Asset Trading Research Articles

Regulating Carbon Assets Insider Trading: A US-EU Comparison to Protect Carbon Market Integrity

Abstract Given that policy information significantly affects the supply and demand of carbon assets, that the major factor impacting carbon asset market price is the size of supply and demand of the assets, and that, unlike commodities, outsiders are not allowed to access policy information, carbon asset insider trading can be implemented majorly as a form of corruption between authorities and entities. In this context, this paper discusses the regulatory vacuum of the US legal system relevant to carbon asset trading. The basis of the argument is in the comparison of legal system between the EU and the US.

Discounting health gain: a different view

ABSTRACT At least since the Age of Enlightenment, good health has been a tenet for society. Healthy societies could learn better, work harder, improve their wealth, and live longer. Today societies focus on life expectancy, as we value long and healthy lives. As illustrated by the provision of COVID-19 vaccines first for the elderly, societies value life-saving actions. Paradoxically, health economic assessments conventionally devalue long-lasting health through the practice of discounting health benefits along with costs. However, health, with its intrinsic and instrumental characteristics, is not synonymous with money cash, a tradeable asset that devalues with time. If improving healthy life expectancy is a societal ambition, it seems counter-intuitive to value future health less as a result of an artificial mathematical construct when evaluating economically new medical interventions. In this paper, we investigate the application of discounting health in healthcare and consider paradoxical findings, especially in relation to disease prevention with vaccination. We argue that there is no economically sustainable argument to discount health gains, except for the benefit of the payer with a goal of spending less on life-saving products. If that is the objective for discounting health, there are other means to achieve the same goal in a more transparent and simpler way. From the long-term perspective of healthcare development, not discounting health gains would encourage research that values long-term effects. This in turn has the potential to benefit the investor, the payer, and the patient/consumer, improving the situation from multiple perspectives.

Economic integration and consumption risk sharing: A comparison of Eurozone and OECD countries

Economic theory predicts that economic integration can facilitate improvements in international consumption risk sharing. We test this proposition by comparing risk sharing among Eurozone members and members of the Organisation for Economic Co-operation and Development (OECD) using a dynamic common correlated effects mean group estimator (DCCEMG). The results reveal that the OECD members outperform the Eurozone members in terms of short run and long run risk sharing, despite the barriers to risk sharing being considerably lower among the latter. We attribute this outcome to lower business cycle synchronization and stock return correlations among OECD members, which creates better opportunities to share risk within the group. The results highlight the trade-off between the benefits of business cycle synchronization and international risk sharing. More tightly integrated economies, facilitate trade in assets that promotes better portfolio diversification, while at the same time are characterized by higher business cycle synchronization that depletes agents of risk sharing opportunities. Consequently, our results give strong support to the hypothesis of “diminishing international diversification potential”. However, the benefits associated with the improved risk sharing are increasing with the number of countries in the monetary union.

Rights of investors in the context of algorithmic Artificial Intelligence technologies and automatization

This study explores the profound impact of digital technologies on economic processes, particularly in the realm of investments. Financial organizations are leveraging digital platforms and algorithmic artificial intelligence (AI) to provide enhanced services to consumers. AI-powered algorithms surpass humans in processing vast amounts of information, leading to improved price discovery and reduced transaction costs. Robo-advisors, empowered by AI, have become pivotal players in financial markets, offering personalized investment portfolios based on risk profiles and objectives. While algorithmic AI technologies eliminate human errors and streamline decision-making, they also introduce new risks. Flaws in algorithm construction, intentional errors, cybersecurity vulnerabilities, and the regulatory challenges of supervising self-learning algorithms are concerns. Additionally, the rise of blockchain platforms has transformed cryptocurrencies and digital tokens into new tradable assets with their own protection challenges. Researchers emphasize that the wealth of data and correlations facilitated by AI can have unintended consequences, and the development of proper supervision and control mechanisms is vital. As the financial landscape continues to evolve, it becomes crucial for investors and regulators to navigate the complexities of digital technologies and address the emerging Global Technology Risk (GTR).

Risk of digital assets: Developments in regulation and implementation

The normative narrative about ‘crypto’ or ‘digital assets' as potential substitutes for the traditional financial systems has the downside that such a new system could present new types of risk, thus requiring dedicated risk management. But is this narrative justified by reality? Actual implementations with a trend to centralisation, generic features of blockchain-based systems to be exploited by new types of intermediaries and recent events (including the collapse of the Terra ecosystem and the bankruptcy of FTX), triggered feedback from central banks and banking supervisors. Recently, the current development of regulations was toughened with a proposal that ‘trading in unbacked digital assets should be treated by regulators like gambling’. This paper avoids any normative discussion of how crypto or digital assets should look theoretically, but focuses on the actual developments. An analysis of the whole stack of layers of blockchain platforms, from influencer marketing to features such as ‘maximum extractable value’, can be condensed to the litmus test of ‘`Cui bono`?’ (‘to whom is it a benefit?’). This perspective reveals that blockchain-based systems are determined by the objectives and intentionality of the (new) intermediaries and are typically a mixture of gaming, gambling and scam. Consequently, the revised high-level recommendations of the Financial Stability Board for ‘global stablecoin’ arrangements, the European MiCA regulation and the amended capital requirements to the Basel framework are benchmarks for the treatment of crypto and digital assets. These are based on the economic objectives: from E-money-like instruments on the balance sheet of (new) intermediaries via traditional securities (equity or credit) to native digital assets, which resemble gambling according to ‘same business, same risk, same regulation’.

Stock Market Volatility Due to Cross-Listing of Tradable Assets

Stock Market Volatility Due to Cross-Listing of Tradable Assets

Research on Investment Strategy Based on Catboost Model and Bollinger Band Channel

As the economy continues to grow, more and more people tend to increase their income through asset investment. This paper devised a trading strategy and evaluated the value of an investment of $1,000 after five years. The Catboost regression model was built to predict the price of gold and bitcoin with an accuracy rate of up to 99%. It showed that the model can effectively make predictions about gold and bitcoin prices. Trading signals for gold and bitcoin were established based on the Bollinger Bands channel theory. Simulated trading of assets began on September 11, 2016, and the total assets after five years were $4,430,859.83. Then this paper conducted sensitivity analysis on the established trading model. Finally, the parameters of the established trading model are optimized to determine an optimal model parameter.

Upaya Perlindungan Hukum Bagi Para Konsumen Pemegang Aset Kripto di Indonesia

This research focuses on analyzing the regulations governing the legal aspects related to crypto assets and their protection. Within this analysis, attention is directed toward the three fundamental pillars of Indonesian law, namely justice, certainty, and expediency. Additionally, this study identifies two pertinent issues. The first pertains to legal regulations concerning the legitimacy of crypto assets. The second involves the form of legal protection provided to holders of crypto assets in general, as well as to those who possess NFT and SFT-based crypto assets specifically. The findings of this study outline several strategies aimed at enhancing regulations governing the trade, use, and circulation of crypto assets in Indonesia. These strategies encompass protective measures that guarantee the quality of block chain technology system performance, ensure value preservation in crypto asset trading, facilitate dispute resolution in crypto asset trading, and endeavor to safeguard Intellectual Property Rights (IPR) in crypto asset trading.

Portfolio allocation and borrowing constraints

Using the US Survey of Consumer Finances, we explore the empirical relationship between borrowing constraints and financial portfolio allocation by American households. To help motivate our empirical analysis we initially draw insights from a mean-variance model of optimal portfolio allocation with three tradable asset classes defined by increasing risk, and establish a link between borrowing restrictions and portfolio allocation in the presence of background risk. Our main contribution, however, lies in estimating the role that borrowing constraints play in shaping households' financial portfolio allocation. This is achieved using an ordered fractional probit model. In addition to revealing the significant empirical role played by household borrowing constraints in determining portfolio allocation, our analysis helps us to resolve ambiguity surrounding the behaviour of the medium-risk asset in our motivational theoretical framework. Further, the empirical distribution of medium-risk assets is found to be remarkably similar to that for high-risk assets, suggesting the presence of a more general ‘risk puzzle’, which our borrowing constraints measures partially explain.

A new hybrid method of recurrent reinforcement learning and BiLSTM for algorithmic trading

Recently, the algorithmic trading of financial assets is rapidly developing with the rise of deep learning. In particular, deep reinforcement learning, as a combination of deep learning and reinforcement learning, stands out among many approaches in the field of decision-making because of its high performance, strong generalization, and high fitting ability. In this paper, we attempt to propose a hybrid method of recurrent reinforcement learning (RRL) and deep learning to figure out the algorithmic trading problem of determining the optimal trading position in the daily trading activities of the stock market. We adopt deep neural network (DNN), long short-term memory neural network (LSTM), and bidirectional long short-term memory neural network (BiLSTM) to automatically extract higher-level abstract feature information from sequential trading data, respectively, and then generate optimal trading strategies by interacting with the environment in a reinforcement learning framework. In particular, the BiLSTM consisting of two LSTM models with opposite directions is able to make full use of the information from both directions in attempting to capture more effective information. In experiments, the daily data of Dow Jones, S&P500, and NASDAQ (from Jan-01, 2005 to Dec-31, 2020) are applied to verify the performance of the newly proposed DNN-RL, LSTM-RL, and BiLSTM-RL trading systems. Experimental results show that the proposed methods significantly outperform the benchmark methods, such as RRL and Buy and Hold, with higher scalability and better robustness. Especially, BiLSTM-RL performs better than other methods.

Stop-loss adjusted labels for machine learning-based trading of risky assets

Since the rise of ML/AI, many researchers and practitioners have been trying to predict future stock price movements. In actual implementations, however, stop-loss is widely adopted to manage risks, which sells an asset if its price goes below a predetermined level. Hence, some buy signals from prediction models could be wasted if stop-loss is triggered. In this study, we propose a stop-loss adjusted labeling scheme to reduce the discrepancy between prediction and decision making. It can be easily incorporated to any ML/AI prediction models. Experimental results on U.S. futures and cryptocurrencies show that this simple tweak significantly reduces risk.

Equity market connectedness across regimes of geopolitical risks: Historical evidence and theory

We use a threshold VAR model to capture the connectedness of the equity returns of the G7 in a regime-contingent manner as defined by low- and high-geopolitical risks (GPR). We find that connectedness is statistically stronger when GPR is at its higher rather than lower regime, but more importantly, this observation can be associated with the threat of adverse geopolitical events, rather than with their actual realization. To explain our empirical observations we employ a model of international trade in assets and international relative asset prices. We introduce uncertainty in future dividend payments combined with agents' ambiguity aversion to changes in the expected dividends. This allows us to model a geopolitical threat as a shock that affects the level of ambiguity about future dividends. At the same time, a geopolitical act is defined as a shock to the current period endowment of a given country, with limited effects on asset prices and returns. Our obtained results have important portfolio allocation implications for investors.

Perspektif Penetapan Pajak Penambahan Nilai Pada Investasi Aset Kripto Di Era Digital

In this pandemic era, investment in crypto assets in Indonesia is increasingly attractive as one of the current breakthroughs in digital transactions. Even though crypto prices are very extreme and have a high risk, they are still more attractive to investors to invest in crypto. To be able to anticipate risks in crypto asset trading in Indonesia, the Government has established a regulatory policy on Value Income Tax on crypto asset investments in 2023, in order to reduce the risks of various digital transactions. The research objectives that become a legal gap in this study are: are there government regulations that can reduce the level of risk in digital transactions in crypto asset investments for business actors in Indonesia, and how can the determination of value added tax by the government be able to provide legal certainty for people who use crypto transactions in Indonesia. The research method used in this paper is normative legal research. This research is directed to be able to provide a description of the implications of legal certainty for value income tax in investing in crypto assets through digital transactions, as a policy to prevent risks for business actors. The results of the study show that the optimal regulation of Value Added Tax (VAT) on Crypto Asset Trading is regulated in the Regulation of the Minister of Finance of the Republic of Indonesia Number 68/PMK.03/2022, can reduce the risks that occur, if there are legal issues in the current crypto asset trading. As well as the implications of any profits derived from bitcoin trading activities being reported to the Directorate General of Taxes. Based on Law Number 42 of 2009 concerning the Third Amendment to Law Number 8 of 1983 concerning Value Added Tax on Goods and Services and Sales Tax on Luxury Goods, it includes a Value Added Tax (VAT) of 10%. And the application of legal certainty in the new regulations in the Regulation of the Minister of Finance of the Republic of Indonesia Number 68/PMK.03/2022.

Digital marketing as digital methods: Repurposing Google Ads for controversy mapping

Digital advertising is central to the business models and technological affordances of digital platforms, creating marketplaces for user attention. Despite the keen theoretical attention to the commodification of audiences in critical new media and data studies, there has been little exploration of how digital marketing can also be understood as a methodological lens to study both controversies and the practices involved in the commodification of attention around the issues that animate such controversies. Addressing this gap in knowledge by drawing on the digital methods approach, this article proposes repurposing the tools, data and practices of digital marketing – what we coin digital marketing epistemology – as new methodological points of observation in controversy mapping. Focusing empirically on Google Ads, we scrutinize the building blocks of advertising campaigns on the platform and their metrics and measurements. We then offer a set of methodological ‘recipes’ for repurposing the Google Ads platform for controversy mapping and illustrate through a series of data sprints how this can contribute not only to a better understanding of the measurement and valuation practices that are involved in converting attention into a tradable asset but also the role these practices play in the unfolding of techno-social controversies.

Analisis Implementasi Zakat Perdagangan dalam Perspektif Ekonomi Islam

This study aims to determine the implementation of trade zakat in the perspective of Islamic economics at the Thrift shop Gembong Surabaya. This type of research is field research with a qualitative descriptive method. Taking purposive sampling. With the data collection method of interviews, observation and documentation. The results of the study show that the implementation of trade zakat at the Gembong Thrift shop in Surabaya is generally carried out, but in practice it is not in accordance with Islamic law. Because the traders there do not know the procedures for implementing trade zakat. Starting from the obligatory conditions of zakat, its distribution, nisab, haul, and the levels of zakat on trade assets. Reviewed based on Islamic economics in general is not in accordance with the rules of Islamic law. This is because traders in carrying out their trade zakat do not use the laws that have been established for trade zakat. There are even those who equate the concept of this trade with alms. However, among them there are those who carry it out correctly and in accordance with the rules of Islamic.

The Dynamic Relationship between the NFT Index and Macroeconomic Factors in Korea

Purpose – This study attempts to analyze the dynamic relationship between the NFT index and macroeconomic factors, and make effective suggestions using research methods in the field of management finance. Design/Methodology/Approach – The study sample was from January 2020 to March 2023. The variables used in the analysis were based on monthly time series data, measured on the basis of the Metaverse–NFT Index (MVNX) index released by Wavebridge Limited in Korea to reflect the growth trend of the NFT market, money supply, US dollar–won exchange rate, gross domestic product, consumer price index, and other macroeconomic indicators. The analytical tools in this study included a unit root test based on the VAR model, the Johansen cointegration Test, Granger causality test, impulse response analysis, and variance decomposition. These methods are very effective in analyzing time series data. Findings – The results of the analysis show that macroeconomic factors, such as money supply, exchange rate, GDP, and consumer price index are important sources of information in predicting the NFT index. Research Implications – The results of this study are expected to provide practical information for NFT users, virtual asset trading platforms, investors, and policymakers related to NFT ecology. Furthermore, this study’s findings are of great significance, being the first global study to explore the relationship between NFTs and macroeconomic factors.

Praktek Jual Beli Crypto Asset di Futures Market Perspektif Hukum Syariah

With his journey initially rejected, crypto assets began to be accepted internationally. In these few years to this day, it shows the process of changing international views on crypto assets. Although crypto assets are not considered as legal tender, Indonesia categorizes crypto assets as assets or commodities. This makes crypto assets under the regulation of the Commodity Futures Trading Supervisory Agency (BAPPEBTI) which is confirmed in Article 1 of the Regulation of the Minister of Trade of the Republic of Indonesia Number 99 of 2018 concerning General Policy for the Implementation of Crypto Assets Futures Trading (Crypto Assets), designated as commodities that can be subject to futures contracts. traded on futures exchanges. The purpose of this study is to examine and analyze the practice of buying and selling crypto assets on the Futures Market in terms of Sharia law. This research when viewed from the source of the data is normative legal research, namely research by examining library materials which are secondary data and also referred to as library research. The results of this study produce practices that occur in crypto asset trading contracts in the futures market, which are basically ribâl fadli practices. In addition, the legal prohibition of trading crypto assets in the futures market is due to the element of maisir. 'This illat appears as the option that small traders have is only one, which is to only sell at maturity. As a result, it automatically cancels the contract so that only the new price is left for the "down payment" and not the "crypto commodity".
 Keywords: Crypto Asset, Futures Market, Sharia Law

When a Series’ Main Protagonist Gets Stolen and Sold – A Study of NFTs and Their Approaches to Copyright Licensing

Abstract Over the past two years non-fungible tokens (NFTs) have become a highly popular, though extremely volatile trade asset. As mere data sets on the blockchain, NFTs are challenging the law to define the legal positions they entail. This article examines NFTs from the angle of copyright licensing, looking at it from a European perspective with the main focus on German law. Based on the technical specifics of NFTs, the paper evaluates the relevant legal position required for a permitted use of tokenized copyrighted content and how such a position may be obtained in the context of NFT trading. A review of the copyright schemes of 21 of the most sought-after NFT collections sets out the current state of licensing and identifies the prevailing legal uncertainties. The fact that these are not merely theoretical is then shown by the turbulent case of a ‘stolen’ ape avatar, tokenized by an NFT, that was set to star in a live-action animated series.

Portfolio liquidation with delayed information

This study investigates the liquidation of a portfolio when there is delayed information such as prices and trading signals. Its motivation stems from the calendar-time effect of information or the momentum effect on market prediction. When the stock price series is modeled by high-order vector autoregressive models, it converges to a system of stochastic delay differential equations in a continuous-time economy. The optimal solution indicates that the agent should trade gradually toward a path-dependent target portfolio to minimize execution costs. We numerically show a surprising novel pattern in which the agent with a liquidation mission sometimes purchases assets first to take advantage of the price predictability stemming from the delay effect. Moreover, optimally utilizing the delay effect has impacts on the time synchronization of trading in different assets and leads to significant cost reduction when the initial market trend is informative.

Information and optimal trading strategies with dark pools

This paper examines the effects of the competition between asset trading venues with different levels of transparency: an opaque dark pool alongside a transparent exchange organized as a limit order book (two-venue market). In a model with asymmetric information, we compare traders’ strategies and market performance in the two-venue market with that of a single-venue market (trading only in the exchange). We show that price informativeness is lower in the two-venue market when informed traders migrate to the dark pool and uninformed investors remain in the exchange. We also find that when orders migrate to the dark pool in the first period, market liquidity is lower (higher) in the two-venue market for high (low) fundamental volatility stocks as traders migrating to the dark pool would have demanded (supplied) liquidity in the exchange. Finally, the expected profits of informed traders are never lower in the two-venue market, but this may not always be true for uninformed traders.