Identifying Arbitrage Opportunities in Retail Markets using Predictive Analytics
38 Pages Posted: 8 Feb 2021 Last revised: 25 Jan 2022
Date Written: January 23, 2022
While the body of literature on arbitrage opportunity identification is well established in the financial, energy, and real-estate markets, research on arbitrage in the retail marketplace is relatively limited. As arbitrage opportunities in this context differ inherently from other markets, we propose a data analytics framework to identify such an arbitrage opportunity by leveraging a machine learning model to predict the optimal purchasing point from the price movement. Our predictive approach is enhanced by incorporating user-generated content which demonstrates its informative power. Overall, the enhanced model attains the precision rate of more than 90 percent while the recall rate is higher than 80 percent in a cross-validation test using the data collected from Amazon Marketplace. In addition, we conduct a field experiment to verify the external validity of the model in a real-life setting. The result shows that our model performs exceptionally well and reaffirms the economic value of our predictive approach.
Keywords: arbitrage, user-generated content, predictive analytics, field experiment, random forest
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