Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments May 31, 2019 erstellt
Beschreibung:
In a rapidly changing world, older data is not as informative as the most recent data. This is known as a concept drift problem in statistics and machine learning. How does a firm adapt in such an environment? To address this research question, we propose a generalized revealed preference approach. We argue that by observing a firm's choices, we can recover the way the firm uses the past data to make business decisions. We apply this approach to study how Prosper Marketplace, an online P2P lending platform, adapts in order to address the concept drift problem. More specifically, we develop a two-sided market model, where Prosper uses the past data and machine learning techniques to assess borrowers' and lenders' preferences, borrowers' risks, and then set interest rate for their loans to maximize his expected profits. By observing his interest rate choices over time and using this structural model, we infer that Prosper assigns different weights to past data points depending on how close the economic environments that generate the data are to the current environment. In the counterfactual, we demonstrate that Prosper may not be using the past data optimally, and it could improve its revenue by changing the way it uses data