Exchange-traded Funds Research Articles

Comparative analysis of profits from Bitcoin and its derivatives using artificial intelligence for hedge

Because there is a discrepancy between how individual investors and investment institutions choose Bitcoin and its new derivatives and Exchange-Traded Funds (ETFs), this paper used Bitcoin and ProShares Bitcoin Strategy ETF (BITO) data and a mixed variational mode decomposition and bidirectional gated cycle unit model to examine the interconnections between Bitcoin and its new derivative ETFs, from which actionable recommendations were developed. As well as conducting financial simulation trading using Bitcoin and BITO, the study expanded to examine other major ETFs. It was found that: (1) Bitcoin data could be employed to forecast and describe BITO; (2) under T+0 trading, Bitcoin was more volatile, profitable, and risky than BITO; and (3) under T+1 trading, Bitcoin was less volatile, profitable, and risky than BITO; however, the T+1 trading was found to have higher volatility, profits, and risk than T+0 trading. This study, therefore, builds a bridge from theory to practice for the prediction and description of new ETFs. Different from previous studies, this study explored the relationships between Bitcoin and BITO using Artificial Intelligence and quantitative financial simulations, which extends the practical and theoretical understanding of the Bitcoin market.

Assessing Awareness and Understanding of Gold ETFs Among Residents of Amravati City

This study investigates the level of awareness and understanding of Gold Exchange-Traded Funds (ETFs) among citizens of Amravati City. As a modern investment option, Gold ETFs offer a convenient and secure method for individuals to invest in gold without the physical burden of ownership. Despite their growing popularity, awareness of Gold ETFs remains limited, particularly in smaller urban areas. Using a quantitative approach, this research collects primary data through surveys to evaluate knowledge, perceptions, and investment intentions regarding Gold ETFs among local residents. The findings aim to inform stakeholders about existing gaps in awareness and suggest strategies for enhancing financial literacy and investment participation.

Forecasting ETF Performance: A Comparative Study of Deep Learning Models and the Fama-French Three-Factor Model

The global financial landscape has witnessed a significant shift towards Exchange-Traded Funds (ETFs), with their market capitalization surpassing USD 10 trillion in 2023, due to advantages such as low management fees, high liquidity, and broad market exposure. As ETFs become increasingly central to investment strategies, accurately forecasting their performance has become crucial. This study addresses this need by comparing the efficacy of deep learning models against the traditional Fama-French three-factor model in predicting daily ETF returns. The methodology employs eight artificial neural network architectures, including ANN, LSTM, GRU, CNN, and their variants, implemented in Python and applied to data ranging from 2010 to 2020, while also exploring the impact of additional factors on forecast accuracy. Empirical results reveal that LSTM and the Fama-French three-factor model exhibit a superior performance in ETF return prediction. This study contributes to the literature on financial forecasting and offers practical insights into investment decision making. By leveraging advanced artificial intelligence techniques, this study aims to enhance the toolkit available for ETF performance analysis, potentially improving investment strategies in this dynamic market segment.

Unlocking ETF price forecasting: Exploring the interconnections with statistical dependence-based graphs and xAI techniques

In the complex landscape of financial markets, accurately predicting Exchange-Traded Fund (ETF) price movements requires advanced methodologies. This research introduces a practical approach that integrates network analysis with graph embeddings, specifically utilizing Node2Vec, to enhance financial prediction models' performance and interpretability. By representing the intricate relationships within financial markets in a lower-dimensional space, we improve the efficiency of AI-driven predictions. A key component of our method is applying the SHAP Explainable AI (xAI) framework, which helps interpret our tree-based models' decision-making process. Using six different tree-based models, our approach delivers accurate predictions while maintaining transparency in model interpretation. This combination of graph embeddings and explainability tools enables stakeholders to understand better the factors influencing financial market behavior, improving decision-making based on AI models.

Dynamic DeFi-G7 stock markets interactions and their potential role in diversifying and hedging strategies

AbstractThis study examines the link between stocks and decentralized finance (DeFi) in terms of returns and volatility. Major G7 exchange-traded funds (ETFs) and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment. Specifically, this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity (VAR-GARCH) model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation (DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair. The volatility spillover analysis proves a contagion effect occurred between different geographic markets, and even between markets of different natures and typologies, during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine. Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times. In addition, DeFi assets, except for the Bazaar (BZR) Protocol, can offer diversification gains when included in financial investment portfolios. These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies, especially during periods of financial crisis.

The countdown to carbon neutrality: Implications for passive investors

In 2019 the European Union (EU) Climate Benchmarks Regulation established a set of standardized emission reduction criteria for financial benchmarks. This study examines the risk-adjusted performance of equity exchange-traded funds (ETFs) tracking EU Climate Transition Benchmarks (CTBs) and EU Paris-Aligned Benchmarks (PABs). Our findings reveal that, after the implementation of the regulation, equity CTB ETFs have exhibited no significant risk-adjusted performance difference compared to their non-sustainable twin ETFs. Conversely, equity PAB ETFs, which adhere to more stringent environmental standards, have underperformed their non-sustainable counterparts. Our results suggest that stringent emission reduction objectives have a negative effect on investors’ returns.

Navigating crises: Gold's role as a safe haven for U.S. sectors

This paper investigates the correlation between U.S. sectors and gold, and whether gold can serve as a safe haven for investors in specific U.S. sectors during the global financial crisis, COVID-19, and the Russia-Ukraine war. We use data from the Standard & Poor's Depository Receipts (SPDR) Select Sector Exchange Traded Fund (ETF) to capture the performance of the respective sectors. Our findings document that gold is a weak safe haven for most U.S. sectors. Gold is not a safe investment for energy, materials, utilities, and consumer staples. Gold does provide vital protection for financial, consumer discretionary, industrial, technology, and healthcare.

Equity Premium Income Exchange-Traded Funds: How Much Income Do You Get to Keep after Taxes?

Equity Premium Income Exchange-Traded Funds: How Much Income Do You Get to Keep after Taxes?

Detection and localization of changes in a panel of densities

We propose a new methodology for identifying and localizing changes in the Fréchet mean of a multivariate time series of probability densities. The functional data objects we study are random densities fs,t indexed by discrete time t and a vector component s, which can be treated as a broadly understood spatial location. Our main objective is to identify the set of components s, where a change occurs with statistical certainty. A challenge of this analysis is that the densities fs,t are not directly observable and must be estimated from sparse and potentially imbalanced data. Such setups are motivated by the analysis of two data sets that we investigate in this work. First, a hitherto unpublished large data set of Brazilian Covid infections and a second, a financial data set derived from intraday prices of U.S. Exchange Traded Funds. Chief statistical advances are the development of change point tests and estimators of components of change for multivariate time series of densities. We prove the theoretical validity of our methodology and investigate its finite sample performance in a simulation study.

The volatility-liquidity dynamics of single-stock ETFs

Single-stock exchange-traded fund (SSETF) is the latest innovation in the ETF arrays of products. We examine the dynamics of the volatility-liquidity relationship between these SSETFs and their underlying stocks. We find that SSETFs have higher volatility and lower liquidity than the underlying stocks. During periods of higher stock volatility, the liquidity of SSETFs is significantly reduced. We also find evidence that SSETF traders can mitigate the liquidity problem by using intermarket sweep orders and routing orders to lit exchanges.

ETFs and tail dependence: Evidence from Chinese stock market

Using Chinese A-share market data, we empirically examine the impact of exchange-traded funds (ETFs) on tail dependence of the underlying securities. Our results show that ETFs can increase the tail dependence of stocks in their basket, showing that the average tail dependence of a stock is higher when it has stronger ETF holding similarity with other stocks. We investigate the role of arbitrage activity and find that ETF holding similarity increases stocks’ ETF arbitrage activity. This effect is primarily on discount arbitrage rather than premium arbitrage, which leads to higher tail dependence among stocks. Alongside propagating demand shocks from the ETF market to underlying securities, ETFs also propagate tail event shock from one stock to other stocks in their baskets. Additionally, arbitrage activities through ETFs add a new layer of non-fundamental tail dependence to the underlying securities. Unlike mutual funds, ETFs lead to more frequent and idiosyncratic tail risk contagion among underlying securities. Our study sheds light on how ETFs provide new channels for risk contagion among underlying securities in emerging markets.

Tradable Risk Factors for Institutional and Retail Investors

Abstract We construct tradable risk factors using combinations of large and liquid mutual funds (long leg) and ETFs (exchange-traded funds) (long and short legs), based on their holdings, for both retail and institutional investors. Exploiting a novel dataset, our tradable factors take into account ETF shorting costs. Assessing the performance of our tradable factors against standard “on-paper” factors, we uncover an implementation shortfall of 2–4 percent annually. Shorting fees and transaction costs contribute to 58 percent of the performance differential between tradable and “on-paper” factors, assigning a non-trivial role to the opportunity cost of not trading the exact “on-paper” portfolio.

On the integration of multiple criteria decision aiding and forecasting: Does it create value in portfolio selection?

A systemic integration of multiple criteria decision aiding (MCDA) and forecasting is presented for enhancing the quality of investment decisions. First, we provide a new methodological approach for combining forward-looking information, as an input to further MCDA analysis and we illustrate that the informational content of the MCDA scores is distinct from that of the original data used as input to the MCDA. Moreover, we provide very compelling evidence that the produced MCDA scores and rankings have incremental information, over and above that being contained in either the original data or the forecasts themselves. Therefore, it can be used successfully in providing methods which will have a high, a priori, chance of resulting in financial performance enhancements; to this end, we have illustrated this finding with a number of different approaches, including asset rotation and portfolio construction. Next, we show that the MCDA ranked variables can be used for both in-sample explaining and out-of-sample forecasting of critical economic variables, such as the real GDP growth or the unemployment rate, so that MCDA might be able to assist in identifying leading indicator properties for economic forecasting. The evaluation of the suggested approach entails extensive empirical testing on the various US economy market sectors, as expressed by the corresponding S&P 500 Exchange Traded Funds (ETFs), for a period of 22 years, documenting specific economic stylized facts, reported for first time in the relevant literature.

Carbon exchange-traded funds market and economic policy uncertainty

This study empirically analyses the relationship between economic policy uncertainty (EPU) and three carbon exchange-traded funds (ETFs) using wavelet coherence analysis as well as dynamic conditional correlation (DCC) from a multivariate GARCH model. The analysis techniques employed allow us to analyze the nature of the relationship which may depend on time and frequency. Our findings indicate that there exists a significant negative correlation between EPU and the ETFs for almost all times and frequencies. However, for some times and frequencies, the identified negative relationship is not significant. Interestingly, we also find that for some times and frequencies, EPU leads the ETFs and for some other times and frequencies ETFs lead the EPU.

Analysis of Performance Factors from U.S. Stock Exchange-Traded Funds Listed Domestically: An Investor"s Perspective

Analysis of Performance Factors from U.S. Stock Exchange-Traded Funds Listed Domestically: An Investor"s Perspective

The spillover and leverage effects and trading volume of FinTech Exchange-Traded Funds

ABSTRACT This paper explores the spillover and leverage effects, as well as trading volume dynamics, of Financial, Technology, and FinTech Exchange-Traded Funds (ETFs) using ARMA-GARCH and ARMA-EGARCH models. The study indicates that these ETF sectors and the stock market index have significant connections and interdependencies, which can transmit shocks and volatility. Market fluctuations significantly impact the ETF sectors, with negative shocks having a more significant impact on volatility. Furthermore, increased volatility is linked to increased trading activity, which indicates active investor adjustments in uncertain periods. The practical implications of these findings for investors, portfolio managers, and policymakers are aimed at supporting portfolio diversification, risk management, and asset allocation decisions. Understanding the effect of market volatility on trading volumes can help improve trading strategies and liquidity management. The paper increases comprehension of spillover and leverage effects and trading volume in ETFs, enabling stakeholders to make well-informed decisions in the financial markets.

Delving into the Exchange-Traded Funds (ETFs) Market: Understanding Market Efficiency

Exchange-traded funds (ETFs) are the most popular products in the financial sector today. There is extensive literature on the multifractal analysis of some stock markets, but not about the multifractal behaviour of the ETF market. This study examines the efficiency of stock index ETFs worldwide from an Efficient Market Hypothesis (EMH) perspective, using the ETFs: Ishares Msci World ETF (URTH), Ishares Russell 1000 ETF (IWB), SPDR S&P 500 ETF TRUST (SPY), Ishares Global Clean En. ETF (ICLN), Ishares USD Green Bond ETF (BGRN), from 1 January 2021 to 24 May 2024. It analyses a pre-conflict and a geopolitical conflict to uncover distinct patterns of behaviour reflecting significant changes in market conditions. Before the conflict, the Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500 and Ishares USD Green Bond ETFs showed signs of anti-persistence in returns, indicating a lack of strong relationship or predictability between short-term price movements. The Ishares Global Clean Energy ETF did not reject the random walk hypothesis, suggesting that returns follow a pattern closer to random, where market prices already efficiently reflect all available information. During the conflict, there was a transition in the ETFs' behaviour patterns, as evidenced by the increases in slope values for Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500, Ishares Global Clean Energy and Ishares USD Green Bond. Thus, the possible transition from anti-persistence to long-term memories in ETF returns during the conflict. For portfolio managers, these findings highlight the need to continually adapt investment strategies to manage risks better and take advantage of opportunities in a dynamic and complex investment environment.

THE MODIFIED MEAN ABSOLUTE DEVIATION INTEGER LINEAR PROGRAMMING MODEL FOR GLOBAL PORTFOLIO ASSET ALLOCATION

This study develops a practical yet robust Mean Absolute Deviation (MAD) Mixed-Integer Linear Programming (MILP) model for a global investment portfolio tailored to the constraints faced by Malaysian retail investors, such as transactional fees and foreign currency exchange spreads, to provide more accurate estimations of portfolio returns. The MAD ILP model is modified to address these factors, aiming to enhance utility and efficiency. The study utilizes a Maybank 12-month fixed deposit cash account and ten selected Exchange-Traded Funds (ETFs) from iShares, Vanguard, and State Street Global Advisors for global portfolio construction. Forecasting techniques were utilized to generate expected returns for the ETFs and foreign currency exchange spreads. Based on the MAD model proposed by Konno and Wijayanayake (2001), the study introduces three modifications: incorporating transaction cost elements specific to Malaysian investors, including foreign currency exchange spreads in return calculations, and transforming the model from mixed-integer to pure integer model to accommodate minimum transaction lot constraint. Verification and validation exercises demonstrate the model’s ability to replicate real-world portfolio construction and reliable simulations. The model offers a practical tool for self-ascribed investors aiming to optimize global investment portfolios while considering unique constraints and transaction costs.

Bitcoin ETF: Opportunities and risk

The year 2024 witnessed a major development in the cryptocurrency industry with the long-awaited approval of spot Bitcoin exchange-traded funds (ETFs). This innovation provides investors with a new, regulated path to gain exposure to Bitcoin through a familiar investment vehicle (Kumar et al., 2024). However, unlike traditional ETFs that directly hold underlying assets, Bitcoin ETFs rely on a creation and redemption process managed by authorized participants (APs). This unique structure introduces distinct characteristics in terms of premium/discount behavior compared to traditional ETFs. This paper investigates the premium and discount patterns observed in Bitcoin ETFs during first four-month period (January 11th, 2024, to May 17th, 2024). Our analysis reveals that these patterns differ significantly from those observed in traditional index ETFs, potentially exposing investors to additional risk factors. By identifying and analyzing these risk factors associated with Bitcoin ETF premiums/discounts, this paper aims to achieve two key objectives: · Enhance market understanding: Equip and market and investors with a deeper comprehension of the unique liquidity risks inherent in Bitcoin ETFs. · Provide a clearer risk management frameworks: Offer a clearer perspective on the risk-return profile of digital asset ETFs, specifically focusing on Bitcoin ETFs. Through a thorough analysis of premium/discount behavior and the underlying factors contributing to it, this paper strives to contribute valuable insights for investors navigating the evolving landscape of digital asset investments

Macroeconomic news and price jumps: evidence from ETFs and LOFs in China

PurposeWe examine the relationship between macroeconomic news and fund price jumps, using high-frequency 5-min intraday data for Exchange Traded Funds (ETFs) and Listed Open-end Funds (LOFs) from 2019 to 2020.Design/methodology/approachWe utilize the non-parametric jump test known as the LM method to detect fund price jumps. In addition, we perform Logistic regression to analyze the relationship between macroeconomic news and fund price jumps. Moreover, we use multiple linear regression to explore the relationship between fund price jumps and subsequent returns.FindingsThe probability of price jumps increases by 22.56% when macroeconomic news is released. Moreover, the returns associated with news-driven price jumps display a reversal pattern, and there is an asymmetric relationship in subsequent returns following macroeconomic shocks. Specifically, funds tend to exhibit lower returns after news-driven price jumps compared to those that are not influenced by news events.Research limitations/implicationsIn today's digital age, investors have unprecedented access to a wealth of information through the Internet and various communication platforms. News and market data can be instantly accessed and disseminated, allowing for swift dissemination of information to investors worldwide. However, despite this enhanced accessibility, investors continue to exhibit overreactions or underreactions to new information.Practical implicationsMacroeconomic news release provide crucial insights into the overall health and performance of the economy. By monitoring and analyzing these indicators, investors can gain valuable information that can guide their investment decisions. Furthermore, by fostering a transparent and reliable information disclosure systems, governments can play a critical role in ensuring the stability and transparency of the funds market.Originality/valueThe paper utilizes 5-min high-frequency data from funds and incorporates a comprehensive macroeconomic news information database. These methodological choices enhance the precision and reliability of the analysis, allowing for a more nuanced understanding of the relationship between macroeconomic news releases and fund price jumps.