A dynamic SBM‐DEA and portfolio formation test approach to the operating efficiency‐stock returns nexus

Mills, Ebenezer Fiifi Emire Atta; Anyomi, Siegfried K.; Baafi, Mavis Agyapomah; Borah, Prasad Siba

doi:10.1002/mde.3583

Cited by 2 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al (2019) used the DEA method to evaluate the carbon emission efficiency of each province and empirically investigated the impact of urbanization on carbon emission efficiency based on the stochastic impacts by regression on population, affluence, and technology extension. Atta et al (2022) evaluated the operational efficiency of Chinese listed real estate companies through SBM–DEA model and panel regression technology and studied its driving factors. To better evaluate the results, the carbon emission efficiency is measured and analyzed by constructing a super-efficiency SBM model (Zhou et al , 2019; Asmita and Dharmesh, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model

Feng²,

Guo³

et al. 2022

IJCCSM

View full text Add to dashboard Cite

Purpose China has proposed two-stage goals of carbon peaking by 2030 and carbon neutralization by 2060. The carbon emission reduction effect of the power industry, especially the thermal power industry, will directly affect the progress of the goal. This paper aims to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency of the thermal power industry and proposes policy suggestions for realizing China’s carbon peak and carbon neutralization goals. Design/methodology/approach This paper evaluates and compares the carbon emission efficiency of the thermal power industry in 29 provinces and regions in China from 2014 to 2019 based on the three-stage slacks-based measure (SBM) of efficiency in data envelopment analysis (DEA) model of undesired output, excluding the influence of environmental factors and random errors. Findings Empirical results show that during the sample period, the carbon emission efficiency of China’s thermal power industry shows a fluctuating upward trend, and the carbon emission efficiency varies greatly among the provincial regions. The carbon emission efficiency of the interregional thermal power industry presents a pattern of “eastern > central > western,” which is consistent with the level of regional economic development. Environmental factors such as economic level and environmental regulation level are conducive to the improvement of carbon emission efficiency of the thermal power industry, but the proportion of thermal power generation and industrial structure is the opposite. Originality/value This paper adopts the three-stage SBM–DEA model of undesired output and takes CO2 as the undesired output to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency in China’s thermal power industry. The results provide a more comprehensive perspective for regional comparative evaluation and influencing factors of carbon emission efficiency in China’s thermal power industry.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model

Feng²,

Guo³

et al. 2022

IJCCSM

View full text Add to dashboard Cite

show abstract

Optimization of short-term stock selection based on volume and price using a non-cooperative parallel DEA model

Shi,

Luo,

Zhang

et al. 2024

RAIRO-Oper. Res.

View full text Add to dashboard Cite

This paper presents a novel approach to portfolio optimization in the field of finance, with a specific focus on short-term yield. Existing literature has mainly utilized fundamental data to predict long-term trends in stock prices, but our proposed methodology utilizes technical indicators based on the theory of chasing up. Furthermore, we address the non-cooperative nature of volume and price fluctuation indicators and introduce non-cooperative theory into the short-term volume and price stock selection scheme for the first time. We propose an optimization of short-term stock selection based on volume and price using a non-cooperative parallel Data Envelopment Analysis (DEA) model, which we apply to Chinese main board listed companies. Our empirical results demonstrate the effectiveness of our model in selecting high-yield stocks in the short term. This paper contributes to the ongoing discussion on portfolio optimization and presents a compelling solution for investors seeking to maximize their financial gains. The proposed methodology can be utilized in practical applications and has significant implications for the financial industry.

show abstract

A dynamic SBM‐DEA and portfolio formation test approach to the operating efficiency‐stock returns nexus

Cited by 2 publications

References 30 publications

The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model

The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model

Optimization of short-term stock selection based on volume and price using a non-cooperative parallel DEA model

Contact Info

Product

Resources

About