Networks in Finance: Asset Pricing, Market Structure and Information

Abstract

This doctoral thesis studies financial markets through the lens of networks theory. The three chapters are united by a theoretical approach, highlighting particular issues in finance: asset pricing, market structure, and information. Chapter 1 is on market structure and financial networks. 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. Chapter 2 is about the dynamics of stock prices and social influence. 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. Chapter 3, joint with Prof. Victoria Vanasco, investigates how social linkages interact with asymmetric information about asset fundamentals to shape market outcomes. We consider a market where traders are embedded in a social network and form beliefs about market size based on their connections. We distinguish two channels underpinning market outcomes: heterogeneous information about asset fundamentals and social linkages. In equilibrium, the network structure determines the information revealed and perceived by traders from price. What, in turn, pin down optimal demands. Asset price is determined by a single network coefficient capturing the direct effect of connections in traders¿ beliefs. The model sheds new light on the implication of the increasing presence of retail investors in financial markets and the emergence of social platforms for engagement and investing.

Date of Award	17 Jul 2023
Original language	English
Supervisor	Pau Milán Solé (Director) & Jesus David Perez Castrillo (Director)