Portfolio Diversification Research Articles

Time-varying causality among whisky, wine, and equity markets

Whisky has entered the category of alternative investment assets, with particularly attractive returns on certain bottles. The literature has shown the diversification benefits of investing in collectible assets, such as fine wine, but analyses dedicated to whisky investment remain scarce. Using different causality tests – parametric, non-parametric, and time-varying – applied to whisky, wine, and the MSCI World indices, we analyze the connection/disconnection between rare whisky, fine wine, and financial markets over a 13-Year period, from January 2010 to January 2023, in weekly frequency. Our results show that whisky and wine respectively provide attractive diversification opportunities when combined with stocks, except during “hectic” periods. Conversely, the combination of whisky and wine appears to be riskier in terms of portfolio diversification, especially since the COVID-19 pandemic.

The effects of reducing renewable power intermittency through portfolio diversification

Intermittency of renewable power sources is a growing concern for grid reliability in the United States and it has not been properly addressed by public policy for very long. One simple way to mitigate this problem is to diversify the renewable energy portfolio. This work studies the case of Texas, a state that not only is the largest wind power producer in the US, but that also has faced considerable diversification in its renewable energy portfolio in the past ten years. A dispatch model is proposed to simulate how thermal generators respond to changes in the intermittency of renewable sources. Additionally, since higher renewable output usually means higher intermittency and vice-versa, a mean–variance analysis is used to study this trade-off. Results indicate that although intermittency decreases the reliability of the grid, reducing it can have adverse effects as increasing the dispatch of polluting power sources and decreasing the revenues of thermal generators, what can decrease important investment in dispatchable power sources in the long-term. Also, results show that the subsidy design adopted by the federal government is an obstacle to portfolio diversification.

Portfolio Optimization: A Return-on-Equity Network Analysis

This article proposes return-on-equity (ROE) networks for portfolio optimization, which integrate the DuPont analysis and graph theory. Portfolio diversification is interpreted as follows: An intercluster relationship of the network structure diversifies business models, whereas an innercluster relationship variegates different industries. The proposed approach is applied to the Chinese stock market. It shows that, in terms of the annualized return, the ROE network optimized portfolio reached <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.20\%$</tex-math> </inline-formula> compared with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.02\%$</tex-math> </inline-formula> of the Shanghai Stock Exchange (SSE) Composite Index. It also shows that portfolios with 100–200 stocks, which are composed of the top 10%–20% ROE stocks, reached the highest return-risk efficiency.

Implementation of K-means Clustering Algorithm for the Indonesian Stock Exchange

In the dynamic field of financial markets, effective analysis and understanding of stock market behavior are very crucial for investors, analysts, and policymakers. This study investigates the implementation of the K-means algorithm for clustering stocks listed on the Indonesian Stock Exchange (IDX). The main objectives of this research include exploring IDX's clustering patterns, identifying groups based on their trading characteristics, and evaluating algorithm performance. Some challenging parts have been addressed, such as data quality, feature selection, determining the optimal number of clusters, scalability, interpretability, and evaluation. Precise data preprocessing, feature engineering, and algorithm optimization provide insight into the clustering structure of the Indonesian stock market, helping investors in portfolio diversification, risk management, and strategic decision-making. The results show the potential of the K-means algorithm in thoroughly uncovering important patterns on the IDX, thereby contributing to the advancement of market analysis methodologies adapted to the Indonesian financial environment.

Do green investments improve portfolio diversification? Evidence from mean conditional value-at-risk optimization

This study investigates how green investment assets improve optimal portfolio diversification in terms of tail downside risk. We use the wavelet conditional value-at-risk ratio to explore the benefits of adding green assets to conventional portfolios. We quantify risk based on the contagion between conventional stock market indices and green environmental assets, including a sustainability index, clean energy, and green bonds. Our findings emphasize the high variance between conventional stock pairs, providing evidence of contagion effects before and during the COVID-19 pandemic. We show that including clean energy and green bond indices in conventional portfolios reduces the extreme risk of portfolios. In addition, we find that the diversification benefits of clean energy, green bonds, and safe-haven investments apply especially in the short term during the pandemic. Finally, we show that the considered portfolios could not decrease long-term risk during the COVID-19 crisis because of the systematic risk spread. Our portfolio optimization design supports the superiority of clean energy and green bonds in portfolio diversification over the sustainability index. These insights can be used by portfolio managers to inform diversification in different investment horizons.

Assessing the connectedness between cryptocurrency environment attention index and green cryptos, energy cryptos, and green financial assets

Using the cross-quantile & wavelet quantile correlation methods, we investigate the connectedness between cryptocurrency environment attention index (ICEA) and green crypto, renewable energy crypto, and green conventional market. The interdependence of ICEA with the considered assets is weak, providing investors with new avenues for reducing systematic risk of their portfolios. The cross-quantile correlations intensity between ICEA and green conventional emerging markets is especially low. ICEA appears as a strong diversifier for the Cardano cryptocurrency and sustainability-conscious industries from the developed economies. The WQC indicates a low level of connectedness of the ICEA with the selected assets. The ICEA does not remain significantly connected with any of the asset. This study provides valuable implications for the investors, portfolio managers and policy markets for the portfolio diversification.

Religious nationalism and home bias

This study explores the impact of religious nationalism on domestic portfolio investment within a country. Examining data from 36 developed and emerging markets spanning the period of 2004-2021, the results suggest that investors from countries with significant religious nationalism sentiment tend to allocate a greater portion of their portfolio to domestic equities, leading to less international diversified portfolios. Religious nationalism promotes cultural uniformity and the preservation of traditional values, prioritizing the protection of religious symbols and customs over embracing innovation. Furthermore, the findings indicate that religious nationalism exacerbates religious discrimination against minority religions, thereby increasing investments in home markets. Moreover, I illustrate that religious nationalism is particularly relevant in emerging markets.

Impacts of cross‐border equity portfolio flow and central bank transparency on financial development: The role of economic freedom and international bonds

AbstractWe investigate the effects of international equity portfolio diversification and central bank independence on financial development using panel data from 49 countries from 2001 to 2016. We find that international equity portfolio diversification improves financial development. We correspondingly examine potential factors through which international equity portfolio diversification may impact financial development and find that central bank transparency provides an important channel for improving financial development. We further find that the relationship between international equity portfolio diversification and financial development is sensitive to economic freedom. Concerning sequencing, we find that foreign equity and debt flow are complementary to financial system development. Our results are robust to endogeneity using the exogenous shock of the 2008 financial crisis. This study offers new empirical evidence on the relationship between international capital and financial development.

Hedging in the Stock Market: Is the Colored Coin Better than the Physically-Backed Diamond Index?

The present paper examines the potential utilization of diamond-connected cryptocurrencies as one novel means to acquire long or short positions in tangible diamonds. We evaluate the equity risk hedging capability of two popular colored coins associated with diamonds, namely PinkCoin and CaratCoin. PinkCoin is chosen over CaratCoin for further analysis due to its extensive transaction data, allowing for a comparison with a benchmark diamond price aggregate. Our results suggest incorporating such tokens in portfolios enables protection against swings in the equity market, particularly during periods of market decline. Moreover, these assets have the attribute of augmenting portfolio diversification within a conventional mean-variance frontier framework. The reconciliation of colored coins’ accessibility and price fluctuations with the safe-haven characteristic of diamonds has resulted in unique diversification benefits comparable to those associated with trading natural gemstones. Although the conclusion above holds significant appeal for investors who favor the inclusion of diamonds in their investment portfolios, it harbors concerns regarding liquidity and the assurance of genuineness.

Green Bonds: A Pathway to Sustainable Investment and Environmental Resilience

Green bonds (or climate bonds) are one of the most used sustainable investment instruments, and under the Paris Climate Agreement of 2015, the climate bond market is expected to thrive soon. Green bonds are gaining increasing popularity among environmentally responsible investors, as well as investors who “simply” attempt to benefit from portfolio diversification, including green issuances, that are close to other fixed bonds. This paper aims to take advantage of previous literature contributions on the green bond market to indicate the way forward for future research. Herein, through a systematic literature review on the green bond market, our goal is to provide investors, main markets actors, and policymakers with some helpful insight on the role of environmental investments in reshaping the financial markets and fostering the sustainability of the economy. Key words: Green bonds; systematic literature review; climate bond introduction, sustainable investment

Forecasting ESG Stock Indices Using a Machine Learning Approach

As the demand for investment products tied to environmental, social and governance (ESG) concerns rises, ESG stock indices have been established. These indices aim to aid investors in navigating and assessing the risks associated with firms based on ESG factors and potential investment returns. The objective of the article is to predict ESG stock indices using a machine learning approach. We use daily data of Dow Jones Sustainability Index (DJSI) World, DJSI Asia Pacific and DJSI Emerging Market from 2018 to 2022 as samples. Two-layer ensemble model – combination of support vector machine (SVM), random forest (RF), long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms – is employed to forecast the indices. The results show that the ensemble model accurately forecasts the indices, with the prediction line closely matching the actual values. It gives the implication that investors are able to improve investment decisions, assist in managing investment risk, and optimize their portfolio diversification. Meanwhile, policymakers are able to anticipate economic trends, inflation and interest rates, assisting in the development of successful economic policies. This research article presents a machine learning approach for predicting ESG stock indices. The proposed model combines SVM, RF, LSTM and GRU algorithms to create a powerful two-layer ensemble model that outperforms individual models. The results show that the ensemble model accurately forecasts ESG stock indices, with the prediction line closely matching the actual values. The model offers insights into the behaviour of different algorithms, highlighting their strengths and limitations. The proposed model can guide decision-making processes, support investment strategies, and ultimately contribute to advancing sustainable investment practices.

Machine learning and deep learning models for stock price prediction, case study: Google Company

Stock prediction is crucial for investors to manage risk, diversify portfolios, and plan for long-term financial goals. Companies use predictions to allocate capital wisely, attract investors, and make strategic decisions. Stock prediction is to analyze competitive stock (e.g. Google, Apple and Microsoft), involving parameters of revenue, profit, PE ratio, growth rate, and to predict the future price of the stock. Based on these assumptions, this project objective is to forecast the trend in Google stock prices with four predictive models: linear regression, XGBoost, Long Short-Term Memory Network (LSTM) and Recurrent neural network (RNN). The dataset is Google stock prediction dataset, which involves 17,598 stock information. Among all four models, LSTM method obtains the best final result with 0.0012 of MSE and 0.95115 of R square. LSTM has the lowest MSE, and its R square is similar to that of other models. For future plan, this project may expand more on feature engineering, model techniques and fine-tuning methods.

Economic Policy Uncertainty and Bank Credit Growth in Indonesia

Objective: This study examines the influence of economic policy uncertainty in countries with the largest capital investments in Indonesia, such as Singapore, China, Hong Kong, Japan, the United States, Korea, and the United Kingdom, on the credit growth of commercial banks in Indonesia. Design/Methods/Approach: The sample of this study is all commercial banks in Indonesia from January 2011 to December 2022. This study uses a quantitative approach, using monthly aggregate data on credit growth of commercial banks in Indonesia and economic policy uncertainty data for each country. Hence, the number of observations in this study amounts to 144. This study uses multiple linear regression with the EViews 12 analysis tool. Findings: The findings in this study show that the influence of economic policy uncertainty in the country with the largest capital investment in Indonesia has various influences. Of the several countries that were observed in the study, Japan was one of the countries that had a significant negative impact on the growth of commercial bank credit in Indonesia. Originality/Value: This study complements several previous studies regarding the impact of economic policy uncertainty on Indonesia's micro and macro economy. Studies regarding the impact of economic policy uncertainty on Indonesia's banking credit growth are still limited. Practical/Policy implication: The findings of this study can be used as a reference for banking managers when making decisions such as credit portfolio diversification. By spreading exposure to various sectors and industries, banks can reduce risks related to economic uncertainty in specific sectors. Banking managers need to design products and services that are more creative and adaptive to help banks remain competitive and attract customer interest amidst an uncertain economic situation.

Portfolio Diversification during Calm and Volatile Markets

Portfolio Diversification during Calm and Volatile Markets

Constructing Portfolio Optimization: Analysis in Indonesia Non-Cyclical Industry (Markowitz Approach and Skewness and Kurtosis)

Purpose: The purpose of this research is to examine the application of portfolio optimization in the context of real-world financial issues, particularly in light of the challenges posed by the COVID-19 pandemic. Traditional portfolio optimization strategies, such as those proposed by Markowitz, often rely on the assumption of normally distributed returns, which may not accurately capture the risks associated with extreme events like the COVID-19 crisis. This study aims to shed new light on portfolio optimization methods by exploring various approaches and considering the implications of non-normally distributed returns on portfolio construction. Methods: This research employs a quantitative approach to analyze portfolio optimization techniques in the face of non-normally distributed returns. Using data from financial markets impacted by the COVID-19 pandemic, the study investigates different portfolio construction methods, including risk-free rate for equally weighted portfolios, optimal risk portfolios, minimum variance weights, and maximum expected returns. Various risk metrics such as variance, Sharpe ratio, and standard deviation are considered to evaluate portfolio performance under different constraints. Results and discussion: The empirical findings highlight the limitations of traditional portfolio optimization techniques, particularly in accurately assessing and managing risk in the presence of non-normally distributed returns. Assets exhibit heavily tailed returns, leading to an underestimation of risk when using standard approaches. The study identifies that certain assets offer high returns but also entail significant risks, necessitating a nuanced approach to portfolio construction. By considering stable distribution models and optimizing for both maximum expected return and minimum variance weight, investors can build more profitable and diversified portfolios while managing risk effectively. Implications of the research: The research findings have important implications for investors and financial practitioners, particularly in navigating uncertain market conditions such as those brought about by the COVID-19 pandemic. By recognizing the limitations of traditional portfolio optimization methods and embracing more sophisticated approaches that account for non-normally distributed returns, investors can make more informed decisions and better manage portfolio risk. These insights can inform the development of robust investment strategies tailored to mitigate the impact of extreme events on portfolio performance. Originality/value: This research contributes to the literature by offering a fresh perspective on portfolio optimization under the backdrop of the COVID-19 pandemic. By systematically evaluating different portfolio construction methods and considering the implications of non-normally distributed returns, the study advances understanding in the field of financial risk management. The identification of stable distribution models and the emphasis on balancing maximum expected return with minimum variance weight provide practical guidance for investors seeking to build resilient and profitable portfolios in turbulent market environments. Overall, this research underscores the importance of adapting portfolio optimization strategies to address the realities of contemporary financial markets.

The performance of renewable energy infrastructure

PurposeRenewable energy infrastructure is an important asset class in the context of reducing global carbon emissions going forward. This includes solar power, wind farms, hydro, battery storage and hydrogen. This paper examines the risk-adjusted performance and diversification benefits of listed renewable energy infrastructure globally over Q1:2009–Q4:2022 to examine the role of renewable energy infrastructure in a global infrastructure portfolio and in a global mixed-asset portfolio. The performance of renewable energy infrastructure is compared with the other major infrastructure sectors and other major asset classes. The strategic investment implications for institutional investors and renewable energy infrastructure in their portfolios going forward are also highlighted. This includes identifying effective pathways for renewable energy infrastructure exposure by institutional investors.Design/methodology/approachUsing quarterly total returns, the risk-adjusted performance and portfolio diversification benefits of global listed renewable energy infrastructure over Q1:2009–Q4:2022 is assessed. Asset allocation diagrams are used to assess the role of renewable energy infrastructure in a global infrastructure portfolio and in a global mixed-asset portfolio.FindingsListed renewable energy infrastructure was seen to underperform the other infrastructure sectors and other major asset classes over 2009–2022. While delivering portfolio diversification benefits, no renewable energy infrastructure was seen in the optimal infrastructure portfolio or mixed-asset portfolio. More impressive performance characteristics were seen by nonlisted infrastructure funds over this period. Practical reasons for these results are provided as well as effective pathways going forward are identified for the fuller inclusion of renewable energy infrastructure in institutional investor portfolios.Practical implicationsInstitutional investors have an important role in supporting reduced global carbon emissions via their investment mandates and asset allocations. Renewable energy infrastructure will be a key asset to assist in the delivery of this important agenda for a greener economy and addressing global warming. Based on this performance analysis, effective pathways are identified for institutional investors of different size assets under management (AUM) to access renewable energy infrastructure. This will see institutional investors embracing critical investment issues as well as environmental and social issues in their investment strategies going forward.Originality/valueThis paper is the first published empirical research analysis on the performance of renewable energy infrastructure at a global level. This research enables empirically validated, more informed and practical decision-making by institutional investors in the renewable energy infrastructure space. The ultimate aim of this paper is to articulate the potential strategic role of renewable energy infrastructure as an important infrastructure sector in the institutional real asset investment space and to identify effective pathways to achieve this renewable energy infrastructure exposure, as institutional investors focus on the strategic issues in reducing global carbon emissions in the context of increased global warming.

Research on Tail Risk Hedging in the Digital Asset Market

In recent years, the digital asset market has experienced rapid growth, but at the same time, its implicit tail risks have gradually raised concerns, especially amidst the global economic downturn caused by the pandemic. This paper aims to study tail risks and propose hedging strategies. Based on the GARCH and VaR models, this paper uses weekly return data from July 2017 to December 2021 for Bitcoin digital cryptocurrency and traditional non-digital asset market indices such as stocks, bonds, gold, and commodities. Utilizing EViews and Excel, the study explores tail risk measurement and portfolio diversification effects in the digital asset market. The main findings are as follows: (1) During the sample period, cryptocurrency returns exhibited significant volatility, with weak correlations with returns from traditional non-digital assets. Further analysis revealed strong hedging effects of digital assets against the bond market but weaker effects against the gold and stock markets, as well as commodity markets; (2) Cryptocurrency in traditional investment portfolios can enhance the risk-return ratio by adding it to the asset mix.

Examining connections between the fourth industrial revolution and energy markets

We investigate the relationship between assets related to the fourth industrial revolution and alternatively dirty and clean energy markets. We examine portfolio diversification benefits and risk reduction gains based on three portfolio construction techniques. Results of static connectedness analysis indicate weak integration between the fourth industrial revolution assets and both clean and dirty energy markets with spillovers being mainly in the short term. Time-varying connectedness analysis shows that the connectedness is time-varying and responsive to external shocks Further, portfolio analysis shows that fourth industrial revolution assets are important for fixed-income portfolios that also comprise stocks and energy assets.

Trade Agreements and Financial Market Integration in Latin America and the US

The primary objective of this study is to examine the extent of financial integration between Latin American and US financial markets, particularly in light of recent efforts to foster integration through trade agreements. Spanning from 1 January 1990 to 31 December 2019, the sample focuses on major market indices and key sectors. Financial integration is quantified using a DCC multivariate GARCH model, incorporating a smooth transition model, structural breaks, and regression-based approaches. Results indicate increased comovement with the US for main market indices in Argentina, Chile, Colombia, Mexico, and Peru, while Brazil shows a decrease. Similar trends are observed in sectoral analyses. This study also reveals heightened correlation post-trade agreements. Structural break analysis highlights significant shifts in dynamic correlations for countries with US free trade agreements. These findings support the argument of increased financial integration, bearing significance for portfolio diversification and international policy formulation.

Volatility spillovers and hedging strategies between impact investing and agricultural commodities

We examine spillover and hedging among impact investing and agricultural commodities. Results demonstrate that impact investing is a prominent spillover transmitter during both calm conditions and crises, while agricultural commodities are typically receivers. Analysis indicates that hedging effectiveness is enhanced by portfolios containing impact investing and agricultural products, with this more so during crises. Additionally, analysis reveals that irrespective of position on the risk aversion spectrum, investors gain utility substantially by including impact investing and agricultural assets, even considering transaction costs. These findings add to the extant literature and offer practical implications for investors, fund managers, and policymakers regarding risk management perspectives and portfolio diversification.