This paper presents a comprehensive comparison of nonparametric tests for jumps in the prices of financial assets. The relative performance of eight tests is examined in a Monte Carlo simulation covering scenarios of both finite and infinite activity jumps, and stochastic volatility models with...

Asset prices observed in financial markets combine equilibrium prices and market microstructure noise. In this paper, we study how to tell apart large shifts in equilibrium prices from noise using high frequency data. We propose a new nonparametric test which allows us to asymptotically remove...

Many models have been suggested to describe how investors manage risk and value risky cash flows. Among them, the most widely used is the Sharpe-Lintner-Black Capital Asset Pricing Model (CAPM). However many anomalies and evidence against this version have been presented. To assume that the CAPM...

Although liquidity has received wide attention in asset pricing literature over the past decades, how stock liquidity is priced in emerging markets remains unclear. We find that liquidity plays an important role in explaining the cross-section and time-series variation in expected returns by...

Bubbles can persist because investors are better off riding bubbles. We define bubbles in a natural way as significant, prolonged deviations from fundamental values measured by the well-known asset pricing models. Our real-time bubble detection system shows that –using US industry returns–...

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing.I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set...

We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional bag-of-words approach, the contextualized representation captures both the syntax and semantics of text, thus...

This paper compares various machine learning models to predict the cross-section of emerging market stock returns. We document that allowing for non-linearities and interactions leads to economically and statistically superior out-of-sample returns compared to traditional linear models. Although...

This paper examines the time-series predictability of aggregate stock returns in 20 emerging markets. In contrast to the aggregate-level findings in US, earnings yield forecasts the time-series of aggregate stock returns in emerging markets. We consider aggregate earnings not as normalizing...

During the global financial crisis, stressed market conditions led to skyrocketing corporate bond spreads that could not be explained by conventional modeling approaches. This paper builds on this observation and sheds light on time-variations in the relationship between systematic risk factors...