The roles of liquidity and delay in financial markets based on an optimal forecasting model

• Published in: PloS one Vol. 18; no. 9; p. e0290869
• Main Authors: Yang, Guo-Hui, Ma, Si-Qi, Bian, Xiao-Dong, Li, Jiang-Cheng
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 01.09.2023; Public Library of Science (PLoS)
• Subjects: Algorithms; Autoregressive models; Bayesian analysis; Behavior; Benchmarks; Capital markets; Criteria; Delay time; Efficiency; Efficient markets; Financial markets; Forecasting; Hypotheses; Investments; Liquidity; Liquidity (Finance); Machine learning; Mathematical models; Medicine and Health Sciences; Methods; Modelling; Phase transitions; Physical Sciences; Probability theory; Quality management; Random variables; Research and Analysis Methods; Securities markets; Securities trading; Social Sciences; Statistical analysis; Statistical physics; Stochastic models; Stochasticity; Stock market crashes; Volatility; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0290869

We investigate the roles of liquidity and delay in financial markets through our proposed optimal forecasting model. The efficiency and liquidity of the financial market are examined using stochastic models that incorporate information delay. Based on machine learning, we estimate the in-sample and out-of-sample forecasting price performances of the six proposed methods using the likelihood function and Bayesian methods, and the out-of-sample prediction performance is compared with the benchmark model ARIMA-GARCH. We discover that the forecasting price performance of the proposed simplified delay stochastic model is superior to that of the benchmark methods by the test methods of a variety of loss function, superior predictive ability test (SPA), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Using data from the Chinese stock market, the best forecasting model assesses the efficiency and liquidity of the financial market while accounting for information delay and trade probability. The rise in trade probability and delay time affects the stability of the return distribution and raises the risk, according to stochastic simulation. The empirical findings show that empirical and best forecasting approaches are compatible, that company size and liquidity (delay time) have an inverse relationship, and that delay time and liquidity have a nonlinear relationship. The most efficient have optimal liquidity.