Duration Of Investment Research Articles

Decision Support System for Recommendation Sharia Banking Investment Products Using Simple Additive Weighting (SAW)

The swift expansion of Islamic banking has resulted in the creation of many investment products, such as mudharabah deposits, sukuk, and Islamic mutual funds, all compliant with sharia rules. Notwithstanding this advancement, the customers' inadequate financial literacy presents considerable obstacles in identifying appropriate investment alternatives that correspond with their objectives and risk appetite. This work seeks to fill this gap by creating a Decision Support System (DSS) that employs the Simple Additive Weighting (SAW) method to aid users in making educated decisions on Islamic banking investments. The SAW technique was selected for its straightforwardness and efficacy in normalizing data and assessing alternatives according to established criteria, including profit potential, risk level, investment duration, and compliance with sharia principles. The research encompassed multiple phases, including the identification of evaluation criteria, the collection and analysis of pertinent data, and the application of the SAW method to rank investment choices. The DSS effectively delivered precise and dependable suggestions, designating Sharia Deposits as the optimal investment choice due to its robust agreement with the established criteria. The results underscore the system's capability to improve financial inclusion and literacy, especially in Indonesia, where comprehension of sharia-compliant financial products is still inadequate. Future improvements to the DSS involve the incorporation of real-time data and the expansion of its application to include more financial industries, such as insurance and microfinance. This study highlights the importance of utilizing technology to address deficiencies in financial literacy and facilitate the sustainable development of the Islamic banking sector.

Evaluation of the Feasibility of Capital Participation Plan by Bangli Regency Government to PT. BPR Bank Daerah Bangli in 2023

This study aims to evaluate the feasibility of investment in capital participation by the Bangli Regency Government in PT BPR Bank Daerah Bangli for the period 2022-2026. This evaluation uses financial analysis methods including Payback Period, Net Present Value (NPV), and Internal Rate of Return (IRR). Based on the results of the analysis, it is known that the capital investment of Rp13 billion during the period generates significant cash flows, with a Payback Period of 4 years and 5.29 months. This shows that the investment capital can be returned faster than the planned investment duration of 5 years, thus indicating that this investment is feasible. The NPV calculation shows a positive value of Rp603,879,838, which indicates that this investment is profitable and provides added economic value to the Bangli Regency Government. Meanwhile, the IRR calculation result of 5.20% also shows that this investment provides a higher rate of return than the cost of capital. Thus, the combination of a positive NPV and an adequate IRR indicates that this investment project is economically feasible and can contribute positively to local finances. The projected profits earned during the analysis period show an increasing trend each year, further strengthening the potential sustainability of this investment. The profit received as dividends in 2022 was recorded at Rp2.71 billion and increased to Rp3.47 billion in 2026. In addition to providing a decent return for the government, this investment also has the potential to increase Regional Original Revenue (PAD) and support other regional development programs. Therefore, the Bangli Regency Government's capital participation in PT BPR Bank Daerah Bangli is considered a strategic step that is low risk but provides significant benefits.

The Effect of Demographics on Investment Choice in Digital Currencies: An Empirical Study of Thai Investors

The purpose of this study is to compare the differences in Thai investors' demographic characteristics that influence their decision to invest in digital currencies. This study was quantitative research. The samples of this study were 400 Thai investors who invest in digitalcurrencies, using non-probability sampling by convenience sampling method. We employed a questionnaire as our research tool, demonstrating content validity of 1.00 and reliability of 0.70 across the entire questionnaire. We analysed the data using a one-way analysis of variance. The research discovered that different demographic factors of Thai investors, such as their age, average monthly income, duration of investments, and amount of money invested, influenced different investment decisions in digital currencies. However, the various demographic factors of Thai investors, such as their gender and level of education, did not influence different investment decisions in digital currencies. Digital currency investment providers can use these research results to develop strategies that accurately and successfully target or qualify investors.

Empirical Analysis of Digital Transformation for Typical Enterprises in China

This study sets out to unravel the complexities of digital transformation challenges faced by traditional enterprises in China, focusing on identifying the multifaceted barriers that impede their progress towards digitalization. Employing a comprehensive review of existing literature coupled with empirical evidence from typical enterprises, the research adopts a qualitative approach to explore the core barriers to digital transformation. The findings reveal that traditional enterprises face significant hurdles in their digital transformation journey, including high transformation costs, prolonged investment durations with uncertain outcomes, and a lack of necessary capabilities for effective digital adoption. However, by strategically combining core factors such as innovation ecosystems, technological infrastructure, and digital components, and placing substantial emphasis on cultivating an internal learning-oriented mindset, enterprises can navigate these challenges effectively. The study illustrates that fostering a learning orientation not only accelerates the application and dissemination of digital technologies but also encourages critical assessment of existing management practices, thereby facilitating the transformation of operations and expansion into new markets. The impact of this research lies in its contribution to both academic discourse and practical implementation, offering valuable insights and recommendations for business leaders and policymakers. By highlighting the critical components of digital transformation and identifying strategies to overcome prevalent obstacles, the study provides a roadmap for traditional enterprises striving to innovate and thrive in the digital age.

Unraveling the spatial dynamics of regional economic disparities in East Java

The objective of this research was to analyze the determinants of regional disparities in East Java Province. This study utilized panel data, incorporating research locations across 38 regencies in East Java, with a data series spanning from 2016 to 2020. Spatial panel data models were employed to address the research objective, specifically examining the impact of spatial and non-spatial factors on disparities in East Java. The study's findings indicated that the Spatial Autoregressive (SAR) model was the most effective for estimating disparities in the region. The effects of each variable are as follows: the average duration of schooling and investment had positive and significant effects on disparities, while the variable of open unemployment rate did not exhibit any significant effect. Additionally, there is a spatial effect in the form of an endogenous interaction effect, signifying that the extent of disparities is influenced by the disparities in neighboring areas.

The risk of additional branch works in the construction of railway projects

Every decision or action taken as part of a construction project involves risk. Unforeseen branch works that may occur during the construction investment are the so-called additional work. They cause risk, both for the contractor and the investor. Skilful management of this risk may lead to minimizing the change in the investment duration or minimizing the change in the cost of the contractual amount. The work proposes a method of analysing the risk of industrial works that may occur during additional works in railway construction investments. A constructed Bayesian network based on the risk component of industrial works was used for the analysis. Bayesian networks are listed as one of the 31 techniques suggested for risk analysis in accordance with the ISO 31010 standard, which enables the correct analysis of the examined problem with satisfactory accuracy. During the construction of the network, historical data was obtained from completed and settled railway infrastructure construction projects, and 125 unique records corresponding to additional works were identified. The created Bayesian network combines technological aspects resulting from the specificity of the implementation of branch works in railway construction projects with a practical assessment of their risk. The proposed network model allows for risk analysis by defining various event scenarios, and has high application capacity resulting from the ease of applying its results in practice in the implementation of railway investments.

Quantifying maternal investment in mammals using allometry.

Maternal investment influences the survival and reproduction of both mothers and their progeny and plays a crucial role in understanding individuals' life-history and population ecology. To reveal the complex mechanisms associated with reproduction and investment, it is necessary to examine variations in maternal investment across species. Comparisons across species call for a standardised method to quantify maternal investment, which remained to be developed. This paper addresses this limitation by introducing the maternal investment metric - MI -for mammalian species, established through the allometric scaling of the litter mass at weaning age by the adult mass and investment duration (i.e. gestation + lactation duration) of a species. Using a database encompassing hundreds of mammalian species, we show that the metric is not highly sensitive to the regression method used to fit the allometric relationship or to the proxy used for adult body mass. The comparison of the maternal investment metric between mammalian subclasses and orders reveals strong differences across taxa. For example, our metric confirms that Eutheria have a higher maternal investment than Metatheria. We discuss how further research could use the maternal investment metric as a valuable tool to understand variation in reproductive strategies.

Measuring return expectations and capital costs in the European private capital markets: An alternative approach

Data collection on return expectations (Ret_Exp) and capital costs is still connected with high barriers and low transparency, especially in European private markets. This study suggests a questionnaire format to gather data from market participants derived from an approach seen in the USA by Slee and Paglia (2010, 2011). Moreover, it suggests that return expectations and cost of capital (CoC) should not be regarded as synonyms. A delta of approximately 1.2 percent can be observed when comparing mean values of both variables over various asset classes as real estate funds, venture capital, mezzanine investments, and direct investment from 213 data points collected in 2019/2020 primarily before the COVID-19 crisis as well as the Russia-Ukraine-conflict. This supports the general theory that return expectations incorporate the cost of capital structure, including a certain premium. In the second step, regressions are applied to identify influential factors on capital costs and return expectations in European private markets. The regressions suggest that the investment duration reduces these values. As a result, short-termism will lead to higher return expectations and capital costs, according to the model. Considering further variables indicates that specialization effects combined with a long investment horizon reduce both metrics for investors.

Fostering sustainable investments through micro-investing platforms

We uncover the underlying factors that influence perceived trade-offs between sustainability and financial returns and risks, and the resulting real-world investment behaviour of micro-investors. Given the direct-to-consumer nature of new age investment platforms, the context for our study is framed within a consumer-centric context. Through a survey and conjoint experiment (383 investors), and analysis of actual investment decisions (for 89,744 micro-investors), we show that individual motives—specifically sustainability values and feelings of empowerment—are key drivers for sustainable investments, influencing willingness to forgo financial returns and the duration of investment. We provide practical implications for fostering sustainable investments through micro-investing platforms.

A search of investors' trading practices in the Bangladesh capital market

PurposeThis research mainly intends to ascertain the stimulus of investor investment tendencies on the amount of capital investment in the share market.Design/methodology/approachUtilizing a sample of 477 individual investors who actively trade on the Bangladesh capital market, this empirical study was conducted. The objective of this examination is to ascertain the investment trading behavior of retail investors in the Bangladesh capital market using multiple regression, hypothesis testing and correlation analysis.FindingsThe coefficients of market categories, preferred share price ranges and investment source reveal negative predictor correlations; all predictors are statistically significant, with the exception of investment source. Positive predictive correlations exist between investor category, financial literacy degree, investment duration, emotional tolerance level, risk consideration, investment monitoring activities, internal sentiment and correct investment selection. Except for risk consideration and investment monitoring activities, all components have statistically significant predictions. The quantity of capital invested in the stock market is heavily influenced by the investment duration, preferred share price ranges, investor type, emotional toleration level and decision-making accuracy level.Research limitations/implicationsThis investigation was conducted exclusively with Bangladeshi individual stockholders. Therefore, the existing study can be extended to institutional investors and conceivably to other divisions. It is possible to conduct this similar study internationally. And the query can enlarge with more sample size and use a more sophisticated econometric model. Despite that the outcomes of this study help the regulatory authorities to arrange more informative seminars and consciousness programs.Practical implicationsThe conclusions have practical implications since they empower investors to modify their portfolios based on elements including share price ranges, investment horizons and emotional stability. To improve chances of success and reach financial objectives, they stress the significance of bettering financial understanding, active monitoring and risk analysis. Results can also be enhanced by distributing ownership over a number of market sectors and price points. The results highlight the value of patience and giving potential returns enough time.Originality/valueThis study on the trading behavior of investors in Bangladesh is unique and based on field study, and the findings of this study will deliver information to the stakeholders of the capital market regarding the investors’ trading behavior belonging to different categories, financial literacy level, investment duration, emotional tolerance level and internal feeling.

A Genetic Algorithm Approach to Optimal Asset Allocation of Defined Contribution Pension Funds: Evidence From India’s National Pension System

Life cycle asset allocation has recently gained popularity and is the default option in pension funds worldwide. A line of recent studies has refuted its glory and argues that a balanced fund with a fixed allocation throughout the investment horizon may yield better results. The question arises from these studies: What should be the balanced fund’s optimum asset allocation weight? Therefore, the present study uses Genetic Algorithm to obtain the optimum asset allocation weight for India’s defined contribution pension plan subscribers. To validate our result, the study compares the results of a Genetic Algorithm to the outcomes of widely used asset allocation techniques, notably the life cycle and equally weighted strategies. This study holds important policy implications as, given the size of the pension asset and the long investment duration, even a trivial increase in return will substantially impact investment outcomes, affecting the subscribers’ well-being in old age.

Literasi Keuangan, Perilaku Keuangan, Overconfidence dan Keputusan Investasi

The number of investors in investing activities continues to increase every year, investment decision making involves knowledge, behavior, and investors' emotions. The aim of the study was to determine the effect of financial literacy, financial behavior and overconfidence on investment decisions and to find out the differences in financial literacy, financial behavior and overconfidence in making investment decisions with an investment duration of less than two years and more than two years. The population in this study were members of the Capital Market Study Group (KSPM) at Udayana University. The total sample is 138 respondents using a saturated sampling technique using a questionnaire as a method of data collection. The data analysis technique used is multiple linear regression and the Mann-Whitney test. The results of the study show that financial literacy, financial behavior and overconfidence have an effect on investment decisions. There are differences in financial literacy and overconfidence, but there are no differences in financial behavior in making investment decisions based on the length of investment.
 Keywords: Investment Decisions; Financial Literacy; Financial Behavior; Overconfidence

Private equity valuation under time-inconsistent preferences

Our research expands on the private equity (PE) valuation theory by incorporating the limited partner’s (LP) time-inconsistent preferences. Our findings show that time inconsistency has a detrimental impact on the LP’s certainty-equivalent valuation, which is dependent on the duration of investment and the value of PE asset. Through a comparative static analysis of the risk-attitude parameter and the future self arrival intensity, we have determined that the negative effects of time inconsistency can be mitigated by a more conservative attitude on the part of LP, while a higher future self arrival intensity exacerbates the impact of LP’s time inconsistency on their certainty-equivalent valuation.

Investigating risk assessment in post-pandemic household cryptocurrency investments: an explainable machine learning approach

This study provides an applicable methodological approach applying artificial intelligence (AI)-based supervised machine learning (ML) algorithms in risk assessment of post-pandemic household cryptocurrency investments and identifies the best performed ML algorithm and the most important risk assessment determinants. The empirical findings from analyzing 13 determinants from 1,000 dataset collected from major cryptocurrency communities online suggest that the logistic regression (LR) algorithm outperforms the remaining six ML algorithms by using performance metrics, lift chart, and ROC chart. Moreover, to make the ML algorithm results explainable and tackle the “black box” issue, the top five most important determinants are discovered, which are the interaction between investment amount and investment duration, investment amount, perception of traditional investments, cryptocurrency literacy, and perception of cryptocurrency volatility. The present study contributes to the literature on risk assessment, especially on the household cryptocurrency investments in the post-pandemic era and the body of knowledge on explainable supervised ML algorithms.

Venture capital activities under uncertainty: US and UK investors behavior.

We investigate how in the context of Corporate Venture Capital (CVC), the investment decisions affect the likelihood of their subsequent exit strategies. We use OLS and probit regression as well as Weibull distribution of residual values, given its reliability and validity for studying lifetime analysis. Based on a sample of 8722 VC-backed ventures with the first investment dates between 1999 and 2018 in United States (US) and United Kingdom (UK), the results show that the presence of CVCs positively affects the funding amounts and the duration of the investment. CVC funds are more generous and more patient than Independent Venture Capital (IVC) funds regarding their investments in ventures. Moreover, the findings provide evidence that the exit strategies are directly influenced by the funding amounts and the duration of the investment which are influenced, in turn, by the fund type. Greater funding increases the likelihood of IPO exit which is reduced by longer investment duration. Our results are robust to alternative estimation methods, namely two-stage treatment-effects regressions. These results help the various stakeholders (VC funds, investors, ventures) make crucial decisions regarding investment amounts and duration, and exit.

Industry-relatedness, geographic proximity and strategic decisions of corporate and independent venture capital-backed companies

ABSTRACT In this paper, we use the essential dichotomy between independent venture capital (IVC) and corporate venture capital (CVC) to investigate the investment mechanisms that lead venture-backed companies to take different successful exit routes, that is, an initial public offering (IPO) or an acquisition. Through an analysis of a sample of 4206 US companies, we find that CVC-backed companies have a longer investment duration and a larger investment amount than IVC-backed companies. Our analysis reveals that geographic distance and industry-relatedness are influential for the success of the company. We show that industry-relatedness is more likely to lead to an acquisition exit while geographic proximity rather fosters IPO exits.

Proposing a New Metric: Private Fund Duration

1. Rafael Castilla 1. is the director of investments and structuring at the Investment Office of the University of Michigan in Ann Arbor, MI. (rafaelec{at}umich.edu) 2. Felicia David-Visser 1. is a senior manager of investments in the Investment Office at the University of Michigan in Ann Arbor, MI. (feliciad{at}umich.edu) 3. David Brophy 1. is a professor of finance and the director of the Center for Venture Capital and Private Equity Finance in the Ross School of Business at the University of Michigan in Ann Arbor, MI. (djbrophy{at}umich.edu) The authors propose a tripartite metric, private fund duration (PFD) (PFD x , PFD y , PFD z ), to assist investors in the analysis of the duration of private funds and investments. In brief, PFD x is the amount-weighted average time of all the contributions to a fund or investment, PFD y is the amount-weighted average time from inception for the fund or investment to return its capital (when distributions equal contributions), and PFD z is the amount-weighted average time for all distributions. An example PFD can be written in years, thus: PFD (3.4, 5.1, 8.0). The authors argue that the PFD is the missing metric that with the multiple on invested capital (MOIC) and the internal rate of return (IRR) can provide a complete summary picture of the cash-flow characteristics of a private fund or investment. The PFD can be used in the monitoring, analysis, and comparison of different private fund and investment opportunities, trends, structures, and strategies, particularly with respect to liquidity and outstanding commitments. The [Online Supplement][1] illustrates one application of PFD using Burgiss data in different asset classes for different vintages: creating benchmarks for PFD. [1]: http://jpm.pm-research.com/lookup/suppl/doi:10.3905/jpm.2022.1.397/-/DC1/jpm.2022.1.397_JPM-Castilla.docx

해외직접투자 기업의 산업분류에 따른 생존율 실증연구

This study was conducted to analyze to compare the survival rate according to the factors affecting the survival of foreign direct investment companies in Korea between 2013 to 2017. In addition, the survival rate of foreign direct investment companies classified according to the Korean standard industry classification is compared. As a result of impact factors on the survival duration of foreign direct investment companies the characteristics of the firm which the Investment Ratio(IR), firm age(FA), and the number of employees(NE) had positive effects. Return on Assets(ROA) of the financial characteristics had positive effects. But other factors did not produce significant results. In the empirical analysis of the survival rate of companies according to industry classification, the survival rate of companies in the real estate industry and financial insurance industry was relatively lower than that of companies in the manufacturing, wholesale & retail business, specialized science & technology service. And the real estate industry and financial insurance industry was investigated with a relatively short survival rate due to the high possibility of risk exposure.

Implications of the Term Structure of Interest Rates for the Duration of Corporate Investment

Implications of the Term Structure of Interest Rates for the Duration of Corporate Investment

Impact of premium collection on investment pattern of Nepalese life insurance companies

The premium collection is one of the main sources for investment and measuring profitability of insurance companies. This research aims to analyze the effect of premium collection on the investment strategy and profitability of the life insurance company. Only four insurance companies have been selected out of the nineteen life insurance companies in Nepal using simple random sampling technique. The purpose of this descriptive and causal-comparative study is to examine the impact of premium collection on investment and profitability in life insurance companies. Both primary and secondary data have been incorporated. Descriptive statistical tools consist of mean, standard deviation, coefficient of variation as well as the inferential statistic consists of correlation and regression analysis as secondary data variables. The findings indicate a positive and significant relationship between total investment and premium collection in the life insurance industry. Additionally, premium collection shows significantly positive relationships with earnings per share and net profit. However, the research also suggests an insignificant relationship between premium collection and return on assets (ROA) and return on equity (ROE). Furthermore, the study highlights the influence of various other factors on premium collection and investment patterns, including firm size, government regulations, investment duration, investment period, and investment objectives. Understanding these factors can aid in formulating an effective structure for premium collection and investment strategies to achieve higher profitability. The research findings shed light on the significance of premium collection in guiding investment decisions in the life insurance sector. The findings of this study can contribute to the formulation of effective premium collection and investment strategies to maximize profits in this industry.