Venture capital (VC) is the main contributor to entrepreneurial firms' funding and thus plays a crucial role in their sustainable development and rapid growth. However, early‐stage VC investors often ...face valuation obstacles to predict firm valuation since entrepreneurial firms lack operational performance records and information asymmetry exists between them. In this paper, an integrated differential evolution algorithm and adaptive moment estimation method scheme (Adam‐ENN) is proposed for early‐stage VC investors to predict entrepreneurial firm valuation. Experimental results show that the proposed machine learning method outperforms the baseline methods. The feature contribution analysis and partial dependence plots were performed to open up the black box of the relationships between entrepreneurial firm valuation and its features. Results indicate that the number of VC investors in the funding syndicate is the most important feature, and VC investors' social capital also plays a significant role in the prediction model. Interestingly, the number of patents cannot convey an effective signal in entrepreneurial firm quality especially in the early‐stage entrepreneurial firm valuation. Finally, this paper helps to guide entrepreneurial firms' valuation using machine learning techniques and offers deep insight into the entrepreneurship financing mechanism from the perspective of VC.
This study focuses on investors' investment decision-making and examines the influence of multiple intelligences in investment decision-making, both rational and irrational. Multiple intelligences ...are a group of Intelligence with its entity and domain. Gardner suggests that the human being has different individual units of intellectual functioning. He labels these units as intelligence, each with identifiable and quantifiable aptitudes. The nine intelligence types differently influence an individual's behaviour and decisions. The multiple intelligence Inventory and a questionnaire on investment decision-making were used for data collection from a hundred investors. Analysis was done with the help of Pearson's correlation, multiple regression chi-square test, and correspondence analysis. Rational decision-making is intellectual, as per the review, and it is proved here that intelligence influences rational decision-making significantly while the other is not influenced. The observed results help to get new visions of investment behaviour, including a new proof of the root causes of investment decisions.
Because of the radical novelty of emerging technologies, investing in such technologies brings high risk and great uncertainty. Whether investors’ experiential knowledge encourages or discourages ...investment in emerging technologies remains under investigation. Juxtaposing the “absorptive capacity” and “competency trap” perspectives, this study proposes a pair of competing hypotheses regarding the influence of experiential knowledge on investor decision-making in emerging technologies. Specifically, we contend that experiential knowledge stimulates investment in emerging technologies based on the absorptive capacity perspective. Meanwhile, drawing on the competency trap perspective, we also argue that experiential knowledge may discourage investment in emerging technologies. Empirical evidence from blockchain-related funding rounds reveals that experiential knowledge negatively influences investment in emerging technologies, which is consistent with the competency trap perspective. We also discover that investor reputation and investor status exacerbate the competency trap implications. Overall, this study sheds light on investment in emerging technologies, the theoretical dilemma of experiential learning, as well as decision-making under uncertainty and ambiguity.
•Investor decision-making regarding emerging technologies is under investigation.•Experiential knowledge impedes investment in emerging technologies.•Investor reputation exacerbates the competency trap effect.•Investor status heightens the competency trap effect.
How building stakeholders (e.g. owners, tenants, operators, and designers) understand impacts of Indoor Air Quality (IAQ) and associated energy costs is unknown. We surveyed 112 stakeholders across ...the United States to ascertain their perceptions of their current IAQ and estimates of benefits and costs of, as well as willingness to pay for, IAQ improvements. Respondents' perceived IAQ scores correlated with the use of high‐efficiency filters but not with any other IAQ‐improving technologies. We elicited their estimates of the impacts of a ventilation–filtration upgrade (VFU), that is, doubling the ventilation rate from 20 to 40 cfm/person (9.5 to 19 l/s/person) and upgrading from a minimum efficiency reporting value 6 to 11 filter, and compared responses to estimates derived from IAQ literature and energy modeling. Minorities of respondents thought the VFU would positively impact productivity (45%), absenteeism (23%), or health (39%). Respondents' annual VFU cost estimates (mean = $257, s.d. = $496, median = $75 per person) were much higher than ours (always <$32 per person), and the only yearly cost a plurality of respondents said they would pay for the VFU was $15 per person. Respondents holding green building credentials were not more likely to affirm the IAQ benefits of the VFU and were less likely to be willing to pay for it.
The present study aims to answer the question of whether herding and overconfidence bias serially mediate the relationship between financial literacy with equity investors’ decision-making in the ...Indian stock market. A survey method was deployed to collect primary data from 436 individual equity investors in the north Indian region. PLS-SEM has been used to examine the serial mediation-based model proposed for the study. Financial literacy was found to have a considerable favourable influence on individual investors’ decision-making. The relationship between financial literacy and investors’ decision-making was found to be serially mediated by herding and overconfidence bias. The study proposes that investors should attend financial market courses, training programs, conferences and seminars to improve their financial literacy and understanding, allowing them to overcome behavioural biases, and will improve their decision-making.
The behavior of investors and their investment decision-making process in the financial markets are guided by psychological (sentiments) and personal characteristics (personality traits). Research in ...recent years has shown the connection between investor sentiment and personality traits and investment decisions. Though academic works in the field of behavioral finance are growing, studies on personality traits and investment decision-making with investor sentiment as a mediator are sparse. To this end, the paper aims to analyze the effects of Indian retail investors’ Big-five personality traits (Neuroticism, Extraversion, Openness to experience, Agreeableness, and Conscientiousness) on their short-term and long-term investment decision-making with the mediating effect of investor sentiment. The study employs the Partial Least Square-Structural Equation Model to test the framed hypotheses. The findings of the study reveal that Neuroticism has a significant positive effect (β=0.352, p<0.05) on investor sentiment. It further shows that Extraversion has a significant positive effect (β=0.186, p<0.05) on long-term decision-making. On the contrary, the consciousness trait has a significant negative effect (β=-0.335, p<0.05) on short-term investment decision-making. Furthermore, the Openness trait demonstrates a significant effect on both short-term and long-term investment decision-making (β=0.357, p<0.05; β=0.007, p<0.05). However, the findings reveal no significant intervening effect of investor sentiment between personality traits and investment decision-making. Thus, the study strongly exerted the impact of investors’ personality traits on their investment decision-making due to the high influence of personal characteristics over sentiment effects.
Investments in industrial energy efficiency are essential for meeting future energy needs. Nevertheless, the industrial sector’s current efforts in energy efficiency investments are insufficient. ...Additional benefits of energy efficiency investments have been suggested to improve the financial attractiveness of energy efficiency investments. Yet, previous research indicates that not all benefits are included when investment opportunities are evaluated, leading to an underestimation of what a firm will gain from the investment. Additionally, previous research lacks conceptual frameworks for describing these additional benefits at an early stage in the investment process. Moreover, various benefit terms are found in currently existing research, but there are a lack of definitions and distinctions attributed to these terms. Therefore, this paper provides a systematic review on the benefit terms of energy efficiency investments, establishes non-energy benefits as the term most relevant for such investments and provides a new definition of the concept. Further, a new framework for categorising non-energy benefits to enable them to be included during the investment process is developed, in which the level of quantifiability and time frame of the non-energy benefits are taken into account. Including non-energy benefits in the investment process can make energy efficiency investments more attractive and increase their priority against other investments. Moreover, non-energy benefits can reinforce drivers as well as counterbalance known barriers to energy efficiency investments. Acknowledging non-energy benefits can thus contribute to an increased adoption level for energy efficiency investments.
•A multi-objective model for energy savings and emissions reduction is proposed.•A hybrid evolutionary algorithm is developed to solve the mixed integer model.•The comparison tests show the better ...performance of the proposed algorithm.•Best compromise solution is screened by using a proposed integrated method.
Coal-mining companies should clearly be investing in energy savings and treatment technologies aimed at reducing both their energy consumption and their pollution levels in order to meet the requirements of government regulations relating to environmental protection; however, when considering the types of technical equipment to use and the timing of their investment, these companies also need to consider their profits and the costs of such investment. We therefore propose a "multi-objective mixed integer non-linear programming" (MMINLP) model of investment in energy savings and emission reductions designed to handle this type of decision-making problem. Given three objectives (maximum profits, minimal energy consumption and minimal pollution), we develop a hybrid mixed-coding ``particle swarm optimization and multi-objective non-dominated sorting genetic algorithm-II" (PSO–NSGA-II) to optimize the continuous and discrete decision variables as a means of helping companies to reach the optimum decision. We also integrate the subtractive clustering-multi-criteria tournament decision-gain analysis method (SC–MTD–GAM) to select the best trade-off solutions on the optimal Pareto fronts. Finally, we carry out a case study of investment decisions on energy savings and emission reductions in the Zhenzhou Coal Industry (Group) Co., Ltd., China, with the results revealing that the proposed model can support decision making for energy savings and emission reductions in coal mining areas. As compared with the NSGA-II and ``non-dominated sorting particle swarm optimization" (NSPSO) algorithms, the proposed PSO–NSGA-II is found to have better convergence, coverage and uniformity.
With the rapid development of traditional energy system, the essential obstacles to the coordination between different energy sources is increasingly obvious, which greatly hinders the further ...improvement of energy efficiency. As a promising energy supply and consumption system, Regional Energy Internet (REI) has significant practical values in improving the coordination of traditional energy and promoting large-scale renewable energy penetration. Due to the large scale, high ambiguity and numerous key interactive criteria, the investment decision making of REI project is a key problem in application management, but it is challenging and has not been solved. Therefore, this paper explores a targeted and scientific REI investment decision making framework through the interval type-2 fuzzy number, Choquet integral and fuzzy synthetic evaluation model according to different preferences of investors to ensure the successful implementation and effective resources integration of potential REI projects. To verify the effectiveness of the framework, a case study with three typical REI projects simulated from a two-stage stochastic optimization model is studied, and corresponding investment countermeasures are proposed as a reference to allocate resources and prevent risk. Besides, since REI investment decision-making has not been deeply studied, this paper contributes to literature and expands knowledge.
•A novel investment decision-making framework of regional energy internet is built.•An investment decision-making criteria system for regional energy internet is built.•Attributes based scenarios for regional energy internet investment are constructed.•An IT2TrFNs and Choquet integral based fuzzy synthetic evaluation is introduced.•Promote the development of interdisciplinary decision-making science.
Slowing down the energy efficiency (EE) improvement rate over the past half a decade and relatively low EE-related investments highlight a need for a more targeted approach to encourage EE ...improvement investment in the Swiss manufacturing sector. The current work is based on an analysis of already implemented energy efficiency measures (EEMs) via target agreements. Four segments of the Swiss manufacturing sector are identified using the K-means clustering based on the energy consumption profiles of the establishments. The EEMs are categorized through text analysis with the help of a machine learning algorithm. The EEMs are ranked using two indicators that can inform different stakeholders: i) EEMs with Internal rates of return well above the reported risk-adjusted hurdle rates are identified for each segment to help industrial decision-makers take ad-hoc, profit-based investment decisions on EE improvement. ii) Levelized costs of saved energy are presented as value ranges for the EEMs that have the largest impact on annual total final energy (TFE) reduction for each segment to help policymakers design a more targeted approach for the promotion of high-impact EEMs. EE system improvements such as insulation of steam systems and process optimization, as well as EE equipment improvements like process equipment insulation, installation of variable frequency drives (VFDs), smart controls, and IE3 motors, were found to be the most profitable options across all clusters. For the electricity cluster, CHP was found to be the most impactful way of EE improvement (high energy savings) but not cost-effective. For fuel-consuming clusters, waste heat recovery measures were found to be the most cost-effective and among the most impactful options for EE improvement, along with fuel substitution measures with varying degrees of cost-effectiveness.
•All establishments are segmented into four clusters using K-means clustering.•All profitable measures: internal rates of return above typical hurdle rates.•Waste heat recovery and substitution: most impactful option for fuel saving.•CHP is the most impactful option for reducing TFE consumption in the electricity cluster.•Most energy efficiency measures appear non-cost-effective for small consumers.
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