Portfolio Theory Research Articles

Revolutionizing finance with bitcoin and blockchain: a literature review and research agenda

PurposeOur analysis is targeted at researchers in the fields of economics and finance, and we place emphasis on the incremental contributions of each paper, key research questions, study methodology, main conclusions and data and identification tactics. By focusing on these critical areas, our review seeks to provide valuable insights and guidance for future research in this rapidly evolving and complex field.Design/methodology/approachThis paper conducts a structured literature review (SLR) of Bitcoin-related articles published in the leading finance, economics and accounting journals between 2018 and 2023. Following Massaro et al. (2016), SLR is a method for examining a corpus of scholarly work to generate new ideas, critical reflections and future research agendas. The goals of SLR are congruent with the three outcomes of critical management research identified by Alvesson and Deetz (2000): insight, critique and transformative redefinition.FindingsThe present state of research on Bitcoin lacks coherence and interconnectedness, leading to a limited understanding of the underlying mechanisms. However, certain areas of research have emerged as significant topics for further exploration. These include the decentralized payment system, equilibrium price, market microstructure, trading patterns and regulation of Bitcoin. In this context, this review serves as a valuable starting point for researchers who are unacquainted with the interdisciplinary field of bitcoin and blockchain research. It is essential to recognize the potential value of research in Bitcoin-related fields in advancing knowledge of the interaction between finance, economics, law and technology. Therefore, future research in this area should focus on adopting innovative and interdisciplinary methods to enhance our comprehension of these intricate and evolving technologies.Originality/valueOur review encompasses the latest research on Bitcoin, including its market microstructure, trading behavior, price patterns and portfolio analysis. It explores Bitcoin's market microstructure, liquidity, derivative markets, price discovery and market efficiency. Studies have also focused on trading behavior, investors' characteristics, market sentiment and price volatility. Furthermore, empirical studies demonstrate the advantages of including Bitcoin in a portfolio. These findings enhance our understanding of Bitcoin's potential impact on the financial industry.

Portfolio management under capital market frictions: a grey clustering approach

International portfolio management is influenced by the existence of “frictions”, factors or events that interfere with trade, which are linked in financial literature to market-specific factors, such as available information, restrictions, investor protection, or market liquidity. Given the wide variety of factors that can be included in these categories, scientific studies typically focus on a reduced number of indicators at a time in order to offer an in depth analysis of their impact. We offer a consolidated view of the perspectives observed in financial literature by proposing a novel index for market frictions that includes all these four components and rank fifteen post-communist East European capital markets based on their index values. We then constructed various scenarios by assuming different levels of importance for the criteria used in index construction. By employing grey clustering analysis, we cluster these capital markets into three categories—strongly recommended, recommended with some reserve, and not recommended—based on the importance given by the decision maker to these factors. The results show that some of the studied markets are in the same cluster, irrespective of the chosen scenario. The only market always included in the “strongly recommended” category is Hungary, indicating that it is a good investment option for international participants. Bulgaria and Slovakia are always regarded as “recommended with reserve” markets, whereas the Republic of Moldova is part of the “not recommended” category. The other markets show a degree of variability that can be explained by different investor perspectives. This study contributes to the existing literature by combining the advantages of grey clustering and portfolio analysis. Investors can use this approach during the decision-making process related to their investments.

Optimal Portfolio Analysis on Stocks Listed in Lq45

The purpose of this study is to determine the optimal portfolio of stocks that are listed in the LQ-45 period (January 2023 – January 2024) and compare the return and risk in stocks that are included in the LQ-45 but not included in the optimal portfolio. The method used is the Single Index Method, which uses the ERB (Excess Return to Beta) assessment reference. The results showed that out of 45 stocks there were 10 stocks that had large ERB, which were included in the optimal portfolio were GGRM, BBTN, KLBF, EXCL, ICBP, MAPI, UNVR, CPIN, INDF, and TBIG, which provided a return of 6.61% per year and a risk of 0.08% per year. Meanwhile, the remaining thirty-five stocks were made up as well as 10 other portfolios. Each of these portfolios consists of 10 randomly selected stocks. These ten portfolios yield higher returns than optimal portfolios. However, they also have a higher risk. The results of the comparison of the coefficient of variation between the optimal portfolio and the other 10 portfolios show that the optimal portfolio is the best portfolio

Solvency analysis of deferred annuities

AbstractWhile the solvency analysis of immediate life annuity portfolios has been extensively studied, the case of deferred annuities has received comparatively much less attention. We assess the importance and effect of stochastic mortality models and interest rates on the solvency analysis of a portfolio of deferred annuity contracts. Our analysis considers three steps: first, the benchmark case where mortality rates and interest rates are both deterministic; then, the case in which only mortality rates are stochastic is explored; finally, the full model where both mortality rates and interest rates are stochastic. The results demonstrate the model risk stemming from the uncertainty in the mortality models and its impact on the evaluation of solvency margins for life annuities. The role of the deferment period is thoroughly discussed and compared to the case of immediate annuities.

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

Purpose: The aim of the study was to examine the role of machine learning in enhancing risk management strategies in financial institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study revealed that integration of machine learning into risk management strategies within financial institutions has demonstrated significant potential for enhancing decision-making processes and mitigating various risks. The study have consistently shown that machine learning algorithms outperform traditional statistical methods in areas such as credit risk assessment, fraud detection, market risk management, and loan portfolio optimization. These advancements have led to improved accuracy, efficiency, and timeliness in risk assessment, enabling financial institutions to make more informed decisions while reducing losses and enhancing overall performance. Unique Contribution to Theory, Practice and Policy: Modern Portfolio Theory (MPT), Efficient Market Hypothesis (EMH) & Agency Theory may be used to anchor future studies on role of machine learning in enhancing risk management strategies in financial institutions. Invest in building robust data infrastructure and governance frameworks to support the implementation of machine learning models in risk management practices. High-quality data is crucial for training accurate and reliable machine learning algorithms. Establish regulatory guidelines and standards for the responsible use of machine learning in risk management within the financial industry. These guidelines should address issues such as model transparency, fairness, and accountability to ensure ethical and responsible practices.

Moderating the Role of Trade Openness on Foreign Portfolio Investment and Economic Growth among East Africa Community Countries

Purpose: This study investigates the impact of foreign portfolio investment (FPI) on economic growth in East Africa using panel data for Kenya, Uganda, Tanzania, Rwanda, and Burundi from 1974-2022. Methodology: The analysis employs a panel vector error correction model to estimate the short and long-run effects of FPI on growth. The study relied on secondary data sources for the period 1974-2022 with an annual frequency, implying 49 years of data. Data on foreign investment variables and macroeconomic control variables were sourced from the World Bank database, National Statistical Bureaus, Central Banks, and Stock Exchanges of the respective countries. Findings: The results indicate that FPI significantly positively impacts economic growth in East Africa. A 1% increase in FPI inflows as a share of Gross Domestic Product (GDP) boosts annual real GDP growth by approximately 10.7% in the long run. The findings are robust to the inclusion of moderating variables like trade openness. Unique Contribution to Theory, Practice and Policy: The empirical analysis aimed to validate the Modern Portfolio Theory by examining if greater foreign portfolio investment flows contributed positively to economic growth in the East African region. The results lend support to policy efforts in East Africa to attract greater FPI through capital market reforms, regional integration, and macroeconomic stability. However, further developing financial markets and enhancing absorptive capacity is essential to leverage FPI more effectively for sustainable growth financing.

Taking a Portfolio approach to wind and solar deployment: The case of the National Electricity Market in Australia

We formulate a new, computationally inexpensive framework based on Mean–Variance Portfolio Theory to determine the optimal allocation of wind and solar capacity, which can leverage the complementarity between intermittent solar and wind generation to meet electricity demand at the lowest cost, with gas acting as reserve generation in times of insufficient wind and solar output, and the variance of the mismatch between demand and the output of wind and solar is taken as the risk indicator. We then apply this framework to the context of the National Electricity Market in Australia. Our result reveals that, assuming sufficient network capacity, the estimated lowest generation cost achievable is $45.26/MWh, where the expected percentage of unserved energy (USE) is 0.0044%. For a risk-averse planner, a level of USE less than 0.0001% may be achieved at the expense of almost doubling the cost to $88.23/MWh. Under the current network capacity constraints, we estimate these costs to increase to $53.09/MWh and $89.90/MWh respectively, while the USE could rise to 0.017%. Moreover, network expansions in Queensland are found to be the most beneficial. On the other hand, a 4-hour battery storage system with power capacity equivalent to 20% of the peak load could help reduce the cost by up to 6.8% alongside further decrease in the USE. Our results are also shown to differ drastically from the conventional, output-based approach. Since the two approaches address two different questions depending on whether demand is flexible, demand flexibility is also an important factor.

Chemotherapy-induced peripheral neuropathy research: A National Institute of Health (NIH) grant portfolio analysis.

Chemotherapy-induced peripheral neuropathy research: A National Institute of Health (NIH) grant portfolio analysis.

From Studio to Runway: Ghanaian Fashion Designers' Creative Processes

Purpose: The fashion scene in Ghana is a dynamic fabric knit together with creative designs. We explore the inspirations and creative processes of Ghanaian fashion designers as they get from studio to catwalk. The objective of the study is to investigate and clarify the inspirations and creative processes that propel Ghanaian fashion designers from ideation to catwalk presentation. Methodology: The paradigm used was a qualitative research approach. This includes observations, interviews, and portfolio analysis of fifteen (15) selected fashion designers in Sekondi Takoradi Metropolis. The collected data were analyzed using thematic analysis. Findings: The findings of the research indicate the diverse range of elements influencing Ghanaian fashion designers' creative processes. This creative journey reflects not only the designers' technical expertise but also their profound connection to their cultural heritage and their ability to translate inspiration into tangible works of art. While the runway remains a traditional showcase for designers, leveraging digital platforms can extend their reach and visibility. In recommendation, designers can explore virtual fashion shows, live-streaming events, or interactive online galleries to showcase their collections to a global audience. Embracing digital innovation can amplify brand exposure and drive engagement with fashion enthusiasts worldwide. Ghanaian designers should collaborate with international platforms, this can help them gain recognition and share their culture with the world. Unique Contribution to Theory, Practice and Policy: The study also contributes to theories of creative processes within design disciplines shedding light on the systematic stages involved in fashion design. By applying entrepreneurship theory, we can analyze how Ghanaian designers identify market opportunities, innovate in design, and create value for their businesses. This was validated in the study where designers conceptualize, iterate, and refine their ideas, thus contributing to the broader discourse on creative practice. From a practical standpoint, this study offers valuable insights for both emerging and established fashion designers, particularly those operating within culture-based industries. The study contributes to theory by documenting the systematic creative processes employed by Ghanaian designers, it provides a roadmap for other practitioners to follow, helping them enhance their design practices and navigate the complexities of the fashion industry.

Co-Bubble transmission across clean and dirty Cryptocurrencies: Network and portfolio analysis

This study proposes a co-bubble network to capture the transmission of co-bubbles across the prices of 37 cryptocurrencies from both static and dynamic perspectives. It considers the periods of the COVID-19 pandemic and the Russo-Ukrainian conflict, and distinguishes clean from dirty cryptocurrencies. The main findings are summarized as follows: Firstly, larger cryptocurrencies, such as Bitcoin, Ethereum, and BNB, have a higher probability of generating co-bubbles in other cryptocurrencies, indicating a strong interdependence among them. Secondly, the co-bubble network experiences notable changes around crisis events, with distinct characteristics observed during the COVID-19 pandemic compared to the Russo-Ukrainian conflict. Thirdly, the transmission of co-bubble influence exhibits time-varying characteristics, and centrality rankings of influential cryptocurrencies vary around the crises. Particularly, after the COVID-19 pandemic, Bitcoin and BNB experience a decline in centrality ranking, while smaller-cap cryptocurrencies show higher centrality rankings, suggesting the transmission of co-bubble effects from large to smaller cryptocurrencies. The centrality rankings of Bitcoin, Ethereum, and BNB show a contrasting pattern, maintaining higher levels in the ongoing post Russo-Ukrainian conflict period. Fourthly, different patterns of co-bubble transmission exist for dirty and clean groups, with dirty cryptocurrencies showing a much higher intensity of co-bubbles during the Russo-Ukrainian conflict. Finally, the portfolio analysis shows that co-bubble network centrality-driven portfolios outperform the baseline portfolio strategy, dirty group portfolio strategy, and clean group portfolio strategy, during the entire sample period and particularly the COVID-19 pandemic. The findings are useful for the decision making of cryptocurrency portfolio managers and policymakers concerned with the behaviour of influential cryptocurrencies and potential risks inferences.

Climate Risk for Coastal Commercial Real Estate in the Language of Modern Portfolio Theory

Climate Risk for Coastal Commercial Real Estate in the Language of Modern Portfolio Theory

Asymmetric connectedness and investment strategies between commodities and Islamic banks: Evidence from gulf cooperative council (GCC) markets

The study uses the data of thirteen Islamic Banks (IB) in the GCC region and sixteen commodities that include soft agriculture, energy, industry, and precious metal commodities. Murabaha transactions makes up most of the revenues of the Islamic banks, whereas commodities set up the biggest portion of the assets among Murabaha transactions, therefore commodities and IB revenues are expected to comove. Interestingly, empirical findings suggest that most of the Islamic banks and commodities are not significantly affected by the shocks from other markets. We further observe that negative shocks have a higher impact on market connectedness among used assets compared to the positive returns and find a significant role of risk variables in explaining the magnitude of spillover between used assets. We perform robustness of our results in sub-samples periods during Shale Oil Revolution, Global Financial Crisis, and the COVID-19. Additionally, the multivariate portfolio analysis shows some risk reduction properties of the majority of the used assets. The performance evaluation measures demonstrate that weights selected based on dynamic connectedness network presents diversification opportunities. These findings help investors and portfolio managers to remain alert to the movements of the risk factors and calibrate there hedging and portfolio management strategies by taking long and short positions as incorporation of Islamic banks and commodities in the GCC region in a portfolio could yield risk reduction benefits and profitability.

Portfolio Validity Test in the Chinese Market

Modern portfolio theory is one of the most classic theories in financial investment theory. It was first released by a famous economist, Markowitz. As the most important critical model in investment science, it provides investors with scientific investment allocation methods, which are favored by many investors. It has been widely used and researched in the world. Modern portfolio theory analyses the historical returns of investment objects and guides future investment behavior. However, it is still controversial whether this theory is reasonable and adequate in Chinas stock market. China's stock market is more susceptible to fluctuations than those in Europe and the US, and many of these factors stem from China's strategic policies in various areas. Since the end of the epidemic, China's economic development has also slowed down. In this context, this paper will construct the maximum Sharpe ratio strategy, market capitalization strategy, and average weight strategy. Through the performance evaluation indexes of these three strategies' performance comparison, this paper will verify whether the portfolio strategy is real and effective in China's stock market and can bring more than significant market returns to investors.

Portfolio Management of Consumer Industry Based on Index Model

This paper explores the application of the index model in portfolio analysis, highlighting its significance in both macro and security analysis for the optimization of portfolios. Utilizing data spanning from October 2018 to November 2023, the study draws several key conclusions. Firstly, it identifies the optional consumption industry as particularly susceptible to market fluctuations, exhibiting higher sensitivity compared to the daily consumption industry. Secondly, an active portfolio comprising Costco, Walmart, Lululemon, and Under Armour, alongside short positions in risk-free securities, yields the optimal portfolio with a maximized Sharpe ratio. However, the study acknowledges limitations, notably the relatively small sample size and the impact of the COVID-19 pandemic, which introduces extreme risks and potential errors in the analysis. Future research directions include modifying the index model to incorporate volatility adjustments and exploring multi-factor investment models to mitigate errors and enhance portfolio efficiency. This study underscores the importance of continuous refinement and adaptation of analytical frameworks in portfolio management amidst dynamic market conditions.

Comparison of Markowitz Model and Index Model in Different Constraints

This paper reviews the application of Markowitz model and index model in portfolio optimization and focuses on comparing the performance under different constraints. The study covers the core concepts and theoretical framework of modern portfolio theory and the development of the two models. The Markowitz model employs quantitative analysis to consider the expected return and risk of assets and aims to maximize investor utility. The index model, on the other hand, achieves investment objectives by replicating the performance of a specific index. Comparative analysis shows that the Markowitz model is suitable for active management, while the index model is more suitable for passive investment. With the advancement of computing technology, the application of Markowitz model will be more accurate and extensive. Meanwhile, as the concept of passive investment becomes popular, the index model will continue to attract more investors. This study is of great significance to investors in choosing investment strategies, and will help academia and the industry to further optimize investment models.

On the Stress-Strength Reliability of Transmuted GEV Random Variables with Applications to Financial Assets Selection.

In reliability contexts, probabilities of the type R=P(X<Y), where X and Y are random variables, have shown to be useful tools to compare the performance of these stochastic entities. By considering that both X and Y follow a transmuted generalized extreme-value (TGEV) distribution, new analytical relationships were derived for R in terms of special functions. The results hereby obtained are more flexible when compared to similar results found in the literature. To highlight the applicability and correctness of our results, we conducted a Monte-Carlo simulation study and investigated the use of the reliability measure P(X<Y) to select among financial assets whose returns were characterized by the random variables X and Y. Our results highlight that R is an interesting alternative to modern portfolio theory, which usually relies on the contrast of involved random variables by a simple comparison of their means and standard deviations.

Application Of Markowitz and Index Models to Real Markets

After Markowitz put forward the portfolio theory in the 1950s, the modern investment theory was developed, and more related theoretical models were created based on it. To find the optimal portfolio in the market, this paper randomly selects 7 stock types, uses three different constraint methods to show the investment characteristics of different group of investors, and uses Index model and Markowitz model to find efficient frontier and the proportion of asset allocation under the condition of maximum Sharpe ratio and minimum variance. The results show that to achieve the maximum Sharpe ratio and find the best combination, it is necessary to purchase AAPL primarily. Secondly, it chooses to purchase assets with less correlation and relatively large return rate, such as MSFT and WMT. The results in this paper shed lights for investor in selecting potential assets and may provide helpful insight for future investments.

Portfolio Analysis of Five Stocks Under Different Constraints Based on Markowitz Model &amp; Index Model

Since the dawn of the 21st century, while there has been an abundance of talented fund managers, truly exceptional asset portfolios have been rare. Addressing this phenomenon, the study delves into the current state of asset management, employing two renowned financial models: the Markowitz model and the Index model. These models are utilized to analyze the returns and risks of five high-performing assets. Additionally, acknowledging the diversity of investor preferences, this paper incorporates five different constraints to cater to a wider spectrum of client needs. The crux of the research is the optimization of the asset portfolio, grounded in these models and constraints. The paper findings indicate that the integration of the Markowitz and Index models with the capital market line and the efficient frontier proves highly effective in the management of risk asset portfolios. In this paper, the Markowitz model consistently excels over the index model in both minvariance and maxsharpe scenarios. This study has important reference value for investors in related fields.

Do socially responsible indices outperform conventional indices? Evidence from before and after the onset of Covid‐19

AbstractAccording to traditional portfolio theories constraints, restrictions, and screens applied in portfolio selection reduces the diversification opportunities which can impact financial performance. Is this true in the case of socially responsible investment (SRI)? To answer this, present study analyzes the performance of Socially Responsible (SR) indices in comparison to conventional indices in an emerging economy. The uniqueness of the study is that it analyses the performance of Shariah, ESG, and thematic indices in a single study. Further, the study measures the impact of Covid‐19 on them. Comparative performance evaluation was conducted by using absolute return analysis and risk‐adjusted measures namely, Sharpe ratio, Treynor ratio, tracking error, information ratio, capital asset pricing model (CAPM), Fama–French three‐factor, and Carhart's four‐factor models. The Structural break was identified, hence analysis was conducted for the total period (January 2017–March 2023) and two sub‐periods, that is, pre and post‐Covid‐19 period. No significant difference was found between the returns of SR indices and conventional indices as against the benchmark index on the basis of absolute return analysis. Sharpe ratio and Treynor ratio both were having negative values for all the SR and conventional indices. Tracking error for all the SR and Conventional indices were very low. The CAPM and both multi‐factor models univocally pointed toward the underperformance of all the SR (except S&amp;P BSE 100 ESG index which had equal performance) and both conventional indices against the benchmark index. Noteworthy point is that only Shariah indices gave the highest returns during post‐Covid period. This research will help in deepening the SRI in the capital market. Companies should increase their ESG scores and make efforts to be listed on the SR indices. Policymakers should announce some kind of rebates, or recognition for star‐performing companies in the field of sustainability to encourage other companies to adopt SR practices in their business operations. The novelty of the current study is that it adds to the socially responsible literature by analyzing the performance of Shariah, ESG, and Thematic indices and conventional indices in a single study in the fastest‐growing economy of India and analyses the impact of Covid‐19 on this performance.

Does the source of oil price shock matter for Indian sectoral stock returns? A time-frequency approach to analyse dynamic connectedness and spillovers

ABSTRACT This paper examines the connectedness and spillovers between decomposed oil shocks and Indian sectoral equities in a time-frequency domain using the most recent Barunik and Krehlik (2018) approach. Our empirical results show that the oil demand shock is the major spillover transmitter across all time horizons followed by the risk shocks; while oil supply shock appears to be a net receiver in all the frequency bands indicating the differential impact of oil shocks. Among the sectors, Basic Materials and Finance receive the highest spillovers from oil shocks in the short-term; while Consumer Discretionary Goods & Services and Industrials join the list in the medium- and long-term. FMCG, Health, Telecom and IT sectors receive the least spillovers in all the bands, making them apt for investments during periods of high volatility. Our empirical results are robust to the application of the alternative TVP-VAR time-frequency parameter framework. The portfolio analysis shows that inclusion of stocks in Oil and sectors like Metal and Telecom significantly reduces the portfolio risk. Dynamic connectedness analysis reveals that the spillovers dramatically increase during times of extreme turmoil, especially during the Global Financial Crisis (2008–2009) and the COVID-19 pandemic. Policy implications of our empirical results are also discussed.