Stock return predictability and stationarity of dividend yield

This paper first investigates the stationarity of dividend yield and then analyzes the predictive ability of the adjusted dividend yield which removes structural changes and high persistence characteristics. Empirical results have found that the dividend yield follows a mean-reverting process in each regime, and the convergence speed depends on the mean and variance. Moreover, the dividend yield is also global stationary. Finally, the adjusted dividend yield can predict future stock returns, and its predictive ability is time-invariant.mean reversion, regime switching, stationarity, stock return predictability

In the Shadow of the United States: The International Transmission Effect of Asset Returns

We examine how the fluctuations in financial and housing markets in U.S. affect the asset returns and GDP in Hong Kong. In contrast to the results from linear specifications, which concludes that the U.S. and Hong Kong are virtually delinked in terms of the asset markets, our regime-switching models indicate that the unexpected shock of US stock returns, followed by the TED spread, has the most significant effect on HK asset returns and GDP, typically in the regime with high return and low volatility. For the in-sample one-step-ahead forecasting, US Term spread stands out to be the best predictor.currency board, fixed nominal exchange rate, international transmission mechanism, hierarchical Markov regime-switching model, vector autoregressive model

Monetary Policy, Term Structure and Asset Return: Comparing REIT, Housing and Stock

This paper confirms that a regime-switching model out-performs a linear VAR model in terms of understanding the system dynamics of asset returns. Impulse responses of REIT returns to either the federal funds rate or the interest rate spread are much larger initially but less persistent. Furthermore, the term structure acts as an amplifier of the impulse response for REIT return, a stabilizer for the housing counterpart under some regime, and, perhaps surprisingly, almost no role for the stock return. In contrast, GDP growth has very marginal effect in the impulse response for all assets.monetary policy; yield curve; REITs; house prices; Markov Regime Switching

Pole analysis on the hadron spectroscopy of Λb→J/ΨpK−\Lambda_b\to J/\Psi p K^-

In this paper we study the $J/\Psi p$ spectroscopy in the process of $\Lambda_b\to J/\Psi p K^-$. The final state interactions of coupled channel $J/\Psi p$ ~-~ $\bar{D} \Sigma_c$~-~$\bar{D}^{*} \Sigma_c$ are constructed based on K-matrix with the Chew-Mandelstam function. We build the $\Lambda_b\to J/\Psi p K^-$ amplitude according to the Au-Morgan-Pennington method. The event shape is fitted and the decay width of $\Lambda_b\to J/\Psi p K^-$ is used to constrain the parameters, too. With the amplitudes we extract out the poles and their residues. Our amplitude and pole analysis suggest that the $P_c(4312)$ should be $\bar{D}\Sigma_c$ molecule, the $P_c(4440)$ could be an S-wave compact pentaquark state, and the structure around $P_c(4457)$ is caused by the cusp effect. The future experimental measurement of the decays of $\Lambda_b\to \bar{D}\Sigma_c K^-$ and $\Lambda_b\to \bar{D}^*\Sigma_c K^-$ would further help to study the nature of these resonances.Comment: updated to the published versio

Development of Computer Vision-Enhanced Smart Golf Ball Retriever

An automatic vehicle system was developed to assist golfers in collecting golf balls from a practice field. Computer vision methodology was utilized to enhance the detection of golf balls in shallow and/or deep grass regions. The free software OpenCV was used in this project because of its powerful features and supported repository. The homemade golf ball picker was built with a smart recognition function for golf balls and can lock onto targets by itself. A set of field tests was completed in which the rate of golf ball recognition was as high as 95%. We report that this homemade smart golf ball picker can reduce the tremendous amount of labor associated with having to gather golf balls scattered throughout a practice field

The Effect of Training Dataset Size on SAR Automatic Target Recognition Using Deep Learning

Synthetic aperture radar (SAR) is an effective remote sensor for target detection and recognition. Deep learning has a great potential for implementing automatic target recognition based on SAR images. In general, Sufficient labeled data are required to train a deep neural network to avoid overfitting. However, the availability of measured SAR images is usually limited due to high cost and security in practice. In this paper, we will investigate the relationship between the recognition performance and training dataset size. The experiments are performed on three classifiers using MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset. The results show us the minimum size of the training set for a particular classification accuracy