Choice modeling (CM) aims to describe and predict choices according to attributes of subjects and options. If we presume each choice making as the formation of link between subjects and options, immediately CM can be bridged to link analysis and prediction (LAP) problem. However, such a mapping is often not trivial and straightforward. In LAP problems, the only available observations are links among objects but their attributes are often inaccessible. Therefore, we extend CM into a latent feature space to avoid the need of explicit attributes. Moreover, LAP is usually based on binary linkage assumption that models observed links as positive instances and unobserved links as negative instances. Instead, we use a weaker assumption that treats unobserved links as pseudo negative instances. Furthermore, most subjects or options may be quite heterogeneous due to the long-tail distribution, which is failed to capture by conventional LAP approaches. To address above challenges, we propose a Bayesian heteroskedastic choice model to represent the non-identically distributed linkages in the LAP problems. Finally, the empirical evaluation on real-world datasets proves the superiority of our approach.
Forecasting CNY exchange rate accurately is a challenging task due to its complex coupling nature, which includes market-level coupling from interactions with multiple financial markets, macro-level coupling from interactions with economic fundamentals and deep coupling from interactions of the two aforementioned kinds of couplings. This study develops a new deep coupled Long Short-Term Memory (LSTM) approach, namely DC-LSTM, to capture the complex couplings for USD/CNY exchange rate forecasting. In this approach, a deep structure consisting of stacked LSTMs is built to model the complex couplings. The experimental results with 10 years data indicate that the proposed approach significantly outperforms seven other benchmarks. The DC-LSTM is verified to be a useful tool to make wise investment decisions through a profitability discussion. The purpose in this paper is to clarify the importance of coupling learning for exchange rate forecasting, and the usefulness of deep coupled model to capture the couplings.
This article examines the impact of cross-shareholding on corporate environmental investment (Env) using Chinese listed firms from 2014 to 2019 as the research setting. The results show that there is a positive impact of cross-shareholding on corporate environmental investment. The finding remains robust to a battery of robustness checks. In addition, the heterogeneity analysis illustrates that the positive impact of cross-shareholding on corporate environmental investment is more pronounced in state-owned firms and high-polluting industries when compared to non-state-owned firms and low-polluting industries, respectively. This study extends the research on cross-shareholding and provides practical implications for corporate sustainable development.
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