“Asset Pricing and Re-sale in Networks” - Job Market Paper
I study asset pricing when re-trade can take place in co-existing and interconnected markets. In my framework, there is a divisible asset and a finite set of traders. They are distributed over a trading network. Traders can acquire shares at a common price, and then they may trade with their connections at possibly different prices. I find that trading centrality, a novel network metric, is a sufficient statistic for the equilibrium. Trading centrality processes information about expected re-trade equilibria, and maps it to traders’ behavior before trade. A trader’s asset acquisition is proportional to his centrality, and the asset common price is defined by aggregating centrality globally. For the re-trades in the network, a trader demands the gap between his optimal level of asset and his centrality; while each price is defined by aggregating centrality locally in the seller’s network. I investigate what market outcomes and welfare arise at different trading networks. Implications for asset issuance and interdealer markets are examined.
“Social Beliefs and Stock Price Booms and Busts”
I develop a dynamic asset pricing model in which subject beliefs about stock price behavior are heterogeneous and susceptible to peer effects. Two types of traders optimally learn from past price realizations and share beliefs in a social network. I show that, at each period, the equilibrium price is a function of traders' beliefs and the network structure. As a result, booms and busts of the price-dividend ratio emerge. The most (least) speculative trader is the most influential during booms (busts). More connected networks exhibit less volatile price dividend ratio, booms and busts episodes last longer, and the average price realization is higher. Also, there is less disagreement in beliefs. However, if traders of the same type are highly interconnected stock market volatility is higher and booms and busts are shorter. The model captures relevant empirical features of stock prices and returns, and it is also consistent with the survey evidence on investor expectations.
“Asset Pricing and Information on Social Networks” with Victoria Vanasco (CREi, UPF and BSE)
“Networked Inflation Expectations: a closer look at Professional Forecasters” with Mridula Duggal (UAB and BSE)