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The Information Content of Taster's Valuation in Tea Auctions of India

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  • Abhinandan Dalal
  • Diganta Mukherjee
  • Subhrajyoty Roy

Abstract

Tea auctions across India occur as an ascending open auction, conducted online. Before the auction, a sample of the tea lot is sent to potential bidders and a group of tea tasters. The seller's reserve price is a confidential function of the tea taster's valuation, which also possibly acts as a signal to the bidders. In this paper, we work with the dataset from a single tea auction house, J Thomas, of tea dust category, on 49 weeks in the time span of 2018-2019, with the following objectives in mind: $\bullet$ Objective classification of the various categories of tea dust (25) into a more manageable, and robust classification of the tea dust, based on source and grades. $\bullet$ Predict which tea lots would be sold in the auction market, and a model for the final price conditioned on sale. $\bullet$ To study the distribution of price and ratio of the sold tea auction lots. $\bullet$ Make a detailed analysis of the information obtained from the tea taster's valuation and its impact on the final auction price. The model used has shown various promising results on cross-validation. The importance of valuation is firmly established through analysis of causal relationship between the valuation and the actual price. The authors hope that this study of the properties and the detailed analysis of the role played by the various factors, would be significant in the decision making process for the players of the auction game, pave the way to remove the manual interference in an attempt to automate the auction procedure, and improve tea quality in markets.

Suggested Citation

  • Abhinandan Dalal & Diganta Mukherjee & Subhrajyoty Roy, 2020. "The Information Content of Taster's Valuation in Tea Auctions of India," Papers 2005.02814, arXiv.org.
  • Handle: RePEc:arx:papers:2005.02814
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    References listed on IDEAS

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    1. Levin, Dan & Smith, James L, 1996. "Optimal Reservation Prices in Auctions," Economic Journal, Royal Economic Society, vol. 106(438), pages 1271-1283, September.
    2. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    3. Grün, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
    4. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
    5. Pearl Judea, 2010. "An Introduction to Causal Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-62, February.
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