Financing Efficiency Evaluation and Influencing Factors of Hydrogen Energy Listed Enterprises in China

Existing studies of financing efficiency concentrate on capital structure and a single external environment or internal management characteristic. Few of the studies include the internal and external financing environments at the same time for hydrogen energy industry financing efficiency. This paper used the data envelopment analysis (DEA) model and the Malmquist index to measure the financing efficiency of 70 hydrogen energy listed enterprises in China from 2014 to 2020 from both static and dynamic perspectives. Then, a tobit model was constructed to explore the influence of external environment and internal factors on the financing efficiency. The contributions of this paper are studying the internal and external financing environments, and integrating financing cost efficiency and capital allocation efficiency into the financing efficiency of hydrogen energy enterprises. The results show that, firstly, the financing efficiency of China’s hydrogen energy listed enterprises showed an upward trend during the years 2014–2020. Secondly, China’s hydrogen energy enterprises mainly gather in the eastern coastal areas, and their financing efficiency is more than that in western areas. Thirdly, the regional economic development level, enterprise scale, financing structure, capital utilization efficiency and profitability have significant effects on the financing efficiency. These results can promote the achievement of “carbon neutrality” in China.

A Literature Review of the Influence of Commercial Credit on the Efficiency of Enterprise Capital Allocation

The real economy is the main body of high-quality development, and the efficiency of capital allocation is an important manifestation of the development of the real economy. Therefore, it is very important to study the efficiency of capital allocation. As a representative of horizontal finance, commercial credit has a significant impact on the improvement of capital allocation efficiency. In view of this, this article combs the literature on commercial credit and capital allocation efficiency from the following aspects: firstly, by studying the literature, combing the literature on the macro-level, micro-level and economic effects of commercial credit; secondly, the measurement method of capital allocation efficiency And the influencing factors are systematically sorted out, and finally sorted out and evaluated the existing literature on the influence of commercial credit on the efficiency of capital allocation.

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Although socially responsible investment (SRI) has developed into an important investment style, only a small number of studies discuss SRI portfolio construction. In view of the overwhelming breakthrough of machine learning in prediction, this paper proposes SRI portfolio construction models by combining a double-screening mechanism considering machine learning prediction and an extended global minimum variance (GMV) model (or extended maximum Sharpe ratio (MSPR) model), which are, respectively, named double-screening socially responsible investment (DSSRI) portfolio models I and II. The proposed models consist of two stages, i.e., stock screening and asset allocation. First, this paper develops a novel double-screening mechanism incorporating environmental, social, and corporate governance (ESG) and return potential criteria to ensure that high-quality stocks with good ESG performance and high-return potential are input into the optimal portfolio. Specifically, to obtain accurate stock return predictions, an extreme learning machine model optimized by the genetic algorithm is employed to predict stock prices. Next, to trade off the financial and ESG objectives of SRI investors, an extended GMV model (or extended MSPR model) considering the ESG factor is introduced to determine the capital allocation proportion of the stocks. We take the A-share market of China as the sample to verify the effectiveness of the proposed models. The empirical results demonstrate that compared with alternative models, the proposed models can yield better annualized return and ESG score performance as well as competitive Sharpe ratio performance.

Life Insurance Companies: Determinants of Cost Efficiency and Profitability

One of the most important aspects of measuring a firm’s performance is its efficiency, through which the firm is expected to envisage effective cost reductions, thereby enhancing profitability. However, most studies conducted to explore the determinants of insurance companies’ performance has concentrated on the accounts earnings information and its components which are known to explain a small proportion of a firm’s performance. Also, studies on insurance either lump all the insurance companies together or pay more attention to non-life insurance, making it difficult to evaluate the fast growing life insurance industry in Ghana. Therefore, this study examines the efficiency of life insurance companies in Ghana utilising data from twelve life insurance companies for a period of 2013-2017. The efficiency scores were calculated using Efficiency Measurement System software. The fixed effect panel regression results show that, the significant determinants of both cost and profit functions are: price of labour, commission, gross premium and net investment income. It was also revealed that, on the average, the life insurance companies were about 71.2% cost efficient and 41.7% profit efficient. Further analysis reveals that, both profit and cost efficiency changes have statistically significant positive effect on firms’ Return on Asset. Policy-makers should institute policies that encourage these companies to operate efficiently in order to make effective capital allocation decisions to avoid collapse.

Global Transmission of Returns among Financial, Traditional Energy, Renewable Energy and Carbon Markets: New Evidence

Connections to world markets facilitate local markets developments to support more efficient capital allocation and greater investment and growth opportunities. Under the framework of cross-market rebalancing theory, in this study, we aim to systematically examine the market connections among world financial, energy, renewable energy and European carbon markets by measuring the return spillovers from 2008 to 2021. We find that the renewable energy market is more closely connected to the world financial and energy markets in the sense of the return transmission, while the carbon market is less connected to them. However, due to improved market regulations and determinations related to fighting climate change, the connections between the carbon market and other markets have gradually intensified. Plotting the return spillover indexes, we observe that strong return spillovers from the renewable energy market to other markets occurred when large investment plans were announced. Regarding the carbon market, regulation changes introduced by the EU Commission to improve and stabilize market environment induced intensified return transmission from carbon market to other markets. Another interesting finding is that the highly intensified return transmission among markets due to the COVID-19 crisis started to loosen when COVAX published the first interim distribution forecast on 3 February 2021.

Anecdotal Bias in Capital Allocation Depends on Anecdote Similarity

Previous research found that anecdotes are more persuasive than statistical data—the anecdotal bias effect. Separate research found that anecdotes that are similar to a target problem are more influential on decision-making than dissimilar anecdotes. Further, previous investigations on anecdotal bias primarily focused on medical decision-making with very little focus on business decision-making. Therefore, we investigated the effect of anecdote similarity on anecdotal bias in capital allocation decisions. Participants were asked to allocate a hypothetical budget between two business projects. One of the projects (the target project) was clearly superior in terms of the provided statistical measures, but some of the participants also saw a description of a project with a conflicting outcome (the anecdotal project). This anecdotal project was always from the same industry as the target project. The anecdote description, however, either contained substantive connections to the target or not. Further, the anecdote conflicted with the statistical measures because it was either successful (positive anecdote) or unsuccessful (negative anecdote). The results showed that participants’ decisions were influenced by anecdotes only when they believed that they were actually relevant to the target project. Further, they still incorporated the statistical measures into their decision. This was found for both positive and negative anecdotes. Further, participants were given information about the way that the anecdotes were sampled that suggested that the statistical information should have been used in all cases. Participants did not use this information in their decisions and still showed an anecdotal bias effect. Therefore, people seem to appropriately use anecdotes based on their relevance, but do not understand the implications of certain statistical concepts.