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This paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory.

By leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment.

Results show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking.

Annual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures.

To the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class.

This paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.

The relationship between farm size and land productivity is a hotly debated issue in the study of agricultural economics and development economics. The purpose of this paper is to explore the causes leading to the inverse productivity relationship by examining the relationship between farm size and factor inputs.

With a large panel data set of farm households in China during 2010–2011, this study uses the factor demand models to examine the relationship between farm size and per-mu labor and non-labor inputs while employing a stochastic frontier production function in determining the difference of labor efforts in farming operation across farm sizes. Moreover, the models for value-added margins and profits are used to further determine producer behavior of small-size farms.

Results of this study show that, as compared to larger farms, smaller farms not only utilize more labor and non-labor inputs per mu, but also benefit from a higher labor effort. Moreover, smaller farms concentrate more on grain output and cash costs while focusing less on the family labor input costs in an effort to maximize value-added margins rather than profits. The higher yields on smaller farms are thus a result of the utilization of a relatively higher level of labor and non-labor inputs along with skilled-oriented precision farming technology. The inverse productivity relationship is explained by the behavior of small-size producers with employment constraints, leading to smaller farms generating a higher yield than larger farms.

While Sen (1966), Feder (1985), Eswaran and Kotwal (1986) and others have theoretically derived the causal relationship between the incomplete factor markets, especially incomplete labor markets, and the inverse productivity, empirical studies to test the causal relationships are limited. In particular, a solid foundation based on an empirical analysis is lacking when it comes to explaining the inverse productivity in China. Results of this study are expected to have significant policy implications in terms of the understanding of small-size producer behavior and the associated mechanism underlying the inverse relationship between farm size and land productivity.

The purpose of this paper is to study the functionality of additively manufactured (AM) parts, mainly depending on their dimensional accuracy and surface finish. However, the products manufactured using AM usually suffer from defects like roughness or uneven surfaces. This paper discusses the various surface quality improvement techniques, including how to reduce surface defects, surface roughness and dimensional accuracy of AM parts.

There are many different types of popular AM methods. Unfortunately, these AM methods are susceptible to different kinds of surface defects in the product. As a result, pre- and postprocessing efforts and control of various AM process parameters are needed to improve the surface quality and reduce surface roughness.

In this paper, the various surface quality improvement methods are categorized based on the type of materials, working principles of AM and types of finishing processes. They have been divided into chemical, thermal, mechanical and hybrid-based categories.

The review has evaluated the possibility of various surface finishing methods for enhancing the surface quality of AM parts. It has also discussed the research perspective of these methods for surface finishing of AM parts at micro- to nanolevel surface roughness and better dimensional accuracy.

This paper represents a comprehensive review of surface quality improvement methods for both metals and polymer-based AM parts.

This paper aims to accommodate the influence of both economic policy uncertainty and geopolitical risks in the relationship between oil price and exchange-rate returns in the Brazil, Russia, India, China and South Africa (BRICS) countries through an interaction term (EPGR).

The authors use continuous wavelet transform (CWT), wavelet coherence (WC) and partial wavelet coherence (PWC). First, the authors apply the CWT to examine the evolution of oil prices, EPGR and exchange rate returns. Second, the authors use WC to investigate the relationship between oil price and exchange rate returns (excluding EPGR). Third, the authors use PWC to account for EPGR’s impact on the oil exchange rate returns dynamics and explore partial correlations in the oil and exchange rate returns dynamics.

The empirical results generally show that EPGR is a key driver in the oil and exchange rate returns nexus.

The relevance of EPGR in influencing exchange rate volatility is confirmed by the findings. As a result, it is critical for government officials and foreign exchange investors to use EPGR as a leading indicator when establishing foreign exchange trading strategies and economic forecasts.

This study is the first, to the best of the authors’ knowledge, to apply a wavelet-based technique to account for EPGR in the relationship between oil and exchange rate returns in the BRICS countries.