PurposeThe purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It is a big challenge for the government to carry out financial market risk management in the big data era.Design/methodology/approachIn this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value.FindingsOwing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike information criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored.Originality/valueUsing the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy.

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Purpose This paper aims to investigate the relation between audit firms’ switch to limited liability partnership (LLP) from limited liability company (LLC) and client firms’ earnings comparability. If LLP auditors, who have a higher liability exposure than LLC auditors, are more consistent in implementing generally accepted accounting principles and executing firm-wide audit methodologies, client firms’ earnings comparability will increase. Design/methodology/approach Using data from China, the authors examine whether client firm-pairs of LLP auditors have higher earnings comparability than client firm-pairs of LLC auditors. The authors also perform cross-sectional tests to shed light on the mechanisms through which auditors’ litigation exposure affects client firms’ comparability. Findings The authors find that firm-pairs in which both firms are audited by LLP auditors exhibit higher earnings comparability than other firm-pairs. This result is stronger when client firms are audited by the same auditor, when client firms are audited by the top 10 auditors and when the auditors are less dependent on the client firms. The authors also document that firm-pairs in which both firms are audited by LLP auditors have lower average analyst earnings forecast error and forecast dispersion. Originality/value To the best of the author’s knowledge, this study is the first to examine the relation between auditor’s litigation exposure and client firms’ earnings comparability. It also extends the literature on audit firm organizational form and audit quality.

With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm.