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Modeling the link between environmental, social, and governance disclosures and scores: the case of publicly traded companies in the Borsa Istanbul Sustainability Index 模拟环境、社会和治理信息披露与得分之间的联系:伊斯坦布尔证券交易所可持续发展指数中的上市公司案例
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-28 DOI: 10.1186/s40854-024-00619-1
Mustafa Tevfik Kartal, Serpil Kılıç Depren, Ugur Korkut Pata, Dilvin Taşkın, Tuba Şavlı
This study constructs a proposed model to investigate the link between environmental, social, and governance (ESG) disclosures and ESG scores for publicly traded companies in the Borsa Istanbul Sustainability (XUSRD) index. In this context, this study considers 66 companies, examining recently structured ESG disclosures for 2022 that were published for the first time as novel data and applying a multilayer perceptron (MLP) artificial neural network algorithm. The relevant results are fourfold. (1) The MLP algorithm has explanatory power (i.e., R2) of 79% in estimating companies’ ESG scores. (2) Common, environment, social, and governance pillars have respective weights of 21.04%, 44.87%, 30.34%, and 3.74% in total ESG scores. (3) The absolute and relative significance of each ESG reporting principle for companies’ ESG scores varies. (4) According to absolute and relative significance, the most effective ESG principle is the common principle, followed by social and environmental principles, whereas governance principles have less significance. Overall, the results demonstrate that applying a linear approach to complete deficient ESG disclosures is inefficient for increasing companies’ ESG scores; instead, companies should focus on the ESG principles that have the highest relative significance. The findings of this study contribute to the literature by defining the most significant ESG principles for stimulating the ESG scores of companies in the XUSRD index.
本研究构建了一个拟议模型,以研究伊斯坦布尔证券交易所可持续发展指数(XUSRD)中上市公司的环境、社会和治理(ESG)披露与 ESG 分数之间的联系。在此背景下,本研究考虑了 66 家公司,对首次发布的 2022 家公司最近结构化的 ESG 披露作为新数据进行了检查,并应用了多层感知器 (MLP) 人工神经网络算法。相关结果有四个方面。(1)MLP 算法在估算公司 ESG 分数方面的解释力(即 R2)为 79%。(2)共同、环境、社会和治理支柱在 ESG 总分中的权重分别为 21.04%、44.87%、30.34% 和 3.74%。(3) 各ESG报告原则对公司ESG得分的绝对和相对重要性各不相同。(4) 从绝对和相对重要性来看,最有效的 ESG 原则是共同原则,其次是社会和环境原则,而治理原则的重要性较低。总之,研究结果表明,采用线性方法来完成ESG披露的不足之处,对于提高公司的ESG得分效率不高;相反,公司应关注相对重要性最高的ESG原则。本研究的结果界定了对刺激 XUSRD 指数中公司的 ESG 分数最重要的 ESG 原则,从而为相关文献做出了贡献。
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引用次数: 0
The volatility mechanism and intelligent fusion forecast of new energy stock prices 新能源股票价格的波动机制与智能融合预测
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-22 DOI: 10.1186/s40854-024-00621-7
Guo-Feng Fan, Ruo-Tong Zhang, Cen-Cen Cao, Li-Ling Peng, Yi-Hsuan Yeh, Wei-Chiang Hong
The new energy industry is strongly supported by the state, and accurate forecasting of stock price can lead to better understanding of its development. However, factors such as cost and ease of use of new energy, as well as economic situation and policy environment, have led to continuous changes in its stock price and increased stock price volatility. By calculating the Lyapunov index and observing the Poincaré surface of the section, we find that the sample of the China Securities Index Green Power 50 Index has chaotic characteristics, and the data indicate strong volatility and uncertainty. This study proposes a new method of stock price index prediction, namely, EWT-S-ALOSVR. Empirical wavelet decomposition extracts features from multiple factors affecting stock prices to form multiple sub-columns with features, significantly reducing the complexity of the stock price series. Support vector regression is well suited for dealing with nonlinear stock price series, and the support vector machine model parameters are selected using random wandering and picking elites via Ant Lion Optimization, making stock price prediction more accurate.
新能源产业是国家大力扶持的产业,准确预测股价可以更好地了解其发展情况。然而,新能源的成本、使用难易程度以及经济形势和政策环境等因素导致其股价不断变化,股价波动性增大。通过计算李雅普诺夫指数和观察截面的波恩卡列面,我们发现中证绿色动力50指数样本具有混沌特征,数据显示出较强的波动性和不确定性。本研究提出了一种新的股价指数预测方法,即 EWT-S-ALOSVR。经验小波分解从影响股价的多个因素中提取特征,形成多个具有特征的子列,大大降低了股价序列的复杂性。支持向量回归非常适合处理非线性股价序列,支持向量机模型参数的选择采用随机游走,并通过蚁狮优化挑选精英,使股价预测更加准确。
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引用次数: 0
Drawdown-based risk indicators for high-frequency financial volumes 基于缩编的高频金融交易量风险指标
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-22 DOI: 10.1186/s40854-023-00593-0
Guglielmo D’Amico, Bice Di Basilio, Filippo Petroni
In stock markets, trading volumes serve as a crucial variable, acting as a measure for a security’s liquidity level. To evaluate liquidity risk exposure, we examine the process of volume drawdown and measures of crash-recovery within fluctuating time frames. These moving time windows shield our financial indicators from being affected by the massive transaction volume, a characteristic of the opening and closing of stock markets. The empirical study is conducted on the high-frequency financial volumes of Tesla, Netflix, and Apple, spanning from April to September 2022. First, we model the financial volume time series for each stock using a semi-Markov model, known as the weighted-indexed semi-Markov chain (WISMC) model. Second, we calculate both real and synthetic drawdown-based risk indicators for comparison purposes. The findings reveal that our risk measures possess statistically different distributions, contingent on the selected time windows. On a global scale, for all assets, financial risk indicators calculated on data derived from the WISMC model closely align with the real ones in terms of Kullback–Leibler divergence.
在股票市场中,交易量是一个关键变量,是衡量证券流动性水平的一个指标。为了评估流动性风险敞口,我们在波动的时间框架内研究交易量缩减的过程和暴跌恢复的措施。这些移动时间窗口使我们的金融指标不会受到股票市场开盘和收盘时大量交易量的影响。实证研究的对象是特斯拉、Netflix 和苹果公司的高频财务交易量,时间跨度为 2022 年 4 月至 9 月。首先,我们使用半马尔可夫模型(即加权指数半马尔可夫链(WISMC)模型)对每只股票的交易量时间序列进行建模。其次,我们计算了基于实际缩减和合成缩减的风险指标,以进行比较。研究结果表明,根据所选时间窗口的不同,我们的风险指标在统计上具有不同的分布。在全球范围内,就所有资产而言,根据 WISMC 模型得出的数据计算出的金融风险指标在库尔贝-莱伯勒离差方面与真实指标非常接近。
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引用次数: 0
User acceptance of social network-backed cryptocurrency: a unified theory of acceptance and use of technology (UTAUT)-based analysis 用户对社交网络支持的加密货币的接受程度:基于技术接受和使用统一理论(UTAUT)的分析
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-21 DOI: 10.1186/s40854-023-00511-4
Márk Recskó, Márta Aranyossy
Turbulent market conditions, well-publicized advantages, and potential individual, social, and environmental risks make blockchain-based cryptocurrencies a popular focus of the public and scientific communities. This paper contributes to the literature on the future of crypto markets by analyzing a promising cryptocurrency innovation from a customer-centric point of view; it explores the factors influencing user acceptance of a hypothetical social network-backed cryptocurrency in Central Europe. The research model adapts an internationally comparative framework and extends the well-established unified theory of acceptance and use of the technology model with the concept of perceived risk and trust. We explore user attitudes with a survey on a large Hungarian sample and analyze the database with consistent partial least square structural equation modeling methodology. The results show that users would be primarily influenced by the expected usefulness of the new technology assuming it is easy to use. Furthermore, our analysis also highlights that while social influence does not seem to sway user opinions, consumers are susceptible to technological risks, and trust is an important determinant of their openness toward innovations in financial services. We contribute to the cryptocurrency literature with a future-centric technological focus and provide new evidence from an under-researched geographic region. The results also have practical implications for business decision-makers and policymakers.
动荡的市场环境、广为人知的优势以及潜在的个人、社会和环境风险使基于区块链的加密货币成为公众和科学界关注的焦点。本文从以客户为中心的角度分析了一种前景广阔的加密货币创新,为有关加密货币市场未来的文献做出了贡献;本文探讨了影响中欧地区用户接受一种假设的社交网络支持的加密货币的因素。研究模型采用了国际比较框架,并通过感知风险和信任的概念扩展了成熟的技术模型接受和使用统一理论。我们通过对大量匈牙利样本进行调查来探究用户态度,并采用一致的偏最小二乘法结构方程建模方法对数据库进行分析。结果表明,假设新技术易于使用,用户将主要受到新技术预期有用性的影响。此外,我们的分析还强调,虽然社会影响似乎不会左右用户的意见,但消费者容易受到技术风险的影响,而信任是决定他们对金融服务创新持开放态度的重要因素。我们以未来技术为中心,为加密货币文献做出了贡献,并从一个研究不足的地区提供了新的证据。研究结果对企业决策者和政策制定者也有实际意义。
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引用次数: 0
Volatility spillovers among leading cryptocurrencies and US energy and technology companies 主要加密货币与美国能源和技术公司之间的波动溢出效应
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-20 DOI: 10.1186/s40854-024-00626-2
Amro Saleem Alamaren, Korhan K. Gokmenoglu, Nigar Taspinar
This study investigates volatility spillovers and network connectedness among four cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin), four energy companies (Exxon Mobil, Chevron, ConocoPhillips, and Nextera Energy), and four mega-technology companies (Apple, Microsoft, Alphabet, and Amazon) in the US. We analyze data for the period November 15, 2017–October 28, 2022 using methodologies in Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Economet 16(2):271–296 2018). Our analysis shows the COVID-19 pandemic amplified volatility spillovers, thereby intensifying the impact of financial contagion between markets. This finding indicates the impact of the pandemic on the US economy heightened risk transmission across markets. Moreover, we show that Bitcoin, Ethereum, Chevron, ConocoPhilips, Apple, and Microsoft are net volatility transmitters, while Tether, BNB, Exxon Mobil, Nextera Energy, Alphabet, and Amazon are net receivers Our results suggest that short-term volatility spillovers outweigh medium- and long-term spillovers, and that investors should be more concerned about short-term repercussions because they do not have enough time to act quickly to protect themselves from market risks when the US market is affected. Furthermore, in contrast to short-term dynamics, longer term patterns display superior hedging efficiency. The net-pairwise directional spillovers show that Alphabet and Amazon are the highest shock transmitters to other companies. The findings in this study have implications for both investors and policymakers.
本研究调查了美国四种加密货币(比特币、以太坊、Tether 和 BNB 币)、四家能源公司(埃克森美孚、雪佛龙、康菲石油和 Nextera 能源)以及四家超大型科技公司(苹果、微软、Alphabet 和亚马逊)之间的波动溢出效应和网络关联性。我们使用 Diebold 和 Yilmaz(Int J Forecast 28(1):57-66, 2012)以及 Baruník 和 Křehlík (J Financ Economet 16(2):271-296 2018)中的方法分析了 2017 年 11 月 15 日至 2022 年 10 月 28 日期间的数据。我们的分析表明,COVID-19 大流行病放大了波动溢出效应,从而加剧了市场间金融传染的影响。这一发现表明,大流行病对美国经济的影响加剧了市场间的风险传递。此外,我们的研究表明,比特币、以太坊、雪佛龙、康菲石油、苹果和微软是波动性的净传播者,而 Tether、BNB、埃克森美孚、Nextera Energy、Alphabet 和亚马逊则是净接收者。我们的研究结果表明,短期波动性溢出效应大于中长期溢出效应,投资者应更关注短期影响,因为当美国市场受到影响时,他们没有足够的时间迅速采取行动保护自己免受市场风险的影响。此外,与短期动态相比,长期模式显示出更高的对冲效率。净对方向溢出效应表明,Alphabet 和亚马逊是对其他公司冲击最大的传播者。本研究的结论对投资者和政策制定者都有借鉴意义。
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引用次数: 0
Detecting DeFi securities violations from token smart contract code 从代币智能合约代码中检测 DeFi 证券违规行为
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-20 DOI: 10.1186/s40854-023-00572-5
Arianna Trozze, Bennett Kleinberg, Toby Davies
Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In recent years, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, particularly various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses. This study aims to determine whether this problem is suited to a machine learning approach, namely, whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’ smart contract code. We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’ tokens’ smart contract code (specifically, opcode-based features). Our final model was a random forest model that achieved an 80% F-1 score against a baseline of 50%. Notably, we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’ Solidity code and conducting cosine similarity analyses. We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library, although this does not account for the entirety of our features. Another contribution of our study is a new dataset, comprising (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of legitimate tokens from a reputable DeFi aggregator. This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.
去中心化金融(DeFi)是在各种区块链上通过智能合约构建和交付金融产品和服务的系统。近年来,DeFi 广受欢迎,市值不断攀升。然而,它也与犯罪有关,特别是各类证券违规行为。DeFi 中缺乏 "了解你的客户 "的要求,这给试图减少潜在违法行为的政府带来了挑战。本研究旨在确定这一问题是否适用于机器学习方法,即我们是否能根据代币的智能合约代码识别可能从事证券违规行为的 DeFi 项目。我们根据从 DeFi 项目代币的智能合约代码中提取的特征(特别是基于 opcode 的特征)构建分类器,从而调整了之前在以太坊上检测特定类型证券违规行为的工作。我们的最终模型是一个随机森林模型,它的 F-1 分数达到了 80%,而基线为 50%。值得注意的是,我们通过分析代币的 Solidity 代码并进行余弦相似性分析,进一步探索了对我们的模型性能最重要的基于代码的特征。我们发现,我们基于操作码的特征可以捕捉到的代码元素之一是 SafeMath 库的实现,尽管这并不代表我们的全部特征。我们研究的另一个贡献是建立了一个新的数据集,其中包括:(a) 一个经过验证的涉及证券违规代币的基本真实数据集;(b) 一组来自知名 DeFi 聚合器的合法代币。本文进一步讨论了检察官在执法工作中使用我们这样的模型的可能性,并将其与更广泛的法律背景联系起来。
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引用次数: 0
Evaluating short- and long-term investment strategies: development and validation of the investment strategies scale (ISS) 评估短期和长期投资战略:制定和验证投资战略量表(ISS)
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-19 DOI: 10.1186/s40854-023-00573-4
Ibrahim Arpaci, Omer Aslan, Mustafa Kevser
In response to the growing importance of understanding individual investment strategies, the present study aimed to develop a new scale for measuring both the short- and long-term investment strategies of individuals. The study assessed the psychometric properties of the investment strategies scale (ISS) using data collected from 1428 individual investors. In the initial study, an exploratory factor analysis (EFA) was conducted to investigate the factor structure of the proposed scale (N = 700). The EFA results yielded a two-factor structure, and Cronbach’s alpha values for short- and long-term investment strategies were 0.90 and 0.88, respectively. A confirmatory factor analysis was performed to validate the factor structure of the scale in the second study (N = 728). The results demonstrated that the two-factor model fit the data well. In the third study, the correlation between Hofstede’s long-term orientation and the two dimensions of the scale was investigated. The results indicated that long-term investment strategies significantly predict long-term orientation, thus confirming the concurrent validity of the scale. These findings demonstrate that the proposed ISS is a reliable and valid instrument for measuring individuals’ short- and long-term investment strategies, contributing to a deeper understanding of investment decision-making processes. This study introduces a novel measurement tool—ISS—specifically designed to comprehensively assess both short- and long-term investment strategies among individual investors.
鉴于了解个人投资策略日益重要,本研究旨在开发一种新的量表,用于测量个人的短期和长期投资策略。本研究利用从 1428 名个人投资者那里收集到的数据,对投资策略量表(ISS)的心理测量特性进行了评估。在初步研究中,进行了探索性因子分析(EFA),以调查拟议量表的因子结构(N = 700)。EFA 结果显示量表具有双因子结构,短期投资策略和长期投资策略的 Cronbach's Alpha 值分别为 0.90 和 0.88。在第二次研究(样本数=728)中,我们进行了确认性因子分析,以验证量表的因子结构。结果表明,双因子模型很好地拟合了数据。在第三项研究中,调查了霍夫斯泰德的长期取向与量表两个维度之间的相关性。结果表明,长期投资战略能显著预测长期取向,从而证实了量表的并行有效性。这些研究结果表明,拟议的国际投资策略量表是测量个人短期和长期投资策略的可靠而有效的工具,有助于加深对投资决策过程的理解。本研究引入了一种新型测量工具--ISS,专门用于全面评估个人投资者的短期和长期投资策略。
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引用次数: 0
Features of different asset types and extreme risk transmission during the COVID-19 crisis COVID-19 危机期间不同资产类型和极端风险传播的特点
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-17 DOI: 10.1186/s40854-023-00510-5
I-Chun Tsai
Unlike the current extensive literature, which discusses which assets can avoid the risks caused by the COVID-19 pandemic, this study examines whether the characteristics of different assets affect the extreme risk transmission of the COVID-19 crisis. This study explores the effects of COVID-19 pandemic–related risk factors (i.e., pandemic severity, pandemic regulations and policies, and vaccination-related variables) on the risk of extreme volatility in asset returns across eight assets. These eight assets belong to the following classes: virtual, financial, energy, commodities, and real assets. To consider the different possible aspects of the COVID-19 impact, this study adopts both empirical methods separately, considering variables related to the pandemic as exogenous shocks and endogenous factors. Using these methods, this study enabled a systematic analysis of the relationship between the features of different asset types and the effects of extreme risk transmission during the COVID-19 crisis. The results show that different types of asset markets are affected by different risk factors. Virtual and commodity assets do not exhibit extreme volatility induced by the COVID-19 pandemic. The energy market, including crude oil, is most affected by the negative impact of the severity of the pandemic, which is unfavorable for investment at the beginning of the pandemic. However, after vaccinations and pandemic regulations controlled the spread of infection, the recovery of the energy market made it more conducive to investment. In addition, this study explains the differences between the hedging characteristics of Bitcoin and gold. The findings of this study can help investors choose asset types systematically when faced with different shocks.
目前大量文献都在讨论哪些资产可以规避 COVID-19 大流行带来的风险,与此不同的是,本研究探讨了不同资产的特征是否会影响 COVID-19 危机的极端风险传递。本研究探讨了 COVID-19 大流行相关风险因素(即大流行严重程度、大流行法规和政策以及疫苗接种相关变量)对八种资产收益极端波动风险的影响。这八种资产属于以下类别:虚拟资产、金融资产、能源资产、商品资产和实物资产。为了考虑 COVID-19 可能产生的不同方面的影响,本研究分别采用了两种实证方法,将与大流行病相关的变量视为外生冲击和内生因素。利用这些方法,本研究得以系统分析不同类型资产的特征与 COVID-19 危机期间极端风险传播影响之间的关系。结果显示,不同类型的资产市场受到不同风险因素的影响。虚拟资产和商品资产没有表现出 COVID-19 大流行所诱发的极端波动。包括原油在内的能源市场受大流行病严重程度的负面影响最大,在大流行病初期不利于投资。然而,在疫苗接种和大流行法规控制了感染传播后,能源市场的复苏使其更有利于投资。此外,本研究还解释了比特币和黄金对冲特性的差异。本研究的结论有助于投资者在面临不同冲击时系统地选择资产类型。
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引用次数: 0
A simplified model for measuring longevity risk for life insurance products 衡量人寿保险产品长寿风险的简化模型
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-16 DOI: 10.1186/s40854-023-00515-0
David Atance, Eliseo Navarro
In this paper, we propose a simple dynamic mortality model to fit and forecast mortality rates for measuring longevity and mortality risks. This proposal is based on a methodology for modelling interest rates, which assumes that changes in spot interest rates depend linearly on a small number of factors. These factors are identified as interest rates with a given maturity. Similarly, we assume that changes in mortality rates depend linearly on changes in a specific mortality rate, which we call the key mortality rate. One of the main advantages of this model is that it allows the development of an easy to implement methodology to measure longevity and mortality risks using simulation techniques. Particularly, we employ the model to calculate the Value-at-Risk and Conditional-Value-at-Risk of an insurance product testing the accuracy and robustness of our proposal using out-of-sample data from six different populations.
在本文中,我们提出了一个简单的动态死亡率模型,用于拟合和预测死亡率,以衡量长寿和死亡率风险。该模型假设即期利率的变化线性取决于少数几个因素。这些因素被确定为特定期限的利率。同样,我们假设死亡率的变化线性取决于特定死亡率的变化,我们称之为关键死亡率。该模型的主要优点之一是,它允许开发一种易于实施的方法,利用模拟技术来衡量长寿和死亡率风险。特别是,我们利用该模型计算了一种保险产品的风险价值和条件风险价值,使用来自六个不同人群的样本外数据测试了我们建议的准确性和稳健性。
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引用次数: 0
Pattern recognition of financial innovation life cycle for renewable energy investments with integer code series and multiple technology S-curves based on Q-ROF DEMATEL 基于 Q-ROF DEMATEL 的整数代码序列和多技术 S 曲线的可再生能源投资金融创新生命周期模式识别
IF 8.4 1区 经济学 Q1 BUSINESS, FINANCE Pub Date : 2024-02-08 DOI: 10.1186/s40854-024-00616-4
Gang Kou, Hasan Dinçer, Serhat Yüksel
The current study evaluates the financial innovation life cycle for renewable energy investments. A novel model is proposed that has two stages. First, the financial innovation life cycle is weighted by the two-generation technology S-curve (TTSC) for renewable energy investments. Second, the TTSC is ranked with integer patterns for renewable energy investments. For this purpose, the decision-making trial and evaluation laboratory (DEMATEL) is considered with q-rung orthopair fuzzy sets (q-ROFSs). A comparative examination is then performed using intuitionistic and Pythagorean fuzzy sets, and we find similar results for all fuzzy sets. Hence, the suggested model is reliable and coherent. Maturity phase 1 is the most significant phase of the financial innovation life cycle for these projects. Aging is the most important period for financial innovation in renewable energy investment projects—renewable energy companies should make strategic decisions after that point. In this situation, decisions should relate to either radical or incremental innovation. If companies do not make decisions during these phases, innovative financial products cannot be improved. As a result, renewable energy companies will not prefer financing products.
本研究评估了可再生能源投资的金融创新生命周期。研究提出了一个包含两个阶段的新模型。首先,根据可再生能源投资的两代技术 S 曲线(TTSC)对金融创新生命周期进行加权。其次,根据可再生能源投资的整数模式对 TTSC 进行排序。为此,决策试验和评估实验室(DEMATEL)考虑使用 q-rung 正对模糊集(q-ROFS)。然后使用直观模糊集和毕达哥拉斯模糊集进行了比较研究,我们发现所有模糊集的结果都相似。因此,建议的模型是可靠和连贯的。成熟期 1 是这些项目金融创新生命周期中最重要的阶段。老化期是可再生能源投资项目金融创新最重要的时期--可再生能源公司应在此阶段后做出战略决策。在这种情况下,决策应与激进创新或渐进创新有关。如果企业不在这些阶段做出决策,创新的金融产品就无法得到改进。因此,可再生能源企业不会优先选择融资产品。
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引用次数: 0
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Financial Innovation
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