Portfolio Management Techniques Research Articles

Towards efficient similarity embedded temporal Transformers via extended timeframe analysis

Price prediction remains a crucial aspect of financial market research as it forms the basis for various trading strategies and portfolio management techniques. However, traditional models such as ARIMA are not effective for multi-horizon forecasting, and current deep learning approaches do not take into account the conditional heteroscedasticity of financial market time series. In this work, we introduce the similarity embedded temporal Transformer (SeTT) algorithms, which extend the state-of-the-art temporal Transformer architecture. These algorithms utilise historical trends in financial time series, as well as statistical principles, to enhance forecasting performance. We conducted a thorough analysis of various hyperparameters including learning rate, local window size, and the choice of similarity function in this extension of the study in a bid to get optimal model performance. We also experimented over an extended timeframe, which allowed us to more accurately assess the performance of the models in different market conditions and across different lengths of time. Overall, our results show that SeTT provides improved performance for financial market prediction, as it outperforms both classical financial models and state-of-the-art deep learning methods, across volatile and non-volatile extrapolation periods, with varying effects of historical volatility on the extrapolation. Despite the availability of a substantial amount of data spanning up to 13 years, optimal results were primarily attained through a historical window of 1–3 years for the extrapolation period under examination.

Analyzing Portfolio Optimization in Cryptocurrency Markets: A Comparative Study of Short-Term Investment Strategies Using Hourly Data Approach

This paper investigates portfolio optimization methodologies and short-term investment strategies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from June 2020 to March 2024. Using hourly data, we apply the Kurtosis Minimization methodology, along with other optimization strategies, to construct and assess portfolios across various rebalancing frequencies. Our empirical analysis reveals significant volatility, skewness, and kurtosis in cryptocurrencies, highlighting the need for sophisticated portfolio management techniques. We discover that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, especially in shorter-term investment horizons, delivering optimal returns to investors. Additionally, our findings emphasize the importance of dynamic portfolio management, stressing the necessity of regular rebalancing in the volatile cryptocurrency market. Overall, this study offers valuable insights into optimizing cryptocurrency portfolios, providing practical guidance for investors and portfolio managers navigating this rapidly evolving market landscape.

Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic

The literature lacks thorough and adequate evidence of the efficiency and herding behavior of clean and renewable energy markets. Therefore, the key objective of this paper is to explore the multifractality and efficiency of six clean energy markets by applying a robust method of Multifractal detrended fluctuation analysis (MFDFA) on daily data over a lengthy period. In addition, to examine the inner dynamics of clean energy markets around the global pandemic (COVID19), the data are further divided into two sub-periods of before and during COVID19. Our sampled clean energy markets exhibit multifractal behavior with a significant impact on the efficiency and intensified presence of multifractality during the COVID19 period. Overall, TXCT and BSEGRNX were the most efficient clean energy markets, but the ranking of TXCT deteriorated significantly in the sub-periods. The presence of multifractality and herding behavior symmetry intensified during the crisis period, which gives a potential for advancing portfolio management techniques. Moreover, our study provides practical implications and new insights for various market participants for better management and understanding of risks.

A State-of-the-Art Review of Probabilistic Portfolio Management for Future Stock Markets

Portfolio management has long been one of the most significant challenges in large- and small-scale investments alike. The primary objective of portfolio management is to make investments with the most favorable rate of return and the lowest amount of risk. On the other hand, time series prediction has garnered significant attention in recent years for predicting the trend of stock prices in the future. The combination of these two approaches, i.e., predicting the future stock price and adopting portfolio management methods in the forecasted time series, has turned out to be a novel research line in the past few years. That is, to have a better understanding of the future, various researchers have attempted to predict the future behavior of stocks and subsequently implement portfolio management techniques on them. However, due to the uncertainty in predicting the future, the reliability of these methodologies is in question, and it is unclear to what extent their results can be relied upon. Therefore, probabilistic approaches have also entered the research arena, and attempts have been made to incorporate uncertainty into future forecasting and portfolio management. This issue has led to the development of probabilistic portfolio management for future data. This review paper begins with a discussion of various time-series prediction methods for stock market data. Next, a classification and evaluation of portfolio management approaches are provided. Afterwards, the Monte Carlo sampling method is discussed as the most prevalent technique for probabilistic analysis of stock market data. The probabilistic portfolio management method is applied to future Shanghai Stock Exchange data in the form of a case study to measure the applicability of this method to real-world projects. The results of this research can serve as a benchmark example for the analysis of other stock market data.

Prioritization Model for IT Project Portfolio Management in Private University: A Literature Review

The swift progress of technology can be harnessed to address the increasing demand for projects, particularly in various organizations like private universities that must cope with resource limitations and make critical decisions. Information Technology (IT) encompasses any technology, such as equipment or techniques, employed by businesses, institutions, or other organizations to process information, including computing, telecommunications technologies, consumer electronics, and broadcasting, as it increasingly digitizes. Choosing between dozens or hundreds of project alternatives presents complex multi-criteria decision-making problems for an organization's portfolio and priorities, necessitating clear-cut techniques, methods, and factor definitions for prioritizing decision-making. This literature review formulates the problem, specifically identifying the criteria for comparison and prioritization models, and seeks a framework and methodology for prioritizing portfolio management in private organizations, particularly universities. The literature review identifies the characteristics of private organizations and the methodologies and practices employed in researching the application of portfolio management priorities and explores the use of portfolio management techniques from prior studies tested in practice and project priority methodologies in private services. The results contribute to enhancing theories on techniques, methods, and, particularly, portfolio project management priorities for the private sector.

The Effect of Capital Expenditure and Operational Expenditure on Investment Decision

The purpose of this research is determines the extent to which capital expenditure decisions are made by listed agricultural sector companies. Companies in Indonesia are related to the value of the company in the long run.The number of companies listed on the IDX during this period was 26, and the sample size used was 10. With the help of regression analysis, the findings reveal that capital expenditure and operational expenditure have significant relationship with investment decisions. it can be concluded that the sector companies listed on the Indonesia Stock Exchange for the period 3 years 2019-2021 capital expenditure variables have a significant positive effect on investment decisions, then operational variables have an insignificant negative effect on investment decisions It is recommended that the management of agricultural sector enterprises should ensure:holistic use of all techniques, exploring risks, real options analysis and growth and portfolio management techniques involving capital assets, in valuing capital investments before taking decision. it can be concluded that the sector companies listed on the Indonesia Stock Exchange for the period 3 years 2019-2021 capital expenditure variables have a significant positive effect on investment decisions, then operational variables have an insignificant negative effect on investment decisions.

Where to invest in cycle parking: A portfolio management approach to spatial transport planning

A lack of cycle parking is a known barrier to promoting the uptake of cycling in urban areas. Unlike cars that can be parked on the roadside with little additional infrastructure, bikes usually require dedicated parking facilities. The existing research and guidance on where cycle parking should be provided primarily focuses on key destinations such as train stations or schools. Thus, there is a gap in knowledge about the amount of general-purpose cycle parking required and how it should be distributed across a city. This paper presents a novel method for analysing and prioritising the spatial distribution of cycle parking. The method draws on established portfolio management techniques but applies them in a spatial context. Using the case study of London, we demonstrate that it is possible to identify areas that have a deficit of cycle parking as well as locations that have the most significant potential for increasing cycling uptake by providing additional cycle parking.

Extending UAF for Model‐Based Capability Planning and Enterprise Portfolio Management

AbstractCapabilities, the lifeblood of an enterprise, can be managed effectively using portfolio management techniques. Capability roadmaps are commonly used to manage the deployment of new and improved capabilities to address key drivers and challenges that the enterprise faces. This paper shows how MBSE can enhance an organization's ability to plan for capability deployments, as well as the ability to more effectively manage its portfolios of systems, services, people, technologies, processes, facilities, and other key enablers for the fielded capabilities. Specific enterprise modeling elements that can facilitate capability planning and portfolio management are presented. These new modeling elements are being incorporated into an update of the Unified Architecture Framework, an OMG standard for modeling an enterprise.

Constrained optimization algorithms for the computation of investable portfolios analytics: evaluation of economic-capital parameters for performance measurement and improvement

PurposeThis paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and investable economic-capital structures in the Gulf Cooperation Council financial markets, subject to applying various operational and financial optimization restrictions under crisis outlooks.Design/methodology/approachThe author implements a robust methodology to assess regulatory economic-capital allocation in a liquidity-adjusted value at risk (LVaR) context, mostly from the standpoint of investable portfolios analytics that have long- and short-sales asset allocation or for those portfolios that contain long-only asset allocation. The optimization route is accomplished by controlling the nonlinear quadratic objective risk function with certain regulatory constraints along with LVaR-GARCH-M (1,1) procedure to forecast conditional risk parameters and expected returns for multiple asset classes.FindingsThe author’s conclusions emphasize that the attained investable economic-capital portfolios lie-off the efficient frontier, yet those long-only portfolios seem to lie near the efficient frontier than portfolios with long- and short-sales assets allocation. In effect, the newly observed market microstructures forms and derived deductions were not apparent in prior research studies (Al Janabi, 2013).Practical implicationsThe attained empirical results are quite interesting for practical portfolio optimization, within the environments of big data analytics, reinforcement machine learning, expert systems and smart financial applications. Furthermore, it is quite promising for multiple-asset portfolio management techniques, performance measurement and improvement analytics, reinforcement machine learning and operations research algorithms in financial institutions operations, above all after the consequences of the 2007–2009 financial crisis.Originality/valueWhile this paper builds on Al Janabi’s (2013) optimization algorithms and modeling techniques, it varies in the sense that it covers the outcomes of a multi-asset portfolio optimization method under severe event market scenarios and by allowing for both long-only and combinations of long-/short-sales multiple asset. The achieved empirical results, optimization parameters and efficient and investable economic-capital figures were not apparent in Al Janabi’s (2013) paper because the prior evaluation were performed under normal market circumstances and without bearing in mind the impacts of the 2007–2009 global financial crunch.

IMPACT OF COVID 19 PANDEMIC ON PROJECT PORTFOLIO MANAGEMENT-A CASE STUDY ON CONSTRUCTION INDUSTRY IN SOUTHERN INDIA

In India, Project Portfolio Management (PPM) is in practice as a tool for prioritizing and managing real estate projects in construction organizations. But due to insufficient funding, improper judgment of experts during the crisis situation, the selection of optimal project portfolio prototype can be viewed as a risk based decision making process involving various risk factors. The objective of this study is to analyze the importance of project portfolio management and the risks associated with it in the construction industry taking into account the impact of novel corona virus COVID 19. This research identifies the adoption of more consistent project governance, risk management techniques and way more careful project portfolio management as the core area of study. A conceptual framework for Project Portfolio Management is also designed after analyzing various parameters of Project Portfolio Management of construction industry with the help of Bayesian framework. The key motive for undertaking this part of examination on real estate sector of Indian construction industry in southern part of India to reduce the impacts and increase the return on investment from the projects by mitigating the effect of risk factors associated in the projects. Project Portfolio Management tools and techniques are very useful for managing multiple construction projects.

Evaluation of Optimal and Coherent Risk-Capital Structures Under Adverse Market Outlooks Using Machine Learning Techniques

This paper broadens research literature associated with the assessment of modern portfolio risk management techniques by presenting a thorough modeling of nonlinear dynamic asset allocation and management under the supposition of illiquid and adverse market settings. This study analyses, from a fund manager’s perspective, the performance of liquidity adjusted risk modeling in obtaining optimal and coherent economic capital structures, subject to meaningful operational and financial constraints as specified by the fund manager. Specifically, the paper proposes a re-engineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (L-VaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions and disallowing both pure long positions and borrowing constraints. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios using machine learning techniques, over the holding period, with the aid of an appropriate scaling of the multiple-assets’ L-VaR matrix along with GARCH-M technique to forecast conditional volatility and expected return. The key methodological contribution is a different and less conservative liquidity scaling factor than the conventional root-t multiplier. Moreover, in this paper, the authors develop a dynamic nonlinear portfolio selection model and an optimization algorithm which allocates both economic capital and trading assets by minimizing L-VaR subject to the constraints that the expected return, trading volume and liquidation horizon should meet the budget limits set by the fund manager. In addition, the paper illustrates how the modified L-VaR method can be used by an equity trading unit in a dynamic asset allocation framework for reporting risk exposure, optimizing economic capital, and assessing risk reduction alternatives. The empirical results strongly confirm the importance of enforcing financially and operationally meaningful nonlinear and dynamic constraints, when they are available, on the L-VaR optimization procedure.

Information Flow from COVID‐19 Pandemic to Islamic and Conventional Equities: An ICEEMDAN‐Induced Transfer Entropy Analysis

With the steady growth in the data set on the COVID‐19 pandemic, empirical works that employ novel and yet appropriate statistical techniques to corroborate previous findings of the pandemic and its consequences on financial markets are necessary. This paper examined the impact of COVID‐19 information flow on the Islamic and conventional equities within the short‐, mid‐, and long‐term horizons to assess possible diversification prospects in the era of the pandemic. To the studied equities markets, a novel technique based on a denoised frequency‐domain entropy paradigm was applied. The operability of entrenched market dynamics in the long‐term horizon of the COVID‐19 pandemic period is reinforced by the results. The findings divulge diversification opportunities between Islamic and conventional equities in the short‐ and mid‐term periods of the COVID‐19 pandemic. The risks on equities from Japan or Bahrain could be diversified by equities from Jordan in the short‐term, while in the intermediate‐term stocks from Japan could diversify with the UAE and USA equities. The results imply that it is imperative for investors and fund managers to employ portfolio management techniques that show how to use benefits together with risk prevention and management across distinct time scales.

Tax-Efficient Portfolio Transition: A Tax-Aware Relaxed-Constraint Approach to Switching Equity Managers

For a taxable investor with a highly appreciated equity portfolio, replacing the portfolio manager is likely to trigger substantial tax liabilities. We focus on transitioning an appreciated equity portfolio to an actively managed strategy. We compare transition from an appreciated portfolio to a traditional long-only tax-agnostic equity strategy with transition to equity strategies utilizing more-advanced portfolio management techniques such as tax-aware rebalancing and relaxed-constraint portfolio construction. We find that transition to a tax-aware relaxed-constraint strategy results in both high implementation efficiency and tax efficiency both during and after the transition. As a result, a tax-aware, relaxed-constraint, post-transition strategy significantly outperforms a traditional tax-agnostic, long-only strategy in its ability to preserve and grow the investor’s after-tax wealth over the long term. We also discuss risks and limitations of the tax-aware, relaxed-constraint approach. <b>TOPICS:</b>Wealth management, equity portfolio management <b>Key Findings</b> ▪ For an individual taxable investor with an appreciated equity portfolio, the tax costs of replacing the portfolio manager may be highly punitive, far outweighing the transaction costs of such replacement. ▪ Advanced portfolio management techniques can help alleviate the tax burden of portfolio transition resulting from manager replacement: We show that transition to a tax-aware relaxed-constraint strategy results in high implementation efficiency and tax efficiency both during and after the transition, leading to strong after-tax performance. ▪ We also show that tax-aware portfolio transition is a complex bespoke solution and stress the need for a careful evaluation of the client-specific situation by a client’s investment advisor and a prospective manager. Nonetheless, our results demonstrate that a tax-aware transition to a relaxed-constraint strategy has the potential for providing a substantial benefit to a taxable investor “locked-in” into a highly appreciated portfolio.

A Modified Hierarchical Risk Parity Framework for Portfolio Management

This article introduces a modified hierarchical risk parity (MHRP) approach that extends the HRP approach by incorporating three intuitive elements commonly used by practitioners. The new approach (1) replaces the sample covariance matrix with an exponentially weighted covariance matrix with Ledoit–Wolf shrinkage; (2) improves diversification across portfolio constituents both within and across clusters by relying on an equal volatility, rather than an inverse variance, allocation approach; and (3) improves diversification across time by applying volatility targeting to portfolios. The author examines the impact of the enhancements on portfolios of commodity trading advisors within a large-scale Monte Carlo simulation framework that accounts for the realistic constraints of institutional investors. The author finds a striking improvement in the out-of-sample Sharpe ratio of 50%, on average, along with a reduction in downside risk. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • The author introduces a modified hierarchical risk parity (MHRP) approach, which incorporates three popular portfolio management techniques into the hierarchical risk parity framework of Lopez de Prado. • He reports a striking improvement in the out-of-sample Sharpe ratio of 50%, on average, for portfolios of commodity trading advisors. • The author argues that the MHRP framework has broad applications for portfolios of traditional and alternative investments.

Optimizing Return on Assets through Investment in Property, Plant and Equipment: Evidence from Listed Nigerian Manufacturing Companies

This study examined the extent to which investment in property, plant &amp; equipment (PPE) made by listed manufacturing companies in Nigeria relate with the return on assets (ROA). The non-usage of composite appraisal techniques, other than traditional budgeting techniques was seen as a major problem of investment decisions on PPE. The study adopted the quantitative panel methodology of the ex post facto and correlational research design. Secondary data were extracted from the fact books of the Nigerian Stock Exchange for the period, 2013 – 2018. The number of manufacturing companies listed in the Stock Exchange during this period was 83, which was also taken as the population of the study. The sample used in the study was 69. Three hypotheses were tested at 0.05 level of significance. Multiple and simple regression analyses were used on the data collected, to find the relationship between the independent and dependent variables. The hypotheses tested indicated in the findings that property, plant and equipment had a significant relationship with return on assets of listed manufacturing firms in Nigeria when there is a joint relationship between variables of property, plant &amp; equipment (PPE) and return on asset (ROA). Based on the findings and conclusion, it was recommended that management of manufacturing companies should ensure a holistic use of all techniques, exploring the real and growth options analyses as well as portfolio management techniques involving productive non-current assets, to earn the benefit of return on assets invested.

ПРИМЕНЕНИЕ AGILE-МЕТОДОВ И LEAN PORTFOLIO MANAGEMENT (LPM) В МОТИВАЦИИ КОМАНДНОЙ РАБОТЫ КОМПАНИИ

The world is changing rapidly, and customer expectations are changing just as quickly. The speed of change encourages companies to invest heavily in digital and agile transformations that scale and bring the benefits of agile teams to the enterprise level. However, few people achieve the desired results. To fully implement digital or agile transformation, commercial and technology teams must achieve and maintain consistency. Full company-wide consistency is a critical condition for competitiveness in today's economy. The difficulty of combining agile teams into an agile company is the resulting gap between the teams that do the work and the teams that distribute the funding. To bridge this gap, managers need the ability to: - increase visibility-visually present work in backlogs (for plans) and demos (for new solutions); - understand the workflow – analyze step-by-step results to assess progress towards achieving the final results; - optimize investment-change strategy or persistently follow a plan, reallocating funds to maximize the delivery of value. In an effort to achieve and maintain consistency, companies often find that traditional portfolio management methods are ineffective. Instead, many implement Lean Portfolio Management techniques to ensure consistency across the organization. The Lean Portfolio Management (LPM) methodology describes the principles of lean management that top management applies to align strategy with execution. Portfolio management teams study the company's strategies and allocate budget funds to implement this strategy. Like any portfolio, an LPM investment portfolio can be creatively defined as well as actively managed throughout the investment lifecycle. LPM focuses on aligning agile development with business strategy and creating value for customers through product and solution development. LPM in combination with agile development methods allows you to increase the flexibility of your business.

Tax-Efficient Portfolio Transition: &lt;i&gt;A Tax-Aware Relaxed-Constraint Approach to Switching Equity Managers&lt;/i&gt;

For a taxable investor with a highly appreciated equity portfolio, replacing the portfolio manager is likely to trigger substantial tax liabilities. We focus on transitioning an appreciated equity portfolio to an actively managed strategy. We compare transition from an appreciated portfolio to a traditional long-only tax-agnostic equity strategy with transition to equity strategies utilizing more advanced portfolio management techniques such as tax-aware rebalancing and relaxed-constraint portfolio construction. We find that transition to a tax-aware relaxed-constraint strategy results in both high implementation efficiency and tax efficiency both during and after the transition. As a result, a tax-aware relaxed-constraint post-transition strategy significantly outperforms a traditional tax-agnostic long-only strategy in its ability to preserve and grow the investors after-tax wealth over the long term. We also discuss risks and limitations of the tax-aware relaxed-constraint approach.

Adaptive and Agile in the Face of Uncertainty and Change: A Toolkit for Managing Complex, Rapidly Changing and Technologically Intensive Environments

As 2017 came to an end, Yahoo, Business Insider, MSN and other news media published articles on “Ten Brands that will disappear in 2018”, citing declining sales, losses, dwindling market share, rising costs, for their parent companies as reasons for these brands’ demise, and reflecting the challenging business environment that many firms face. Managing the external environment effectively with the appropriate types of internal processes that will allow the firm to respond quickly and effectively to its competitors can be a major challenge, that not all organizations are equipped to deal with. For organizations operating in complex and technologically intensive environments, the need to adapt to change is critical. Staying competitive in a fast moving and challenging external environment that heavily relies on technology, requires the organization to be innovative on a continual basis. Agility, adaptability and flexibility in thinking and processes is required at all levels – from development of organizational strategies to implementing them at the project level. Organizations can greatly benefit from the use of portfolio, program and project management methods and processes that allow them to be quickly aware of changes in the external environment, and remain responsive to its changing customer needs. This paper evaluates the optimal set of methods and processes from the organization’s strategic level down to its execution level. The author and provides an integrated toolkit of portfolio, program and project management techniques for organizations to manage their business in complex and dynamic environments, to equip them to deal with evolving customer needs.

Assessing the effects of information and communication technologies on organizational development: business values perspectives

ABSTRACTInformation and communication technology (ICT) projects for organizational development deal with market challenges, information handling, and the integration of multiple information systems (IS) in an organization. This research investigates how ICT projects (IS systems, etc.) affect the strategic, social, and human development in an organization. Previous studies have highlighted the advantages of ICT portfolio management techniques and return on investment approaches; the current research focused primarily on measuring business value on investment perspective. Therefore, based on the findings from the literature review, an integrated framework was proposed and validated using the case study in Saudi Arabia to evaluate the effects of ICT/IS projects from a managerial perspective. The framework consisted of a list of processes, criteria, and sub-criteria for different kinds of extracted features to measure the impact of ICT/IS projects. Our findings demonstrated that the effects of ICT projects are not limited to social and economic development, but are also categorized as strategic, managerial, informational, operational, transactional, organizational, infrastructure, and transformational development. It is hoped that the findings of the current study can inform ICT decision makers, experts, and researchers who have investigated and are doing research in this area.

Optimisation of the decision-making process of investment in public projects through the use of practices of portfolio management

This article aims to explore the portfolio management tools and techniques that can assist policy makers to improve the quality of public spending. The researchers were motivated by Brazil being a country that provides one of the worst returns of taxes collected for the well-being of society [IBPT, (2013), p.2]. Several studies have shown the tools and techniques for portfolio management can improve the performance of organisations. The article was written in the form of a theoretical study, using descriptive and bibliographic research. The study sought to identify the best practices existing on portfolio management to describe a basic model that can be deployed in any public organisation.