Uncovering the impacts of structural similarity of financial indicators on stock returns at different quantile levels

Xi, Xian; Gao, Xiangyun; Zhang, Jinsheng; Zheng, Huiling; Ding, Jiazheng

doi:10.1016/j.irfa.2021.101787

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…From 2000 to 2017, the greater part of the budget summaries of recorded organizations has been effectively obtained, including an aggregate of 32,283 fiscal reports of 3,025 recorded organizations. Based on perusing countless writing [16][17][18][19], this paper constructed six first-level indicators according to the above financial statement data, namely, solvency index, profitability index, operating ability index, development ability index, cash flow index, and risk level index. Under this, we figured out 25 auxiliary markers as indicated by the current writing on monetary danger and monetary extortion.…”

Section: Financial Index Constructionmentioning

confidence: 99%

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

Wei

2022

Wireless Communications and Mobile Computing

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At present, the process of security issuance in China has changed from examination and approval system to approval system. With the increasing stock that enters the capital market through the IPO, establishing an effective financial risk monitoring and control system of listed companies has great practical significance and application value. In the perspective of big data, this paper constructs a new practical financial early warning integration model. A total of 32,283 financial statements of 3,025 listed companies are collected from 2000 to 2017. First, this paper collected and built a financial crisis prediction database: a total of 32,283 financial statements of 3,025 listed companies occurred from year 2000 to 2017 and corresponding financial indicators including 6 primary indicators and 25 secondary indicators are collected. Next, the financial crisis model based on financial indicators is proposed and trained by the classic machine learning method such as random forest and gradient boosting decision tree. And then, through natural language processing and deep learning technology, this paper also builds a financial crisis prediction model based on financial statement text. Finally, an ensemble model is proposed and its performances are evaluated; the results indicate that the proposed ensemble model performed the best and significantly outperforms other benchmark methods for financial crisis prediction and identification, indicating that it can be employed as an intelligent identification system to enhance identification accuracy for early warning and identification of financial crisis of listed companies in China stock marketing.

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Section: Financial Index Constructionmentioning

confidence: 99%

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

Wei

2022

Wireless Communications and Mobile Computing

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“…The ground of our proposal is that rms with similar features tend to show the same level of development in terms of the considered characteristics (see e.g., [13] and [27]).…”

Section: Introductionmentioning

confidence: 95%

“…In the quoted paper, the authors use a threshold method to construct the correlation network of Chinese stocks and then study the structural properties of the network and topological stability. In [13], the authors compute the structural similarity of nancial indicators using normalised Euclidean distance. They construct networks based on rm performance and analyse the topological characteristics and parameters of the networks to represent the level of similarity among rms.…”

Section: Introductionmentioning

confidence: 99%

Similarity-based heterogeneity and cohesiveness of networked companies issuing minibonds

Cerqueti

Deffains‐Crapsky

Storani

2022

Chaos, Solitons & Fractals

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“…Xian et al (2021) [18] point out that, in this context, (1) the strength of the structural similarity of the investment project indicators, (2) the average negative effects extracted from the similarities in the old projects are important for the new projects and (3) the impact of the cohesion of positive similarities applied to new investment projects.…”

Section: Review Of Specialized Literaturementioning

confidence: 99%

Similarities and Proximity Symmetries for Decisions of Complex Valuation of Mining Resources in Anthropically Affected Areas

et al. 2022

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After 1990, when the economic system changed in Romania, the mining industry was the most controversial field from a productive-economic point of view and subject to reforms and transformations for efficiency. Currently (2022), there are nine main mining perimeters in which the production of useful, energetic, and nonenergetic mineral substances is operational, and in others it has decreased or stopped. Still active mining areas need economic and ecological assessments to identify similarities and proximity symmetries for informed exploitation decisions and feasible complex resource utilization. The main objective of our study is to define a framework for the theoretical and practical contribution to the substantiation of decisions and expressions of interest regarding future investments in mining projects for useful and energetic and non-energetic mineral substances in Romania. Investments in the mining industry are expensive, with major risks and subunit success rates for specific geotechnological conditions. The purpose of the research is to provide the methodology for using some variables of similarities from proximity mining deposits in the stage of exploitation or post-exploitation affected by anthropogenic activity in the national geological territory through a case study of Romania. With the help of statistical scales, the research results highlight that in the exploitation and post-mining perimeters in Romania, the states of “affect” and “post-affect” anthropic, respectively, of eco-economic damage are in a maximum proportion of approximately 36% in relation to the ideal situations of no affect. For a mining investment project, knowing similar or symmetrical exploitation and post-exploitation properties and situations, and from the geological deposits in the vicinity, premises are created for optimized strategic and tactical decisions, based on reality and, above all, for the provision of expressions of interest for new investments that have a programmed, expected success rate.

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Uncovering the impacts of structural similarity of financial indicators on stock returns at different quantile levels

Cited by 5 publications

References 38 publications

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

Similarity-based heterogeneity and cohesiveness of networked companies issuing minibonds

Similarities and Proximity Symmetries for Decisions of Complex Valuation of Mining Resources in Anthropically Affected Areas

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