Important Investment Decisions Research Articles

Drone microrelief analysis to predict the presence of naturally regenerated seedlings

Three-dimensional (3D) mapping and unmanned aerial vehicles (UAVs) are essential components of the future development of forestry technology. Regeneration of forest stands must be ensured according to the law in the required quality and species composition. Forest management focuses on the optimization of economic costs and quality-assured seedlings. Predicting the suitability of the plots’ environment for natural forest regeneration can contribute to better strategic planning and save time and money by reducing manual work. Although the savings may be considered negligible on small forested plots, they are significant for large cleared areas, such as those harvested after large beetle infestations or strong windstorms, which are increasingly common in European forests. We present a methodology based on spatial analysis and 3D mapping to study the microrelief and surrounding of recently cleared areas. We collected data on four plots in the spring and autumn of a single year after the harvest of four Norway spruce [Picea abies (L.) Karst.] stands near Radlice, Czechia using a multirotor Phantom 4 Pro UAV with a red, green, blue (RGB) camera. We used RGB imagery to compute microrelief data at a very high spatial resolution and the surrounding forest stands after harvesting. We used the microrelief data to estimate the amount of water accumulation and incoming solar radiation across the sites. Based on presence data of newly-established seedlings, we used linear mixed effects models to create a suitability map for each site. Model variables included topographic wetness index, solar area radiation, fencing, type of soil preparation, and distance to the nearest mature forest edge. The topographic wetness index and fencing had strong positive influence on seedling establishment, while solar radiation had a negative influence. Our proposed methodology could be used to predict spontaneous regeneration on cleared harvest areas, or it can estimate how much area is suitable for regeneration, which can lead to important investment decisions.

Portfolio Based on AR Model Using 5 Stocks

An investment portfolio is a collection of stocks, bonds, and financial derivatives held by investors or financial institutions, aimed at diversifying risks and maximizing returns. This article selects stocks of Manulife, BOA, Apple, PLC, and National Pharmaceutical Group from October 21, 2022 to August 9, 2023. This article uses AR model, minimum variance model, and maximum Sharpe's ratio model to predict stock prices, and establishes investment portfolios based on the predicted values. Firstly, based on the first 75% of the data, this article predicts the last 25% of the data. Secondly, based on the prediction, the minimum variance model and the maximum Sharpe ratio model are applied and compared with SP500 within the same time frame. However, the results indicate that the SP500 index performs best in terms of yield. This study suggests that purchasing market index funds is an important investment decision for investors during the volatile US dollar interest rate hike cycle.

IMPACT OF FIXED ASSETS IN FIRM PROFITABILITY

Capital budgeting decisions are one the most important investment decisions to be made by a company. Fixed assets contribute to the major portion of capital requirement of a firm. The study tries to find weather there is any direct relationship between fixed assets and financial performance represented by firms profitability. Three models namely OLS pooled regression model, Random Effects Model and Fixed Effects model are used in the study to evaluate the impact of fixed assets on financial performance. All three models concur that there exist significant relationship between the dependent and independent variables and that financial performance is influenced by the level of investment in fixed assets KEY WORDS: Fixed Assets, Firm Profitability, Operating Margin, Fixed Assets Turnover, Foreign Exchange Rates, Interest rate, INV/COGS ratio,

Young Adults' Investment Decisions in Surabaya: The Influence of Financial Literacy and Risk Perception

Important investment decisions allocate an investor's assets in light of further consideration. As youthful investors have dominated the investor population in recent years, investors should be cautious of the elements that influence investment decisions. Despite its significance, few or no studies have examined the influence of financial literacy and risk perception on investment decisions among young adults in Surabaya. This study seeks to determine the impact of financial literacy and perception of risk in investment decision-making, specifically young adults in Surabaya, both simultaneously and individually. By distributing questionnaires to a total of 89 respondents, a basic random sampling method was utilized. Then, IBM SPSS Statistics is used to conduct a multiple Linear regression analysis on the data. The findings demonstrated that financial literacy and risk perception have a significant positive relationship with the investment decisions of young adults in Surabaya, both concurrently and separately. In addition, this research enables young adults in Surabaya to comprehend their investment decision, allowing them to comprehend the factors that influence it and obtain useful benefit.

Intervention of Public Policy on Behavior Change of Street Children in Indonesia

Poverty has a contribution to increase the number of street children then, street children are minors who maintain their lives on the streets. Many children stay in the street to make a living every day. Because they are the country's most important resource, children, the policy and investment decisions made today will have significant implications for the country's future. The aim of this analysis is to analyze the implementation of public policy on behaviour change of street children in Indonesia. The method used in this analysis is Narrative Policy Analysis (NPA). NPA is a research method by using a narrative approach. Policies in dealing with street children in Indonesia are still top-down, so their implementation is measured through the indicators put forward by Edward III which include: communication, resources, arrangements, and bureaucratic structures. However, the needs of street children must also be known through a collaborative approach by combining top-down and bottom-up methods. In conclusion, direct individual support from government officials, the social community, and families also play a role in changing the behavior of street children. Policies that are repressive and do not understand the condition of street children tend not to change the behavior of street children. Policies should concern the real needs of street children and find out the vital causes of why they choose to live on the streets to prevent those condition. This analysis focused only on narrative policy to overcome children’s welfare. Thus, this analysis has a limit in comparing the condition of street children in various cities of Indonesia.

Exploring the use of dual-processing in commercial property development decision-making under conditions of risk and uncertainty

PurposeCommercial property development builds floor space in anticipation of potential, but unknown, future demand, making it particularly prone to risk and uncertainty. The research explores the degree to which property developer decision-making is objective and rational and the degree to which it relies on behavioural instincts and intuition. Decision-making theory, including heuristics, is considered and its prevalence in the field of commercial property development is examined.Design/methodology/approachA “dual-processing” decision-making model, comprising intuitive System 1 and objective System 2 processing, is proposed and tested. Inductive research using template analysis of interviews, with “high status” commercial property developers, explored whether the model offers an accurate representation of developers' behaviour and effective lens through which to examine decisions made under conditions of risk and uncertainty.FindingsParticipants believed they adopted objective and rational approaches to complex commercial property development decisions. Analysis of interviews reveals that System 1 heuristics and intuition play significant roles in decision-making behaviour, leading to potential bias and systematic error. The research concludes that the dual-processing model provides a useful lens through which to better understand the decision-making approach adopted by commercial property developers.Originality/valueThe research represents the rare application of behavioural theory to the realm of commercial property development and provides new and original insight as to how important investment decisions are made under conditions of risk and uncertainty, with implications for professional practice.

The inflation-hedging performance of industrial metals in the world's most industrialized countries

Can inflation risks be hedged by industrial metals? This is the poser that this study intends to address. We examine this possibility for seven (7) globally traded industrial metals in twenty (20) most industrialized countries of the world. The depth of industrialization process of these countries and the increasing level of financialization of commodity markets motivate us to consider the hedging potential of these industrial metals. Summarily, we find that each of the industrial metals can hedge inflation risks in at least one country. When asymmetries are accounted for, the inflation-hedging ability of the metals improves. We also note that on country-specific basis, the hedging performance of the metals is stronger after the 2008 financial crisis. However, the average hedging performance based on the panel data analysis is susceptible to financial shocks. Lastly, the inflation-based predictive model offers a better forecast performance for the commodity returns in most cases than the historical average model. Important investment and policy decisions can be made respectively by commodity investors and policy makers based on the findings established in this study.

AHP 분석에 의한 콘텐츠 스타트업 투자결정요인 연구

It is challenging for content-related startups to thrive over the long term due to the inadequate conditions in a range of aspects, such as capital, commercialization skills, and expertise. In addition, preparations for investment attraction are undertaken in a vague way due to the paucity of knowledge about investment concerns. In this regard, this study examined how investors that specialize in content startups actually evaluate and pursue investment opportunities. To this end, a survey was performed among specialists in the domestic investment sector, and AHP analysis was conducted in order to determine the relative significance of investment determinants. The findings indicated that Investors in content startups regarded 'entrepreneur (team)' as the most significant element in determining investment, and 'expertise in the industry' as the most significant factor in terms of overall relevance and priority for subfactors. Furthermore, the analysis by respondents' affiliations revealed that entrepreneur (team) considered as the most important factor investment decisions by venture capital, followed by accelerators and financial firms. These results suggest that in the case of content startups, the founder and team members' internal human factors must be enhanced in order to entice investment.

Optimal Allocation of Retirement Portfolios

A retiree with a savings account balance, but without a pension, is confronted with an important investment decision that has to satisfy two conflicting objectives. Without a pension, the function of the savings is to provide post-employment income to the retiree. At the same time, most retirees want to leave an estate to their heirs. Guaranteed income can be acquired by investing in an annuity. However, that decision takes funds away from investment alternatives that might grow the estate. The decision is made even more complicated because one does not know how long one will live. A long life expectancy may require more annuities, and a short life expectancy could promote more risky investments. However there are very mixed opinions about both strategies. A framework has been developed to assess consequences and the trade-offs of alternative investment strategies. We propose a stochastic programming model to frame this complicated problem. The objective is to maximize expected estate value, subject to cash outflow constraints. The model is motivated by the Markowitz mean-variance approach, but with risk measured by CVaR and additional sophisticated constraints. The cash outflow shortages are penalized in the objective function of the problem. We use the kernel method to build position adjustment functions that control how much is invested in each asset. These adjustments nonlinearly depend upon asset returns in previous years. A case study was conducted using two variations of the model. The parameters used in this case study correspond to a typical retirement situation. The case study shows that if the market forecasts are pessimistic, it is optimal to invest in an annuity. The case study results, codes, and data are posted on our website.

DETERMINANTS OF INTERNET FINANCIAL REPORTING AND DISCLOSURE OF LISTED FIRMS IN TANZANIA: THE CASE OF DAR ES SALAAM STOCK EXCHANGE

This paper aims to examine the potential drivers that may influence the level of corporate internet financial reporting and disclosure by listed firms in Tanzania. In total, 15 listed companies were analyzed in this study during the year 2015 to 2020. By using the panel multiple regression techniques, the paper has established the strength and direction of the effect of seven drivers on the internet financial reporting index namely: size, leverage profitability, liquidity, the board size, non-executive directors, and audit firm type. The findings of the study revealed that firm size, leverage, liquidity, board size, non-executive directors, and audit firm type were found to be positive and significant on IFR while profitability was found to be negative and significant on IFR. The study finding is important as it assists in informing regulators about the characteristics of Tanzanian firms that are, and are not, satisfying investors’ demand for updated/online financial information and build a mandatory framework for IFR instead of using it voluntarily because the internet is increasingly used by firms to disseminate financial information. It also assists current and potential stakeholders to know the factors that influence corporate internet financial reporting in Tanzania. In practice, online financial reporting can be used as an effective tool for improving investors’ decision-making process and investors use IFR to make important financial investment decisions

Application of neural network to speed-up equilibrium calculations in compositional reservoir simulation

Compositional reservoir simulation is an important tool to model fluid flow in oil and gas reservoirs. Important investment decisions regarding oil recovery methods are based on simulation results, where hundred or even thousand of different runs are performed. In this work, a new methodology using artificial intelligence to learn the thermodynamic equilibrium is proposed. This algorithm is used to replace the classical equilibrium workflow in reservoir simulation. The new method avoids the stability test for single-phase cells in most cases and provides an accurate two-phase flash initial estimate. The classical and the new workflow are compared for a gas-oil mixing case, showing a simulation time speed-up of approximately 50%. The new method can be used in compositional reservoir simulations.

Dice manufacturing company: the acquisition decision

Learning outcomes The learning outcomes of this paper is as follows: understand the issues that faced private Egyptian textile producers following the January 2011 revolution and how that impacted their business model. Evaluate whether Dice’s inorganic expansion through acquiring Alex Clothing Company is a sound strategic decision given the economic uncertainty in Egypt. Analyze the acquisition decision through projection evaluation techniques, including net present value (NPV), internal rate of return (IRR) and modified IRR (MIRR), to measure whether the acquisition will add value to Dice. Discuss non-financial issues post-acquisition that are not captured by traditional capital budgeting and project evaluation techniques. Case overview/synopsis Dice Manufacturing Company, an established and successful textile manufacturing family business, is facing an important investment decision with regard to inorganic expansion through the acquisition of Alex Clothing Company and its subsidiary United Dyers. The case is intended to be discussed in an undergraduate corporate finance class. The case setting is inside Dice Manufacturing Company, where one of the founders, Nagy Toma and his CFO Victor ElMalek are analyzing the acquisition decision in January 2015. The protagonist is Victor ElMalek, who has to recommend a course of action for the company owners. The case allows students to apply capital budgeting and project valuation methods to make a decision on whether the acquisition brings value to Dice and to analyze issues management can face post-acquisition. The case follows through the history of Dice, presenting its business model and changes that accompanied the 2011 revolution. It then moves on to outline the acquisition opportunity and provides data for students to analyze through traditional project valuation techniques, including NPV, IRR and MIRR. Complexity academic level Undergraduate. Subject code CSS 1: Accounting and Finance. Supplementary materials Teaching notes are available for educators only.

Do expert surveys underrate lower-income countries?

Perceptions of countries’ policy environments and innovation efforts drive important investment and regulatory decisions, but those perceptions may be biased. One particular concern is that evaluations of richer countries might be inflated compared to evaluations of poorer countries, as general stereotypes of national success spill over into assessments of research capability. Comparison of World Economic Forum survey assessments of businesses’ research spending with official spending figures reveals that, among countries spending comparable amounts on research, richer countries receive higher survey ratings. Richer countries’ educational outcomes are similarly rated higher than similarly performing poorer countries’. These findings suggest that perceptions may systematically disadvantage economically poorer countries, thereby misdirecting real-world investments and policy decisions derived from those rankings.

The Impact of Stock Characteristics on Its Market Price in Jordanian Commercial Banks

The study aims at identifying the impact of stock characteristics represented by (Earnings Per Share (EPS), Book Value Ratio (BVR), Dividends Per Share (DPS), Dividends Payout Ratio (DPR), Market to Book Ratio (MBR), Price Earnings Ratio (PER), and Yield Per Share (YPE)) on the market stock price in the 13 commercial banks in Jordan during the period from 2005 to 2018. Multiple Linear Regression has been used to illustrate the impact of the independent variables and the controlling variables on the dependent variable.
 
 The study has found that there is a significant impact of stock characteristics on its market price at the Jordanian commercial banks. The study also found a statistically significant impact for each book value ratio, dividends per share, market to book ratio, price-earnings ratio, and yield per share on the market price at the Jordanian commercial banks. However, there was no statistically significant effect for each of the earnings per share and dividend’s payout ratio on the market price at the Jordanian commercial banks. The study recommends that investors, analysts, and decision-makers use the characteristics of stocks when carrying out analyses before making important investment decisions that can affect their wealth in the future through forecasting stock prices.

Adaptive Market Hypothesis

Purpose: To investigate the implications of the Addaptive Market Hypothesis (AMH) on Turkish stock exchange market (Borsa Istanbul) indices as an emerging economy. BIST-100, BIST-30 and BIST-All indices are subjected to the analyses for the period between January 2002 and April 2017. Design/Methodology/Approach: Two-year rolling windows and daily test values were calculated by using linear methods (Variance Ratio Test) and nonlinear methods (BDS test) to investigate the market efficiency. Findings: According to the Variance Ratio Test results, index returns are unpredictable, that is, the market is efficient, while the results of nonlinear analysis show the existence of adaptive market hypothesis. In particular, all three indices display efficiency in the 2013-2016 period implying that returns were not predictable in this period. The results of the non-linear analysis show that the market is efficient from time to time and sometimes deviates from efficiency, indicating the validity of the adaptive market hypothesis in Borsa Istanbul. Practical Implications: The changes in the market efficiency from time to time should be considered while taking important investment decisions. Moreover, according to AMH, since trends, panics, bubbles and crashes exist in the market, arbitrage opportunities arise time to time, and market timing is an important issue to catch the profit opportunities. Therefore, as a further study, matching the important events with the efficiency of the market could provide more insights about timing the market. Originality/Value: To the best of authors’ knowledge, this is the first comprehensive study that examines the index based AMH in Borsa Istanbul. This study is believed to contribute to the literature by giving insights about the evolution of market efficiency in Turkey.

Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage

Decision makers rely on models to make important regulatory, policy, and investment decisions. For power systems, these models must capture (i) the future challenges introduced by the integration of large quantities of variable renewable energy sources and (ii) the role that energy storage technologies should play. In this paper, we explore several different approaches to selecting representative days for generation expansion planning models, focusing on capturing these dynamics. Further, we propose a new methodology for adjusting the outputs of clustering algorithms that provides three advantages: the targeted level of net demand is captured, the underlying net demand shapes that define ramping challenges are accurately represented, and the relationship between annual energy and peak demand is captured. This weighting methodology reduces the magnitude of the error in the representative day based generation expansion planning models estimation of costs by 61% on average. The results also demonstrate the importance of carefully performing the clustering of representative days for both system costs and technology mix. In most cases improvements to the total cost of different representative day based expansion plans are realised where conventional generation capacity is substituted for energy storage. Based on the energy storage technology selected we conclude this capacity is being used to address ramping challenges as opposed to shifting renewable generation from off to on peak periods, reinforcing the importance of capturing detailed intraday dynamics in the representative day selection process.

Optimal planning of technology roadmap under uncertainty

The selection and planning of technical projects is an important and challenging investment decision for companies as significant amount of capital is often involved. With the growing complexity and scale, managing technical research projects and technology roadmap (TRM) are greatly affected by uncertainties than ever before. However, existing approaches for addressing these problems are restricted to deterministic environments. In this study, a general methodology based on graph theory and mathematical programming for R&D projects planning subject to uncertainty is proposed to maximize profit and to find precedence relations according to technological trends for given budgets and time. We first put forward a new graph model and its mathematical definition to represent the relations among technologies. The network contains nodes to represent technologies and edges to denote feasible paths between two technology nodes. To deal with uncertainty, a network-based novel robust optimization model as well as a chance constrained model is developed. Finally, we apply the proposed model and solution approach to the TRM of Smart Home industry. The numerical study shows that the proposed method can effectively and efficiently solve the optimization problems for technical project planning, path designing, and project management, under uncertainty.

Conditional conservatism and labor investment efficiency

Prior literature documents that asymmetric timely recognition of losses versus gains (also known as conditional conservatism) can induce management to make more efficient investment decisions by mitigating information asymmetry between management and investors and providing early signals about the profitability of projects that are undertaken. In this paper, we investigate the impact of conservatism on an important investment decision that has been overlooked, namely, investment in labor. We find that conservatism is negatively associated with labor investment inefficiency; more specifically, conservatism reduces inefficient investment practices on the labor market, including over-hiring, under-firing, under-hiring, and over-firing. Our results hold after controlling for managerial ability, corporate governance, and other investments.

Decision-making in the management of property investment by Australian superannuation funds

Decision-making in property investment by superannuation funds is an important investment decision, but it is different to their decision-making on other asset classes included in their asset portfolios. The large value and heterogeneous nature of individual pieces of real estate make the market for real estate relatively illiquid and subject to larger transaction costs than other asset classes. Based on interview surveys of Australian superannuation funds, using the analytical hierarchical process (AHP), we identified strategic decision-making as being the most important factor used by the superannuation funds when making decisions on the management of their property investment portfolio. Comments during the interviews indicated that their decisions were influenced by restrictions in their fund’s investment mandate and the level of funds that they had to invest. The AHP technique has allowed this research to provide a more in-depth understanding of the management of decision-making factors than previous surveys.

Discounted Cash Flow Analysis

This technical note outlines the principles and application of proper discounted cash flow (DCF) analysis in the context of evaluating business investment projects. The note provides several specific examples, emphasizes building intuition for DCF modeling, and reviews several common errors novices to the subject make in such analysis. Excerpt UVA-F-1728 Rev. Oct. 2, 2020 Discounted Cash Flow Analysis Business managers face a steady flow of business investment opportunities. These important investment decisions take many forms: an extension of an existing product, software to reduce production costs, or a machine purchase to improve product quality. With so many investment choices, managers quickly appreciate that some amount of rigor is required to discriminate between those opportunities that merit going ahead with the investment from those that do not. In making these sorts of decisions, astute managers consider the perspective of their investors. To that end, this note outlines principles and examples of proper discounted cash flow (DCF) analysis in the context of evaluating business investment projects. The fundamental premise of the DCF approach is that the value associated with a business investment is measured by the expected cash flow gains to investors above what they might expect to generate on their own with investments of similar risk. DCF analysis provides an important tool that allows managers to model the gains to investors associated with investment opportunities. DCF analysis seeks to precisely identify the present value of economic net benefits to business investors associated with an investment (the net present value, or NPV). By identifying the investment cash flows, the manager is able to identify the exact source of the value gains and losses to investors relative to a baseline of not making the investment at all. To provide context for the discussion, this note begins with the example of a capacity expansion decision for a crop-dusting business. An Investment Decision for Dusty Barnes . . .