time proximal to an external event, using five multimethod studies and point to features that help reap online sharing and firm value benefits.
Predicting Gamestop Reddit Saga?
Quality of information in echo chambers is not very high! Save your money by listening to others who disagree with you. Joint with Scott Counts at Microsoft Research, Ben Zhao and his amazing students.
The Psychology of Job Loss
Losing our jobs can make us angry or sad, but can people being angry or sad predict unemployment? We use 1.2B tweets to find out. ICWSM 2016. Joint work with Scott Counts at Microsoft Research and Davide Proserpio at USC.
Macro Prediction Book chapter
Traditional macroeconomic data used by economic agents to make decisions are noisy, lack richness, and produced with considerable lag. This chapter explores how alternative, web-scale data sources (“Big Data”) can help. We present a case study using a common alternative data source- web search to predict one of the most important data releases- non-farm payrolls (NFP). We discuss the efficacy of various machine learning (ML) techniques, the live performance of alternative data prediction models and the typical problems faced in practice.
Privacy vs. Alpha
Do we really need to sacrifice privacy to make better investment decisions? Or maybe too much personal information is not only socially risky but also simply adds trading noise! Joint work with Neil Seeman , the Chairman of leading internet-based global trend tracker (RIWI).