Big Data, Small Credit: The Digital Revolution and Its Impact on Emerging Market Consumers

This research report sheds light on a new cadre of technology companies who are disrupting the credit scoring business in emerging markets. Using non-financial data -- such as social media activity and mobile phone usage patterns -- complex algorithms and big data analytics are forever changing the economics of how we identify, score, and underwrite credit to consumers who have been invisible to lenders until now

The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public’s perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit “big data.

Information Processing and Stock Market Volatility - Evidence from Real Estate Investment Trusts

This study proposes novel measurements of investor psychology utilizing Big Data and reveals the impact of different measures on asset price volatility. We construct a news sentiment measure based on news articles reflecting information supply. News content is transformed into quantitative data utilizing sentiment analysis. We further investigate a metric for investor attention based on search queries from the web representing information demand. Consequently, we investigate how asset price volatility is attributable to information processing by investors. The main contribution of this paper is the analysis of novel insights into information processing by investors in the presence of Big Data. In particular, we find that the impact of news content (information supply) generally is much stronger than the effect of Big Data search behavior (information demand). While our results confirm the negativity-bias of investors, we find that measures additionally incorporating positive content outperform measures solely based on negative connotations