We investigate the movements of the yield curve after the release of major U.S. macroeconomic announcements through the lenses of an arbitrage-free dynamic term structure model with macroeconomic fundamentals. Combining estimated yield responses obtained using high-frequency data with model...

This paper presents an empirical investigation of scaling and multifractal properties of US Dollar–Deutschemark (USD–DEM) returns. The data set is ten years of 5-min returns. The cumulative return distributions of positive and negative tails at different time intervals are linear in the...

High Frequency Trading is pervasive across all electronic financial markets. As algorithms replace an increasing number of tasks previously performed by humans, cascading effects similar to the Flash Crash of May 6th 2010 become more likely. In this study, we bring together a number of different...

We investigate the movements of the yield curve after the release of major U.S. macroeconomic announcements through the lenses of an arbitrage-free dynamic term structure model with macroeconomic fundamentals. Combining estimated yield responses obtained using high-frequency data with model...

The paper presents an evolutionary economic model for the price evolution of stocks. Treating a stock market as a self-organized system governed by a fast purchase process and slow variations of demand and supply the model suggests that the short term price distribution has the form a logistic...

In the present paper an attempt has been made to estimate how political stability affects stock prices. This analysis refers to EU, Japan and the United States for the period (1997–2012). The findings of present model are in agreement with (Nixon, 2014); (Voth, 2001). The econometric model...

Conventional measurements of risk premiums are biased if the estimation models are potentially misspecified and unstable. Say, factor interactions is one of the crucial omitted specifications that standard models cannot involve. Motivated by this argument, we propose an interpretable...

With approximately 900 million observations we conduct, to our knowledge, the largest study ever of intraday stock return predictability using machine learning techniques finding consistent out-of-sample predictability across market, sector, and individual stock returns at various time horizons....

We propose a new modeling approach for the cross-section of returns. Our model, Factorization Asset Pricing Model (FAPM), allows for predictor interactions by introducing second-order observable characteristics interactions regarding the unobservable high-order loadings. If the characteristics...

Using a semi-supervised topic model on 7,000,000 New York Times articles spanning 160 years, we test whether topics of media discourse predict future stock and bond market returns to test rational and behavioral hypotheses about market valuation of disaster risk. Focusing on media discourse...