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1 – 10 of 545Michael O'Neill and Gulasekaran Rajaguru
The authors analyse six actively traded VIX Exchange Traded Products (ETPs) including 1x long, −1x inverse and 2x leveraged products. The authors assess their impact on the VIX…
Abstract
Purpose
The authors analyse six actively traded VIX Exchange Traded Products (ETPs) including 1x long, −1x inverse and 2x leveraged products. The authors assess their impact on the VIX Futures index benchmark.
Design/methodology/approach
Long-run causal relations between daily price movements in ETPs and futures are established, and the impact of rebalancing activity of leveraged and inverse ETPs evidenced through causal relations in the last 30 min of daily trading.
Findings
High frequency lead lag relations are observed, demonstrating opportunities for arbitrage, although these tend to be short-lived and only material in times of market dislocation.
Originality/value
The causal relations between VXX and VIX Futures are well established with leads and lags generally found to be short-lived and arbitrage relations holding. The authors go further to capture 1x long, −1x inverse as well as 2x leveraged ETNs and the corresponding ETFs, to give a broad representation across the ETP market. The authors establish causal relations between inverse and leveraged products where causal relations are not yet documented.
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Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a…
Abstract
Purpose
Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.
Design/methodology/approach
Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.
Findings
The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.
Practical implications
One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.
Originality/value
This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.
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This review aims to provide an overview of research from different academic disciplines to chart some of the key developments in retail cryptocurrency trading against the backdrop…
Abstract
Purpose
This review aims to provide an overview of research from different academic disciplines to chart some of the key developments in retail cryptocurrency trading against the backdrop of the wider trading landscape, and how it has evolved in recent years. The purpose of this review is to provide researchers with a broad perspective to highlight the complex range of factors that drive cryptocurrency trading among retail investors.
Design/methodology/approach
Peer-reviewed literature from the social sciences, economics, marketing and branding disciplines is synthesised to explicate influential factors among retail cryptocurrency investors.
Findings
Online retail trading communities can create narratives that ascribe value to cryptocurrencies leading to consumer herding behaviours. The principles that underpin emotional branding and Fear of Missing Out can promote trading behaviour driven by heuristic processing and cognitive biases. Concurrently, the tenets of controversial marketing and the anti-establishment nature of Bitcoin and other cryptocurrencies serve to bolster in-group out-group categorisations fostering continued investment and market volatility. Consequently, Bitcoin and cryptocurrency trading more broadly offer a powerful combination of excitement from risk-taking akin to gambling buffered by the sanctity of social inclusion.
Originality/value
A broader, unique perspective on retail cryptocurrency trading which assists in better understanding the complexities that underpin its appeal to retail investors.
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Stefano Piserà and Helen Chiappini
The aim of the paper is to investigate the risk-hedging and/or safe haven properties of environmental, social and governance (ESG) index during the COVID-19 in China.
Abstract
Purpose
The aim of the paper is to investigate the risk-hedging and/or safe haven properties of environmental, social and governance (ESG) index during the COVID-19 in China.
Design/methodology/approach
This paper employs the DCC, VCC, CCC as well as Newey–West estimator regression.
Findings
The findings provide empirical evidence of the risk hedging properties of ESG indexes as well as of the environmental, social and governance thematic indexes during the outbreak of the COVID-19 crisis. The results also support the superior risk hedging properties of ESG indexes over cryptocurrency. However, the authors do not find any safe haven properties of ESG, Bitcoin, gold and West Texas Intermediate (WTI).
Practical implications
The paper offers therefore, practical policy implications for asset managers, central bankers and investors suggesting the pandemic risk-hedging opportunities of ESG investments.
Originality/value
The study represents one of the first empirical contributions examining safe-haven and hedging properties of ESG indexes compared to traditional and innovative safe haven assets, during the eruption of the COVID-19 crisis.
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Xiaojie Xu and Yun Zhang
For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction…
Abstract
Purpose
For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.
Design/methodology/approach
In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?
Findings
The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.
Originality/value
The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.
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Dyliane Mouri Silva de Souza and Orleans Silva Martins
This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.
Abstract
Purpose
This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.
Design/methodology/approach
The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.
Findings
In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.
Originality/value
The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.
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Thabo J. Gopane, Noel T. Moyo and Lesego F. Setaka
Stirred by scant regard for market phases in portfolio performance assessments, the current paper investigates the active versus passive investment strategies under the bull and…
Abstract
Purpose
Stirred by scant regard for market phases in portfolio performance assessments, the current paper investigates the active versus passive investment strategies under the bull and bear market conditions in emerging markets focusing on South Africa as a case study.
Design/methodology/approach
Methodologically, the measures of Jensen's alpha and Treynor index are applied to the monthly returns of 20 funds from January 2010 to June 2022.
Findings
The results are enlightening; though they contradict developed market evidence, they are consistent with emerging market trends. The findings show that actively managed funds outperform the market benchmark and passive investing style under bear and normal market conditions. Passive investment strategy outperforms both market benchmark and actively investing style under bull market conditions.
Practical implications
In the face of improved market efficiency, increased liquidity and recent technological impact, the findings of this study have practical application. The study outcomes should inform and update global investors, especially asset managers interested in emerging markets; however, the limitations of the study should also be considered.
Originality/value
While limited studies consider market conditions when comparing and contrasting the performance of passive versus active investing, such consideration is lacking in emerging markets. The current study corrects this literature imbalance.
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Ashish Kumar, Shikha Sharma, Ritu Vashistha, Vikas Srivastava, Mosab I. Tabash, Ziaul Haque Munim and Andrea Paltrinieri
International Journal of Emerging Markets (IJoEM) is a leading journal that publishes high-quality research focused on emerging markets. In 2020, IJoEM celebrated its fifteenth…
Abstract
Purpose
International Journal of Emerging Markets (IJoEM) is a leading journal that publishes high-quality research focused on emerging markets. In 2020, IJoEM celebrated its fifteenth anniversary, and the objective of this paper is to conduct a retrospective analysis to commensurate IJoEM's milestone.
Design/methodology/approach
Data used in this study were extracted using the Scopus database. Bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of a journal. Mapping of bibliographic data is carried using VOSviewer.
Findings
Study findings indicate that IJoEM has been growing for publications and citations since its inception. Four significant research directions emerged, i.e. consumer behaviour, financial markets, financial institutions and corporate governance and strategic dimensions based on cluster analysis of IJoEM's publications. The identified future research directions are focused on emergent investments opportunities, trends in behavioural finance, emerging role technology-financial companies, changing trends in corporate governance and the rising importance of strategic management in emerging markets.
Originality/value
To the best of the authors' knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJoEM. The study presents the key themes and trends emerging from a leading journal considered a high-quality research journal for research on emerging markets by academicians, scholars and practitioners.
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Md. Bokhtiar Hasan, Md Mamunur Rashid, Md. Naiem Hossain, Mir Mahmudur Rahman and Md. Ruhul Amin
This research explores the spillovers and portfolio implications for green bonds and environmental, social and governance (ESG) assets in the context of the rapidly expanding…
Abstract
Purpose
This research explores the spillovers and portfolio implications for green bonds and environmental, social and governance (ESG) assets in the context of the rapidly expanding trend in green finance investments and the need for a green recovery in the post-COVID-19 era.
Design/methodology/approach
This study utilizes Diebold and Yilmaz’s (2014) spillover method and portfolio strategies (hedge ratio, optimal weights and hedging effectiveness) for the data starting from February 29, 2012, to March 14, 2022.
Findings
The study’s findings reveal that the lower volatility spillover is evidenced between the green bonds and ESG stocks during tranquil and turbulent periods (e.g. COVID-19 and Russia-Ukraine War). Furthermore, hedging costs are lower both in normal times and during economic slumps. Investing the bulk of the funds in green bonds makes it possible to achieve maximum hedging effectiveness between the S&P green bond (GB) and the S&P 500 ESG.
Practical implications
Both investors and policymakers may use these findings to make wise investment and policy choices to achieve post-COVID environmental sustainability.
Originality/value
Unlike previous research, this is the first to explore the interconnectedness among the major global and country-specific green bonds and ESG assets. The major findings of this study about the lower volatility spillovers and hedging costs between green bonds and ESG assets during the tranquil and turbulent periods may contribute to the post-COVID investment portfolio for environmental sustainability.
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Jorge Bacca-Acosta, Melva Inés Gómez-Caicedo, Mercedes Gaitán-Angulo, Paula Robayo-Acuña, Janitza Ariza-Salazar, Álvaro Luis Mercado Suárez and Nelson Orlando Alarcón Villamil
This study aims to examine how the adoption of digital technologies affects the business competitiveness of countries in Latin American and European countries.
Abstract
Purpose
This study aims to examine how the adoption of digital technologies affects the business competitiveness of countries in Latin American and European countries.
Design/methodology/approach
This study used a structural model based on factors representing the pillars of the Global Competitiveness Index: financial system, adoption of information and communication technologies (ICT), skills, labor market, product market, macroeconomic stability, business dynamism and gross domestic product (GDP) purchasing power parity (PPP) as a percentage of the total world value. The authors considered 17 Latin American and 28 European countries. The model was analyzed by partial least squares-structural equation modeling.
Findings
ICT adoption in Latin American countries is a strong predictor of business dynamism (66% of the variance), skills (81% of the variance), product market (75% of the variance), labor market (42% of the variance) and financial system (49% of the variance). Similarly, ICT adoption in European countries is a strong predictor of business dynamism (35.6% of the variance), skills (72.2% of the variance), product market (51.6% of the variance), labor market (81.7% of the variance, but with a negative path coefficient) and financial system (38% of the variance).
Practical implications
Latin American countries should create policies to build skills to increase ICT adoption, and improve business and labor market dynamism. A theoretical implication is that the authors propose two structural models based on the GCI that best explains competitiveness in Europe and Latin America.
Originality/value
Using GCI data, the authors present empirical evidence on the predictors of competitiveness across 17 Latin American and 28 European countries with a special focus on the adoption of digital technologies.
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