If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to provide empirical quantitative evidence concerning small business financing in China and highlight the financing problems faced by small to medium-sized enterprises (SMEs) in developing their businesses. Design/methodology/approach -A semi-structured questionnaire survey was conducted to collect data from a sample of 60 small businesses in three cities in China. Descriptive methods and the SPSS statistical software package were used to analyse the data and interpret the results. Findings -The data gathered covered current topic in research including the capital structure of SMEs at start-up, the types and extent of funding shortage, the preference of financial resources as SMEs grow, the significant factors, which help SMEs secure bank loans and the influence of a firm's size, age and the like. The findings generally support financial theories and previous studies about SMEs but also offer the basis for new arguments about financing SMEs in China.Research limitations/implications -The sample size is relatively small and statistical analysis is relatively straightforward. Practical implication -The present study will be of interest to policy makers developing new strategies and policies to support the financing of SMEs in China. Originality/value -The results from this study contribute to the understanding of current problems in financing Chinese small business enterprises. These include findings, which were not presented in other similar studies.

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Probabilistic models help us encode latent structures that both model the data and are ideally also useful for specific downstream tasks. Among these, mixture models and their time-series counterparts, hidden Markov models, identify discrete components in the data. In this work, we focus on a constrained capacity setting, where we want to learn a model with relatively few components (e.g. for interpretability purposes). To maintain prediction performance, we introduce prediction-focused modeling for mixtures, which automatically selects the dimensions relevant to the prediction task. Our approach identifies relevant signal from the input, outperforms models that are not prediction-focused, and is easy to optimize; we also characterize when prediction-focused modeling can be expected to work.