The purpose of this research is financing options for high-tech enterprises in China during their early-stage. This study develops a three-phase model to analyze the financing choices of Chinese high-tech firms between banking and venture capital while considering information asymmetry. The findings reveal that opaque information disclosure favors venture capital financing, while transparent disclosure leads to bank loans for equally qualified high-tech enterprises. Data analysis further confirms that both bank loans and venture capital are viable options for early-stage Chinese high-tech enterprises. These findings offer valuable guidance for high-tech firms and investors navigating China’s financial landscape. Moreover, policy management departments can leverage these insights to make informed decisions and facilitate the financing of high-tech enterprises. JEL Classification: C70, C23, D22, G24.

This study introduces a novel forecast combination method for monthly Japanese tourism demand, analyzed at both aggregated and disaggregated levels, including tourist, business, and other travel purposes. The sample period spans from January 1996 to December 2018. Initially, the time series data were decomposed into high and low frequencies using the Ensemble Empirical Mode Decomposition (EEMD) technique. Following this, Autoregressive Integrated Moving Average (ARIMA), Neural Network (NN), and Support Vector Machine (SVM) forecasting models were applied to each decomposed component individually. The forecasts from these models were then combined to produce the final predictions. Our findings indicate that the two-stage forecast combination method significantly enhances forecasting accuracy in most cases. Consequently, the combined forecasts utilizing EEMD outperform those generated by individual models.

We employed a non-parametric causality test based on Singular Spectrum Analysis (SSA) and used the Vector Error Correction Model (VECM) and Information Share Model (IS) to measure the relationship between the futures and spot prices for seven major agricultural commodities in China from 2009 to 2017. We found that the agricultural futures market has potential leading information in price discovery. The results of an Impulse Response Function (IRF) analysis also showed that the spot prices react to shocks from the future market and have a lasting impact. This confirms our findings reported for the causality test and information share analysis.