Using a large sample of Japanese firms, we investigate whether the level of foreign ownership in a firm is inversely related to information asymmetry between firm (managers) and market (outside investors). Since information asymmetry is not directly observable and, thus, is difficult to measure empirically, our analysis focuses on the link between foreign shareholding and a measurable consequence of information asymmetry; that is, the timing and magnitude of intertemporal return-earnings associations. The empirical results support our hypothesis, and subsequent tests based on residual foreign ownership show that the relation between foreign ownership and information asymmetry is robust to the addition of various control variables such as market capitalization and cross-corporate holdings. We also show that foreign investors tend to avoid stocks with high cross-corporate holdings. Overall, our results suggest that foreign (institutional) investors are likely to be efficient processors of public information and are attracted to Japanese firms with low information asymmetry.

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To study the influence of information on the behavior of stock markets, a common strategy in previous studies has been to concatenate the features of various information sources into one compound feature vector, a procedure that makes it more difficult to distinguish the effects of different information sources. We maintain that capturing the intrinsic relations among multiple information sources is important for predicting stock trends. The challenge lies in modeling the complex space of various sources and types of information and studying the effects of this information on stock market behavior. For this purpose, we introduce a tensor-based information framework to predict stock movements. Specifically, our framework models the complex investor information environment with tensors. A global dimensionality-reduction algorithm is used to capture the links among various information sources in a tensor, and a sequence of tensors is used to represent information gathered over time. Finally, a tensor-based predictive model to forecast stock movements, which is in essence a high-order tensor regression learning problem, is presented. Experiments performed on an entire year of data for China Securities Index stocks demonstrate that a trading system based on our framework outperforms the classic Top- N trading strategy and two state-of-the-art media-aware trading algorithms.

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The (P)- and (M)-3-azonia[6]helicenyl β-cyclodextrins exhibit L/D selectivities of up to 12.4 and P/M preferences of up to 28.2 upon complexation with underivatized proteinogenic amino acids in aqueous solution at pH 7.3.