The financial integration of China: New evidence on temporally aggregated data for the A-share market

In spite of high trade openness, existing empirical work, using daily data, has not found any evidence of international financial integration of China. In this paper we examine to what extent the Chinese A-share market, de jure protected from foreign influences by capital controls, is actually integrated with global or regional markets. We study a long sample (October 1992 through March 2005) of active trading, within the framework of a regime-switching error correction model. We confirm the role of temporal aggregation in cointegration tests. With daily or mid-week closing prices, we do not find any long run relationship with either the New York or the Hong Kong market, thus replicating previous findings. However, the use of weekly averaged prices implies that, up to late 1996, the Shanghai A-share market index was cointegrated with the S&P500. Subsequently, this relationship broke down and a long run relationship with the Hang Seng index gradually arose. Information flows, as well as the prospects of de jure financial opening, and the growing awareness of valuation concepts among Chinese domestic investors, in the presence of multiple listing of Mainland firms, help explain the evidence of financial integration in spite of capital controlsChina's A-share market, Markov-switching ECM, temporal aggregation, international financial integration

Understanding the Chinese stock market: international comparison and policy implications

The definitions of the bear, sidewalk and bull markets are ambiguous in the existing literature. This makes it difficult for practitioners to distinguish between different market conditions. In this paper, we propose statistical definitions of the bear, sidewalk and bull markets, which correspond to the three states in our hidden semi-Markov model. We apply this analysis to the daily returns of the Chinese stock market and seven developed markets. Using the Viterbi algorithm to globally decode the most likely sequence of the market conditions, we systematically find the precise timing of the bear, sidewalk and bull markets for all the eight markets. Through the comparison of the estimation and decoding results, many unique characteristics of the Chinese stock market are revealed, such as ‘crazy bull’, ‘frequent and quick bear’ and ‘no buffer zone’. In China, the bull market is more volatile than in developed markets, the bear market occurs more frequently than in developed markets, and the sidewalk market has not functioned as a buffer zone since 2005. Possible causes of these unique characteristics are also discussed and implications for policy-making are suggested

Decoding Chinese stock market returns: three-state hidden semi-Markov model

In this paper, we employ a three-state hidden semi-Markov model (HSMM) to explain the time-varying distribution of the Chinese stock market returns since 2005. Our results indicate that the time-varying distribution depends on the hidden states, which are represented by three market conditions, namely the bear, sidewalk, and bull markets. We find that the inflation, the PMI, and the exchange rate are significantly related to the market conditions in China. A simple trading strategy based on expanding window decoding shows profitability with a Sharpe ratio of 1.14

Transcribing Content from Structural Images with Spotlight Mechanism

Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured symbols), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what" solution. Specifically, we first decide "where-to-look" through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine the framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18

Experimental studies of the acoustic wave field near a borehole

A monopole or a dipole source in a fluid borehole generates acoustic waves, part of which propagate along the borehole and the other part enter the formation propagating as P- or S-waves. The refracted waves propagating along the borehole wall are used to determine P- and S-wave velocities. However, a significant fraction of the seismic energy radiates into the formation. In this laboratory study, we measure the acoustic waves in the borehole and the seismic waves in the formation at different distances from the borehole. We use scaled borehole models made of Lucite and of concrete to simulate a soft and a hard formation, respectively. The waveforms are measured in the boreholes as well as in the formations with different radial distances from the axis of the borehole. The results show that the investigation depth of the wave measured in the borehole is less than one half of the wavelength. The seismic energy radiating into the formation and scattered from interfaces and heterogeneities can be used for imaging the formation.Massachusetts Institute of Technology. Earth Resources Laboratory (Founding Member Consortium