Financial Trading Research Articles

The far side of capitalism: Institutions and trade financing in Manila during the long eighteenth century

AbstractSustained long‐distance trade in the early modern era necessitated institutional mechanisms capable of solving three interrelated challenges: the need to mobilize an unprecedented volume of capital and to lock it in for long periods of time, ways of mitigating the principal–agent problem across continents, and methods to internalize and distribute the high risks associated with intercontinental sailing. The case of Manila represents an alternative institutional approach to achieving market impersonality and solving the three fundamental challenges without the need for joint‐stock corporations, and extending beyond private and cultural networks. By adapting urban religious institutions such as brotherhoods and using legacy funds to facilitate pooling savings, Manileños managed to establish a capital market capable of mobilizing large resources towards trade finance during the long eighteenth century.

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

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

Bridging the trade finance gap for export-oriented SMEs: A systematic literature review

Small and Medium Enterprises (SMEs) are the most efficient segment of all global economies. They often struggle to adjust to the constantly changing requests imposed by the dynamic international markets, especially those in developing countries. Inadequate access to trade finance is one of the main constraints faced by SMEs engaged in international trade exports. The aim of this paper is to explore the factors affecting access to trade finance by export-oriented SME and the remedial steps taken to bridge the trade finance gap in the Sri Lankan context as well as the global context. The research methodology employed is a comprehensive systematic review of literature. Based on the review of selected 64 articles, we find that banking sector weaknesses, internal weakness within the SMEs and the insufficiency of government support have an effect on the access to trade finance by the export oriented SMEs. In addition, a number of remedial measures in bridging the trade finance gap are also identified. This systematic literature review addresses the research gap existing due to the limited availability of literature on the same domain. The findings signal towards government policymakers and stake holders in revamping their initiatives towards bridging the global trade finance gap, successfully. And creates as an optimal foundation for future empirical research endeavors.

An innovative method for short-term forecasting of blockchain cryptocurrency price

Cryptocurrency market sentiment is relatively unstable, which makes cryptocurrency price an attribute of high volatility. Accurate forecasting methods help to clarify the volatility trend of the cryptocurrency price, thereby reducing the investment risk of participants in the cryptocurrency market. Therefore, this research proposed a new method for short-term forecasting of the cryptocurrency price based on a small sample. This study took three typical blockchain cryptocurrencies (Bitcoin, Ethereum, Litecoin) as experimental objects, chose data intervals with different volatility trends in the U.S. stock indices between 2022 and 2023 as sample data, and used grey correlation analysis to select core affecting variables. Furthermore, this study built a grey multivariate convolution model with prioritized accumulating novel information for conducting prediction experiments on blockchain cryptocurrency price. The research findings demonstrate that the proposed model achieves high prediction accuracy in all experiments, and the model accuracy is superior to the comparison models. This study proposes a scientific prediction approach for blockchain cryptocurrency price, which can guide financial investors in developing and analyzing quantitative financial trading strategies to a certain extent. Meanwhile, this study provides a specific reference for relevant government departments to strengthen cryptocurrency regulation, prevent financial risks, and maintain financial stability.

The Impact of AI on International Trade: Opportunities and Challenges

This study aims to explore the transformative potential of Artificial Intelligence (AI) in international trade, focusing on its key roles in optimizing trade operations, enhancing trade finance, and expanding market access. In trade optimization, AI leverages advanced machine learning and predictive analytics to enhance demand forecasting, route optimization, and customs procedures, leading to more efficient logistics and inventory management. In trade finance, AI can automate document processing and risk assessment, increasing access to finance and enhancing transactional transparency, particularly through integration with blockchain technology. In terms of market access, AI-driven analytics can identify consumer trends and competitive dynamics, enabling personalized marketing and overcoming linguistic and cultural barriers. Due to the lack of quantitative data, this study employed qualitative research methods, specifically a multiple-case-study approach. The case studies of leading companies such as Alibaba, DHL, and Maersk showcase how they leverage AI to optimize their trade operations, improve customer service, and achieve greater efficiency. These real-world examples demonstrate AI’s practical applications and significant benefits in the global trade landscape. However, the adoption of AI in international trade is not without challenges. These include issues around data quality, ethical concerns, technological complexity, and public perception. Policy recommendations highlight the need for a robust data infrastructure, establishing ethical AI guidelines, and fostering international cooperation to align data protection regulations.

AI and blockchain technology for secure and transparent financial transactions

The integration of Artificial Intelligence (AI) and blockchain technology presents a transformative approach to enhancing the security and transparency of financial transactions. Blockchain’s decentralized and immutable ledger system ensures that transaction records are tamper-proof and transparent, significantly reducing the risk of fraud and errors. AI, with its advanced data analytics and machine learning capabilities, further augments this ecosystem by providing real-time monitoring, predictive analytics, and automated decision-making processes. Together, these technologies enable more efficient and secure financial operations, from cross-border payments to trade finance and asset management. This paper explores the synergistic potential of AI and blockchain in revolutionizing the financial sector, highlighting key applications, benefits, and challenges. By leveraging the strengths of both technologies, financial institutions can achieve unprecedented levels of security, efficiency, and trust in their operations.

Optimizing Test Data Management Strategies in Banking Domain Projects

The steady rise in processing power over the past 20 years has resulted in an enormous volume of data. Furthermore, anybody may now easily create and consume material in any format thanks to recent advancements in Web technology. Large volumes of data are regularly gathered by banking systems, including trade finance data, SWIFT and telex communications, client information, details about transactions, risk profiles, credit card details, limit and collateral details, the compliance or Anti Money Laundering (AML)-related data, and limit and collateral details. Every day, thousands of choices are made at banks. These choices pertain to credit, default, beginning a relationship, investments, AML, and illicit funding, among other things. To make these crucial choices, one must rely on a variety of data and drill down capabilities offered by the banking systems. We created a set of specifications for the kinds of data that should be included in a product catalogue. We ascertained what data the departments need by using a survey and questionnaire of the retailer's staff. We ensured that there was no one standard for the information organisation and then put out our own plan. This enormous amount of data may be mined for information and intriguing patterns, which can then be used to the decision-making process. This article examines and summarises a number of data mining methods that have applications in the banking industry. An overview of data mining methods and procedures is given. It also sheds light on how these methods may be applied in the banking industry to facilitate and enhance decision-making.

Gold Price Inflation and Its Implications for Financial Markets: Strategies for Mitigation

Abstract: Gold prices are among the most important indicators in the global financial market. The international gold price has continued to rise in recent years, repeatedly setting new records, accompanied by a boom in gold consumption and investment. The rapid rise in gold prices is due to a combination of factors. Supply and demand, economic conditions, and geopolitical events can lead to price fluctuations. Among them, economic conditions depend on monetary policy, inflation expectations, and market volatility, which may affect the trend of gold prices. The rise in gold prices has exacerbated market volatility and put higher demands on risk management for financial institutions. At the same time, the rise in gold prices has also affected the entire financial industry and changed traditional asset allocation strategies. This article examines the influence of increasing gold prices on the financial market and proposes potential preventive measures or solutions through a survey of literatures, theoretical analysis and logical reasoning. At the same time, this can help financial traders plan their funds reasonably, reduce the impact of inflation and currency devaluation, and better cope with changes in international trade and exchange rates, thus allowing the financial market to operate better.

Ghanaian Small and Medium-Scale Enterprises (SMEs') Readiness for African Continental Free Trade Area (AfCFTA) Implementation

This paper seeks to assess Ghanaian Small and Medium-Scale Enterprises (SMEs') readiness for the African Continental Free Trade Area (AfCFTA) Implementation. The research instrument used to collect data was questionnaires and the study employed descriptive research design. The study randomly selected 250 SMEs from a population of 480 SMEs that were registered members of the Ghana National Chamber of Commerce and Industry, Cape Coast Branch and operating within manufacturing, pharmaceutical, herbal, tourism, hospitality, food and beverage, agribusiness, garment, leather, and textiles. Data collected from the SMEs was analysed using SPSS, version 28. The findings from the study shows that, there is a significant gap between Ghanaian SMEs' readiness for AfCFTA implementation. The study finds out that although majority of SMEs are capable of innovation and developing new products, they also have limited knowledge of export documentation, trade information and infrastructure, trade finance, and funding for expansion. They also have low knowledge and lack a basic understanding of the benefits of AfCFTA, its advantages, components, protocols, and Rules of Origin. The study again revealed that the obstacles facing Ghanaian SMEs exportation are high cost of production, border delays, poor infrastructure, high inflation, difficulty in accessing credit, and language problems. Based on these findings, the study recommended that SMEs needs to be retooled particularly in the area of AfCFTA Rules of Origin, its objectives, components, and Ghana’s trade policy to improve their readiness for AfCFTA implementation. Again, businesses should concentrate more on value addition, and be dedicated to looking for ways to enhance their current or existing products to outperform comparable products from other African nations.

Online probabilistic knowledge distillation on cryptocurrency trading using Deep Reinforcement Learning

Leveraging Deep Reinforcement Learning (DRL) for training agents for financial trading has gained significant attention in recent years. However, training these agents in noisy financial environments remains challenging and unstable, significantly impacting their performance as trading agents, as the recent literature has also showcased. This paper introduces a novel distillation method for DRL agents, aiming to improve the training stability of DRL agents. The proposed method transfers knowledge from a teacher ensemble to a student model, incorporating both the action probability distribution knowledge from the output layer, as well as the knowledge from the intermediate layers of the teacher’s network. Furthermore, the proposed method also works in an online fashion, allowing for eliminating the separate teacher training process typically involved in many DRL distillation pipelines, simplifying the distillation process. The proposed method is extensively evaluated on a large-scale cryptocurrency trading setup, demonstrating its ability to both lead to significant improvements in trading accuracy and obtained profit, as well as increase the stability of the training process.

Navigating crises: Gold's role as a safe haven for U.S. sectors

This paper investigates the correlation between U.S. sectors and gold, and whether gold can serve as a safe haven for investors in specific U.S. sectors during the global financial crisis, COVID-19, and the Russia-Ukraine war. We use data from the Standard & Poor's Depository Receipts (SPDR) Select Sector Exchange Traded Fund (ETF) to capture the performance of the respective sectors. Our findings document that gold is a weak safe haven for most U.S. sectors. Gold is not a safe investment for energy, materials, utilities, and consumer staples. Gold does provide vital protection for financial, consumer discretionary, industrial, technology, and healthcare.

An adaptive financial trading strategy based on proximal policy optimization and financial signal representation

Trading strategies play a crucial role in financial trading. However, due to the significant amount of noise present in financial signals, traditional trading strategies and those based on price prediction often fail to achieve optimal results in real market conditions. Nowadays, with the pervasive noise in financial signals, developing successful trading strategies to achieve high returns has become one of the most prominent and challenging research areas in modern finance. Therefore, an adaptive financial trading strategy, called proximal policy optimization based on financial signal representation trading strategy (FSRPPO), is proposed. This strategy employs financial signal representation technology (FSR), which combines complete ensemble extreme-point symmetric mode decomposition with adaptive noise (CEEMDAN) and modified rescaled range analysis (MRS), to accurately and robustly represent dynamic market states, enhancing profitability. Additionally, the designed reward function reduces trading frequency to lower costs, while the designed action space increases trading flexibility to reduce risks. The experimental results on real stock data demonstrate the outstanding profitability and good risk avoidance ability of our proposed trading strategy, which means that the proposed model can effectively filter out noise from financial signals, extract valuable information, and provide reliable decision support for investors.

Detection and localization of changes in a panel of densities

We propose a new methodology for identifying and localizing changes in the Fréchet mean of a multivariate time series of probability densities. The functional data objects we study are random densities fs,t indexed by discrete time t and a vector component s, which can be treated as a broadly understood spatial location. Our main objective is to identify the set of components s, where a change occurs with statistical certainty. A challenge of this analysis is that the densities fs,t are not directly observable and must be estimated from sparse and potentially imbalanced data. Such setups are motivated by the analysis of two data sets that we investigate in this work. First, a hitherto unpublished large data set of Brazilian Covid infections and a second, a financial data set derived from intraday prices of U.S. Exchange Traded Funds. Chief statistical advances are the development of change point tests and estimators of components of change for multivariate time series of densities. We prove the theoretical validity of our methodology and investigate its finite sample performance in a simulation study.

Research on the Yuan Universe Financial Trading Platform Under Innovative Business Model

This paper focuses on the innovative business model and its technology application of the Yuan-Universe financial trading platform. Firstly, the global trend and competitive status of cosmic finance are discussed through market analysis. Later, the paper analyzes in depth the role of Web3.0, blockchain and AI in improving the performance of the trading platform. In the business model section, this paper discusses the profit models such as transaction withdrawal and information consulting, and evaluates their financial sustainability. In addition, the paper analyzes the potential technical and market risks of the platform and discusses the management strategies. Finally, the paper summarizes the business and technological innovation of the yuan universe financial trading platform, and prospects the future development. This study provides insights for understanding new models of financial services and provides reference for the development of fintech.

The Role of the U.S. Dollar in Global Trade Finance: A Study of Its Dominance and Future Prospects

This paper digs deep on what reason has led the U.S. dollar to be a dominant force for years mainly due to its stability, liquidity, and greater usage within international markets. It has made it the world's reserve currency did not just serve as an enabler for far-flung international trades and investments but also led to the establishment of the United States in terms of standing in economic and geopolitical standing. Historical factors concerning the dollar's increasing popularity over time are addressed in this paper-from the post- WWII establishment of the Bretton Woods system to the moments well after the death of the gold standard. It further explores shifting threats to the supremacy of the dollar and key drivers that might change the financial environment in the world. These are the economic ascension of emerging markets, particularly China, seeking to internationalize the yuan; extension of the BRICS bloc, composed of Brazil, Russia, India, China, and South Africa, and their joint thrust toward depolarization. Digital currencies and blockchain technology provide an emerging alternative that would allow countries to bypass traditional banking systems or lessen their dependency on the dollar when making cross-border transactions. The discussion explores how geopolitical stress - whether a trade war or the imposition of sanctions, as examples - may push the shift away from the dollar, based on a focus on increasing foreign reserves' diversity and insulation against the over-reach of the US. In addition, the paper went through how adjustments in global financial regulation, technological innovation, and central banks' policies may have an impact on the safe-haven status the dollar enjoys. Through a combination of both quantitative and qualitative methods, this paper examines the continued relevance of the dollar at the international level in terms of trade and finance. Indeed, it envisions possible scenarios: the continued reign of the dollar, the emergence of rival currencies, and the new politics of global economic crises over currency dynamics. But if nothing else, it provides a more refined and multi- dimensional view of what is going on with the dollar today and what may go on in the future in an increasingly multipolar world economy. These insights will be critical when policymakers, financial institutions, and global businesses negotiate their way through uncertainty in changing currency landscapes. Keywords: U.S. dollar, global trade, finance reserve, currency economic, uncertainty emerging economies, digital currencies, geopolitical shifts currency competition.

Exploring the Dynamic Behavior of Crude Oil Prices in Times of Crisis: Quantifying the Aftershock Sequence of the COVID-19 Pandemic

Crude oil prices crashed and dropped into negative territory at the onset of the COVID-19 pandemic. This extreme event triggered a series of great-magnitude aftershocks. We seek to investigate the cascading dynamics and the characteristics of the series immediately following the oil market crash. Utilizing a robust method named the Omori law, we quantify the correlations of these events. This research presents empirical regularity concerning the number of times that the absolute value of the percentage change in the oil index exceeds a given threshold value. During the COVID-19 crisis, the West Texas Intermediate (WTI) oil prices exhibit greater volatility compared to the Brent oil prices, with higher relaxation values at all threshold levels. This indicates that larger aftershocks decay more rapidly, and the period of turbulence for the WTI is shorter than that of Brent and the stock market indices. We also demonstrate that the power law’s exponent value increases with the threshold value’s magnitude. By proposing this alternative method of modeling extreme events, we add to the current body of literature, and the findings demonstrate its practical use for decision-making authorities—particularly financial traders who model high-volatility products like derivatives.

On the integration of multiple criteria decision aiding and forecasting: Does it create value in portfolio selection?

A systemic integration of multiple criteria decision aiding (MCDA) and forecasting is presented for enhancing the quality of investment decisions. First, we provide a new methodological approach for combining forward-looking information, as an input to further MCDA analysis and we illustrate that the informational content of the MCDA scores is distinct from that of the original data used as input to the MCDA. Moreover, we provide very compelling evidence that the produced MCDA scores and rankings have incremental information, over and above that being contained in either the original data or the forecasts themselves. Therefore, it can be used successfully in providing methods which will have a high, a priori, chance of resulting in financial performance enhancements; to this end, we have illustrated this finding with a number of different approaches, including asset rotation and portfolio construction. Next, we show that the MCDA ranked variables can be used for both in-sample explaining and out-of-sample forecasting of critical economic variables, such as the real GDP growth or the unemployment rate, so that MCDA might be able to assist in identifying leading indicator properties for economic forecasting. The evaluation of the suggested approach entails extensive empirical testing on the various US economy market sectors, as expressed by the corresponding S&P 500 Exchange Traded Funds (ETFs), for a period of 22 years, documenting specific economic stylized facts, reported for first time in the relevant literature.

Algorithmic trading and machine learning: Advanced techniques for market prediction and strategy development

This paper provides an in-depth examination of advanced techniques in algorithmic trading and machine learning, focusing on their impact on market prediction and trading strategies. As financial markets evolve, the need for sophisticated analytical methods has become paramount to gaining a competitive edge. The study covers a range of techniques including time series analysis, natural language processing (NLP), and deep learning models, highlighting their contributions to enhancing predictive accuracy and trading efficiency. The paper explores the importance of feature engineering, model selection, and risk management in developing robust trading strategies. It also addresses the challenges and limitations inherent in financial modeling, such as data quality, overfitting, and computational complexity. Additionally, the paper examines emerging trends and technologies, including quantum computing, federated learning, and ESG integration, which are poised to shape the future of financial markets. By synthesizing insights from various advanced techniques and their practical applications, this paper offers a comprehensive overview of the current state and future directions of algorithmic trading and machine learning in finance. It underscores the importance of continuous innovation and adaptation in maintaining a competitive advantage in the dynamic landscape of financial trading.

Low Financing Trade Credit Inventory for High Deterioration under Time Dependent Holding Cost

Low Financing Trade Credit Inventory for High Deterioration under Time Dependent Holding Cost

Hedge funds and the positive idiosyncratic volatility effect

Abstract While it is established that idiosyncratic volatility is negatively priced in the cross-section of stock returns, the relation between idiosyncratic volatility and hedge fund returns is largely unexplored. We document that hedge funds with high idiosyncratic volatility earn higher future risk-adjusted returns of 6 percent p.a. than hedge funds with low idiosyncratic volatility. The outperformance arises because hedge funds trade high idiosyncratic volatility stocks wisely. They pick high volatility stocks when they are underpriced and short-sell high volatility stocks when they are overpriced. Our results support the notion that hedge funds’ idiosyncratic volatility is a measure of managerial skill.