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Improving household asset allocation (HAA) ability can enable households to increase consumption and promote the steady growth of China's economy. However, HAA decisions in China are suboptimal. This may be related to households' limited financial literacy. Based on 2017 and 2019 China Household Finance Survey data, this study examines the role of financial literacy in improving HAA. It finds that the higher the household's financial literacy, the higher the household's score in the asset allocation evaluation because households with higher financial literacy pay greater attention to economic and financial news and are more inclined to seek advice from investment advisors. Further analysis shows that the optimization effect of financial literacy on HAA is greater in households that are wealthy, higher educated, and located in areas with better regional financial development. In conclusion, helping households improve their financial literacy is an important step toward improving HAA ability. [ABSTRACT FROM AUTHOR]

With the development of technology and the abundance of data, many novel methods like artificial intelligence and machine learning have emerged for quantitative finance. This work tries to build a framework with screening function to help investors create a portfolio of stocks based on data from multiple sources, including historical trading data, factor data, financial data and media data. The framework integrates scoring and screening models. The scoring model consists of Seq2Seq model using historical trading data and a factor model using a new bottom-up discretization method while the screening model is composed of a novel discriminative model and a media model based on the weighted stock relation graph. Two types of model are fused to select portfolio with screening ability. This framework has been verified in China's A-share market, and is proved to be effective. We also noticed that the fused model is sensitive to the scale of selected stocks and the length of prediction period, which means it can be quickly adjusted according to our trading strategy. • Multisource data are fused for stock selection. • The fusion model has a great performance for the screening ability. • Seq2Seq model and t-merge factor model are built as scoring models. • The discrimination model and media sentimental model are screening models. • Models are analyzed and validated on China's stock market. [ABSTRACT FROM AUTHOR]