Asset Trading Research Articles

Investigating Blockchain Technology's Integration in Financial Services

This paper introduces a comprehensive analytical framework designed to assess the efficacy of blockchain technology in financial accounting and its broader implications for the financial sector. The analysis explores how blockchain can revolutionize financial accounting by enhancing both efficiency and accuracy, facilitating real-time financial reporting, and expediting audit processes. It also examines blockchain's current deployment across various financial domains, including insurance, banking, payment systems, asset trading, lending, and remittances, offering a detailed look at the technology's penetration and impact. The study identifies significant hurdles, such as elevated transaction fees and prevailing technical constraints, which currently impede broader adoption. It also discusses potential regulatory challenges and the need for sector-wide collaboration to realize the full benefits of blockchain. The results of this study provide critical insights for both researchers and developers within the financial sector, highlighting the transformative potential of blockchain and proposing directions for future technological advancements and application areas. This examination not only underscores the immediate benefits and challenges but also sets the stage for subsequent innovation in integrating blockchain into established financial systems, potentially leading to more robust, transparent, and efficient financial services.

Liquidity Preference, Interest Rate Spread, and the Transformation of the US Financial System

This article examines empirical patterns of interest rate spreads between yields on long-term corporate bonds and the Federal Funds Rate (FFR) in the United States over the period from 1960 through 2023. Since the early 1980s, discernible patterns of interest spreads have emerged, revealing marked deviations in corporate bond yields from the FFR. We focus on financial market forces particularly arising from financial intermediaries in analyzing these patterns. To this end, the article develops a theoretical framework based on the concept of liquidity preference in Keynes’s 1937 papers and the works of Hyman Minsky and Victoria Chick. We also conduct cointegration analyses for corporate bond yields, the FFR, and financial intermediaries’ leverage ratios. Overall, our empirical results indicate that the relationship between the FFR and corporate bond yields—particularly with respect to their long-run relationship and interest rate markups—has markedly changed since the 1980s and is significantly affected by financial intermediaries’ asset/capital leverage ratios. As a policy implication, regulatory reforms aimed at enhancing the Federal Reserve Bank’s capacity to steer long-term rates and contain intermediaries’ balance sheet leverage, particularly those centered on risky asset trading to increase gains from price differentials, are required JEL Classification: B26, E11, E43, E52

Beliefs about the Stock Market and Investment Choices: Evidence from a Survey and a Field Experiment

Abstract We survey retail investors at an online bank to study how beliefs about the autocorrelation of aggregate stock returns shape investment decisions measured in administrative account data. Individuals’ beliefs exhibit substantial heterogeneity and predict trading responses to market movements. We inform half of our respondents that, historically, the autocorrelation was close to zero, which causes them to update their perceived current autocorrelation and return expectations. The treatment shifts respondents’ equity purchases during the COVID-19 crash months later in the direction implied by the intervention. Our results provide causal evidence about the drivers of disagreement and trade in asset markets. (JEL D83, D84, G11, G12, G41, G50)

Engagement in New Gambling Practices and its Association with Gambling Disorder, Impulsivity, and Cognitive Distortions: Findings from a Nationally Representative Sample of Spanish Gamblers.

Most epidemiological surveys focus on adult gambling behaviors related to traditional gambling forms, while studies on novel forms often focus on loot boxes and cryptocurrency trading individually. This study examines the co-ocurrence of emergent gambling and gambling-like practices, analyzing the demographic and psychological characteristics of involved gamblers. A cross-sectional study surveyed 1429 Spanish individuals aged 18-65, using a web-based questionnaire. The survey assessed participation in 19 gambling (e.g., lotteries, sports betting) and gambling-like activities (e.g., trading of cryptocurrencies and other assets, buying loot-boxes), along with sociodemographic and substance use. Problem gambling (PGSI), Impulsivity (UPPS-P), and cognitive distortions (Labrador's cognitive distortions scale) were also assessed. Participants who gambled over the past year (n = 921) were classified into four groups: traditional gambling (TG) only (64.5%, n = 594), TG with trading activities (27.5%, n = 253), TG with gambling withing video games or streaming platforms (2.5%, n = 23), and TG with both trading and video gambling (5.5%, n = 51). Most gamblers engaged exclusively in traditional formats, but 35.5% also participated in novel gambling forms. Those involved in both trading and video gambling were generally younger, male, with higher levels of impulsivity and gambling-related cognitive distortions compared to TG-only gamblers (p < 0.001). This group also exhibited higher rates of problem gambling and substance use (p < 0.001). This study emphasize the importance of including emerging gambling activities, which are particularly prevalent among high-risk gamblers, in epidemiological surveys. Identifying new gambling patterns and associated risk factors could help optimize public policies and develop more effective regulatory and prevention strategies.

Multiperiod Dynamic Portfolio Choice: When High Dimensionality Meets Return Predictability

Multiperiod portfolio choice is the central problem in active asset management. Multiperiod dynamic portfolios are notoriously difficult to solve, especially when there are hundreds of tradable assets as well as a large number of state variables. In this article, we develop a novel two-step methodology to solve the multiperiod dynamic portfolio choice problem with high dimensional assets in the presence of return predictability conditional on a large number of state predictors. Specifically, in the first step, we propose the new Risk-Premium Projected-PCA (RP-PPCA) method to reduce the dimension of tradable assets. This method achieves Dimension Reduction (DR) by estimating latent factors with explanatory power in both time series variation and expected return in high-dimension-low-sample-size data. In the second step, we use dynamic programming to solve the multiperiod portfolio choice problem, and in each recursive step, we adopt an Adjusted semiparametric Model Averaging (AMA) method to avoid the curse of dimensionality associated with a large set of state variables while remaining computationally efficient. Thus, we name this two-step approach DRAMA, which stands for a combination of a new dimension reduction method and an adjusted semiparametric model averaging method. Analytically, we show that the portfolios constructed by the DRAMA are approximately optimal under mild assumptions. Moreover, our numerical results based on empirical data from US stock markets show that the proposed portfolios have excellent out-of-sample performances.

Designing a Billionaire's Tax

With ProPublica’s dramatic revelation in 2021 of the low “true tax rates” of America’s billionaires serving as a catalyst, the exclusion of unrealized gains from the base of the federal income tax has been challenged to an extent unprecedented in the century-plus history of the tax. In late 2021, a proposal by Senate Finance Committee Chair Ron Wyden (D, OR) to tax billionaires’ unrealized gains in tradable assets might have become law, but for the opposition of Senator Joe Manchin (D, WV). And in early 2022, the Treasury Department of President Joe Biden included a minimum tax on the unrealized gains of the ultrarich in its tax reform proposals. A narrower reform, taxing billionaires who borrow against unrealized appreciation to finance lavish consumption expenditures, has also been urged. All three of these proposed reforms—but especially the first two—would transform the fundamental character of the income tax as applied to the wealthiest Americans. This Article describes the three approaches, considers design challenges under each and evaluates their relative merits.

Study on the Legal Adaptation of Digital Asset Trading and Securities Laws

This thesis discusses the connection between digital asset trading and securities law, and analyzes the role and importance of securities law in digital asset trading. As an emerging market, digital asset trading involves the issues of investor protection, information disclosure, and compliance of trading platforms. Securities law provides legal protection and regulatory framework for digital asset trading by regulating market behavior, protecting investors' rights and interests and maintaining market order. Investors should be aware of local securities laws and comply with compliance requirements to ensure the legality and safety of transactions. Regulators should actively follow up the market development and continuously improve the regulatory mechanism of digital asset trading to promote the healthy development of the market.

Research on Energy Trading Mechanism Based on Individual Level Carbon Quota

High economic growth is accompanied by substantial consumption of fossil energy and significant negative externalities on the ecological environment. The global warming effect resulting from environmental pollution caused by energy has brought energy carbon emissions into the forefront of social attention. Establishing a carbon trading market is an essential measure to achieve the “double carbon” goal, with individual and household carbon emissions accounting for 70% of China’s total emissions. Constructing an individual-level carbon trading market will facilitate the efficient realization of this goal. However, addressing the challenge of handling vast amounts of data and network congestion in relation to frequent but small-scale individual carbon trading has become an urgent issue that needs to be resolved. In light of this, the present study designs a digital technology-based framework for the carbon market trading system and proposes an individual carbon asset price-based model for carbon market trading, aiming to establish a research framework for the carbon quota market. Furthermore, blockchain technology is employed as the underlying technology in the proposed carbon trading market model to cater to individual-level carbon trading services and achieve optimal matching between carbon quota suppliers, thereby enhancing profitability of the carbon trading platform. The numerical results obtained from the model demonstrate that in absence of government subsidy mechanisms, individual-level carbon trading can effectively reduce total consumer emissions. The present study successfully overcomes the carbon lock-in effect of consumer groups and achieves the generation and trading of individual carbon assets despite capital constraints. This study facilitates accumulation and trade of individual carbon resources, reduces overall consumer emissions, enhances environmental benefits at societal level, and provides a foundation for governmental decision-making.

Capital controls and trade policy

How does optimal capital-flow management change with prevailing trade policies? We study the joint optimal determination of capital controls and trade tariffs in a two-country, two-good model with trade in goods and assets. Because countries are large in both markets, a country-planner can achieve higher domestic welfare by departing from free trade in addition to levying capital controls, despite the cooperative optimal allocation being efficient. However, time variation in the optimal tariff induces households to over- or under-borrow through its effects on the path of the real exchange rate. As a result, optimal capital controls are generally smaller when trade policy is constrained (i.e., by a Free-Trade Agreement), but, absent retaliation, can be larger depending on the paths of underlying fundamentals.

Tax Regulations On Cryptocurrency Transactions In Indonesia

This study explores the evolving landscape of cryptocurrency taxation in Indonesia, focusing on regulatory developments and challenges. Tracing the trajectory from the introduction of Bitcoin in 2009 to the establishment of a formal crypto exchange in 2023, this research emphasizes the government's commitment to regulate and facilitate crypto asset trading. Despite this progress, challenges such as regulatory uncertainties, security concerns, necessitating a profound understanding of cryptocurrency dynamics to navigate the global financial landscape. This research focuses on exploring Indonesia's regulatory response, particularly in taxation, employing a qualitative descriptive methodology that integrates primary data from legal frameworks and secondary data through literature reviews, document analyses, and interviews with cryptocurrency traders and tax officials. This study contributes not only to academic discourse but also provides insights for fostering the growth of the cryptocurrency ecosystem while managing potential tax-related risks.

A blockchain-based privacy protecting framework with multi-channel access control model for asset trading

A blockchain-based privacy protecting framework with multi-channel access control model for asset trading

Algorithmic trading, what if it is just an illusion? Evidence from experimental asset markets

We experimentally investigate whether and how the potential presence of algorithmic trading (AT) in human-only asset markets can influence humans’ price forecasts, trading activities and price dynamics. Two trading strategies commonly employed by high-frequency traders, spoofing (SP) - associated with market manipulation - and market making (MM) - seen as liquidity provision - are considered. These experiments reveal that, first, the mere expectation of SP traders can, at first, impair price convergence towards fundamentals. Second, the expected presence of AT, especially MM traders, induce larger initial price forecasts deviations from fundamentals. Third, despite the absence of AT in our experiments, the information about the presence of AT, employing MM strategy, is sufficient to alter subjects trading behavior over time and the impact of past realized prices on subjects’ order prices.

Does “Paper Oil” Matter? Energy Markets’ Financialization and Co-Movements with Equity Markets

We revisit, and document new facts regarding, the financialization of U.S. energy markets in 2000–2010. We show that, after controlling for macroeconomic factors and physical energy market fundamentals, the strength of energy markets’ co-movements with the U.S. stock market is positively related to the energy paper market activity of hedge funds that trade both asset classes. This relation weakens when credit risk is elevated. We find, in contrast, no link with the aggregate positions of commodity index traders in energy futures markets. Our findings have implications for the ongoing debate regarding the financialization of commodities.

AN examination of linear factor models in U.K. stock returns in the presence of dynamic trading

This study uses the approach of Ferson and Siegel, Rev Financ Stud 22:2735–2758 (2009), and Ferson, Siegel and Wang, J Financ Quant Anal, forthcoming, (2024) to examine the unconditional mean–variance efficiency, in the presence of conditioning information (UMV), of ten linear factor models in U.K. stock returns. The study finds that the UMV efficiency of all the multifactor models is strongly rejected in U.K. stock returns in two different sets of test assets. This rejection is mainly driven by allowing dynamic trading in the test assets and factors. The optimal use of conditioning information also has a significant impact in relative model comparison tests. In relative model comparison tests based on UMV efficiency, the best performing model is the eight-factor model of Chib and Zeng, J Bus Econ Stat 38:771–783 (2020) model.

Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration

Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China’s green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes.

Advance Sequential Models for Asset Trading: Leveraging LSTM &amp; RNN with Stochastic RSI and MACD Indicators

Abstract: The goal of stock market prediction is to forecast a company's financial stocks' future worth. The application of machine learning, which creates predictions based on the values of the current stock market indices by training on their prior values, is a recent trend in stock market prediction technologies. Machine learning utilises many models to facilitate accurate and reliable prediction. The study focuses on stock value prediction using regression and LSTM-based machine learning. Considered factors are volume, low, high, open, and closed. We also employ algorithmic trading, which is a technique for automatically executing orders based on pre-programmed trading instructions that take volume, price, and time into account. This kind of trading aims to take advantage of a computer's speed and computational power in comparison to human traders. Algorithmic trading has become more popular in the twenty-first century, prompting traders to respond. According to a 2019 research, 92% of traders use algorithms rather than people. The moving average convergence &amp; divergence and the relative strength index are our primary tools. One technical tool employed in financial market analysis is the relative strength index, or RSI. Charting a stock's historical and present strength or weakness with retail and institutional traders is the goal. Investment banks, mutual funds, pension funds, and hedge funds all utilise it extensively when they need to perform trades. based on the closing prices of a recent trading period, too fast for a human or the market. It is important to distinguish the indicator from relative strength. The RSI measures the speed and size of price changes and is categorised as a momentum oscillator. The rate at which prices rise or fall is known as momentum. The ratio of higher closes to lower closes, where closures are defined as averages of absolute values of price changes, is the relative strength, or RS. The RSI determines momentum by dividing higher closes by overall closes, or stocks with more or stronger positive moves. The RSI is most commonly employed during a 14-day period and is scaled from 0 to 100, with 70 and 30 representing the high and low readings, respectively. Alternately shorter or longer outlooks are associated with shorter or longer time frames. Although they happen less frequently, high and low levels— 80 and 20, or 90 and 10—indicate increased momentum. J. Welles Wilder created the relative strength index, which was first published in the June 1978 issue of commodities magazine and in his 1978 book New concepts in technical trading systems, and in commodities magazine in the June 1978 issue.

Profitability trend prediction in crypto financial markets using Fibonacci technical indicator and hybrid CNN model

Cryptocurrency has become a popular trading asset due to its security, anonymity, and decentralization. However, predicting the direction of the financial market can be challenging, leading to difficult financial decisions and potential losses. The purpose of this study is to gain insights into the impact of Fibonacci technical indicator (TI) and multi-class classification based on trend direction and price-strength (trend-strength) to improve the performance and profitability of artificial intelligence (AI) models, particularly hybrid convolutional neural network (CNN) incorporating long short-term memory (LSTM), and to modify it to reduce its complexity. The main contribution of this paper lies in its introduction of Fibonacci TI, demonstrating its impact on financial prediction, and incorporation of a multi-classification technique focusing on trend strength, thereby enhancing the depth and accuracy of predictions. Lastly, profitability analysis sheds light on the tangible benefits of utilizing Fibonacci and multi-classification. The research methodology employed to carry out profitability analysis is based on a hybrid investment strategy—direction and strength by employing a six-stage predictive system: data collection, preprocessing, sampling, training and prediction, investment simulation, and evaluation. Empirical findings show that the Fibonacci TI has improved its performance (44% configurations) and profitability (68% configurations) of AI models. Hybrid CNNs showed most performance improvements particularly the C-LSTM model for trend (binary-0.0023) and trend-strength (4 class-0.0020) and 6 class-0.0099). Hybrid CNNs showed improved profitability, particularly in CLSTM, and performance in CLSTM mod. Trend-strength prediction showed max improvements in long strategy ROI (6.89%) and average ROIs for long-short strategy. Regarding the choice between hybrid CNNs, the C-LSTM mod is a viable option for trend-strength prediction at 4-class and 6-class due to better performance and profitability.

The Elusive Gains from Nationally Oriented Monetary Policy

Abstract The gains from monetary policy cooperation depend on real and financial distortions in the economy and evolve dynamically with prevailing economic conditions. We show that, with international trade in assets, these gains are driven by asymmetric cross-border developments in productivity and savings, and can reach multiples of the cost of economic fluctuations. When financial flows are restricted to nonstate-contingent bonds, the gains from cooperation grow with the size of global imbalances, i.e. net-foreign-asset positions.

Cross-chain asset trading scheme for notaries based on edge cloud storage

Blockchain has penetrated in various fields, such as finance, healthcare, supply chain, and intelligent transportation, but the value exchange between different blockchains limits their expansion. Cross-chain technology, such as notary mechanism, enables asset exchanges between different blockchain networks. However, existing research still confronts problems such as single inherent value evaluation, collusion risk, credit evaluation and unreasonable resource allocation, making it difficult to ensure the security of cross-chain asset transactions. So this paper proposes a cross-chain asset trading scheme based on edge cloud storage to improve the reliability of notaries and the security of cross-chain value exchange. Firstly, introduce the entropy weight method to determine indicators and adopt multi indicator evaluation to reduce the risk of collusion between notaries and users; Secondly, design a multi-indicator credit evaluation method to improve the accuracy of the evaluation; Finally, design a new and old notary node share allocation method to improve the rationality of resource allocation.The experiment shows that the scheme designed in this paper can reduce the risk of collusion, more accurately screen out high credit nodes to act as notaries, and make resource allocation more reasonable.

Cross-Chain Digital Asset System for Secure Trading and Payment

Blockchain as a ledger technology is attractive without the need for central servers. There are many types of blockchains in different fields, such as digital asset trading and payment, which have business interactions. In the fields, the digital asset and payment information on their blockchains need to be securely cooperative with each other. However, some business activities are on different blockchains, which have different consensus algorithms and network architectures, thus limiting the interoperability among these activities and making each blockchain an island. Cross-chain technology can connect different blockchains and realize the interoperability and sharing of information among them. This work designs a cross-chain digital asset system for secure trading and payment. It builds two parallel chains, i.e., digital asset chain (DAC) and payment chain (PC), and their functions are analyzed and designed. The cross-chain, i.e., relay chain (RC), is used to realize cross-chain interoperability, where the cross-chain message format and authority setting are designed to endow parallel chains with the ability to recognize. The decentralized characteristic of the RC allows cross-chain messages to be safely transmitted to ensure secure trading and payment. Through testing and analysis, the proposed system can provide more secure trading and payment than its peers.