Enhance Market Efficiency Research Articles

The AI revolution: are crypto markets more efficient after ChatGPT 3?

This study examines the efficiency of crypto markets in the context of the AI revolution, focusing on AI-Crypto sectors. By employing the Adjusted Market Inefficiency Magnitude (AMIM) and conducting quantile efficiency and liquidity analyses, we observe dynamic fluctuations in market efficiency across sectors like Generative AI, AI Big Data, Cybersecurity, and Distributed Computing. Our findings reveal that most AI-Crypto sectors present a tendency towards higher efficiency in extreme market conditions. The introduction of ChatGPT 3 significantly enhanced market efficiency, with sectors associated with AI experiencing positive mean returns and increased liquidity. Our results suggest that market efficiency in these sectors is not static but evolves with technological innovations and sector-specific characteristics, in that AI-related sectors are driving the recent market dynamics, with increased liquidity and efficiency observed post-ChatGPT 3 launch. This research extends previous work on crypto market efficiency and explores the incorporation of information in AI tokenized projects, underscoring the transformative impact of AI on the crypto market landscape.

Impact of windfall tax on market dynamics: A Cournot oligopoly model with exogenous shocks

This study explores the nuanced impact of windfall taxes on market equilibrium, introducing an innovative approach within the Cournot oligopoly framework. The paper uses the 0–1 test for chaos to dissect how profit taxation can stabilize market behaviors under bounded rationality. It suggests that higher taxes may prevent collusion, thereby promoting a more competitive environment. The paper demonstrates that while windfall taxes leave regular markets almost unaffected, they can protect firms in chaotic states from adverse outcomes. This research underscores the necessity for policymakers to tailor windfall tax strategies to specific market conditions, potentially driving enhanced market efficiency. Our insights advocate for the judicious application of windfall taxes, which could significantly shape future economic policies.

Enhancing Consumer-to-Consumer (C2C) E-Commerce through Blockchain: A Model-Driven Approach

The rapid progress of Information and Communication Technology (ICT), especially the Internet, has changed lifestyles in profound ways, including sharing ideas, virtual interactions, digital entertainment, and online transactions. It has resulted in businesses globally turning to electronic commerce (e-commerce) to market products. E-commerce has revolutionized operations with features such as centralized storage and detailed product information in the marketing process. However, the inefficiency and lack of transparency in these centralized systems lead to high costs and limited user control, posing a significant challenge. Challenges include commission fees from E-Commerce providers, which hinder business growth. The research aimed to propose a more efficient and transparent model for Consumer-to-Consumer (C2C) e-commerce using blockchain technology. The C2C model enhanced user transactions and minimized third-party dependence, but centralization increased costs and limited seller access. Thus, a decentralized blockchain approach was proposed for greater transparency in e-commerce. The research innovatively applied blockchain to C2C e-commerce, enhancing market efficiency and transparency. The research method applied was a combination of Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis and practical application. The result shows that the approach succeeds in reducing high costs, transparency of data storage, and dependence on third parties. Blockchain reduces third-party involvement and promotes a fairer business environment because it uses a tamper-proof database for transparency, security, and efficiency in the ever-growing e-commerce ecosystem. Blockchain ensures automated transactions, real-time data tracking, and data security.

Principles versus rules based standards: Differential impact on accounting quality and relevance

AbstractThis research explores the transition from rules‐based to principles‐based accounting standards, particularly focusing on the implementation of ASC 606, and its impact on the quality and value relevance of financial reporting. The study draws on the debate between the detailed, prescriptive nature of rules‐based standards versus the flexible, judgment‐reliant principles‐based standards. Through a comprehensive empirical analysis, it finds that the adoption of ASC 606 has led to a significant improvement in accounting quality, supporting the theory that principles‐based standards, which emphasize reflecting the economic substance of transactions, result in more accurate and informative financial statements. Additionally, the paper reveals that this transition has varied effects across different industries, with the most pronounced improvements in sectors characterized by complex customer contracts and multiple performance obligations. The research further indicates an increase in the value relevance of financial reporting post‐ASC 606 adoption, suggesting that financial statements now provide more relevant information for investors, thereby enhancing market efficiency. These findings contribute to the ongoing discourse on the optimal approach to financial reporting standards, highlighting the benefits of principles‐based standards while also acknowledging the need for strong regulatory frameworks and professional judgment to mitigate the risks of earnings management and ensure high‐quality financial reporting.

Shades of grand corruption among allocative efficiency and institutional settings. The case of Italy

This paper examines the underexplored impact of the procedures in public contracts on market allocation efficiency that are at risk of corruption. We integrated the GMM-SYS estimator with analyses of a balanced panel database of all the provinces in Italy between the period 2013–2021. The use of a high level of spatial granularity in an under-researched context revealed two distinct scenarios. In competitive markets, a more entrepreneurial and less widespread form of corruption emerged which may enhance market efficiency (bottom-up pattern). Conversely, in a context with limited market competition and low institutional quality, corruption indicators may be less useful due to potential pre-bribery behavior (top-down pattern). Our study underscores the capacity of corrupt suppliers to exploit market opportunities and secure selection. We found that corruption risk practices within public contracts have the potential to improve market efficiency, thus rendering them appealing to corrupt suppliers due to their capacity to deliver economic advantages. This also applies to provinces with high-quality governance. Additionally, the paper highlights the limitations of indicators in alternative contexts. The paper has significant implications for policymakers in defining assignation criteria in high-quality contexts, but also offers insights into the effectiveness of grand corruption indicators for academics.

Do corporate carbon emissions affect risk and capital costs?

This study explores the impact of corporate carbon emissions on idiosyncratic risk and capital costs. Leveraging a dataset comprising 1016 company-year observations from Australian-listed companies over the period 2007 to 2020, our panel regression analysis reveals significant associations. We find that companies with higher carbon emissions experience elevated idiosyncratic risk, contributing to increased capital costs in both debt and equity markets. Our results remain robust after using alternative model specifications, accounting for the diverse nature of corporate carbon performance among industries, shaped by unique industrial factors. These results highlight the significance of carbon emissions pricing within financial markets and emphasize the importance of standardized corporate carbon disclosure in enhancing market efficiency, facilitating more accurate valuation, and guiding resource allocation in the broader economy.

Enhancing Vegetable Price Forecasting Accuracy: A Hybrid SARIMA- DWT-GANN Approach

In the context of Malaysia's agricultural sector, the development of a robust vegetable price forecasting model holds paramount importance. The pricing dynamics of vegetables can significantly affect various stakeholders, including farmers, distributors, and consumers. The agricultural sector in Malaysia encounters persistent challenges such as supply-demand imbalances, seasonal variations, and market uncertainties, which can lead to income disparities for farmers and disruption in supply chains. Addressing these issues requires accurate and timely predictions of vegetable prices to enhance planning, resource allocation, and decision-making. This study introduces an innovative SARIMA-DWT-GANN hybrid model for vegetable price forecasting. By fusing the strengths of traditional time series modeling with the capabilities of neural networks, the proposed model offers a comprehensive solution to capture both linear and non-linear price patterns. The results demonstrate the superiority of the SARIMA-DWT-GANN model over the individual SARIMA model, as evident from correlation coefficients that closely approach unity and p-values confirming statistical significance. The model's ability to predict price changes has significant implications for making informed decisions throughout the agricultural supply chain. This research provides a robust forecasting tool that not only enhances market efficiency and profitability but also offers a promising solution to address the challenges in Malaysia's agricultural sector.

Marketing of agricultural products through e-NAM in Tamil Nadu state

Integrating technology in agricultural marketing has marked a transformative milestone in Tamil Nadu, where agriculture is a cornerstone of the economy. The National Agriculture Market (e-NAM) has redefined traditional methods, creating a unified national market for agricultural commodities. This study focuses on marketing selected agricultural products through e-NAM in Tamil Nadu, analyzing trade records from 2017 to 2021, and forecasting up to 2025-2026. Focusing on efficiency and transparency, the study explores the impact of e-NAM on the agricultural trade market. Secondary data spanning 2017-2021 from the Department of Agriculture, Cooperation & Farmers Welfare, and the National Agricultural Market were collected and analyzed using charts and forecasting methods. Trade records for arrived and traded lots, quantities, and values in Tamil Nadu displayed consistent growth. The arrival of lots increased steadily, reflecting potential market expansion. Traded lots and quantities showcased dynamic growth, indicating an expanding trade market. Forecasted values predict a positive trajectory with continuous growth up to 2025-2026. The study underscores the transformative impact of e-NAM in Tamil Nadu. The positive trade quantity and value trends reveal a dynamic and expanding market. Forecasted projections suggest sustained growth, emphasizing the role of the platform in enhancing market efficiency and economic development. While optimistic, acknowledging external factors is crucial for stakeholders to navigate potential challenges. The success of e-NAM exemplifies technologies transformative power in fostering sustainable development in the Tamil Nadu agrarian sector.

An Analysis on the Development of Online Art Sales Industry and the Sustained Competitive Advantages of its Players Based on Porter’s Five Forces Model

The rapid growth of online art sales post-2010 necessitates an examination of industry trends and competitive dynamics. This study investigates the current competitive structure, highlighting the need for online art platforms to cultivate trust and innovate technologically to thrive in a crowded market. Adopting a mixed methodology combining The Platform Economy and Long Tail Theory, the research identifies strengths and weaknesses, revealing parallels with e-commerce retail. Industry analysis, employing Porter’s Five Forces Model, uncovers competition intensity, while a case study on artnet utilizes the VRIO Model to assess sustainable advantages. Traffic metrics and qualitative insights from an interview with a platform representative supplement quantitative data, informing recommendations for enhancing market efficiency and adaptability. Data was sourced from websites and a questionnaire-based email interview.

A Comprehensive Mapping of the Coconut Value Chain Actors in Western Tamil Nadu, India

The coconut industry in Western Tamil Nadu, India, epitomizes the intricate interplay of actors and activities within the agricultural value chain. Despite India's prominent stature as the leading coconut producer globally, challenges persist, including price disparities and limited value addition. This study investigates the coconut value chain in Western Tamil Nadu, mapping its actors and activities to elucidate opportunities for enhancing efficiency and sustainability. Employing a multi-stage sampling technique, data were collected from 300 coconut farmers and various stakeholders. Six value chains were identified, encompassing domestic, processing, and export markets. Primary actors, including farmers, harvest contractors, commission agents, wholesalers, retailers, processors, farmer producer companies (FPCs), and exporters, were analyzed in terms of their roles and activities. Results highlight the significance of value chain mapping in understanding market dynamics and optimizing resource allocation. Channel III, facilitated by FPCs, emerged as a promising avenue for reducing costs and enhancing market efficiency. This study emphasizes the importance of informed decision-making and strategic interventions for fostering a sustainable and vibrant coconut industry in Western Tamil Nadu and beyond.

Optimisation of Game Strategies Under Information Asymmetry Between Insurance Companies and Consumers

The information asymmetry common in insurance markets has a profound impact on market operations and outcomes, and game theory provides a framework for understanding and responding to this problem. This paper explores the manifestation of information asymmetry in the insurance market and the application of game theory, as well as the optimisation of strategies between insurers and consumers. On the part of insurance companies, game strategies are optimised through measures such as improving information transparency, enhancing risk assessment and establishing long-term cooperative relationships, while consumers actively participate in the market game by enhancing their awareness of self-protection, improving their insurance knowledge and choosing reputable insurance companies. These optimisation measures help to improve the market environment, enhance market efficiency and achieve a win-win situation in the insurance market, laying the foundation for the healthy development and sustainable stability of the insurance market.

International Currency Circulation and Monetary Policy

This paper explores the challenges in the internationalization of local currencies and the establishment of a robust International Currency Circulation (ICC) mechanism. Employing an innovative Agent-Based Model (ABM) grounded in Behavioral Finance, our research examines the interdependence between currency circulation and inter-bank treasury bond market. Through simulations, we analyze the impacts of monetary policies, increased overseas holdings, and investor sentiments on treasury bond prices and market activities. Our findings underscore the pivotal role of a robust monetary policy, the strategic optimization of market structures through overseas institutions, and the non-linear relationship between investor sentiment and treasury bond prices. This study contributes valuable insights for policymakers, investors, and academics, emphasizing measures to enhance market efficiency and stability in the realm of international currency circulation.

REVIEW OF AI TECHNIQUES IN FINANCIAL FORECASTING: APPLICATIONS IN STOCK MARKET ANALYSIS

This scholarly inquiry delves into the burgeoning intersection of Artificial Intelligence (AI) and financial forecasting, particularly within the stock market domain. The study's backdrop is set against the rapid evolution of AI techniques, which have significantly altered the landscape of financial analysis. The primary aim is to dissect and evaluate the impact of AI on stock market predictions, juxtaposing its capabilities against traditional forecasting methods while navigating through the ethical and practical complexities inherent in AI implementation. The scope of the paper encompasses a comprehensive review of AI's evolution in financial analysis, its comparative effectiveness, and the sector-specific applications in stock markets. Methodologically, the study employs a systematic review of existing literature, focusing on peer-reviewed articles that shed light on the performance, challenges, and future prospects of AI in stock market forecasting. The findings reveal AI's profound potential in enhancing market efficiency and volatility understanding, albeit tempered by challenges such as data quality issues, model interpretability, and the need for robust regulatory frameworks. The main conclusions underscore AI's transformative role in financial forecasting, highlighting its ability to analyze vast datasets and predict market trends with heightened accuracy. However, the study also acknowledges the limitations within AI models, emphasizing the necessity for a balanced approach that integrates AI with traditional methods and continuous algorithmic refinement. Recommendations advocate for collaborative efforts between technologists, ethicists, and financial experts to develop ethically sound, transparent, and effective AI applications. In summary, this paper offers a panoramic view of AI's role in financial forecasting, serving as a guidepost for future explorations in this field. It underscores the immense possibilities and intricate challenges of AI in the dynamic landscape of stock market analysis, paving the way for a new era of data-driven decision-making in finance.
 Keywords: Artificial Intelligence, Financial Forecasting, Stock Market Prediction, Machine Learning, Ethical Considerations, Regulatory Frameworks.

Energy Performance Certificates and the Capitalization of Utility Costs in Rents: The Potential Role of Asymmetric Information and Uncertainty

This paper is the first to investigate the relationship between the energy efficiency of dwellings, measured by the energy performance certificate (EPC), and utility cost inclusion in rental prices. First, we investigate potential drivers behind the decision to include utility costs in rents. We find that labeled dwellings are more likely to include utility costs and that this likelihood is higher among energy-efficient dwellings than among inefficient dwellings. Next, we surprisingly find that utility costs seem to be under-capitalized in energy-inefficient dwellings. These results are confirmed with the counterfactual decomposition approach. Overall, the findings indicate that the EPC labeling policy may be important for both landlord and tenant decision-making and may enhance market efficiency.

THE ROLE OF COMPETITION IN PRIVATE ENTERPRISE AND ITS IMPLICATIONS FOR MARKET EFFICIENCY

Background: Competition plays a pivotal role in private enterprise systems, impacting resource allocation, pricing, and overall market efficiency. This study investigates the influence of competition on these economic factors, particularly focusing on how monopolistic practices and barriers to entry affect market outcomes. Methods: The paper employs an econometric analysis to evaluate the impact of various sectors on GDP, utilizing regression models to quantify the contributions of agriculture, industry, manufacturing, and services. The analysis integrates theoretical perspectives on competition and monopolistic behaviour with empirical data to assess how different market conditions influence economic efficiency. Results: The findings reveal significant effects of competition on economic outcomes. Agriculture, industry, manufacturing, and services all contribute differently to GDP, with services showing the highest impact. The analysis also highlights the negative implications of monopolistic practices, including resource misallocation and higher prices, while barriers to entry and strategic behaviour further influence competitive dynamics. Conclusions: Effective competition fosters optimal resource utilization and market efficiency, benefiting consumers and the broader economy. Monopolistic behaviour and entry barriers can lead to inefficiencies and suboptimal outcomes. The study underscores the need for robust competition policy and regulation to enhance market efficiency and drive economic growth.

The Impact of AI on Sustainable Development of The Labor Market

With the emergence of generative AI technologies like ChatGPT, the conversation has shifted increasingly toward the possibility that machines will take over human jobs. The rise of artificial intelligence (AI) poses a double challenge to job security and the labor market organization while also bringing about remarkable efficiencies and introducing fresh job sectors. The complex landscape requires intentional government intervention based on fundamental economic principles. Information asymmetry is emphasized in discussing AI’s incorporation into the labor market, utilizing Joseph Stiglitz’s information theory. Discrepancies in access to information about emerging technologies and the required skills for the AI-driven economy could exacerbate market inefficiencies and result in job loss. Stiglitz’s remarks underscore the crucial need for governments to actively address these information gaps to enhance market efficiency and promote worker well-being. Potential actions could involve promoting partnerships between educational institutions and businesses to align curriculum with evolving labor market demands and mandating that companies reveal the incorporation of AI into their practices to enhance the accuracy of workforce development plans. This paper analyses the role of government in harmonizing AI’s impact on the labor market, AI’s influence on labor market dynamics, and navigating job displacement in the AI era. In addition, the relevant recommendations are proposed.

A policy model for regulating and standardizing financial advisory services in Nigeria’s capital markets

This paper presents a policy model for regulating and standardizing financial advisory services in Nigeria’s capital markets, addressing the urgent need for structured oversight to enhance market integrity and investor protection. The model proposes a comprehensive regulatory framework that integrates stringent standards for financial advisors, uniform guidelines for service delivery, and robust compliance mechanisms to ensure adherence to best practices. It emphasizes the establishment of a central regulatory body responsible for setting and enforcing standards, alongside a system of periodic audits and performance evaluations to maintain high-quality advisory services. Key components of the proposed policy include the development of a certification process for financial advisors, mandatory training programs, and the implementation of transparent disclosure requirements to aid investors in making informed decisions. The model also advocates for leveraging technological advancements, such as digital platforms for real-time monitoring and data analytics, to streamline regulatory processes and enhance transparency in financial advisory practices. The policy model aims to address existing gaps in regulatory oversight by providing clear guidelines and enforcement mechanisms to prevent conflicts of interest, ensure ethical conduct, and foster investor confidence. By aligning Nigeria’s capital markets with international best practices and integrating technological solutions, the model seeks to create a more resilient and trustworthy financial advisory environment. Expected outcomes include improved service quality, increased investor protection, and enhanced market efficiency. The model’s implementation will contribute to greater market stability and confidence, supporting sustainable growth in Nigeria’s capital markets. This approach not only addresses current regulatory challenges but also positions Nigeria as a leader in financial advisory standards within the global financial community.

Does Attenuating Endowment Effect Improve Efficiency? An Overview of Applications

This article explores the impact of the endowment effect on market efficiency, with a specific focus on attenuating this effect to improve overall efficiency. The analysis is grounded in a classical three-dimensional theoretical framework encompassing willingness to pay (WTP), willingness to accept (WTA), and loss aversion. The article discusses strategies to narrow the gap between WTA and WTP and reduce loss aversion as means to mitigate the endowment effect and enhance market efficiency. Drawing on literature reviews and case studies, the paper examines three applications of the endowment effect in the context of P2P payment, organizational co-determination, and the U.S. drinking water supply. The findings suggest that attenuating the endowment effect has the potential to improve efficiency, foster cooperation, and provide practical methods for enhancing market outcomes. To improve the future study, this paper provides insight to mitigate the endowment effect by combining closing the WTA-WTP discrepancy and reducing the loss aversion, introducing proxy variables based on specific contexts for empirical study.

Limits of arbitrage and their impact on market efficiency: Evidence from China

This paper examines the impact of limits of arbitrage (LOA) on market efficiency by considering a large sample of Chinese stocks. Intraday market efficiency is measured using two widely used measures: the intraday return predictability measure and the variance ratio measure. We find that LOA plays a major role in driving market efficiency. We also use an exogenous shock, the introduction of Shenzhen Stock Connect, which increases trade volume and lowers LOA, to examine the impact of LOA on market efficiency. Our results indicate that this event improves market efficiency regarding both our measures. Therefore, we conclude that policy makers in China are acting in the right direction to elevate China's status in world markets by adapting policies that inherently make the country more attractive to global investors by lowering LOA and enhancing market efficiency.

The Impact Automated Market Makers on Stock Prices and Returns

Examining the impact Automated market makers have on stock prices and returns is the focus of this study. Automated market makers are state-of-the-art financial instruments that facilitate the bilateral trade of digital assets in the DeFi industry. Market participants trade against pools of liquidity rather than individual buyers and sellers. Market returns, price changes, and liquidity levels will be analyzed both before and after the introduction of automated market makers. Well take a look at how automated market makers affect transaction costs, liquidity, and trading volume. Major stock markets that employ automated market makers will be surveyed for data collection and analysis. The effect of liquidity ratios on market performance will be studied statistically. Understanding the impact Automated market makers have on market returns and pricing is critical for traders, regulators, and researchers. Improved investment returns and the development of decentralized financial ecosystems may result from incorporating the results into market structure and liquidity. The implementation of automated market makers on centralized stock exchanges has been aimed at enhancing market efficiency. This research investigates the impact of automated market makers on stock prices and returns. Using monthly market returns from the closing index and equity price data, the study examines the effects before and after the implementation of automation. The study utilizes a longitudinal research design, analyzing listed firms with data spanning the study period. The findings contribute to the understanding of the influence of automated market makers on stock prices and returns, particularly in regions with limited literature on the subject.