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This study aims to comprehensively examine the relationship between financial reporting quality (FRQ) and investment efficiency (IE). The central thrust of this research endeavor is to empirically analyze the impact of FRQ on diverse facets of investment, including overinvestment, underinvestment and overall IE.

Using a sample of 13,902 firm-year observations from publicly listed US companies, this study uses the generalized method of moment (GMM) in conjunction with three distinct measures for FRQ under three different investment settings, considering firm liquidity and industry performance.

This study offers interesting insights into the intricate relationship between FRQ and IE. The results indicate a strong positive relation between the two constructs. In particular, the research reveals a negative link between FRQ and underinvestment, and an inverse relationship between FRQ and overinvestment. These findings suggest that FRQ is one of the key drivers of IE and that by enhancing FRQ, businesses can better optimize their investments.

This study highlights the significant implication of the effect of FRQ on IE, as it enables businesses to optimize their investments by improving their decision-making processes and better risk assessment of associated projects, resulting in more efficient capital allocation. A higher degree of FRQ increases investors’ confidence in a company’s financial statements, resulting in higher liquidity. It can benefit regulators to set higher standards and promote transparency.

The study examines the relationship between FRQ and IE. The study finds a strong positive relation between FRQ and IE, with FRQ being a key driver of IE. The paper’s original contribution lies in its comprehensive examination of the complex relationship between FRQ and IE, using robust analytical techniques by applying GMM and taking into consideration firms liquidity and industry performance.

This paper aims to investigate the internal and external determinants of firms’ efficiency and develop optimal corporate governance risk benchmarks for the manufacturing sector across different countries.

Corporate governance risk data were acquired from Institutional Shareholder Services Europe SA. Data on firms’ efficiency and for explanatory and control variables were taken from the DataStream database. The generalised directional distance function data envelopment analysis (GDDF-DEA) model and its stochastic extension provided corporate efficiency measures and optimal corporate governance benchmarks. The authors used ordinary least squares multiple regression analysis with wild bootstrapping to test the study hypotheses.

The authors found significant differences between firms’ optimal and actual efficiency input/output variables and corporate governance risks in the manufacturing sector across countries. Internal firm characteristics such as group affiliations, product market competition and insider ownership and external institutional factors such as the legal system, the rule of law, control of corruption, law enforcement and cultural values are vital determinants of firms’ efficiency.

This paper provides valuable guidance to enable corporate managers, regulators and policymakers to enhance firms’ efficiency and corporate governance practices.

This paper develops optimal corporate governance risk benchmarks and identifies the most critical internal and external factors affecting firms’ efficiency in the manufacturing sector in various countries. It also used a novel GDDF-DEA model, with the multi-parametric model for bias correction of efficiency estimator.