We use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic...

Using Indian bank-level data, we examine the cross-sectional returns predictability for banking stocks in view of the distinct industry parameters prevalent in the financial services space. We find the existence of abnormal returns in banking stocks. We also observe that the celebrated...

Purpose: The aim of this paper is to examine the role of liquidity in asset pricing in a tiny market, such as the Portuguese. The unique setting of the Lisbon Stock Exchange with regards to changes in classification from an emerging to a developed stock market, allows an original answer to...

This study develops an agent-based computational stock market model in which each trader’s buying and selling decisions are endogenously determined by multiple factors: namely, firm profitability, past stock price movement, and imitation of other traders. Each trader can switch from being a...

The firm size and value anomalies are the global-level counterpart for explaining the cross-sectional variations of equity returns. The purpose of this paper is to examine the size, value effects and the explanatory power of three well-known pricing models - CAPM, three-and five-factor across...

Three concepts: stochastic discount factors, multi-beta pricing and mean-variance efficiency, are at the core of modern empirical asset pricing. This chapter reviews these paradigms and the relations among them, concentrating on conditional asset-pricing models where lagged variables serve as...

Employing asset-pricing models over the period 2012 to 2017, this study examines whether a search attention index (SAI) explains the variation in the weekly excess return of stocks. The study finds that the estimated abnormal return of a portfolio based on search intensity is significantly high...