Asset Classes Research Articles

Optimizing Portfolio Allocation in the Indian Defense Sector: A Python-Based Approach

This paper presents a detailed analysis of portfolio construction strategies aimed at maximizing risk-adjusted returns for investors, utilizing Python-based methodologies. By leveraging historical data on stock returns and risks, a meticulous selection process identified 5 stocks with superior risk-return profiles, outperforming the dataset's average measures with higher returns and lower risks. The selected stocks from the defense industry formed the foundation of an optimal portfolio designed to minimize sector-specific risks while maximizing growth potential. Through rigorous portfolio optimization techniques, including Markowitz's mean-variance optimization, an optimal portfolio allocation was identified with a return of 62.40% and volatility of 27.74%. Insights from the study provide valuable perspectives for investors seeking realistic and sustainable wealth accumulation strategies. The research also highlights future avenues for exploration, such as integrating alternative asset classes, incorporating behavioral finance insights, and leveraging advancements in risk management technologies. This study contributes to advancing portfolio theory and offers practical guidance for investment decision-making.This study demonstrates the effectiveness of Python in financial analysis and portfolio optimization while furthering the theory of portfolios and providing valuable guidance for making investment decisions. Keywords: Returns, Risk, Volatility, Optimal Portfolio Construction, Defense Sector, Markowitz Portfolio Theory,

Cryptocurrency Adoption and Financial Innovation

The advent of cryptocurrencies and blockchain technology has sparked a revolutionary Shift in the financial sector. This study sets out on a wide-ranging investigation to understand the nuanced dynamics, repercussions, and potential future paths of this shifting environment in the UK and USA. The primary goals of the research are to examine how cryptocurrencies affect financial markets and conventional banking systems; to examine how blockchain technology might be used in the financial sector; to assess policy and regulatory considerations; and to predict and plan for the future. This research digs into how cryptocurrencies have revolutionized the banking and finance sectors. Analysis of adoption rates, market volatility, and integration methods sheds light on the changing position of cryptocurrencies in investment portfolios, reconfiguration of asset classes, and coping mechanisms of conventional financial institutions. When looking at the financial sector as a whole, the transformational potential of blockchain technology becomes clear. The advent of DeFi, smart contracts, and asset tokenization offers new prospects to improve financial transactions, increase transparency, and broaden participation in the investment market. The research analyzes cryptocurrencies and blockchain technology from a policy and regulatory perspective. The delicate balancing act between stimulating innovation and guaranteeing consumer protection, market integrity, and financial stability is highlighted by a comparison of the regulatory methods adopted in the United Kingdom and United States, as well as proposals from international organizations. The research identifies potential future paths for these technologies and their implications. Opportunities and challenges that will influence the future of finance emerge, with a focus on central bank digital currencies (CBDCs), sustainable blockchain solutions, and interdisciplinary collaborations. As this deep dive comes to a close, the transformational power of cryptocurrencies and blockchain technology is highlighted. It sheds light on the forces that are altering the structures of the world’s financial markets, conventional banking structures, and regulatory frameworks. The findings and critical assessment stress the need for well-considered choices, ethical innovation, and interdisciplinary cooperation in order to succeed in an ever-changing environment. To further democratize access, improve transparency, and reshape the economic fabric of our planet, the future of finance resides at the confluence of tradition and innovation, where cryptocurrencies and blockchain technology exist. Cryptocurrency adoption has catalyzed changes in consumer behavior and investment patterns. While some view digital currencies as speculative assets, others embrace them as alternative forms of money and store of value. This diversity of perspectives underscores the need for a nuanced understanding of cryptocurrency adoption and its implications for financial systems. The significance of this study lies in its contribution to our understanding of the evolving relationship between cryptocurrency adoption and financial innovation. By elucidating the drivers, challenges, and implications of this phenomenon, policymakers, industry stakeholders, and researchers can make informed decisions to harness the transformative potential of digital currencies while mitigating associated risks. Cryptocurrency adoption and the financial innovations it has spurred are transforming the way we think about money and financial services. While challenges remain, the potential benefits of a more decentralized, efficient, and inclusive financial system are significant. As the technology matures, regulatory frameworks adapt, and user confidence grows, cryptocurrency adoption is likely tocontinue its upward trajectory. Cryptocurrency adoption and its associated financial innovations have the potential to usher in a paradigm shift in the way we manage and exchange value.

Green Shocks: The Spillover Effects of Green Equity Indices on Global Market Dynamics

This study investigates the impact of green equity indices on global market dynamics using a time-varying parameter vector autoregression (TVP-VAR) model. We uncover a significant shift in the role of the global market, transitioning from a shock transmitter to a shock receiver, as the influence of green finance grows. By directly comparing green equity indices with their corresponding global parent indices, we adopt a global perspective that transcends the limitations of studies focusing on specific regions, such as the USA, China, or Europe. This novel approach minimizes the potential biases in the transmission channels within regional markets, enabling a more comprehensive understanding of the relationship between green finance and global market dynamics. Moreover, by focusing on equity indices we ensure a consistent comparison of financial instruments, avoiding the complexities that arise when comparing different asset classes such as green bonds and conventional equities. For global investors, our results highlight the importance of dynamic and flexible hedging strategies that adapt to the distinct characteristics of green assets and their growing influence on the global market. Risk managers should incorporate these time-varying spillover effects into their models to better assess and mitigate potential risks. Policymakers should consider the growing influence of green finance on the broader market when formulating regulations and incentives to support sustainable investing, as our findings underscore the increasing importance of this sector in shaping market dynamics.

Forecasting and Analysis of Cryptocurrency Trends

The development of financial technology has given rise to a new class of asset known as cryptocurrency, which has presented a significant potential for study. Forecasting cryptocurrency prices is challenging because of their dynamic and volatile nature. There are hundreds of different cryptocurrencies in circulation today. With prospects in peer-to-peer payments, remittance, e-commerce and retail, and media & entertainment, the cryptocurrency business appears to have a bright future. From 2019 to2024, the worldwide cryptocurrency industry is anticipated to expand at a 32% CAGR. The distributed ledger technology's immutability and transparency, the rise in remittances from poor nations, shifting monetary laws, and a sharp rise in venture capital investments are the main factors propelling this market's growth. We created a short-term prediction (between five and thirty days) for the cryptocurrencies using the ARIMA model. When it came to predicting cryptocurrency time series, we discovered that the suggested method outperformed the ARIMA-ARFIMA models in both periods of gradual rising (falling) and transition dynamics (change of trend). Index Terms: digital currency,cryptocurrency, and ARIMA model.

Detour-RS: Reroute Attack Vulnerability Assessment with Awareness of the Layout and Resource

Recent decades have witnessed a remarkable pace of innovation and performance improvements in integrated circuits (ICs), which have become indispensable in an array of critical applications ranging from military infrastructure to personal healthcare. Meanwhile, recent developments have brought physical security to the forefront of concern, particularly considering the valuable assets handled and stored within ICs. Among the various invasive attack vectors, micro-probing attacks have risen as a particularly menacing threat. These attacks leverage advanced focused ion beam (FIB) systems to enable post-silicon secret eavesdropping and circuit modifications with minimal traceability. As an evolved variant of micro-probing attacks, reroute attacks possess the ability to actively disable built-in shielding measures, granting access to the security-sensitive signals concealed beneath. To address and counter these emerging challenges, we introduce a layout-level framework known as Detour-RS. This framework is designed to automatically assess potential vulnerabilities, offering a systematic approach to identifying and mitigating exploitable weaknesses. Specifically, we employed a combination of linear and nonlinear programming-based approaches to identify the layout-aware attack costs in reroute attempts given specific target assets. The experimental results indicate that shielded designs outperform non-shielded structures against reroute attacks. Furthermore, among the two-layer shield configurations, the orthogonal layout exhibits better performance compared to the parallel arrangement. Furthermore, we explore both independent and dependent scenarios, where the latter accounts for potential interference among circuit edit locations. Notably, our results demonstrate a substantial near 50% increase in attack cost when employing the more realistic dependent estimation approach. In addition, we also propose time and gas consumption metrics to evaluate the resource consumption of the attackers, which provides a perspective for evaluating reroute attack efforts. We have collected the results for different categories of target assets and also the average resource consumption for each via, required during FIB reroute attack.

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.

Development of Company-Specific Emission Factors with Confidence Intervals for Natural Gas Customer Meters in Southern California.

Methane is both a significant and short-lived greenhouse gas compared to CO2, and reducing methane emissions from natural gas distribution systems may offer cost-effective reduction opportunities. We report substantial new direct leak rate measurements from customer meter set assemblies (MSAs) in Southern California. In a novel way, emission factors are defined in terms of aboveground Hazardous and Nonhazardous leak categories, which take direct advantage of readily available industry leak data. We also studied leaks that were not detected as part of normal leak survey procedures. As a result, this yields company-specific emission factors that can be used to track progress in reducing methane emissions. This approach also has the advantage of explicitly accounting for the skewed or fat-tail distribution of leak rates by treating high flow rate MSA leaks separately from low flow rate MSA leaks. The Southern California Gas (SoCalGas) methane emission factors, based on 485 leak rate measurements by direct enclosure, were 4.55 (95% confidence interval: 2.32 to 7.14) kg/day for Hazardous leaks, 0.149 (0.119 to 0.183) kg/day for Nonhazardous leaks, and 0.0039 (0.0003 to 0.0198) kg/day for Non-Detected leaks. The percentage of surveyed meters with nondetected leaks was 29.1% (24.3 to 34.6%). Based on a robust Monte Carlo analysis, total leak emissions from MSAs for the SoCalGas system were reduced by 35% based on data from 2015 to 2022. These reductions were attributed to surveying a larger number of MSAs and accelerated leak repair rates. In traditional population-based emission inventories, an individual emission factor for a given asset category is multiplied by the total population of MSAs within the category. This approach simply cannot capture the reduction in leak numbers and methane emissions resulting from leak mitigation and prevention programs.

Formation of the model for the commercialization of the company's digital intellectual assets

Purpose: of the study is algorithmic modeling of the commercialization of digital intellectual assets (CIAS), taking into account pricing factors, risk and the level of commercial potential, based on the identifi cation of CIAS and determination of their specifics. Methods: general scientific methods of comparative analysis, systematization and generalization, identifi cation of distinctive features and price-forming factors that take into account the specifics of the objects of research are used. To form a model of the commercialization of digital intellectual assets, the algorithmic approach, the method of expert assessments in calculating the indicator of commercial potential, approaches and methods of valuation in determining the initial value of assets were used.Results: the author formulated the defi nition of a digital intellectual asset (DIA), establishes its relationship with related categories of assets, identifi es two categories of DIA indicating common and distinctive properties. Based on the results of the identifi cation of DIA, the factors aff ecting the value and commercial potential of various types of DIA are determined. The method for calculating the indicator of the commercial potential, taking into account the factors considered, is proposed. The tools of the commercialization, as well as approaches and methods of estimating the initial cost for various categories of DIA are disclosed. The algorithmic model of the DIA commercialization is constructed. Conclusions and Relevance: the algorithmic model of commercialization of new types of assets, digital intellectual assets, proposed in the article takes into account their specifi cs and factors aff ecting the initial cost and commercial potential. The model can be used by Russian companies to increase their competitiveness in the context of the development of the digital rights and technologies market. Further research should be devoted to the specifi cation of the commercialization algorithm for certain categories of DIA: 1) intellectual property objects in digital form; 2) digital (digital utilitarian) rights to such objects and their use, implemented exclusively according to the rules of the information system (investment platform).

Evolution of Intangible Property to Crypto Assets: Legal Pragmatism in Anglo-American Common Law and Chinese Civil Law

Abstract The emergence of crypto assets, a novel category of assets, has posed significant challenges to property law across various legal systems. The integration of these intangible assets into property law frameworks proves to be complicated for many civil law jurisdictions, which tend to adhere to dogmatic approaches. In contrast, Anglo-American common law and Chinese civil law systems, despite their divergent origins, have exhibited legally pragmatic responses to intangible property, including crypto assets. By examining the development of their property laws concerning property conceptualization and the treatment of emerging intangible assets, this article explores how the historical foundations of Anglo-American common law in feudal tradition and Chinese civil law in communist ideologies have fostered adaptable and flexible notions of property. This adaptability is argued to be a strength within both the Anglo-American common law and Chinese civil law systems, enabling them to accommodate innovative intangible assets more easily compared to their civilian counterparts.

Time-varying causality among whisky, wine, and equity markets

Whisky has entered the category of alternative investment assets, with particularly attractive returns on certain bottles. The literature has shown the diversification benefits of investing in collectible assets, such as fine wine, but analyses dedicated to whisky investment remain scarce. Using different causality tests – parametric, non-parametric, and time-varying – applied to whisky, wine, and the MSCI World indices, we analyze the connection/disconnection between rare whisky, fine wine, and financial markets over a 13-Year period, from January 2010 to January 2023, in weekly frequency. Our results show that whisky and wine respectively provide attractive diversification opportunities when combined with stocks, except during “hectic” periods. Conversely, the combination of whisky and wine appears to be riskier in terms of portfolio diversification, especially since the COVID-19 pandemic.

The dynamic connectedness between collateralized loan obligations and major asset classes: a TVP-VAR approach and portfolio hedging strategies for investors

Motivated by the increasing demand for alternative assets that can contribute to reducing portfolio risk, this paper examines the volatility spillovers between collateralized loan obligations (CLOs) and various in-demand investment instruments, including equities, bonds, crude oil, commodities, gold, bitcoin, shipping and real estate. The applied methodology comprehends the time-varying parameter vector autoregressive (TVP-VAR) modification of the classical spillover approach, for the period from January 1, 2012, to August 31, 2023. The empirical findings show moderate levels of dynamic connectedness; albeit several external shocks strengthened the interconnection among the assets. Moreover, we compare the ability of CLOs for hedging, during the overall sample period and multiple subperiods, by estimating hedge ratios and optimal portfolio weights, in order to inform investors about feasible portfolio adjustments. Our results indicate that CLOs constitute an effective hedging tool, irrespective of the period covered, as the short position in their volatility provides high hedging effectiveness for investors holding long positions in the volatility of all the remaining assets.

Tokenized Assets on Public Blockchains: How Transparent is the Blockchain?

With the proliferation of programmable blockchains with smart contract capabilities, new blockchain technology use cases have emerged that involve the tokenization of conventional financial assets and related smart contract-based financial services. While early blockchains like Bitcoin introduced native cryptocurrencies as new asset classes, in recent years, market participants have noted the potential for blockchain and distributed ledger technologies (DLT) to be used to trade tokenized versions of bonds, money funds, and commodities, among other assets (GFMA, 2023, p. 6).

Fiscal Year-Ends and Financial Benefits: The Role of Prospective Measurement Dates in Tax Reform

ABSTRACT The Tax Cuts and Jobs Act (TCJA) of 2017 dramatically reshaped the tax landscape for companies and reduced incentives for multinationals to leave foreign earnings abroad. We study a provision of the law that allowed some multinationals to take simple measures to reduce their tax burden more than other firms. The TCJA levied a tax on accumulated income held in foreign subsidiaries of U.S. firms. The implementation of this tax left firms an avenue to protect shareholder wealth, as foreign income retained as “cash” was taxed at a higher rate (15.5 percent) than foreign income retained in other asset classes (8 percent). Measurement rules allowed some firms significantly more time to shift foreign cash holdings to “noncash” assets and lower their tax burden. Using staggered, firm-specific measurement dates, we find that fiscal year-end firms with more time to respond to the law reduced their cash levels more than other firms. JEL Classifications: E62; H21; H25.

Economics and accounting intangible assets

This article will outline the essence of intangible assets owned by economic agents in economic and accounting aspects. The concept and classification of intangible assets are presented. The main accounts that represent these assets in accounting are presented. The indicators used to evaluate and account for intangible assets are also disclosed, and a methodology for their calculation and accounting is provided. And accordingly, a theory is given for recording business transactions related directly to intangible assets.

A robust approach for inference on style analysis coefficients

Style analysis is an asset class factor model aiming at obtaining information on the internal allocation of a financial portfolio and at comparing portfolios with similar investment strategies. The aim of the paper is to investigate the use of quantile regression to draw inferences on style coefficients. In particular, we compare an approximation widely used from practitioners, the Lobosco–Di Bartolomeo solution, with robust estimators based on constrained median regression. The inference process exploits a rolling window procedure based on subsampling theory. Different sets of outliers have been simulated in order to show differences in the efficiency, in the coverage error and in the length of the resulting confidence intervals. The proposed solution shows better performance in presence of outliers in Y, in X, or in X and Y, in terms both of empirical coverage and of interval lengths, i.e. whereas the performance of the classical solution deteriorates.

Machine learning and the cross-section of cryptocurrency returns

We employ a repertoire of machine learning models to investigate the cross-sectional return predictability in cryptocurrency markets. While all methods generate substantial economic gains—unlike in other asset classes—the benefits from model complexity are limited. Return predictability derives mainly from a handful of simple characteristics, such as market price, past alpha, illiquidity, and momentum. Contrary to the stock market, abnormal returns in cryptocurrencies originate from the long leg of the trade and persist over time. Furthermore, despite high portfolio turnover, most machine learning strategies remain profitable after trading costs. However, alphas are concentrated in hard-to-trade assets and critically depend on harvesting extreme returns on small, illiquid, and volatile coins.

Learning deep news sentiment representations for macro-finance

This paper introduces custom neural network techniques to the problem of latent economic factor extraction for voluminous news analytics data. In the context of macro-financial news, we derive low-dimensional representations of time series that arise in textual sentiment analyses spanning various topics. We explore three applications for compressed news sentiment data: nowcasting GDP growth, explaining asset class returns in a panel data analysis, and time series momentum investment. Our empirical study shows that nonlinear data representations based on supervised autoencoder architectures compare favorably to alternatives across all applications. In specific, we demonstrate that augmenting autoencoders with supervision tasks based on common asset class returns and market characteristics disciplines the dimension reduction and naturally supports the transparency of resulting representations. Taken together, our findings position supervised autoencoders as attractive competitor models alongside PCA and PLS approaches.

Relationship between the Rainfall Index and Stock/Agri-commodity Market Indices: Empirical Evidence from India

The rainfall, stock, and agri-commodity indices are the economic indicators for Indian economy. Therefore, research is needed to explore the complex relationship between rainfall and economic indicators and its longterm implications. Only a few studies are available on this relationship, and no study gives complete, conclusive evidence on this relationship. Therefore, the present study focused on determining whether there is any relationship between the rainfall index and stock/agri-commodity indices. This study employed the advanced statistical tools like ADF test, correlation, Granger causality test, and GARCH (1,1) model. The research utilized the monthly average rainfall index and the Sensex index from 1991 to 2020. Similarly, the study employed the monthly average Guarex agri-commodity index for the period from 2018 to 2020. The study found that there is no relationship and causality relationship between the rainfall index and the Sensex index. The GARCH (1,1) model shows that the rainfall index has no impact on the volatility of the Sensex index. There is a moderately negative correlation between the rainfall index and the Guarex index, and rainfall affects the volatility of the Guarex index. This shows that the rainfall index can be a distinct asset class that can be traded through rainfall derivatives. In order to launch rainfall derivatives in India, NCDEX and Skeymet Industries have already approached the SEBI for permission.

Valuation, Accounting Principles, and Classification of Assets in the Metaverse

This study explores the valuation methods, accounting principles, and asset classification systems needed for accurate financial reporting in the Metaverse. The unique characteristics of virtual assets in the Metaverse pose challenges for traditional valuation methods and require the development of comprehensive and adaptable approaches. This study utilizes qualitative research methods, including in-depth interviews with accounting professionals in Bath and Bristol, UK. Fourteen accountants were selected based on their expertise in accounting and their understanding of the Metaverse. The findings reveal that effective governance, regulatory mechanisms, and community-driven protocols influence asset values in the Metaverse. It is shaped by factors such as engagement, scarcity, and competition. Tailored accounting principles should address legal recognition, accurate tracking, jurisdictional restrictions, and risk management. Compliance with regulations, transparent reporting, and collaboration with regulatory authorities are crucial, alongside integrating innovative technologies like blockchain for robust accounting practices.

Problems and Prospects of Unification of the Concepts of “Tokens”, “Digital Assets” and “Digital Rights”

Introduction: the accelerated digital transformation of the economy leads to a lack of unity in understanding the essence of new digital objects and a lag in the legal regulation from the pace of digitalization. As a result, when characterizing new digital objects, there arise terminological inaccuracies, discussions regarding the expediency and correctness of legislative innovations, and the gaps in the legal regulation of digital objects of legal relations. As a result, the purpose of the current study is to define the essence and delineate the concepts of “token”, “digital assets” and “digital rights”. To achieve this goal, the following tasks are defined: to study the definitions of the above-mentioned concepts available in the legal doctrine, their role in the civil legislation, to develop a systematic understanding of the digital objects selected for research. Methods: the methodological framework for the study is a set of methods: the terminological analysis of individual digital objects – tokens, digital assets and digital rights; the generalization of the studied materials by induction to determine the subordination of the described digital objects; the generalization and comparison of doctrinal concepts on the relationship between the concepts of “digital assets” and “digital rights”. Based on the conducted research, the following results are achieved: the doctrinal differences are revealed in the understanding of the term “token”, in the correctness of the identification of “digital rights” as an object of civil rights, in the correlation of the concepts of “token”, “digital asset” and “digital rights”; the classification of digital assets is of scientific interest in the framework of further research. According to the results of the scientific research, the author has made a number of conclusions, in particular: 1) the lack of unity in the definitions of new digital terms generates discussions regarding the current norms of the Russian legislation and hinders the introduction of promising digital structures into civil circulation; 2) in order to unify digital terminology, the following changes to the Russian legislation are proposed: digital assets should be distinguished as a separate category of objects of civil rights; digital rights should be defined as a kind of digital asset expressed in monetary form; a token should be defined as a means of balance sheet accounting of a digital asset and the authentication of users of an information system.