Chengbo Fu

I. In March 2010, China launched its margin-trading and short-selling (MTSS) program with a 90-stock pilot. Eight subsequent expansion rounds extended eligibility to more than 2,400 stocks. The ref...

Geographical distance is a critical yet underexplored determinant in venture capital (VC) exits. While proximity mitigates information asymmetry, excessive local concentration may foster homogeneous competition and diseconomies of scale, creating a geographical proximity paradox that complicates VC exit outcomes. Drawing on 96,798 VC investment events in China from 2010 to 2023, this study examines the nonlinear relation between geographical distance and VC exits. The results reveal that: (1) Geographical distance exhibits a significant inverted U-shaped relation with main VC exits: moderate distance improves exit likelihood by alleviating clustering-induced competition, whereas excessive distance suppresses exits by increasing information costs. (2) VC networks partially mediate this relation, as distance pressures appear to stimulate network capacity that contributes to exits. (3) Digital economy development flattens the curve by reducing information frictions, whereas intense regional economic activity steepens it by amplifying competition in proximate clusters and information costs in distant markets. These findings reframe geographical distance from a linear constraint to a dynamic factor with optimal thresholds, offering strategic guidance on spatial portfolio allocation for VCs.

Purpose: This study examines how oil market volatility and clean energy trends impact the stock performance of automakers, specifically comparing traditional manufacturers with electric vehicle (EV) producers such as BYD and Tesla. The objective is to assess the extent to which traditional automakers are sensitive to oil market fluctuations, while EV manufacturers align more closely with clean energy dynamics, particularly during global market crises. Design/methodology/approach: Using daily data from January 2013 to December 2023, we conduct linear regressions, GARCH, DCC-GARCH and the Diebold–Yilmaz connectedness approaches in the analysis. We use these econometric models to capture volatility patterns, correlations and cross-market spillovers. Findings: Traditional manufacturers are affected by both oil prices and clean energy development. While traditional automakers remain more vulnerable to oil price volatility, global leading EV manufacturers BYD and Tesla are less sensitive to oil price shocks and show strong alignment with clean energy indices. Significant volatility spillovers are observed during global crises, such as the COVID-19 pandemic and the Russia–Ukraine conflict. Originality/value: The paper uniquely integrates clean energy indices into the analysis of oil price impacts on automaker stocks. By comparing traditional and EV manufacturers using advanced econometric models, it sheds light on the literature of energy markets and sustainable financial markets.