Exchange-traded Funds Research Articles

Minimum-variance portfolio optimization: an asset pricing model approach

ABSTRACT This paper employs novel asset pricing model methods to construct the global minimum-variance portfolio G. Out-of-sample analyses of U.S. stock returns show that optimized G portfolios have relatively higher expected returns, lower variance, and higher Sharpe ratios than those based on traditional variance-covariance matrix estimation methods. Robustness checks confirm these findings and show that our G portfolio has lower variance than currently available minimum-variance exchange traded funds (ETFs). We conclude that asset pricing models can be used to build high performing G portfolios. Implications to portfolio management are discussed.

Comparative Analysis of Mutual Funds and Gold ETFs: A Study on Investment Performance

This project analysis the returns of specific large-cap equity mutual funds and Gold Exchange Traded Funds (ETFs) to determine the best investment options for investors looking to balance growth and stability. To create a well-rounded portfolio, the study assesses the performance metrics and risk factors of these investment options. Large-cap equity funds are examined for their ability to offer growth in investment value, while gold ETFs are evaluated for their function as protection against market fluctuations and rising prices. A Decision matrix framework is utilized to methodically assess every fund and ETF based on factors including past performance and level of risk. This method based on data allows for a comprehensive analysis, pointing out the advantages and drawbacks of equity and gold investments. The research provides information on strategic asset allocation, highlighting diversification to maximize profits and handle risk efficiently. The results are meant to assist investors in making knowledgeable, eco-friendly investment choices in a changing financial environment.

Do ETFs increase the comovements of their underlying assets? Evidence from a switch in ETF replication technique

We investigate the impact of Exchange-Traded Funds (ETFs) on the comovements of their constituent securities using a novel identification that exploits the switch from synthetic to physical replication of a large French ETF. After the switch, constituent stocks experience greater commonality, in both returns and liquidity. For both the full sample of ETF constituents and the least liquid ETF constituents, a larger part of the variation in individual stock returns or liquidity is explained by market-wide variations. We present evidence that ETF creation and redemption is the transmission mechanism of the comovements. Moreover, we show that the comovements do not appear excessive.

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.

An ETF-based measure of stock price fragility

Equity mutual fund flows are commonly employed to measure stock price fragility - a stock’s exposure to non-fundamental demand risk. However, this approach may be biased by confounding fundamental information, potentially underestimating risk exposure. We propose an alternative method that uses the primary market data of exchange-traded funds (ETFs). This approach overcomes many limitations of mutual fund data, incorporates the influence of a broader set of investor demand, and strongly predicts stock return volatility and return comovement. Our study highlights the significant role that the arbitrage trading activity of ETFs play in signaling non-fundamental demand shocks.

Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty

This study investigates the profitability of portfolios that integrate asymmetric fractality within the Black–Litterman (BL) framework. It predicts 10-day-ahead exchange-traded fund (ETF) prices using recurrent neural networks (RNNs) based on historical price information and technical indicators; these predictions are utilized as BL views. While constructing the BL portfolio, the Hurst exponent obtained from the asymmetric multifractal detrended fluctuation analysis is employed to determine the uncertainty associated with the views. The Hurst exponent describes the long-range persistence in time-series data, which can also be interpreted as the uncertainty in time-series predictions. Additionally, uncertainty is measured using asymmetric fractality to account for the financial time series’ asymmetric characteristics. Then, backtesting is conducted on portfolios comprising 10 countries’ ETFs, rebalanced on a 10-day basis. While benchmarking to a Markowitz portfolio and the MSCI world index, profitability is assessed using the Sharpe ratio, maximum drawdown, and sub-period analysis. The results reveal that the proposed model enhances the overall portfolio return and demonstrates particularly strong performance during negative trends. Moreover, it identifies ongoing investment opportunities, even in recent periods. These findings underscore the potential of fractality in adjusting uncertainty for diverse portfolio optimization applications.

Beyond REITs, Investing in Real Estate Funds

This study examines the performance of U.S. real estate funds of both exchange-traded funds (ETFs) and mutual funds, including passive ETFs, active ETFs, retail-class mutual funds, institutional-class mutual funds, and index mutual funds. The active ETF portfolio stands out with the highest returns and Sharpe ratio and consistently higher alphas in the factor models. It exhibits more prominent performance in downtrend markets and in recent years, whereas its peer retail-class mutual fund portfolio has consistently shown inferior performance. The passive ETF and the index mutual fund portfolios have never surpassed the performance of the active ETF portfolio. Neither choosing the cheap index strategy nor paying for high-cost mutual fund managers proves to be the optimal option for investors. The outperforming portfolio is found to be active ETFs, which sheds light on the continuing surge in their fund flows.

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.

The Benefits of China Funds During Globalization and in a New World Order

Examining data from their inception in the 1990s to 2022, we find that both China mutual funds and China exchange-traded funds (ETFs) outperformed their benchmarks, the S&P 500 index fund and SPY, before the global financial crisis starting in the summer of 2007 but underperformed afterward. We show that the real benefits of China funds in the post-crisis period since May 2009 lie in their hedge effect against U.S. stock market downturns. Current policies in both countries indicate the emergence of a new world order of selective segregation in some sensitive sectors but a continuous trading partnership because each country needs the other. For example, China needs computer chips from the United States, whereas the United States needs key chip-making minerals from China. Paradoxically, this new world order creates growth opportunities in both countries, which is conducive to investing in China funds and makes it necessary for China funds to invest directly in Chinese shares to better serve the U.S. investors.

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.

Risk spillovers and optimal hedging in commodity ETFs: A TVP-VAR Approach

Despite the growing popularity of commodity exchange traded funds (ETFs), research on their risk transmission dynamics is lacking. The study employs a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model to analyze volatility transmission among commodity ETFs during significant events like the COVID-19 pandemic and geopolitical conflicts. It aims to minimize interconnectedness among ETFs to reduce associated risk using the Mean Co-skewness optimal portfolio (MCoP) technique. The findings emphasize the importance of adaptability in portfolio management and provide actionable information for investors seeking to optimize allocation and manage risk exposure effectively in dynamic market environments.

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.