Search results for: loan portfolio

Authors: Pranvera Shehaj

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Unlike any prior analysis on the withholding tax rates negotiated in tax treaties, this study looks at the treaty heterogeneity content, by investigating the impact of the residence country’s double tax relief method and of tax-sparing agreements, on the difference between developing countries’ domestic withholding taxes on dividends on one side, and treaty negotiated withholding taxes at source on portfolio dividends on the other side. Using a dyadic panel dataset of asymmetric double tax treaties between 2005 and 2019, this study suggests first that the difference between domestic and negotiated WHTs on portfolio dividends is higher when the OECD member uses the credit method, as compared to when it uses the exemption method. Second, results suggest that the inclusion of tax-sparing provisions vanishes the positive effect of the credit method at home on the difference between domestic and negotiated WHTs on portfolio dividends, incentivizing developing countries to negotiate higher withholding taxes.

Keywords: double tax treaties, asymmetric investments, withholding tax, dividends, double tax relief method, tax sparing

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Authors: Matthias Nnadi

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This paper investigates the relationship between premiums and returns in the short and long terms in European merger and acquisition (M&A) deals. The study employs Calendar Time Portfolio (CTP) model and find strong evidence that in the long run, premiums have a positive impact on performance, and we also establish evidence of a significant difference between the abnormal returns of the high premium paying portfolio and the low premium paying ones. Even in cases where all sub-portfolios show negative abnormal returns, the high premium category still outperforms the low premium category. Our findings have implications for companies engaging in acquisitions.

Keywords: mergers, premium, performance, returns, acquisitions

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Authors: Tea Poklepović, Zdravka Aljinović, Mario Matković

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Modern Portfolio Theory (MPT) according to Markowitz states that investors form mean-variance efficient portfolios which maximizes their utility. Markowitz proposed the standard deviation as a simple measure for portfolio risk and the lower semi-variance as the only risk measure of interest to rational investors. This paper uses a third volatility estimator based on intraday data and compares three efficient frontiers on the Croatian Stock Market. The results show that range-based volatility estimator outperforms both mean-variance and lower semi-variance model.

Keywords: variance, lower semi-variance, range-based volatility, MPT

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Authors: Guohong Wang, Hao Huang, Rui Xing, Liyan Tang, Yu Wang

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Start-ups are consistently under pressure to overcome liabilities of newness and smallness. They must focus on assembling resource and engaging constant renewal and repeated entrepreneurial activities to survive and grow. As an important form of resource, knowledge is constantly vital to start-ups, which will help start-ups with developing new product in hence forming competitive advantage. However, significant knowledge is usually needed to be identified and exploited from external entities, which makes it difficult to achieve knowledge transfer; with limited resources, it can be quite challenging for start-ups balancing the exploration and exploitation of knowledge. The research on knowledge transfer has become a relatively well-developed domain by indicating that knowledge transfer can be achieved through plenty of patterns, yet it is still under-explored that what processes and organizational practices help start-ups facilitating knowledge transfer for new product in the context portfolio entrepreneurship. Resource orchestration theory emphasizes the initiative and active management of company or the manager to explain the fulfillment of resource utility, which will help understand the process of managing knowledge as a certain kind of resource in start-ups. Drawing on the resource orchestration theory, this research aims to explore how knowledge transfer can be facilitated through resource orchestration. A qualitative single-case study of a sodium-ion battery new venture was conducted. The case company is sampled deliberately from representative industrial agglomeration areas in Liaoning Province, China. It is found that distinctive resource orchestration sub-processes are leveraged to facilitate knowledge transfer: (i) resource structuring makes knowledge available across the portfolio; (ii) resource bundling makes combines internal and external knowledge to form new knowledge; and (iii) resource harmonizing balances specific knowledge configurations across the portfolio. Meanwhile, by purposefully reallocating knowledge configurations to new product development in a certain new venture (exploration) and gradually adjusting knowledge configurations to being applied to existing products across the portfolio (exploitation), resource orchestration processes as a whole make exploration and exploitation of knowledge balanced. This study contributes to the knowledge management literature through proposing a resource orchestration view and depicting how knowledge transfer can be facilitated through different resource orchestration processes and mechanisms. In addition, by revealing the balancing process of exploration and exploitation of knowledge, and laying stress on the significance of the idea of making exploration and exploitation of knowledge balanced in the context of portfolio entrepreneurship, this study also adds specific efforts to entrepreneurship and strategy management literature.

Keywords: exploration and exploitation, knowledge transfer, new product development, portfolio entrepreneur, resource orchestration

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Authors: Yasmine Essafi Zouari, Aya Nasreddine

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Following a long inflationary period from the post-war era to the mid-1980s (+10.1% annually), France went through a moderate inflation period between 1986 and 2001 (+2.1% annually) and even lower inflation between 2002 and 2016 (+1.4% annually). In 2022, inflation in France increased rapidly and reached 4.5% over one year in March, according to INSEE estimates. Over a long period, even low inflation has an impact on portfolio value and households’ purchasing power. In such a context, inflation hedging should remain an important issue for investors. In particular, long-term investors, who are concerned with the protection of their wealth, seek to hold effective hedging assets. Considering a mixed-asset portfolio composed of housing assets (residential real estate in 150 Grand Paris communes) as well as financial assets, and using both correlation and regression analysis, results confirm the attribute of the direct housing investment as an inflation hedge especially particularly against its unexpected component. Further, cash and bonds were found to provide respectively a partial and an over hedge against unexpected inflation. Stocks act as a perverse hedge against unexpected inflation and provide no significant positive hedge against expected inflation.

Keywords: direct housing, inflation, hedging ability, optimal portfolio, Grand Paris metropolis

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Authors: Darrol Stanley, Levan Efremidze, Jannie Rossouw

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We investigate the predictability of the USD/ZAR (South African Rand) exchange rate with sample entropy analytics for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. These results are preliminary and do not yet account for reasonable transactions costs, although these are very small in currency markets.

Keywords: currency trading, entropy, market timing, risk factor model

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Authors: Amit Kumar Bardhan, Barnali Nag, Chandra Sekhar Mishra

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Microfinance is considered as one of the key drivers of financial inclusion and pro-poor financial growth. Microfinance in India became popular through Self Help Group (SHG) movement initiated by NABARD. In terms of outreach and loan portfolio, SHG Bank Linkage programme (SHG-BLP) has emerged as the largest microfinance initiative in the world. The success of financial inclusion lies in the successful implementation of SHG-BLP. SHGs are generally promoted by social welfare organisations like NGOs, welfare societies, government agencies, Co-operatives etc. and even banks are also involved in SHG formation. Thus, the pro-poor implementation of the scheme largely depends on the credibility of the SHG Promoting Institutions (SHPIs). The rural poor lack education, skills and financial literacy and hence need continuous support and proper training right from planning to implementation. In this study, we have made an attempt to inspect the reasons behind low penetration of SHG financing to the poorest of the poor both from demand and supply side perspective. Banks, SHPIs, and SHGs are three key essential stakeholders in SHG-BLP programmes. All of them have a vital role in programme implementation. The objective of this paper is to find out the drivers and hurdles in the path of financial inclusion through SHG-BLP and the role of SHPIs in reaching out to the ultra poor. We try to address questions like 'what are the challenges faced by SHPIs in targeting the poor?' and, 'what are factors behind the low credit linkage of SHGs?' Our work is based on a qualitative study of SHG programmes in semi-urban towns in the states of West Bengal and Odisha in India. Data are collected through unstructured questionnaire and in-depth interview from the members of SHGs, SHPIs and designated banks. The study provides some valuable insights about the programme and a comprehensive view of problems and challenges faced by SGH, SHPIs, and banks. On the basis of our understanding from the survey, some findings and policy recommendations that seem relevant are: increasing level of non-performing assets (NPA) of commercial banks and wilful default in expectation of loan waiver and subsidy are the prime reasons behind low rate of credit linkage of SHGs. Regular changes in SHG schemes and no incentive for after linkage follow up results in dysfunctional SHGs. Government schemes are mostly focused on creation of SHG and less on livelihood promotion. As a result, in spite of increasing (YoY) trend of number of SHGs promoted, there is no real impact on welfare growth. Government and other SHPIs should focus on resource based SHG promotion rather only increasing the number of SHGs.

Keywords: financial inclusion, inclusive growth, microfinance, Self-Help Group (SHG), Self-Help Group Promoting Institution (SHPI)

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Authors: Ali Al-Shehri, Abdulaziz Al-Qasim, Abdulkarim Sofi, Ali Yousef

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The oil and gas industry has played a major role in improving the prosperity of mankind and driving the world economy. According to the International Energy Agency (IEA) and Integrated Environmental Assessment (EIA) estimates, the world will continue to rely heavily on hydrocarbons for decades to come. This growing energy demand mandates taking sustainability measures to prolong the availability of reliable and affordable energy sources, and ensure lowering its environmental impact. Unlike any other industry, the oil and gas upstream operations are energy-intensive and scattered over large zonal areas. These challenging conditions require unique sustainability solutions. In recent years there has been a concerted effort by the oil and gas industry to develop and deploy innovative technologies to: maximize efficiency, reduce carbon footprint, reduce CO2 emissions, and optimize resources and material consumption. In the past, the main driver for research and development (R&D) in the exploration and production sector was primarily driven by maximizing profit through higher hydrocarbon recovery and new discoveries. Environmental-friendly and sustainable technologies are increasingly being deployed to balance sustainability and profitability. Analyzing technology and its sustainability impact is increasingly being used in corporate decision-making for improved portfolio management and allocating valuable resources toward technology R&D.This paper articulates and discusses a novel workflow to identify strategic sustainable technologies for improved portfolio management by addressing existing and future upstream challenges. It uses a systematic approach that relies on sustainability key performance indicators (KPI’s) including energy efficiency quotient, carbon footprint, and CO2 emissions. The paper provides examples of various technologies including CCS, reducing water cuts, automation, using renewables, energy efficiency, etc. The use of 4IR technologies such as Artificial Intelligence, Machine Learning, and Data Analytics are also discussed. Overlapping technologies, areas of collaboration and synergistic relationships are identified. The unique sustainability analyses provide improved decision-making on technology portfolio management.

Keywords: sustainability, oil& gas, technology portfolio, key performance indicator

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Authors: Jeonghwan Jeon

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Recently, the industry and academia have been planning development through industry/university cooperation (IUC), and the government has been promoting alternative methods to achieve successful IUC. Representatively, business cultivation involves the lead university (regarding IUC), research and development (R&D), company support, professional manpower cultivation, and marketing, etc., and the scale of support expands every year. Research is performed by many academic researchers to achieve IUC and although satisfaction of their results is high, expectations are not being met and study of the main factor is insufficient. Therefore, this research improves on theirs by analysing the main factors influencing their satisfaction. Each factor is analysed by AHP, and portfolio analysis is performed on the importance and current satisfaction level. This will help improve satisfaction of business participants and ensure effective IUC in the future.

Keywords: industry/university cooperation, satisfaction, portfolio analysis, business participant

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Authors: Jeonghwan Jeon

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Recently, the industry and academia have been planning development through industry/university cooperation (IUC), and the government has been promoting alternative methods to achieve successful IUC. Representatively, business cultivation involves the lead university (regarding IUC), research and development (R&D), company support, professional manpower cultivation, and marketing, etc., and the scale of support expands every year. Research is performed by many academic researchers to achieve IUC and although satisfaction of their results is high, expectations are not being met and study of the main factor is insufficient. Therefore, this research improves on theirs by analysing the main factors influencing their satisfaction. Each factor is analysed by AHP, and portfolio analysis is performed on the importance and current satisfaction level. This will help improve satisfaction of business participants and ensure effective IUC in the future.

Keywords: industry/university cooperation, satisfaction, portfolio analysis, research and development

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Authors: Ismail Kimuli

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With a steadfast economic development, the Greater Kampala metropolitan area (GKMA) faces increasing pressures to increasetheshare of low-carbon electricity in the energy balance, abate CO2 emissions and also restructure the transportation sector for a sustainable 2050. GKMA, is Uganda’s commercial, political, social, and industrial hub with a population of 4.1 million, contributing 60% tothe nation’s GDP and accounts for 80% of Uganda’s industrial sector.However, with the rampant anthropogenic interference that causes climate change, CO2 emissions in the metropolitan are contributing to global warming. Many economies across the globe are addressing this challengethrough development and analysis of sustainable energy portfolios.A sustainable energy portfolio is a low-carbon scenario. The study reviews the literature to establish the current energy management situation of GKMA and finds it wanting in addressing the immediate challenges associated with energy management of the metropolitan. Then, the study develops and examines a sustainable energy portfolio for GKMA using TIMES-VEDA and then presents it as an investigative low-carbon energy scenario that could propel the metropolitan sustainably towards 2050.Sustainability is plausible by optimizing the total primary energy supply, generating low-carbon electricity from hydropower and PV-solar renewables, improving heating technologies for residential & commercial sectors, and switching 90% of land passengers from road to a Kampala metro for a sustainable mid-century.

Keywords: GKMA, sustainability, TIMES-VEDA, low-carbon scenario

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Authors: Vijay Kumar, Ron Bird

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The study investigates the effect of bank-specific, industry-specific and macroeconomic variables on the profitability of conventional and Islamic banks. Our sample comprises 1,781 bank-year observations of 205 banks from four countries in the Asian region for the period 2004-2014. Our results suggest that credit quality, cost management and bank size are the keys factors that contribute positively to bank profitability in Asia. The banks with high non-performing loans and high cost-to-income ratio are more likely to be exposed to losses. The impacts of the bank-specific variables are stronger than are the industry-specific and macroeconomic variables. We find that Malaysian banks are the least profitable compared to the banks in Bangladesh, Indonesia and Pakistan. There is strong evidence to suggest that conventional banks are more profitable than Islamic banks. Our results suggest that the impact of capital adequacy ratio and bank size and loan to deposit ratio vary across Islamic and conventional banks and across different subsamples.

Keywords: capital adequacy ratio, Islamic banks, non-performing loan ratio, ownership

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

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A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian automotive sector, stock market decisions, equity portfolio analysis, decision tree classifiers, statistical data analysis

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Authors: Obioma Uche

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Due to the recent volatility in oil & gas prices as well as increased development of non-conventional resources, it has become even more essential to critically evaluate the profitability of petroleum prospects prior to making any investment decisions. Traditionally, simple Monte Carlo (MC) algorithms have been used to randomly sample probability distributions of economic and geological factors (e.g. price, OPEX, CAPEX, reserves, productive life, etc.) in order to obtain probability distributions for profitability metrics such as Net Present Value (NPV). In recent years, second-order MC algorithms have been shown to offer an advantage over simple MC techniques due to the added consideration of uncertainties associated with the probability distributions of the relevant variables. Here, a strategy for the application of the second-order MC technique to a case study is demonstrated to analyze its effectiveness as a tool for portfolio management.

Keywords: Monte Carlo algorithms, portfolio management, profitability, risk analysis

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Authors: Jiahui Yang, John Quigley, Lesley Walls

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In this paper, the authors develop a stochastic model regarding the investment in supplier delivery performance development from a buyer’s perspective. The authors propose a multivariate model through a Multinomial-Dirichlet distribution within an Empirical Bayesian inference framework, representing both the epistemic and aleatory uncertainties in deliveries. A closed form solution is obtained and the lower and upper bound for both optimal investment level and expected profit under uncertainty are derived. The theoretical properties provide decision makers with useful insights regarding supplier delivery performance improvement problems where multiple delivery statuses are involved. The authors also extend the model from a single supplier investment into a supplier portfolio, using a Lagrangian method to obtain a theoretical expression for an optimal investment level and overall expected profit. The model enables a buyer to know how the marginal expected profit/investment level of each supplier changes with respect to the budget and which supplier should be invested in when additional budget is available. An application of this model is illustrated in a simulation study. Overall, the main contribution of this study is to provide an optimal investment decision making framework for supplier development, taking into account multiple delivery statuses as well as multiple projects.

Keywords: decision making, empirical bayesian, portfolio optimization, supplier development, supply chain management

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Authors: Mohammad Q. M. Momani

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The objective of this study is to examine whether the value relevance of ownership structure changed as the Amman Stock Exchange market conditions changed. Using data from 2005 to 2014, the study finds that the performance of portfolios that contain firms with concentrated ownership structure declines significantly during the post-crisis period. These portfolios exhibit poor performance relative to portfolios that contain firms with dispersed ownership structure during the post-crisis period. The results argue that uninspired performance of the Amman Stock Exchange during the post-crisis period, increased the incentives for controlling shareholders to expropriate. Investors recognized these incentives and discounted firms that were more likely to expropriate.

Keywords: value relevance, ownership structure, portfolio performance, Jordan, ASE

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Authors: Vinayak Bassi, Rajpreet Singh

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Over the years, with the emergence of sophisticated computers and algorithms, finance has been quantified using computational prowess. Asset valuation has been one of the key components of quantitative finance. In fact, it has become one of the embryonic steps in determining risk related to a portfolio, the main goal of quantitative finance. This study comprises a drawing comparison between valuation output generated by two stochastic dynamic models, namely Black-Scholes and Dupire’s bi-dimensionality model. Both of these models are formulated for computing the valuation function for a portfolio of European options using Monte Carlo simulation methods. Although Monte Carlo algorithms have a slower convergence rate than calculus-based simulation techniques (like FDM), they work quite effectively over high-dimensional dynamic models. A fidelity gap is analyzed between the static (historical) and stochastic inputs for a sample portfolio of underlying assets. In order to enhance the performance efficiency of the model, the study emphasized the use of variable reduction methods and customizing random number generators to implement parallelization. An attempt has been made to further implement the Dupire’s model on a GPU to achieve higher computational performance. Furthermore, ideas have been discussed around the performance enhancement and bottleneck identification related to the implementation of options-pricing models on GPUs.

Keywords: monte carlo, stochastic models, computational finance, parallel programming, scientific computing

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Authors: Nisreen Naji Al-Khawaldeh, Lara Ahmad Mansour El-Awar

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The present study investigated the strategies MA translation students used for translating technical terms, the most common obstacles they encountered in translating such terms, and the motives behind using such terms as they are in their original form despite their translatability into Arabic. To achieve these objectives, a translation test was administered to 100 MA students specialising in translation at both Hashemite University and The University of Jordan. It consisted of two parts: (a) 50 English technical terms to be translated (b) two questions to be answered concerning the challenges or problems encountered while translating the previous technical terms and the motives that drive them to use most of the English technical terms as they are despite their translatability into Arabic. The analysis of the results revealed that MA translation students faced problems in translating technical terms, namely the inability to find the equivalent form for the given technical terms, the use of literal translation, and the wider use of loan-words type. Besides, the students used different strategies to translate the technical terms, namely borrowing (i.e., loan- words), paraphrasing, synonymy, naturalization, equivalence, and literal translation. Moreover, it was also revealed that most technical terms were used as they are in the source language despite their translatability into Arabic because these technical terms are easier to use in English rather than in Arabic. Also, when these terms were introduced to the Arab world, they were introduced in English, not in Arabic. So, the brain links these objects to their English terms.

Keywords: arabic, english, technical terms, translation strategies, translation problems

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Authors: Pulak Swain, A. K. Ojha

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Portfolio optimization is based on dealing with the problems of efficient asset allocation. Risk and Expected return are two conflicting criteria in such problems, where the investor prefers the return to be high and the risk to be low. Using multi-objective approach we can solve those type of problems. However the information which we have for the input parameters are generally ambiguous and the input values can fluctuate around some nominal values. We can not ignore the uncertainty in input values, as they can affect the asset allocation drastically. So we use Robust Optimization approach to the problems where the input parameters comes under box uncertainty. In this paper, we solve the multi criteria robust problem with the help of  E- constraint method.

Keywords: portfolio optimization, multi-objective optimization, ϵ - constraint method, box uncertainty, robust optimization

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Authors: Iulia E. Falcan

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The transition of the European power sector towards a clean, renewable energy (RE) system faces the challenge of meeting power demand in times of low wind speed and low solar radiation, at a reasonable cost. This is likely to be achieved through a combination of 1) energy storage technologies, 2) development of the cross-border power grid, 3) installed overcapacity of RE and 4) dispatchable power sources – such as biomass. This paper uses NASA; derived hourly data on weather patterns of sixteen European countries for the past twenty-five years, and load data from the European Network of Transmission System Operators-Electricity (ENTSO-E), to develop a stochastic optimization model. This model aims to understand the synergies between the four classes of technologies mentioned above and to determine the optimal configuration of the energy technologies portfolio. While this issue has been addressed before, it was done so using deterministic models that extrapolated historic data on weather patterns and power demand, as well as ignoring the risk of an unbalanced grid-risk stemming from both the supply and the demand side. This paper aims to explicitly account for the inherent uncertainty in the energy system transition. It articulates two levels of uncertainty: a) the inherent uncertainty in future weather patterns and b) the uncertainty of fully meeting power demand. The first level of uncertainty is addressed by developing probability distributions for future weather data and thus expected power output from RE technologies, rather than known future power output. The latter level of uncertainty is operationalized by introducing a Conditional Value at Risk (CVaR) constraint in the portfolio optimization problem. By setting the risk threshold at different levels – 1%, 5% and 10%, important insights are revealed regarding the synergies of the different energy technologies, i.e., the circumstances under which they behave as either complements or substitutes to each other. The paper concludes that allowing for uncertainty in expected power output - rather than extrapolating historic data - paints a more realistic picture and reveals important departures from results of deterministic models. In addition, explicitly acknowledging the risk of an unbalanced grid - and assigning it different thresholds - reveals non-linearity in the cost functions of different technology portfolio configurations. This finding has significant implications for the design of the European energy mix.

Keywords: cross-border grid extension, energy storage technologies, energy system transition, stochastic portfolio optimization

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Authors: Abdelmounaim Lahrech, Hajar Bousfiha

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This paper examines equity market integration between Morocco and France and its consequent implications on international portfolio diversification. In the absence of stock market linkages, Morocco can act as a diversification destination to European investors, allowing higher returns at a comparable level of risk in developed markets. In contrast, this attractiveness is limited if both financial markets show significant linkage. The research empirically measures financial market’s integration in by capturing the conditional correlation between the two markets using the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model. Then, the research uses the Dynamic Conditional Correlation (DCC) model of Engle (2002) to track the correlations. The research findings show that there is no important increase over the years in the correlation between the Moroccan and the French equity markets, even though France is considered Morocco’s first trading partner. Failing to prove evidence of the stock index linkage between the two countries, the volatility series of each market were assumed to change over time separately. Yet, the study reveals that despite the important historical and economic linkages between Morocco and France, there is no evidence that equity markets follow. The small correlations and their stationarity over time show that over the 10 years studied, correlations were fluctuating around a stable mean with no significant change at their level. Different explanations can be attributed to the absence of market linkage between the two equity markets.

Keywords: equity market linkage, DCC GARCH, international portfolio diversification, Morocco, France

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Authors: Ghita Benayad

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Asset allocation, which affects the long-term profitability of portfolios by distributing assets to fulfill a range of investment objectives, is the cornerstone of investment management in the dynamic and complicated world of financial markets. This paper offers a technique for optimizing strategic asset allocation with the goal of improving portfolio performance by addressing the inherent complexity and uncertainty of the market through the use of Principal Component Analysis (PCA) in a multi-objective modeling framework. The study's first section starts with a critical evaluation of conventional asset allocation techniques, highlighting how poorly they are able to capture the intricate relationships between assets and the volatile nature of the market. In order to overcome these challenges, the project suggests a PCA-driven methodology that isolates important characteristics influencing asset returns by decreasing the dimensionality of the investment universe. This decrease provides a stronger basis for asset allocation decisions by facilitating a clearer understanding of market structures and behaviors. Using a multi-objective optimization model, the project builds on this foundation by taking into account a number of performance metrics at once, including risk minimization, return maximization, and the accomplishment of predetermined investment goals like regulatory compliance or sustainability standards. This model provides a more comprehensive understanding of investor preferences and portfolio performance in comparison to conventional single-objective optimization techniques. While applying the PCA-driven multi-objective optimization model to historical market data, aiming to construct portfolios better under different market situations. As compared to portfolios produced from conventional asset allocation methodologies, the results show that portfolios optimized using the proposed method display improved risk-adjusted returns, more resilience to market downturns, and better alignment with specified investment objectives. The study also looks at the implications of this PCA technique for portfolio management, including the prospect that it might give investors a more advanced framework for navigating financial markets. The findings suggest that by combining PCA with multi-objective optimization, investors may obtain a more strategic and informed asset allocation that is responsive to both market conditions and individual investment preferences. In conclusion, this capstone project improves the field of financial engineering by creating a sophisticated asset allocation optimization model that integrates PCA with multi-objective optimization. In addition to raising concerns about the condition of asset allocation today, the proposed method of portfolio management opens up new avenues for research and application in the area of investment techniques.

Keywords: asset allocation, portfolio optimization, principle component analysis, multi-objective modelling, financial market

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Authors: Viviana Ventre, Giacomo Di Tollo, Roberta Martino

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The portfolio selection problem includes the evaluation of many criteria that are difficult to compare directly and is characterized by uncertain elements. The portfolio selection problem can be modeled as a group decision problem in which several experts are invited to present their assessment. In this context, it is important to study and analyze the process of reaching a consensus among group members. Indeed, due to the various diversities among experts, reaching consensus is not necessarily always simple and easily achievable. Moreover, the concept of consensus is accompanied by the concept of false consensus, which is particularly interesting in the dynamics of group decision-making processes. False consensus can alter the evaluation and selection phase of the alternative and is the consequence of the decision maker's inability to recognize that his preferences are conditioned by subjective structures. The present work aims to investigate the dynamics of consensus attainment in a group decision problem in which equivalent portfolios are proposed. In particular, the study aims to analyze the impact of the subjective structure of the decision-maker during the evaluation and selection phase of the alternatives. Therefore, the experimental framework is divided into three phases. In the first phase, experts are sent to evaluate the characteristics of all portfolios individually, without peer comparison, arriving independently at the selection of the preferred portfolio. The experts' evaluations are used to obtain individual Analytical Hierarchical Processes that define the weight that each expert gives to all criteria with respect to the proposed alternatives. This step provides insight into how the decision maker's decision process develops, step by step, from goal analysis to alternative selection. The second phase includes the description of the decision maker's state through Markov chains. In fact, the individual weights obtained in the first phase can be reviewed and described as transition weights from one state to another. Thus, with the construction of the individual transition matrices, the possible next state of the expert is determined from the individual weights at the end of the first phase. Finally, the experts meet, and the process of reaching consensus is analyzed by considering the single individual state obtained at the previous stage and the false consensus bias. The work contributes to the study of the impact of subjective structures, quantified through the Analytical Hierarchical Process, and how they combine with the false consensus bias in group decision-making dynamics and the consensus reaching process in problems involving the selection of equivalent portfolios.

Keywords: analytical hierarchical process, consensus building, false consensus effect, markov chains, portfolio selection problem

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Authors: Abdelouahab Errida, Bouchra Lotfi, Elalami Semma

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In this paper, we try to address the topic of multiple change management by adopting an engineered research methodology, conducted within a Moroccan company during its implementation of several change projects that aim at improving its supply chain management performance. Firstly, we present the key concepts related to our research, namely change management, multiproject management and supply chain management. Then, we try to assess how the change management and multi-project management are applied in this company. Finally, we try to propose an approach that will help managers in dealing with multiple change projects. This approach proposes to integrate change management, project management and multi-project management for managing change projects according to three organizational levels: executive level, project portfolio level and change project level.

Keywords: change management, multi-project management, project management, change portfolio, supply chain management,

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Authors: Walid Chkili

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This paper investigates the dynamic relationship between gold and stock markets using data for BRICS counties. For this purpose, we estimate three multivariate GARCH models (namely CCC, DCC and BEKK) for weekly stock and gold data. Our main objective is to examine time variations in conditional correlations between the two assets and to check the effectiveness use of gold as a hedge for equity markets. Empirical results reveal that dynamic conditional correlations switch between positive and negative values over the period under study. This correlation is negative during the major financial crises suggesting that gold can act as a safe haven during the major stress period of stock markets. We also evaluate the implications for portfolio diversification and hedging effectiveness for the pair gold/stock. Our findings suggest that adding gold in the stock portfolio enhance its risk-adjusted return.

Keywords: gold, financial markets, hedge, multivariate GARCH

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Authors: Chokri Slim

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The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims.

Keywords: credit risk management, multiple linear regression, principal components analysis, artificial neural networks, support vector machines

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Authors: Zohreh Soltani, Seyedmohammadhossein Hosseinian

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Reduction of energy consumption in built infrastructure, through the installation of energy-efficient technologies, is a major approach to achieving sustainability. In practice, the viability of energy efficiency projects strongly depends on the cost reimbursement and profitability. These projects are subject to failure if the actual cost savings do not reimburse the project cost in a timely manner. In such cases, refinancing could be a solution to benefit from the long-term returns of the project if implemented wisely. However, very little is still known about the effect of refinancing options on financial performance of energy efficiency projects. To fill this gap, the present study investigates the financial behavior of energy efficiency projects with focus on refinancing options, such as Leveraged Loans. A System Dynamics (SD) model is introduced, and the model application is presented using an actual case-study data. The case study results indicate that while high-interest start-ups make using Leveraged Loan inevitable, refinancing can rescue the project and bring about profitability. This paper also presents some managerial implications of refinancing energy efficiency projects based on the case-study analysis. Results of this study help implementing financially viable energy efficiency projects, so the community could benefit from their environmental advantages widely.

Keywords: energy efficiency projects, leveraged loan, refinancing, sustainability

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Authors: Surayyo Shaamirova, Anwar Hasan Abdullah Othman

Abstract:

This study investigates the co-integration association between the top 10 cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Bitcoin Cash, EOS, Cardano, Litecoin, Stellar, IOTA, and NEO. The study applies Johansen Juselius co-integration test to examine the long-run co-integration and utilize the Engle and Granger casualty test to examine the short-run relationship. The findings of the study show that there is a strong co-integration relationship among the cryptocurrencies; however, in the short run, there is no causal relationship among the crypto currencies. These results, therefore, suggest that there are portfolio diversification opportunities in the cryptocurrencies industry when it comes to long run investment decisions, on the other hand, the cryptocurrencies industry shows the characteristics of efficiency in the short-run. This is an indication of a non-speculation investment in the cryptocurrencies industry in the short term investment.

Keywords: cryptocurrencies, Johansen-Juselius co-integration test, Engle and Granger casualty test, portfolio diversification

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Authors: A. Lefebre, M. Willekens, K. Debackere

Abstract:

Since the 1990s, patent applications have been booming, especially in the field of telecommunications. However, this increase in patent filings has been associated with an (alleged) decrease in patent quality. The plethora of low-quality patents devalues the high-quality ones, thus weakening the incentives for inventors to patent inventions. Despite the rich literature on strategic patenting, previous research has neglected to emphasize the importance of patent portfolio management and its impact on patent quality. In this paper, we compare related patent portfolios vs. nonrelated patents and investigate whether the patent quality and innovativeness differ between the two types. In the analyses, patent quality is proxied by five individual proxies (number of inventors, claims, renewal years, designated states, and grant lag), and these proxies are then aggregated into a quality index. Innovativeness is proxied by two measures: the originality and radicalness index. Results suggest that related patent portfolios have, on average, a lower patent quality compared to nonrelated patents, thus suggesting that firms use them for strategic purposes rather than for the extended protection they could offer. Even upon testing the individual proxies as a dependent variable, we find evidence that related patent portfolios are of lower quality compared to nonrelated patents, although not all results show significant coefficients. Furthermore, these proxies provide evidence of the importance of adding fixed effects to the model. Since prior research has found that these proxies are inherently flawed and never fully capture the concept of patent quality, we have chosen to run the analyses with individual proxies as supplementary analyses; however, we stick with the comprehensive index as our main model. This ensures that the results are not dependent upon one certain proxy but allows for multiple views of the concept. The presence of divisional applications might be linked to the level of innovativeness of the underlying invention. It could be the case that the parent application is so important that firms are going through the administrative burden of filing for divisional applications to ensure the protection of the invention and the preemption of competition. However, it could also be the case that the preempting is a result of divisional applications being used strategically as a backup plan and prolonging strategy, thus negatively impacting the innovation in the portfolio. Upon testing the level of novelty and innovation in the related patent portfolios by means of the originality and radicalness index, we find evidence for a significant negative association with related patent portfolios. The minimum innovation that has been brought on by the patents in the related patent portfolio is lower compared to the minimum innovation that can be found in nonrelated portfolios, providing evidence for the second argument.

Keywords: patent portfolio management, patent quality, related patent portfolios, strategic patenting

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Authors: Till Gramberg

Abstract:

In the dynamic world of machine and plant engineering, stagnation in the growth of new product sales compels companies to reconsider their business models. The increasing shift toward service orientation, known as "servitization," along with challenges posed by digitalization and sustainability, necessitates an adaptation of product portfolio management (PPM). Against this backdrop, this study investigates the current challenges and requirements of PPM in this industrial context and develops a framework for the application of generative artificial intelligence (AI) to enhance agility and efficiency in PPM processes. The research approach of this study is based on a mixed-method design. Initially, qualitative interviews with industry experts were conducted to gain a deep understanding of the specific challenges and requirements in PPM. These interviews were analyzed using the Gioia method, painting a detailed picture of the existing issues and needs within the sector. This was complemented by a quantitative online survey. The combination of qualitative and quantitative research enabled a comprehensive understanding of the current challenges in the practical application of machine and plant engineering PPM. Based on these insights, a specific framework for the application of generative AI in PPM was developed. This framework aims to assist companies in implementing faster and more agile processes, systematically integrating dynamic requirements from trends such as digitalization and sustainability into their PPM process. Utilizing generative AI technologies, companies can more quickly identify and respond to trends and market changes, allowing for a more efficient and targeted adaptation of the product portfolio. The study emphasizes the importance of an agile and reactive approach to PPM in a rapidly changing environment. It demonstrates how generative AI can serve as a powerful tool to manage the complexity of a diversified and continually evolving product portfolio. The developed framework offers practical guidelines and strategies for companies to improve their PPM processes by leveraging the latest technological advancements while maintaining ecological and social responsibility. This paper significantly contributes to deepening the understanding of the application of generative AI in PPM and provides a framework for companies to manage their product portfolios more effectively and adapt to changing market conditions. The findings underscore the relevance of continuous adaptation and innovation in PPM strategies and demonstrate the potential of generative AI for proactive and future-oriented business management.

Keywords: servitization, product portfolio management, generative AI, disruptive innovation, machine and plant engineering

Procedia PDF Downloads 83