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This study aims to examine the venture capital (VC) industry in China. It has demonstrated a history of high growth with significant variations over time. The authors have examined the trends and determinants of VC investments in China over a 20-year period from 1995 to 2014. They find that the aggregate amount of VC investments, the total number of venture deals and the average amount of venture investments per deal in China are all significantly impacted by macroeconomic conditions (i.e. GDP, export, money supply), technology innovations and financial market indicators (i.e. initial public offerings (IPOs), interest rate, price-to-earnings ratio, etc.). They also find that the 2007 China A-Share stock market crash and the subsequent global financial crisis have motivated VCists in China to adjust their investment strategies and risk levels by allocating more capital to later-stage investments and securing more deals with later-round financings. However, after the 2008 global financial crisis, the China’s venture industry has recovered faster compared to the US counterpart response.

The authors first perform trend analysis of VC investments at an aggregate level, by stages of development, and across industry from 1995 to 2014.To test H1 and H2, the authors use multiple regression models with lagged explanatory variables. To test H3, the authors use univariate tests to compare the measures of VC investments at an aggregate level, stage funds ratios, stage deals ratios and financing series ratios during both a five-year and seven-year time windows around the 2007 A-Share stock market crash and the subsequent financial crisis.

The development of the VC industry in China has demonstrated a history of high growth with significant variation over time. The authors find that the aggregate amount of VC investments, the total number of venture deals and the average amount of venture investments per deal in China are all significantly impacted by macroeconomic conditions (i.e. GDP, export, money supply), technology innovations and financial market indicators (i.e. IPOs, interest rate, price-to-earnings ratio, etc.). The authors also find that the 2007 China A-Share stock market crash and the subsequent global financial crisis have motivated VCists in China to adjust their investment strategies and risk by allocating more capital to later-stage investments and securing more deals with later-round financings. However, the China VC industry has recovered faster compared to the USA just after the 2008 global financial crisis.

There are also limitations in the study. The VC data in China in the earlier 1990s might not be very reliable due to the quality of statistics. Therefore, the trend analysis and discussions mainly focus on the time after 2000. Also, the authors cannot find VC financing sequence data for the analysis. Second, there is no doubt that the policy impact from Chinese transforming economic system and government policies on its VC industry is substantial (Su and Wang, 2013). However, they cannot find an appropriate variable to be included in the empirical models to consider this effect. Further study on this area would provide meaningful information. Third, although the authors have done comparison study between the VC industry in China in this study and the VC industry in the US documented in Ning et al. (2015) and discussed some interesting findings, more in-depth research in this area will be very useful.

The findings have meaningful implications for VCists and start-up companies seeking equity financings in China. VCists should closely monitor macroeconomic and market conditions to make appropriate adjustments to their risk and investment strategies. Entrepreneurs seeking equity financings for their business could also monitor the identified macroeconomic and market indicators, which can help them with their timing and to negotiate a better equity financing deal. VC financing is more likely to succeed when key macroeconomic and market indicators become favorable.

This paper contributes to the literature by testing the supply and demand theory on the VC market proposed by Poterba (1989) and Gompers and Lerner (1998) from the macroeconomic perspective using 20 years’ VC data from China. The authors also examine how the 2007 A-Share stock market crash and the subsequent financial crisis affected VCists to adjust their risk levels and investment strategies. It provides useful information for international academia and policymakers to understand the quick rise of China VC industry. The authors also find that the macroeconomic drivers of VC industry are somewhat different under different economic systems.

The impact of globalization and worldwide competition has become excruciatingly noticeable in China. The purpose of this study is to examine China’s transformation of state‐owned enterprises (SOEs) to competitive, capable organizations by identifying the dominant challenges and forces for change to State‐Owned Enterprises in China (SOE), the nature of SOE responses to those forces, and the degree of SOE success in making the necessary transformations to compete in a global business environment.

This paper tests two moving average technical trading rules for four Asian markets. Our results indicate that moving average rules do indeed have predictive power and can discern recurring price patterns for profitable trading. Moreover, our results support the hypothesis that technical trading rules can outperform the buy‐and‐hold strategy. Break‐even one‐way trading costs are estimated to be high for all four markets. To confirm the test outcome, robust tests based on bootstrap and the related t‐tests among the markets are also carried out. We conclude from the statistical results that moving average rules are valid and indeed have predictive power. It is implied that the trading rules may be used to design a trading strategy that will beat the buy‐and‐hold strategy in the Hong Kong, Singapore, South Korea, and Taiwan markets. The contribution of the current study is that this is the first validation test of trading rules using four markets at a similar development stage and culture tradition; and in the tests, we use most current and longer periods than the periods used in previous literature. Our robust tests are unique and considered distribution‐free.

A new method of representing non‐spherical, smooth‐surfaced, axi‐symmetrical particles in discrete element (DE) simulation using model particles comprising overlapping spheres of arbitrary size whose centres are fixed in position relative to each other along the major axis of symmetry of the particle is presented. Contact detection and calculation of force‐deformation and particle movement is achieved using standard DE techniques modified to integrate the behaviour of each element sphere with that of the multi‐element particle to which it belongs. The method enables the dynamic behaviour of particles of high aspect ratio and irregular curvature (in two dimensions) to be modelled. The use of spheres to represent a particle takes advantage of the computational speed and accuracy of contact detection for spheres, which should make the method comparable in computational efficiency to alternative schemes for representing non‐spherical particles.