Traditional Asset Classes Research Articles

Relationship between Bitcoin and the stock market – can bitcoin serve as a safe haven for investors?

Aim: As a new asset class, Bitcoin and other cryptocurren-cies can be interesting for investors in the context of return stabilization, especially in times of crisis. We aimed to anal-yse whether Bitcoin can serve as a safe haven for investors in times of crisis.Methods: The data covers the period from September 17, 2014, to April 29, 2021, with 382 observations. Yahoo! Finance served as the source for the Bitcoin prices and Investing.com for the values of the Standard & Poor’s 500 (S&P500) Index. We used the maximum likelihood method to estimate the dynamic conditional correlation model.Results: Due to the high volatility during the analysed peri-od, Bitcoin achieved a higher risk-adjusted return compared to the S&P500 Index. The DCC model showed a positive cor-relation between the returns of the S&P500 and Bitcoin during the analysed period.Conclusions: Our results suggest that Bitcoin may not serve as a safe haven for investors in times of crisis. However, its role in this context should be further evaluated by examin-ing its relationship with other traditional asset classes (gold, commodities) and other types of cryptocurrencies such as stablecoins.

Intellectual Property as Business Loan Collateral: A Taxonomy of Institutional and Economic Determinants

Abstract As a promising strategy to secure debt financing, firms can use their intellectual property rights (IPR) as collateral. Despite an ongoing shift to a more technology-based economy, the collateralization of IPR is still trailing behind the use of more traditional asset classes. In this paper, we address the challenges and opportunities of using IPR as collateral from a legal and economic angle by also exploring the role of and consequences for IP law. We develop a new taxonomy, i.e., a classification of the key determinants of using IPR as collateral. The taxonomy defines two pillars that govern the use of IPR collateral that distinguish between institutional and economic determinants. The institutional determinants cover contract law, IPR registries, and banking regulations. The economic determinants constitute the influence of IPR characteristics on the trade-off between the economic costs and benefits of collateralizing IPR. We apply the derived taxonomy to the legal and economic status quo in several industrialized economies to identify potential impediments to IPR-backed debt financing. Taken together, our taxonomy can be viewed as providing guidance for future research in the fields of law and economics on IPR as loan collateral for businesses.

Gaining a Seat at the Table: Enhancing the Attractiveness of Online Lending for Institutional Investors

This paper investigates the suitability of online loans as an investment through the lens of a portfolio optimization framework. We propose general characteristics-based portfolio policy (GCPP), a framework which overcomes unique challenges associated with building a portfolio of online loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive data set spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average annualized internal rate of return (IRR) of 8.86% to 13.08%, significantly outperforming an equal-weight portfolio of loans. To assess the attractiveness of online loans, we then compare the performance of the GCPP portfolio to traditional investment vehicles including stocks, bonds, and real estate. The results demonstrate that a portfolio of online loans earns competitive or higher rates of return compared to traditional asset classes with limited comovement. These results indicate that online loans are an attractive novel asset class for investors. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.

Bitcoin replication using machine learning

Cryptocurrencies are characterized by high volatility and low correlations with traditional asset classes, and present an intriguing investment opportunity. However, their inherent risks and regulatory uncertainties make direct investment challenging for many investors. This paper addresses this challenge by proposing a replication framework that employs machine learning to create synthetic portfolios that replicate the risk-adjusted return profile and diversification benefits of Bitcoin, by far the largest cryptocurrency by market share. We show that the synthetic portfolios offer a compelling alternative to direct investment in Bitcoin, delivering superior risk-adjusted returns net of trading costs while mitigating the risks that are associated with holding Bitcoin directly. Furthermore, the synthetic portfolios provide better diversification benefits and lower tail risk.

Do entrepreneurs use cryptocurrency to hedge against their business risk?

This paper studies if entrepreneurs use cryptocurrency to hedge against business risk. Cryptocurrency is a newly emerging asset class that distinct from other traditional asset classes and less been analyzed from the entrepreneur's perspective. To implement the analyses, we address the endogeneity issue of being an entrepreneur using parents background and show that entrepreneurs prefer risk diversification hypotheses to hedge against their business risks.

INVESTING IN CRYPTOCURRENCY AS AN ALTERNATIVE WAY OF FINANCIAL INVESTMENTS

In the realm of finance, cryptocurrency has emerged as a captivating alternative to traditional investment avenues, such as stocks, bonds, and real estate. Its decentralized nature, unfettered by government or financial institution control, presents a unique proposition in the world of wealth management. The allure of cryptocurrency lies in its potential for substantial returns. Its value has experienced remarkable growth in recent years, offering investors the prospect of significant financial gains. Additionally, cryptocurrency serves as a valuable tool for portfolio diversification, as its performance is often uncorrelated with traditional asset classes. Moreover, cryptocurrency boasts portability and accessibility, making it a convenient and inclusive investment option. Its digital nature allows for seamless storage and transferability, while its global reach enables anyone with an internet connection to participate in the cryptocurrency ecosystem. However, alongside these potential benefits, cryptocurrency also harbors inherent risks. Its volatility poses a challenge, as its value can fluctuate dramatically in a short period. Regulatory uncertainty looms, as governments worldwide grapple with the implications of cryptocurrency and may impose restrictive measures. Furthermore, security concerns persist, as cryptocurrency exchanges and wallets have fallen prey to cyberattacks, jeopardizing investors' assets. In light of these considerations, investing in cryptocurrency demands a thoughtful approach. Investors must carefully assess their risk tolerance and align their investment goals with the inherent risks associated with cryptocurrency. Thorough research and a well-diversified portfolio are crucial for navigating the complexities of this emerging asset class. The purpose of the article is to analyze the profitability and safety of investments in cryptocurrency and study the different methods associated with it.

Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes

We examine extreme connectedness between the artificial intelligence (AI) tokens, artificial intelligence ETFs, and other asset classes, using the quantile VAR approach of Ando et al., (2022). We find moderate connectedness levels at mean and median quantiles, with AI ETFs (AI tokens) acting as strong (weak) net emitters (receivers) of return spillovers. Findings, confirmed by alternative testing, suggest that, during normal market conditions, AI tokens may offer utility as diversifiers for portfolios of traditional assets. However, at both lower and upper quantiles, connectedness levels increase, consistent with AI tokens and ETFs being sensitive to extreme shocks. Results suggest that AI tokens and ETFs do not diversify the risk of other assets during extreme market conditions. Finally, AI tokens, especially, may offer effective hedging at low cost for traditional assets (gold, equity, real estate, bonds, and currency), except for the oil and cryptocurrency market. Investors including AI assets in portfolios need to diligently monitor for changing market conditions.

Spotting the stock and crypto markets’ rings of fire: measuring change proximities among spillover dependencies within inter and intra-market asset classes

Crypto assets have lately become the chief interest of investors around the world. The excitement around, along with the promise of the nascent technology led to enormous speculation by impulsive investors. Despite a shaky understanding of the backbone technology, the price mechanism, and the business model, investors’ risk appetites pushed crypto market values to record highs. In addition, pricings are largely based on the perception of the market, making crypto assets naturally embedded with extreme volatility. Perhaps unsurprisingly, the new asset class has become an integral part of the investor’s portfolio, which traditionally consists of stock, commodities, forex, or any type of derivative. Therefore, it is critical to unearth possible connections between crypto currencies and traditional asset classes, scrutinizing correlational upheavals. Numerous research studies have focused on connectedness issues among the stock market, commodities, or other traditional asset classes. Scant attention has been paid, however, to similar issues when cryptos join the mix. We fill this void by studying the connectedness of the two biggest crypto assets to the stock market, both in terms of returns and volatility, through the Diebold Francis spillover model. In addition, through a novel bidirectional algorithm that is gaining currency in statistical inference, we locate times around which the nature of such connectedness alters. Subsequently, using Hausdorff-type metrics on such estimated changes, we cluster spillover patterns to describe changes in the dependencies between which two assets are evidenced to correlate with those between which other two. Creating an induced network from the cluster, we highlight which specific dependencies function as crucial hubs, how the impacts of drastic changes such as COVID-19 ripple through the networks—the Rings of Fire—of spillover dependencies.

Do cryptocurrencies integrate with the indices of equity, sustainability, clean energy, and crude oil? A wavelet coherency approach

AbstractThe paper examines market co‐movement between pairs of financial assets in the time‐frequency domain. Recent finance literature confirms the integration of cryptocurrencies and financial assets, which may bring more investments with the possibility of surplus liquidity in the cryptocurrency segment, leading to financial instability. The novelty of this paper is examining the integration of cryptocurrencies and the indices of equity, sustainability, renewable energy, and crude oil for the daily observations from 2015 to 2021 by using the wavelet coherency method. The empirical results signify no integration in the short‐term scales and grow stronger in the medium‐term scales, especially during the COVID‐19 period, and further exhibit weaker heterogeneous associations in the long‐term scales. However, the sustainability, clean energy indices follow similar dynamics of the equity market and crypto pairs. In contrast, the global crude oil index showcases the minor integration with cryptocurrencies compared with other traditional asset classes. Hence, the cryptocurrency market fails to confirm the safe haven features, especially during the COVID‐19 periods (Medium‐term), which facilitate the domestic and international investors expecting to hedge their price risk in equity markets using cryptocurrencies may have to look for short‐term. The lead–lag heterogeneous effects of the asset‐pairs may pave arbitrage opportunities for investors.

Can cryptocurrency solve the problem of financial constraint in corporates? A literature review and theoretical perspective

Purpose This paper aims to attempt to examine some of the unique features of cryptocurrency and the reasons for its growing market acceptability. Given the expanding size of cryptocurrency markets, the present study strives to identify whether it can be used as an alternative financial asset in place of traditional financial assets to meet firms' financial constraints. It also provides issues for future research in the area of cryptocurrency markets. Design/methodology/approach This paper analysed 94 research papers from databases such as ScienceDirect, Proquest, EBSCO, Emerald Insight and Web of Science. Articles connected to cryptocurrency, financial assets and corporate financial constraints research were explored. VOSviewer software has been used to visualise the specified body of literature and identify eight clusters in previous literature using keyword and abstract analysis. Findings Studies reveal that cryptocurrency markets are independent of traditional financial markets and cryptocurrency returns have less correlation with traditional financial asset classes. This can be an advantage to firms, especially during times of crisis when traditional financial assets are impacted by significantly lower returns, while cryptocurrencies can serve as an alternative. Realtime data reveals that during the pandemic, cryptocurrencies had the maximum growth in returns which also happened to be a time when firms faced severe cash constraints. While accepting cryptocurrency as a means of exchange is still under review by regulatory authorities, it can be considered an alternative asset for investment purposes. Firms can take advantage of it to overcome financial constraints and thus reap the gains from holding crypto assets for precautionary reasons. Originality/value The present study investigates using cryptocurrency as an alternative financial asset to solve the financial constraint problem in corporates. The issues regarding volatility, cyber securities, gold returns, long-term and short-term returns have been some of the most prominent studies in the area of cryptocurrency. The present study uses eight theme-based clusters to identify the role of cryptocurrency as an alternative investment class and examines evidence-based research regarding the financial returns from holding cryptocurrency over certain traditional asset classes such as gold, currency or stocks. In recent years, it has been found that investors' growing interest in holding cryptocurrency as part of their financial portfolio has led to the substantial appreciation of cryptocurrency prices. To the best of the authors’ knowledge, the study will be a novel attempt to identify the role of cryptocurrency as an antidote to the companies’ financial constraints and liquidity issues.

Bitcoin connectedness to traditional asset classes in times of COVID-19

Bitcoin connectedness to traditional asset classes in times of COVID-19

Bitcoin connectedness to traditional asset-classes in times of COVID-19

Bitcoin connectedness to traditional asset-classes in times of COVID-19

Tactical Asset Allocation, Risk Premia, and the Business Cycle: A Macro Regime Approach

Market conditions change over the course of the business cycle. When are investors compensated to take risk? And what type of risk? We propose a practical regime-based framework for tactical asset allocation (TAA), combining leading economic indicators and global risk appetite to identify four macro regimes: recovery, expansion, slowdown, and contraction. We document distinct performance characteristics across regimes for traditional asset classes and their underlying risk factors, focusing on the term premium, credit premium, and equity premium. We provide simple and practical examples of TAA strategies for long-only multi-asset, and fixed income portfolios with the potential to generate attractive excess returns. Results are statistically significant and economically relevant after transaction costs, with information ratios between 0.70 and 0.80.

Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning

The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.

Observing Cryptocurrencies through Robust Anomaly Scores.

The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.

Risk hedging properties of infrastructure: a quantile regression approach

PurposeThis paper aims to examine the hedge, diversifier and safe haven properties of the global listed infrastructure sector and subsector indices against two traditional asset classes, stocks and bonds, and four alternative asset classes, including commodities, real estate, private equity and hedge funds during extreme negative stock market movements.Design/methodology/approachUsing dynamic conditional correlation and quantile regression, the authors analyze a data set of 12 indices comprising listed infrastructure and traditional asset classes from 2010 to 2019.FindingsOverall, the findings indicate that listed infrastructure acts as an effective diversifier but not as a strong safe haven or hedge when considered in a multiasset context. With minor exceptions, listed infrastructure cannot be concluded as a safe haven against other asset classes under investigation.Practical implicationsThe present study has implications for institutional investors looking to incorporate infrastructure in their multiasset portfolios for increased portfolio diversification benefits.Originality/valueDespite the increased influence of infrastructure as an asset class, to the best of the authors’ knowledge, this is the first study to investigate the hedge, safe haven and diversifying properties of infrastructure in a multi-asset context.

Water and traditional asset classes

Data from the most representative water exchange-traded index funds (ETFs) at the worldwide level has been selected to analyze their role in the performance of portfolios that follow the main US and European stock, bond and volatility benchmarks. The empirical analysis has been performed using cross- correlation analysis and Quantile Regression methodology. Our results indicate that water assets can play the roles of diversifier, hedge and safe haven for equity, fixed income and volatility indices, respectively.

Measuring Sector Cyclicality: A Factor-Based Approach

Equity sectors are often ascribed static economic classifications that fail to consider their dynamic and time-varying fundamental characteristics across business cycles. Leveraging research findings on factor cyclicality, the authors propose a simple and practical methodology to categorize sectors as cyclical or defensive by estimating their sensitivity to a cyclical multifactor portfolio. Their results reveal that although some sectors have exhibited persistent cyclical or defensive features, most sectors have experienced noteworthy changes over time. Using a predictive business cycle regime framework, they document the effectiveness of this dynamic factor-based sector classification approach and provide examples of sector rotation strategies that have historically generated attractive excess returns, outperforming static alternatives. Their results are consistent with a factor investing paradigm, recognizing that factors are important drivers of portfolio risk and performance, and traditional asset classes can be seen as implementation vehicles of intended factor and macro exposures.

Integrating Private Equity in a Liquid Multi-Asset Portfolio

In this article, the authors define a comprehensive, rigorous and intuitive portfolio construction framework that accounts for the key aspects of private equity investing in multi-asset portfolios. These aspects are normally ignored in more conventional asset allocation approaches such as mean–variance, mainly because there are no readily available approaches that can handle the distinct assumptions of illiquid assets. In particular, the framework accounts for the illiquid feature of private equity, as well as for its cash flow features, and highlights how these fit together for constructing multi-asset portfolios with other traditional asset classes. Also, because private equity is a form of active investing, by building on previous research, the authors provide an approach that allows integrating active preferences into the asset allocation decision and accounting for active and passive risk–return trade-offs.

Analyzing diversification benefits of cryptocurrencies through backfill simulation

The cryptocurrency market provides an interesting diversification opportunity for asset allocation due to its fundamental differences compared to traditional asset classes. Even though the cryptocurrency market experienced a surge until 2017, the market has relatively stabilized since the bubble along with increased participation from institutional investors. Instead of only performing analysis for the recent years, which is short for testing asset allocation benefits, we focus on the post-bubble market condition but backfill cryptocurrency returns into the past in order to analyze a longer investment horizon. In our backfill simulation, investment in cryptocurrencies show higher return but mixed results in terms of portfolio efficiency for risk-based optimized portfolios. Even though risk-based models allocate a small portion in cryptocurrencies, its high volatility limits diversification benefits since even small allocations often lead to higher portfolio risk.