This paper uses fractional cointegration analysis to examine whether long-run relations exist between securitized real estate returns and three sets of variables frequently used in the literature as the factors driving securitized real estate returns. That is, we examine whether such...

We examine the statistical power of fundamental and behavioural factors with regards to stock returns of the Dow Jones Industrials Index. With a novel sentiment dataset from over 3.6 million Reuters news articles, we find significant correlations between Reuters sentiment and stock returns. We...

This study investigates the cross-country impact of U.S. equity market skewness risk. We find that a large decrease in the U.S. market skewness significantly predicts high future returns on international equity markets. The predictability remains significant after controlling for a set of U.S....

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

The predictability of stock market is of great interest to both reseachers and investors. Despite voluminous evidence of in-sample predictability, the out-of-sample predictability of stock returns remains an ongoing debate. In this paper, motivated by both the financial theories and the well...

The classical APT model is of the form r j - E(r j) = beta j(I - EI) + epsilon j, where r j - E(r j) is the earning deviation (called basic ariance-profit) of the security j, I is a common factor. This paper considers the impact on the securities return caused by the skewness and kurtosis of the...

This paper is the first to utilize a direct test for periodic, partially collapsing speculative bubbles in US REIT prices. A long history of data is employed for the All, Mortgage and Equity REIT categories. This approach is more powerful than existing tests and is based on the formulation of a...

We propose a local adaptive multiplicative error model (MEM) accommodating timevarying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time....