Abstract: Run-of-river (RoR) hydropower projects represent a viable, clean, and cost-effective alternative to dam-based plants and provide decentralized power production. However, RoR schemes’ cost-effectiveness depends on the proper selection of site and design flow, which is a challenging task because it requires multivariate analysis. In this respect, this study presents the development of an investment decision support tool for assessing the optimal size of an RoR scheme considering the technical, environmental, and cost constraints. The net present value (NPV) from a project perspective is used as an objective function for supporting the investment decision. The tool has been tested by applying it to an actual RoR project recently proposed in Colombia. The obtained results show that the optimum point in financial terms does not match the flow that maximizes energy generation from exploiting the river's available flow. For the case study, the flow that maximizes energy corresponds to a value of 5.1 m3/s. In comparison, an amount of 2.1 m3/s maximizes the investors NPV. Finally, a sensitivity analysis is performed to determine the NPV as a function of the debt rate changes and the electricity prices and the CapEx. Even for the worst-case scenario, the optimal size represents a positive business case with an NPV of 2.2 USD million and an internal rate of return (IRR) 1.5 times higher than the discount rate.
Abstract: This paper presents preference programming technique based multiple criteria decision making analysis for selecting a facility location for a new organization or expansion of an existing facility which is of vital importance for a decision support system and strategic planning process. The implementation of decision support systems is considered crucial to sustain competitive advantage and profitability persistence in turbulent environment. As an effective strategic management and decision making is necessary, multiple criteria decision making analysis supports the decision makers to formulate and implement the right strategy. The investment cost associated with acquiring the property and facility construction makes the facility location selection problem a long-term strategic investment decision, which rationalize the best location selection which results in higher economic benefits through increased productivity and optimal distribution network. Selecting the proper facility location from a given set of alternatives is a difficult task, as many potential qualitative and quantitative multiple conflicting criteria are to be considered. This paper solves a facility location selection problem using preference programming, which is an effective multiple criteria decision making analysis tool applied to deal with complex decision problems in the operational research environment. The ranking results of preference programming are compared with WSM, TOPSIS and VIKOR methods.
Abstract: In the era of big data, public investors are faced with more complicated information related to investment decisions than ever before. To survive in the fierce competition, it has become increasingly urgent for investors to combine multi-source knowledge and evaluate the companies’ true value efficiently. For this, a rule-based ontology reasoning method is proposed to support steel companies’ value assessment. Considering the delay in financial disclosure and based on cost-benefit analysis, this paper introduces the supply chain enterprises financial analysis and constructs the ontology model used to value the value of steel company. In addition, domain knowledge is formally expressed with the help of Web Ontology Language (OWL) language and SWRL (Semantic Web Rule Language) rules. Finally, a case study on a steel company in China proved the effectiveness of the method we proposed.
Abstract: This paper explores whether stock characteristics influence the herding formation among investors in the US equity market. To extend the research scope of the existing literature, this paper further examines the role that stock risk characteristics play in the US equity market, and the way they influence investors’ decision-making. First, empirical results show that whether general stocks or high-risk stocks, there are no herding behaviors among the investors in the US equity market during the whole research period or during four great events. Moreover, stock characteristics have great influence on investors’ trading decisions. Finally, there is a bidirectional lead-lag relationship of the herding formation between high-risk stocks and low-risk stocks, but the influence of high-risk stocks on the low-risk stocks is stronger than that of low-risk stocks on the high-risk stocks.
Abstract: One of the most important roles of financial reporting is improving the firms’ investment decisions; however, there is not much supporting evidence for this claim in emerging markets like Iran. In this study, the effect of financial reporting transparency in investment efficiency of Iranian firms has been investigated. In order to do this, 336 listed companies on Tehran Stock Exchange (TSE) has been selected for time period 2012 to 2015 as research sample. For testing our main hypothesis, we classified sample firms into two groups based on their deviation from expected investment: under-investment and over-investment cases. The results indicate that there is positive significant relationship between financial transparency and investment efficiency. In the other words, transparency can mitigate both underinvestment and overinvestment situations.
Abstract: The technological paradigm of the disaster
management field, especially in the case of governmental
intervention strategies, is generally based on rapid and flexible
accommodation solutions. From various technical solution patterns
used to address the immediate housing needs of disaster victims, the
adaptive re-use of existing buildings can be considered to be both
low-cost and practical. However, there is a scarcity of analytical
methods to screen, select and adapt buildings to help decision makers
in cases of emergency. Following an extensive literature review, this
paper aims to highlight key points and problem areas associated with
the adaptive re-use of buildings within the disaster management
context. In other disciplines such as real estate management, the
adaptive re-use potential (ARP) of existing buildings is typically
based on the prioritization of a set of technical and non-technical
criteria which are then weighted to arrive at an economically viable
investment decision. After a disaster, however, the assessment of the
ARP of buildings requires consideration of different/additional layers
of analysis which stem from general disaster management principles
and the peculiarities of different types of disasters, as well as of their
victims. In this paper, a discussion of the development of an adaptive
re-use potential (ARP) assessment model is presented. It is thought
that governmental and non-governmental decision makers who are
required to take quick decisions to accommodate displaced masses
following disasters are likely to benefit from the implementation of
such a model.
Abstract: One of the biggest challenges entering a market with a carsharing or any other shared mobility (SM) service is sound investment decision-making. To support this process, the authors think that a city index evaluating different criteria is necessary. The goal of such an index is to benchmark cities along a set of external measures to answer the main two challenges: financially viability and the understanding of its specific requirements. The authors have consulted several shared mobility projects and industry experts to create such a Shared Mobility City Index (SMCI). The current proposal of the SMCI consists of 11 individual index measures: general data (demographics, geography, climate and city culture), shared mobility landscape (current SM providers, public transit options, commuting patterns and driving culture) and political vision and goals (vision of the Mayor, sustainability plan, bylaws/tenders supporting SM). To evaluate the suitability of the index, 16 cities on the East Coast of North America were selected and secondary research was conducted. The main sources of this study were census data, organisational records, independent press releases and informational websites. Only non-academic sources where used because the relevant data for the chosen cities is not published in academia. Applying the index measures to the selected cities resulted in three major findings. Firstly, density (city area divided by number of inhabitants) is not an indicator for the number of SM services offered: the city with the lowest density has five bike and carsharing options. Secondly, there is a direct correlation between commuting patterns and how many shared mobility services are offered. New York, Toronto and Washington DC have the highest public transit ridership and the most shared mobility providers. Lastly, except one, all surveyed cities support shared mobility with their sustainability plan. The current version of the shared mobility index is proving a practical tool to evaluate cities, and to understand functional, political, social and environmental considerations. More cities will have to be evaluated to refine the criteria further. However, the current version of the index can be used to assess cities on their suitability for shared mobility services and will assist investors deciding which city is a financially viable market.
Abstract: Stock investment decisions are often made based on current events of the global economy and the analysis of historical data. Conversely, visual representation could assist investors’ gain deeper understanding and better insight on stock market trends more efficiently. The trend analysis is based on long-term data collection. The study adopts a hybrid method that combines the Clustering algorithm and Force-directed algorithm to overcome the scalability problem when visualizing large data. This method exemplifies the potential relationships between each stock, as well as determining the degree of strength and connectivity, which will provide investors another understanding of the stock relationship for reference. Information derived from visualization will also help them make an informed decision. The results of the experiments show that the proposed method is able to produced visualized data aesthetically by providing clearer views for connectivity and edge weights.
Abstract: Strategic investment decisions are characterized by
high innovation potential and long-term effects on the
competitiveness of enterprises. Due to the uncertainty and risks
involved in this complex decision making process, the need arises for
well-structured support activities. A method that considers cost and
the long-term added value is the cost-benefit effectiveness estimation.
One of those methods is the “profitability estimation focused on
benefits – PEFB”-method developed at the Institute of Management
Cybernetics at RWTH Aachen University. The method copes with
the challenges associated with strategic investment decisions by
integrating long-term non-monetary aspects whilst also mapping the
chronological sequence of an investment within the organization’s
target system. Thus, this method is characterized as a holistic
approach for the evaluation of costs and benefits of an investment.
This participation-oriented method was applied to business
environments in many workshops. The results of the workshops are a
library of more than 96 cost aspects, as well as 122 benefit aspects.
These aspects are preprocessed and comparatively analyzed with
regards to their alignment to a series of risk levels. For the first time,
an accumulation and a distribution of cost and benefit aspects
regarding their impact and probability of occurrence are given. The
results give evidence that the PEFB-method combines precise
measures of financial accounting with the incorporation of benefits.
Finally, the results constitute the basics for using information
technology and data science for decision support when applying
within the PEFB-method.
Abstract: This paper proposes a linear model for optimizing
domestic energy consumption in Romania. The particularity of the
model is that it is putting in competition both tangible technologies
and thermal insulation projects with different financing modes.
The model is optimizing the energy system by minimizing the
global discounted cost in household sector, by integrating residential
lighting, space heating, hot water, combined space heating – hot
water, as well as space cooling, in a monolithic model. Another
demand sector included is the passenger transport.
This paper focuses on space heating part, analyzing technical and
economic issues related to investment decisions to envelope and
insulate buildings, in order to minimize energy consumption.
Abstract: This work proposes a fuzzy methodology to support
the investment decisions. While choosing among competitive
investment projects, the methodology makes ranking of projects
using the new aggregation OWA operator – AsPOWA, presented in
the environment of possibility uncertainty. For numerical evaluation
of the weighting vector associated with the AsPOWA operator the
mathematical programming problem is constructed. On the basis of
the AsPOWA operator the projects’ group ranking maximum criteria
is constructed. The methodology also allows making the most
profitable investments into several of the project using the method
developed by the authors for discrete possibilistic bicriteria problems.
The article provides an example of the investment decision-making
that explains the work of the proposed methodology.
Abstract: The primary purpose of this study is to understand the differences in the relationship between working capital management efficiency, working capital investment decisions and working capital finance decisions and the profitability of firms within the context of two African developing economies, Kenya and Nigeria. The study finds that there is a significant difference in the relationship between the firm’s profitability and the working capital variables which suggests different challenges for working capital management in each of these countries.
Abstract: This study investigated published financial statement as correlate of investment decision among commercial bank stakeholders in Nigeria. A correlation research design was used in the study. 180 users of published financial statement were purposively sampled from Lagos and Ibadan. Data generated were analyzed using Pearson correlation and regression. The findings of the study revealed that, balance sheet is negatively related with investment decision (r= -.483; p
Abstract: Prior research has not effectively investigated how the
profitability of Chinese branches affect FDIs in China [1, 2], so this
study for the first time incorporates realistic earnings information
to systematically investigate effects of innovation, imitation, and
profit factors of FDI diffusions from Taiwan to China. Our nonlinear
least square (NLS) model, which incorporates earnings factors,
forms a nonlinear ordinary differential equation (ODE) in numerical
simulation programs. The model parameters are obtained through
a genetic algorithms (GA) technique and then optimized with the
collected data for the best accuracy. Particularly, Taiwanese regulatory
FDI restrictions are also considered in our modified model to meet
the realistic conditions. To validate the model-s effectiveness, this
investigation compares the prediction accuracy of modified model
with the conventional diffusion model, which does not take account
of the profitability factors.
The results clearly demonstrate the internal influence to be positive,
as early FDI adopters- consistent praises of FDI attract potential firms
to make the same move. The former erects a behavior model for the
latter to imitate their foreign investment decision. Particularly, the
results of modified diffusion models show that the earnings from
Chinese branches are positively related to the internal influence. In
general, the imitating tendency of potential consumers is substantially
hindered by the losses in the Chinese branches, and these firms would
invest less into China. The FDI inflow extension depends on earnings
of Chinese branches, and companies will adjust their FDI strategies
based on the returns. Since this research has proved that earning is
an influential factor on FDI dynamics, our revised model explicitly
performs superior in prediction ability than conventional diffusion
model.
Abstract: Majority of Business Software Systems (BSS)
Development and Enhancement Projects (D&EP) fail to meet criteria
of their effectiveness, what leads to the considerable financial losses.
One of the fundamental reasons for such projects- exceptionally low
success rate are improperly derived estimates for their costs and time.
In the case of BSS D&EP these attributes are determined by the work
effort, meanwhile reliable and objective effort estimation still appears
to be a great challenge to the software engineering. Thus this paper is
aimed at presenting the most important synthetic conclusions coming
from the author-s own studies concerning the main factors of
effective BSS D&EP work effort estimation. Thanks to the rational
investment decisions made on the basis of reliable and objective
criteria it is possible to reduce losses caused not only by abandoned
projects but also by large scale of overrunning the time and costs of
BSS D&EP execution.
Abstract: Real options theory suggests that managerial flexibility embedded within irreversible investments can account for a significant value in project valuation. Although the argument has become the dominant focus of capital investment theory over decades, yet recent survey literature in capital budgeting indicates that corporate practitioners still do not explicitly apply real options in investment decisions. In this paper, we explore how real options decision criteria can be transformed into equivalent capital budgeting criteria under the consideration of uncertainty, assuming that underlying stochastic process follows a geometric Brownian motion (GBM), a mixed diffusion-jump (MX), or a mean-reverting process (MR). These equivalent valuation techniques can be readily decomposed into conventional investment rules and “option impacts", the latter of which describe the impacts on optimal investment rules with the option value considered. Based on numerical analysis and Monte Carlo simulation, three major findings are derived. First, it is shown that real options could be successfully integrated into the mindset of conventional capital budgeting. Second, the inclusion of option impacts tends to delay investment. It is indicated that the delay effect is the most significant under a GBM process and the least significant under a MR process. Third, it is optimal to adopt the new capital budgeting criteria in investment decision-making and adopting a suboptimal investment rule without considering real options could lead to a substantial loss in value.
Abstract: Literature reveals that many investors rely on technical trading rules when making investment decisions. If stock markets are efficient, one cannot achieve superior results by using these trading rules. However, if market inefficiencies are present, profitable opportunities may arise. The aim of this study is to investigate the effectiveness of technical trading rules in 34 emerging stock markets. The performance of the rules is evaluated by utilizing White-s Reality Check and the Superior Predictive Ability test of Hansen, along with an adjustment for transaction costs. These tests are able to evaluate whether the best model performs better than a buy-and-hold benchmark. Further, they provide an answer to data snooping problems, which is essential to obtain unbiased outcomes. Based on our results we conclude that technical trading rules are not able to outperform a naïve buy-and-hold benchmark on a consistent basis. However, we do find significant trading rule profits in 4 of the 34 investigated markets. We also present evidence that technical analysis is more profitable in crisis situations. Nevertheless, this result is relatively weak.