Trade Networks under Asymmetric Information

Buyer and seller interactions are analyzed with intermediaries called traders using a network structure. Goods are traded in the market through those networks. Each seller and buyer is linked to a trader through a network. We introduce asymmetric information on the valuation of goods by sellers and buyers. We deal with a two-stage game with incomplete information. The trader tries to maximize his profit. In Chapter 1, we analyze even network structures with one seller-one trader-one buyer and two buyers-one trader-two sellers and the asymmetric network cases with one seller-one trader-two buyers and two sellers-one trader-one buyer. We find that he sometimes offers different prices to sellers or to buyers when the penalty is almost zero in the second even network. We obtain that the trader sometimes offers the same price to all three parties to receive the maximum profit in the second asymmetric case. In Chapter 2, we allow there to be multiple traders and analyze how buyer and seller prices are influenced by competition among traders in a model of uncertainty. Sometimes, the seller and buyer both benefit from the competition between the two traders. The traders compete in the sense of a Bertrand duopoly to choose the price where each trader aims to maximize his profit. In other network structures, we note that the sellers who are not subject to the competition between two traders suffer from the consequences of the monopoly competition. We obtain that there will not be one fixed price that the traders offer the second seller and the second buyer in the last two network structures depending on the conditions on the valuations for the traded good. Middlemen, either as individuals or realtors, seem to have influential effects on the pricing strategy in the housing market. In Chapter 3, we analyze the contribution of the middlemen in the housing market as an application to trade networks. We work on a specific dataset related to the housing prices for two main neighborhoods in Atlanta, Georgia. The characteristics of those neighborhoods differ in terms of its distance to downtown or high-valued residential houses. We compare and contrast the returns for those two neighborhoods with distinct properties in order to investigate if any of these neighborhoods yield higher returns for the middlemen