We reconsider the issue of price discovery in spot and futures markets. We use a threshold error correction model to allow for arbitrage opportunities to have an impact on the return dynamics. We estimate the model using quote midpoints, and we modify the model to account for time-varying...

In a complete market for short-lived assets, we investigate long run wealth-driven selection on a general class of investment rules that depend on endogenously determined current and past prices. We find that market instability, leading to asset mis-pricing and informational efficiencies, is a...

Purpose - The present research aims to examine a range of momentum trading strategies for the tourism and hospitality sector. Design/methodology/approach - The paper followed the methodology of Jegadeesh and Titman (1993) to construct the portfolios. In this methodology, all portfolios were...

Using a very large data set with more than 9,700 stocks listed on NYSE, AMEX and NASDAQ, we analyze overnight price jumps and report short-term investor overreaction to information shocks and document return reversal and predictability up to five days. For negative and positive overnight jumps,...

This paper investigates changes in the price discovery portions for two popular securities based on the S&P 500 index, namely the S&P 500 E-mini futures and the SPDR Exchange Traded Fund (Ticker SPY) for the period Jan 2002 through Dec 2013. We show a significant change in the price discovery of...

Supported by several recent investigations, the empirical pricing kernel (EPK) puzzle might be considered a stylized fact. Based on an economic model with state dependent preferences for the financial investors, we want to emphasize a microeconomic view that succeeds in explaining the puzzle. We...

Bayesian learning provides a core concept of information processing in financial markets. Typically it is assumed that market participants perfectly know the quality of released news. However, in practice, news' precision is rarely disclosed. Therefore, we extend standard Bayesian learning...

Bayesian learning provides the core concept of processing noisy information. In standard Bayesian frameworks, assessing the price impact of information requires perfect knowledge of news’ precision. In practice, however, precision is rarely dis- closed. Therefore, we extend standard Bayesian...

Bayesian learning provides the core concept of information processing in financial markets. Typically, it is assumed that market participants know perfectly the quality of released news. However, in practice, news' precision is rarely disclosed. Therefore, we extend standard Bayesian learning,...