Analysing the behavioural finance impact of 'fake news' phenomena on financial markets: a representative agent model and empirical validation

This paper proposes an original behavioural finance representative agent model, to explain how fake news’ empirical price impacts can persist in finance despite contradicting the efficient-market hypothesis. The model reconciles empirically-observed price overreactions to fake news with empirically-observed price underreactions to real news, and predicts a novel secondary impact of fake news: that fake news in a security amplifies underreactions to subsequent real news for the security. Evaluating the model against a large-sample event study of the 2019 Chinese ADR Delisting Threat fake news and debunking event, this paper finds strong qualitative validation for its model’s dynamics and predictions.

Convergence in models of misspecified learning

We establish convergence of beliefs and actions in a class of one‐dimensional learning settings in which the agent's model is misspecified, she chooses actions endogenously, and the actions affect how she misinterprets information. Our stochastic‐approximation‐based methods rely on two crucial features: that the state and action spaces are continuous, and that the agent's posterior admits a one‐dimensional summary statistic. Through a basic model with a normal–normal updating structure and a generalization in which the agent's misinterpretation of information can depend on her current beliefs in a flexible way, we show that these features are compatible with a number of specifications of how exactly the agent updates. Applications of our framework include learning by a person who has an incorrect model of a technology she uses or is overconfident about herself, learning by a representative agent who may misunderstand macroeconomic outcomes, and learning by a firm that has an incorrect parametric model of demand.

Inequality

By whatever indicator it is assessed, inequality has been rising in recent years. This book considers it a macroeconomic issue and innovates by including it among its topics. The chapter begins by reviewing evidence and facts on inequality, measurement issues, and the relation with poverty. The macroeconomic models of income distribution reviewed next include vintage models, endogenous growth models, and whether inequality can be accommodated in ‘representative-agent’ models. Attention then turns to ‘factor’ (labour-capital) income shares, which have also been changing recently, reviewing both the relevant analytics and the possible processes that underlie this change. The chapter concludes with recent debates on determinants of inequality, the evolution of the labour share (the ‘r-g’ question), and the future of income distribution.

Distributional Aspects of Economic Systems

This paper proposes a methodology to jointly analyze the distributions of capital and labor and of saving and consumption across the population. Hinging on the novel concept of income composition inequality and on its technical assessment through a specific indicator, this paper classifies economic systems by bringing together these two distributions in a two-dimensional box. Economic systems can be classified as Kaldorian Systems or as Representative Agent Systems depending on their position in the box. In Kaldorian Systems, the rich individuals save capital income and the poor individuals consume labor income. In Representative Agent Systems, all individuals are identical in terms of ownership and behaviors. The paper illustrates this methodology via an empirical application to the European context, in which two major clusters of economic systems – Mediterranean and Northern European – emerge. Furthermore, this paper illustrates how the classification proposed can be useful in understanding a country’s long-run performance in terms of capital accumulation, inequality and growth. (Stone Center on Socio-Economic Inequality Working Paper)