Based on the sample of Chinese A-listed firms from 2003 to 2020, this paper investigates the effect of common institutional ownership on labor income share. The result shows that common institutional ownership can significantly increase firms’ labor income share. Mechanism tests indicate that common ownership can: 1) alleviate financial constraints by reducing the debt financing costs and increasing the trade credit financing, thus increasing the labor income share; 2) improve corporate innovation and therefore enhances the demand for highly-skilled labor, which eventually boost labor income share. Competitive hypothesis test represents that common institutional ownership can reduce the monopoly power of enterprises and decrease monopoly rent, so as to increase the proportion of labor in the distribution. Further analyses present that the network formed by the common ownership can effectively exert the financing support role of SOEs and the knowledge spillover effect of innovative-advantage firms, which contributes to the labor income share increasing of other related firms in the network connection. This study not only enriches the economic consequences of common institutional ownership, but also provides policy guidance for the government to further optimize the income-distribution pattern by deepening the reform of the financial market.

In the era of big data, asset pricing is influenced by various factors, which are extracted from multi-source heterogeneous information, such as high frequency market and sentiment information, low frequency firm characteristic and macroeconomic information. Especially, low frequency policy information plays a significant role in the long-term pricing in China but it is barely investigated due to its textual form. To this end, we first extract policy variables from major national development plans (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using Natural Language Processing (NLP) technique and Dynamic Topic Model (DTM). However, traditional models are inadequate for mixed frequency data modeling and feature extraction. Then, we propose a mixed frequency deep factor asset pricing model (MIDAS-DF) that solves the asset pricing problems under the mixed frequency data environment through mixed data sampling (MIDAS) technique and deep learning architecture. Time-varying latent factors and factor loadings can be modeled from mixed frequency data directly in a nonlinear and data-driven way. Thus, the MIDAS-DF model is able to learn the nonlinear joint-patterns hidden in multi-source heterogeneous information. Our empirical studies of 4939 stocks on the Chinese A-share market from January 2003 to July 2022 demonstrate that low frequency policy information has profound impacts on asset pricing, which anchors the long-term pricing direction, and high frequency market and sentiment information have significant influences on stock prices, which optimize the short-term pricing accuracy, they together enhance the pricing effects. Consequently, pricing effects the MIDAS-DF model outperform the five competing models on individual stocks, various test portfolios, and investment portfolios. Our research about heterogeneous information provides implications to the government and regulators for decision-support in policy-making and our investment portfolio is of great importance for investors’ financial decisions.