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- W3134019027 abstract "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 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 is capable of generating as much as a 113.31% profit margin." @default.
- W3134019027 created "2021-03-15" @default.
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- W3134019027 date "2021-01-01" @default.
- W3134019027 modified "2023-09-26" @default.
- W3134019027 title "Identifying Arbitrage Opportunities in Retail Markets using Predictive Analytics" @default.
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- W3134019027 doi "https://doi.org/10.2139/ssrn.3764048" @default.
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