Portfolio Diversification Strategies Research Articles

Determinants of stock prices in telecommunication industry: An application of fundamental analysis

The present study attempted to assess the impact of fundamental ratios on the share prices of selected telecommunication companies in India. India has dramatically expanded over the past ten years to become the second-biggest telecoms market worldwide, with 1.17 billion users. The Indian telecom industry has proliferated thanks in part to the government of India’s liberal and reformist policies and strong customer demand. It has become a lucrative investment sector for investors due to its recent and prospective growth. Data on 13 telecom firms indexed in the S&P BSE telecommunication index from 2013 to 2022 were taken from companies’ annual reports, the BSE website (Bombay Stock Exchange), and other secondary sources. Six firm-specific fundamental factors viz. Debt to Equity ratio (D/E), Current ratio (CR), Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to earnings ratio (P/E), Return on equity (ROE), and three country-specific fundamental factors viz. Gross Domestic Product, Inflation rate, and S&P BSE Sensex return were considered. Fixed effect panel regression through Generalized Least Square (GLS) model was performed to find inferences. Debt Equity ratio and Inflation rate were found to impact share price negatively. Conversely, the Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to Earnings ratio (P/E), and Return on Equity (ROE) positively impacted selected companies’ share prices. The study results will benefit individual & institutional investors in formulating their investment and portfolio diversification strategies for gaining a high effective rate of return on their investments.

Volatility and International Interactions in Financial Markets: An Analysis of the Turkish Stock Exchange and G7 Countries

Using mean and variance causality analysis, this study examines the volatility relationship between Turkish and G7 stock markets. Weekly return data from May 29, 2009, to June 6, 2023, is utilized for the analysis. The Hong mean and variance causality analysis method is employed as the methodology. Based on the results of the study, Turkey and Japan's stock markets have a significant mean causality relationship. Moreover, the variance causality analysis demonstrates a strong relationship between Turkey and stock markets of Canada, France, Germany, Japan, and the United States. The findings contribute to portfolio diversification strategies and highlight the importance of understanding the dynamics of international financial markets.

Equity markets and ESG dynamics: Assessing spillovers and portfolio strategies through time-varying parameters

Motivated by the recent surge of socially responsible activism, we examine the return and volatility transmission between the global Environmental, Governance, and Social index and traditional equity markets of G7 countries. For this purpose, we employ the TVP-VAR model on the returns and volatilities from 04 April 2011 to 09 September 2023. The variance co-variance matrix of the TVP-VAR model is used for portfolio analysis. Our finding indicates a significant level of connectedness between green and traditional equity markets. Moreover, green equity functions as a transmitter of both return and volatility spillovers to the traditional equity markets. In addition, our analysis of dynamic connectedness reflects a spike in both total return and volatility spillover amid the COVID-19 period. This indicates that the relationship between green and traditional equity markets intensifies during times of market stress and uncertainty which further infer implications for portfolio diversification. Furthermore, we calculated optimal asset allocation within portfolios and identified effective hedging ratios for different ESG and G7 equity pairs. The implications of the study include informing portfolio diversification strategies, and guiding decision-making for policymakers, hedge fund managers, and portfolio professionals amidst market uncertainties, based on identified market dynamics and connectedness.

Japanese stock market sectoral dynamics: A time and frequency analysis

AbstractThis study examines the Japanese stock market connectedness across different sectors, focusing on both the time and frequency dimensions. The dataset used spans from January 1999 to April 2022 and employs various methodologies, including time‐varying parameter vector autoregressions, TVP‐VAR frequency dependency, and Quantile coherency. The empirical findings reveal that cyclical or aggressive stocks predominantly act as net transmitters of shocks across sectors. Moreover, short‐term spillovers are more significant compared to intermediate‐term spillovers, indicating that Japanese sectors are more pronounced to market shocks in the short run. The spillover effects are also asymmetric and vary over time. Additionally, the real estate sector exhibits diversification benefits across different time horizons, while the energy sector provides protection primarily in the short run. This research contributes to the development of financial policies aimed at reducing sectoral imbalances and promoting stable growth. Furthermore, it offers insights for investors seeking to devise optimal portfolio diversification strategies.

Tracing the dynamic impact of energy transitions on equity market volatility in an era of financial turbulence

This study investigates the dynamic relationship between the shift towards clean energy and stock market fluctuations, covering the period from January 2004 to August 2021 by applying time-varying parameters vector autoregressions (TVP-VAR). The results reported strong spillovers between clean energy markets and stocks, while dirty ones demonstrated disconnected spillovers. Asymmetry in the spillovers detected dominance of negative (bad) spillovers due to various uncertain economic and financial circumstances. The most significant negative (bad) events tracked in time-varying attributes are the global financial crisis, European Debt Crisis, Shale Oil Crisis, Brexit, US interest rate agenda, and the COVID-19 pandemic. Meanwhile, negative asymmetries prevailed in time-varying analysis, echoing the pre-eminence of negative crisis periods. We identified clean energy markets and stocks as net spillover emitters and dirty energy markets as net recipients. These findings have profound implications for a range of stakeholders, including investors, policymakers, and market participants, offering valuable insights for portfolio diversification and risk management strategies amidst the ongoing energy paradigm shift.

The implications of non‐synchronous trading in G‐7 financial markets

AbstractWe investigate the effects of non‐synchronous trading on volatility spillover for the G‐7 equity markets during the Eurozone sovereign debt crisis (ESDC) and the Covid‐19 pandemic crisis. For data synchronisation we utilise ΜΑ(1) adjusted return series to estimate the Baba‐Engle‐Kraft‐Kroner (BEKK) and the dynamic conditional correlation (DCC) models. We also consider the use of realised kernels as explanatory variables in the variance equation. In this set up, the contagion effects during crises periods are more perceptible, as the spikes are easier to interpret. We also check the robustness of our main results by applying, wavelet coherence analysis to G‐7 major equity indices with realised kernels, as well as local Gaussian correlations (LGC). Our findings suggest the empirical significance of the synchronisation effects for the US and the other G‐7 equity markets. We also conclude that realised kernels is an effective tool for mitigating non‐synchronous effects. These results underline the significance of quantifying the synchronisation effects in equity markets as well as international portfolio diversification strategies.

Relationships among return and liquidity of cryptocurrencies

The cryptocurrency market is a complex and rapidly evolving financial landscape in which understanding the inter- and intra-asset dependencies among key financial variables, such as return and liquidity, is crucial. In this study, we analyze daily return and liquidity data for six major cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Binance Coin, Litecoin, and Dogecoin, spanning the period from June 3, 2020, to November 30, 2022. Liquidity is estimated using three low-frequency proxies: the Amihud ratio and the Abdi and Ranaldo (AR) and Corwin and Schultz (CS) estimators. To account for autoregressive and persistent effects, we apply the autoregressive integrated moving average-generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) model and subsequently utilize the copula method to examine the interdependent relationships between the return on and liquidity of the six cryptocurrencies. Our analysis reveals strong cross-asset lower-tail dependence in return and significant cross-asset upper-tail dependence in illiquidity measures, with more pronounced dependence observed in specific cryptocurrency pairs, primarily involving Bitcoin, Ethereum, and Litecoin. We also observe that returns tend to be higher when liquidity is lower in the cryptocurrency market. Our findings have significant implications for portfolio diversification, asset allocation, risk management, and trading strategy development for investors and traders, as well as regulatory policy-making for regulators. This study contributes to a deeper understanding of the cryptocurrency marketplace and can help inform investment decision making and regulatory policies in this emerging financial domain.

IPOs as Catalysts for Retail Investor Wealth Creation: Evidence from the Indian Stock Market

The study delves into the dynamics of wealth creation for retail investors in the context of Initial Public Offerings (IPOs) within the Indian equity markets. Against the backdrop of a burgeoning retail investor base, the research offers a comprehensive analysis spanning April 2012 to December 2022, evaluating short-term and long-term IPO performance metrics. The study reveals nuanced insights into wealth creation dynamics by scrutinizing listing day returns, first-week to third-year returns, and market-adjusted returns vis-à-vis the Nifty50 Index. Notably, the research suggests that while IPOs exhibit promise with positive listing day returns, sustaining growth over extended horizons poses challenges. The hypothesis of inflation serving as a pragmatic hurdle rate for retail investors is explored, offering a novel perspective on wealth preservation. It also sheds light on the significant variability in the Real Rate of Returns (RRRs) among IPOs, with some contributing to wealth generation while others result in wealth erosion. This underscores the importance of retail investors carefully assessing and managing risks, emphasizing the need for prudent portfolio diversification and effective risk mitigation strategies, particularly over longer investment horizons.

Sectoral Comparison of Herding During Global Financial Crisis and Covid-19: Comparative Analysis of Shariah Compliant and Conventional Stocks

The worldwide financial environment has experienced substantial upheavals amid both the Global Financial Crisis (GFC) and the COVID-19 pandemic. Gaining insight into investor behavior, with a specific focus on herd behavior, during these periods is essential for a comprehensive understanding of market dynamics. The authors intend to compare and analyze investor herding behavior in the Pakistani Stock market, specifically focusing on shariah-compliant and conventional stocks during both the Global Financial Crisis (GFC) and the COVID-19 pandemic. The study explores how stock return dispersions behave in response to significant upward and downward movements in the market index. Additionally, the research distinguishes between the overall and sector-specific performance of Shariah-compliant and conventional stocks. To examine participant herding behavior, the authors applied the cross-sectional absolute deviation model (CSAD) to the daily data of the Karachi stock market. The results indicate that both Shariah-compliant and conventional stocks exhibited a weak form of herding during the GFC. Furthermore, different sectors displayed varying degrees of herding intensity during this crisis. Notably, a substantial increase in herding behavior was observed during the COVID-19 pandemic. These findings have crucial implications for portfolio diversification strategies during financial crises, emphasizing the identification of safe havens by constructing portfolios across diverse segments and sectors. This research contributes to the existing knowledge on herding behavior by examining two distinct hypotheses related to conventional and shariah-compliant stocks, and the empirical evidence supports these hypotheses.

Time-Varying Nexus and causality in the quantile between Google investor sentiment and cryptocurrency returns

Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research. Indeed, this study aims to uncover the role of Google investor sentiment on cryptocurrency returns (including Bitcoin, Litecoin, Ethereum, and Tether), especially during the 2017-18 bubble (January 01, 2017, to December 31, 2018) and the COVID-19 pandemic (January 01, 2020, to March 15, 2022). To achieve this, we use two techniques: quantile causality and wavelet coherence.First, the quantile causality test unveils that investors’ optimistic sentiments have notably higher cryptocurrency returns, whereas pessimistic sentiment has significantly opposite effects. Moreover, the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant. This result supports the role of Tether as a stablecoin in portfolio diversification strategies. In fact, the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.

The impact of liquidity conditions on the time-varying link between U.S. municipal green bonds and major risky markets during the COVID-19 crisis: A machine learning approach

In this study, we aim to investigate the role of the liquidity risk channel in affecting the dynamic conditional correlations (DCCs) between the U.S. municipal green bonds and risky assets (energy commodities and stocks) by accounting for other macroeconomic risks and uncertainties during the COVID-19 crisis period. To explore the underlying mechanism behind the DCCs, we first derive the DCCs and then use novel machine learning techniques, such as CatBoost, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) models and select the most effective one based on its performance metrics. We also employ the Shapley Additive Explanations (SHAP) method to gain a greater understanding of machine learning predictions. The baseline findings suggest the critical role of the rising term spread in increasing the DCCs between the U.S. municipal green bonds and risky assets during the COVID-19 crisis. Equally important, we observe that this role is more pronounced at high levels of the term spread and that the term spread frequently interacts with funding liquidity conditions (TED spread). Our analysis holds valuable implications for the implementation of informed policy sets and the development of optimal portfolio diversification strategies in global markets.

Comovement between agricultural commodities and stock returns of commodity-dependant sub-saharan Africa countries amidst the COVID-19 pandemic

The study analyses the comovement between agricultural commodities (cocoa, coffee, corn, cotton, and soybeans) and sub-Saharan African equities (BRVM, Ghana, Kenya, Mauritius, and Uganda) heavily exposed to shocks from the COVID-19 pandemic spanning from January 2017 to December 2022. Through bivariate and multivariate wavelet analysis, the study identifies the adaptive and asymmetrical nature of the sampled markets both in the pre-pandemic and pandemic periods. The results highlight commodities as the main driving force behind equities, with few exceptions, and reveal these markets' safe-haven and hedge potential during the pandemic and the normal periods, except for the multivariate case. The bivariate findings suggest that global investors can consider investing in agricultural commodities and the sampled equities for enhanced portfolio diversification. By examining the interconnectedness of the sampled market across different timeframes and investment horizons, the study has uncovered significant implications for policy-making, portfolio diversification strategies, and risk management approaches.

An exploration of the mathematical structure and behavioural biases of 21st century financial crises

In this paper we contrast the dynamics of the 2022 Ukraine invasion financial crisis with notable financial crises of the 21st century — the dot-com bubble, global financial crisis and COVID-19. We study the similarity in market dynamics and associated implications for equity investors between various financial market crises and we introduce new mathematical techniques to do so. First, we study the strength of collective dynamics during different market crises, and compare suitable portfolio diversification strategies with respect to the unique number of sectors and stocks for optimal systematic risk reduction. Next, we introduce a new linear operator method to quantify distributional distance between equity returns during various crises. Our method allows us to fairly compare underlying stock and sector performance during different time periods, normalising for those collective dynamics driven by the overall market. Finally, we introduce a new combinatorial portfolio optimisation framework driven by random sampling to investigate whether particular equities and equity sectors are more effective in maximising investor risk-adjusted returns during market crises.

Relative informative power and stock return predictability: a new perspective from Egypt

Purpose This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms. Design/methodology/approach The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model. Findings The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners. Practical implications This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies. Originality/value In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

The Effectiveness of Different Portfolio Diversification Strategies on Reducing Risk: A Survey Based on Financial Advisors (2021)

The Effectiveness of Different Portfolio Diversification Strategies on Reducing Risk: A Survey Based on Financial Advisors (2021)

Rippling effect of liquidity risk in the sovereign term structure

PurposeThe study attempts to identify the linkages in the term structure of illiquidity and the impact of global and domestic factors on sovereign bonds in emerging Asia. The objective of the study ensues on defining the direction of illiquidity spillover across bonds of varying tenors.Design/methodology/approachThis study explores the joint dynamics of contemporary liquidity risk premia and its time-varying effect on the term structure spectrum using the Diebold and Yilmaz (2012) spillover framework.FindingsA substantial relationship was found to exist between the liquidity of bonds with closer terms to maturity. The macroeconomic environment primarily impacts the liquidity of 10-year bonds, and they spiral down to the subsequent bond liquidity, exhibiting a rippling effect. The authors further show that the direction of liquidity shock transmission is from long- to medium- and thence to short-term bonds. Among the global factors, foreign investments and S & P 500 VIX significantly affect the liquidity of 10-year bonds.Research limitations/implicationsThe study has several implications for academicians, policymakers and domestic and global investment professionals. The drivers of liquidity risk and the transmission across the term structure help investors in designing efficient portfolio diversification strategies. The results are relevant for cross-border investors in the valuation of emerging Asian sovereign bonds while deciding on asset allocations and hedging strategies. The monetary regulators strive on a continuous basis to improve the liquidity in sovereign bond markets in order to ensure efficient funding of development activities. This study finds that short-term bonds are more liquid than long-term bonds. Their auction framework with higher series of short-term bond issues helps to provide the required liquidity in the markets.Practical implicationsThe term structure of illiquidity is upward sloping, inferring a higher underlying liquidity risk of long-term bonds compared to short-term bonds. This finding suggests that a higher representation of short-term bonds in the auction framework helps to enhance the overall market liquidity.Originality/valueThis study offers insights into the debate on the shape of the term structure of illiquidity and the point of origination of liquidity shocks. Further, the direction of spillover across a wide spectrum of bonds is also demonstrated.

MOVEMENT ANALYSIS GOLD PRICE DURING COVID-19 YEARS 2020 - 2021

Gold is a type of investment instrument that can be used as an alternative choice for investors
 and can be used as one of the portfolio diversification strategies. One of the considerations in investment activities in the capital market is the rate of return of the investment instrument. The ups and downs of gold price movements in this study are influenced by three factors, namely the rupiah exchange rate against the USD, inflation and the Indonesian Interest Rate (SBI), which we now know better as BI 7-day. The BI rate or BI 7-day has a function as a suppressor of the inflation rate so that economic conditions are maintained. The research design is in the form of quantitative research using secondary data while the research design used is a causal design and uses a Static tical Package for Social Sciences (SPSS) for data processing. The results of the first study show that there is an influence between the Rupiah exchange rate and the USD affecting the movement of gold during Covid-19 in 2020-2021. The results of the second study show that there is an influence between inflation affecting the movement of gold during Covid -19 in 2020-2021. The results of the third study show that there is no influence between BI 7-day on the movement of gold during Covid-19 in 2020-2021.

Dynamic relationship of volatility of returns across different markets: evidence from selected next 11 countries

PurposeThis paper aims to examine whether the volatility of returns in commodity (gold, oil), bond and forex markets is related over time to the volatility of returns in equity markets of Bangladesh, Indonesia, Pakistan, Philippines, Turkey and Vietnam. In addition, the authors analyze the integration of the commodity, bond, forex and equity markets across these markets.Design/methodology/approachThe dynamic conditional correlation GARCH (DCC-GARCH) model is used to capture the time-varying conditional correlation among markets. The authors use daily data of stock prices, oil prices, gold prices, exchange rates and 10 years' bond yields of the six countries from Datastream and investing.com from January 2001 to April 2021.FindingsFindings reveal that the parameters of dynamic correlation are statistically significant which indicates the importance of time-varying co-movements. Estimation of the DCC-GARCH model suggests that the stock market is significantly correlated with bond, forex, gold and oil markets in all six countries.Practical implicationsThis study has practical implications for policymakers and investment professionals. A better understanding of dynamic linkages among the markets would help in constructing effective hedging and portfolio diversification strategies. Policy makers can get insight to build proper strategies in order to insulate the economy from factors that cause volatility.Originality/valueSeveral studies have investigated the linkage between commodity and stock markets and the volatility spillover effect, but very little attention is given to study the interrelationship between groups of market segments of different economies. No study has comparatively examined the dynamic relationship of multiple markets of a group of emerging countries simultaneously.

Practical Applications of Questioning the Wisdom of Crowds to Design Portfolio Diversification Strategies

In <b><i>Questioning the Wisdom of Crowds to Design Portfolio Diversification Strategies</i></b>, from the Winter 2023 issue of <b><i>The Journal of Alternative Investments</i></b>, <b>Vadim Zlotnikov</b>, <b>Igor Halperin</b>, <b>Lisa Huang</b>, <b>Cathy Pena</b> (all of <b>Fidelity</b>), and <b>Mikhail Stukalo</b> (of <b>Gainy</b>, a fintech startup), develop a diversification strategy based on measures of crowdedness. They design a portfolio with the positive characteristics of uncrowded stocks that also harvests the skill of active managers. They create a measure of crowdedness using holdings data on active managers. Stocks can become crowded based on conviction, reflecting manager skill. But they also can become crowded due to a consensus view, which leads to problematic characteristics; crowded stocks can exhibit negatively skewed returns with high forward-looking volatility. The authors also construct a long–short portfolio based on two different measures of crowdedness. The portfolio is beta-neutral and can be used to improve the Sharpe ratio of a 60/40 portfolio by dampening volatility. Maintaining smart money investors’ insights, the equity component does not sacrifice returns, as other defensive strategies do.

Dynamic Conditional Correlation and Volatility Spillover between Conventional and Islamic Stock Markets: Evidence from Developed and Emerging Countries

This study aims to investigate the dynamic conditional correlation and volatility spillover between the conventional and Islamic stock markets in developed and emerging countries in order to develop better portfolio and asset allocation strategies. We used both multivariate GARCH (MGARCH) and multi-scales-based maximal overlap discrete wavelet transform (MODWT) approaches to investigate dynamic conditional correlation and volatility spillover between conventional and Islamic stock markets in developed and emerging countries. The results show that conventional and Islamic markets move together in the long run for a specific time horizon and present time-varying volatility and dynamic conditional correlation, while volatility movement changes due to financial catastrophes and market conditions. Further, the findings point out that Chinese conventional and Islamic stock indexes showed higher volatility, whereas Malaysian conventional and Islamic stock indexes showed comparatively lower volatility during the global financial crisis. This study provides fresh insights and practical implications for risk management, asset allocation, and portfolio diversification strategies that evaluate stock market reactions to the crisis in the international avenues of finance literature.