Enhance Market Efficiency Research Articles

ETFs and the price volatility of underlying bonds

AbstractWe investigate whether exchange traded funds (ETFs) distort bond prices or increase price volatility. Contrary to concerns, we find that ETF ownership of corporate bonds is linked to reduced price volatility, likely due to ETFs absorbing bond illiquidity. Monthly net ETF inflows (outflows) increase (decrease) volatility for investment‐grade bonds but have no effect on high‐yield bonds. Additionally, daily ETF flows negatively correlate with same‐day bond returns. Overall, our results suggest that bond ETFs enhance market efficiency, acting as a liquidity buffer rather than contributing to demand pressure as seen with stock ETFs.

Examining the Impact of Macroeconomic Variables on the NEPSE Index: VECM Approach

NEPSE plays a pivotal role in Nepal’s financial ecosystem, fostering economic growth, corporate development, and investment opportunities. The study of macroeconomic variables’ impact on the NEPSE Index is significant as it provides insights for investors and policymakers to make informed decisions, enhancing market efficiency and economic stability. This study examines the impact of macroeconomic variables on the NEPSE Index using secondary data from 1994 to 2023 and the VECM approach, revealing a long-run equilibrium among variables with significant influences from GDP, WTI, IR, and M2. The analysis highlights rapid adjustments towards equilibrium, negligible effects from lagged GDP and IR changes, and asymmetric causal relationships observed in Engle-Granger tests. The regression analysis shows moderate explanatory power with significant model fit indicators and reveals no residual autocorrelation or serial correlation, suggesting robustness in modeling the NEPSE Index dynamics. Recommendations include leveraging these findings for targeted policy interventions to enhance market stability and investor confidence.

A Reputation-Based Pricing Strategy for Distributed Diverse Entity Systems: Enhancing Market Efficiency Through Real-Time Reputation Updates

Although existing studies address the reduction of default rates by adjusting electricity trading rankings based on reputation values, the mechanisms for penalizing electricity trading defaults remain incomplete. Therefore, this paper proposes a real-time reputation-based pricing method for distributed diverse entity systems to mitigate electricity trading defaults. First, a reputation reward and penalty mechanism evaluates the trading behavior of diverse entities. Next, a ‘price-dominant, reputation-auxiliary’ pricing concept guides the process. Following this, a reputation-driven pricing strategy model for distributed adjustable resources allows for bid adjustments based on real-time market dynamics. Upon electricity trading completion, the reputation values of all entities are recalculated and disclosed, enabling entities to adjust future pricing and electricity trading quantities to optimize their profits. This method effectively reduces default rates while alleviating the impact of market electricity tradings on peak-to-valley fluctuations. Finally, simulations conducted on the MATLAB 2018b platform confirm the rationality and feasibility of the proposed real-time reputation-based pricing strategy within distributed diverse entity systems.

Price signals across onion markets in Tamil Nadu

This research investigates price volatility and market integration within select small onion markets across Tamil Nadu, India. Using monthly wholesale price data from the Dindigul, Chennai, Coimbatore, and Idukki markets for the period 2014 to 2023, the study employs an array of econometric methods to analyze price dynamics and market interconnections. The ARCHGARCH model analysis revealed high price volatility persistence in selected small onion markets with the Coimbatore market displaying the highest levels of volatility. The Augmented Dickey-Fuller test established stationarity within the price series, while cointegration tests indicated a long-term equilibrium relationship across the markets analyzed in Tamil Nadu. Further examination through the Granger causality test revealed that price movements in the Coimbatore market had a causal impact on the Dindigul market. Additionally, the markets in Coimbatore, Dindigul, and Chennai were shown to exert influence over the Idukki market in Kerala. The Vector Error Correction Model (VECM) analysis demonstrated that in response to disequilibrium across the Coimbatore, Chennai, Dindigul, and Idukki markets, short -run price adjustments occurred at rates of 41%, 45%, 46%, and 48%, respectively, working towards restoring long-run equilibrium. In the Dindigul market, price adjustments with a one-month lag had a notable influence on current prices within Coimbatore, Chennai, and Dindigul. These findings underscore the interconnected nature of small onion markets within the region and shed light on price behavior patterns. Insights derived from this study could inform policy measures aimed at stabilizing prices and enhancing market efficiency in the small onion markets.

Research on the Role of Risk Hedging and Arbitrage Strategies in Financial Derivatives Investment

In financial derivatives investment, risk hedging and arbitrage strategies play a key role. Risk hedging aims to reduce the uncertainty caused by asset price fluctuations and provide investors with stable returns through derivatives such as futures, options, and credit default swaps (CDS). This strategy is widely used by enterprises and financial institutions to manage commodity prices, exchange rates, and credit risks. Arbitrage strategies use market price differences, especially pricing differences in different markets or at various time points, to seek risk-free or low-risk returns. This paper reviews existing literature on financial derivatives, sorting out the role of risk hedging in financial derivatives investment. It summarizes the basic principles, application cases, advantages, and limitations of risk hedging. Additionally, the research delves into the role of arbitrage strategies in financial derivatives investment, covering its basic principles, types, application cases, advantages, and limitations. This research finds that hedging strategies, through instruments such as futures, options, and credit default swaps, provide effective risk management by stabilizing returns and reducing the impact of market volatility. Arbitrage strategies, on the other hand, exploit market inefficiencies to generate low-risk or risk-free profits, thereby enhancing market efficiency and liquidity. Despite these advantages, this paper also highlights certain limitations, such as the complexity and cost of hedging instruments, and the diminishing arbitrage opportunities as markets become more efficient.

Government Restriction, Trading Network, and Bond Yield: A Quasi-Natural Experiment Based on the Chinese Inter-Bank Bond Market

ABSTRACT Whether government regulation can improve market efficiency is a contentious and significant issue. This study examines spot transaction data from China’s bond market in 2018 and concludes that government regulation directly corrects market pricing. Additionally, it empowers the secondary market to improve its price discovery and risk identification functions, thereby enhancing market efficiency. The study does this by using the introduction of pricing restrictions on local government bond issuance in August 2018 as a quasi-natural experiment. Unlike existing studies that focus more on ordinary markets, this study provides a representative example of financial markets for the public interest theory of regulation.

Ownership structure and market competition in the Gulf cooperation council

Purpose This study aims to examine the impact of sector market competition on the ownership structure of publicly listed firms in the Gulf Cooperation Council (GCC) countries. Design/methodology/approach The authors employed the system-Generalized Method of Moments (GMM) model for panel data, utilizing over 93,000 data points from 6,215 firm-years across six GCC countries from 2010 to 2020. Findings The results reveal a statistically significant negative relationship between total block holders and institutional ownership for both Herfindahl–Hirschman and Tobin’s-Q competition proxies. This finding suggests that companies operating in monopolistic or oligarchic sectors are likely to attract block holder investors. Moreover, various firm- and country-level factors, including return on assets, growth, size, gross domestic product and political crisis, also exhibit significant relationships with specific ownership variables. Practical implications Investigating the influence of competition on block holders’ ownership in the GCC region may provide new insights into the role of competitive markets in fostering economic development, promoting investor protection and shareholder rights, enhancing market efficiency and competitiveness and implementing effective reform policies and strategies. Originality/value Despite the significant contribution of GCC countries to global oil commodities, limited market competition research has been conducted in these markets. This study aims to fill this gap by investigating the impact of various firm-, industry-, and country-level factors on firm ownership structure in the GCC region, focusing on the influence of market competition.

Enhancing Carbon Trading Mechanisms through Innovative Collaboration: Case Studies from Developing Nations

Against a backdrop of global climate change mitigation efforts, carbon trading has emerged as a critical mechanism, yet developing countries often lack the necessary infrastructure and collaborative frameworks. The research investigates how intermediaries facilitate stakeholders and employ innovative practices to foster effective carbon trading markets. Methodologically, it employs a qualitative approach, conducting in-depth case studies of four intermediaries through 32 semi-structured interviews and archival document. Findings underscore the pivotal role of digital platforms in enabling real-time trading, rigorous standardization processes to ensure market credibility, and multi-stakeholder engagement strategies that promote inclusive participation. This research contributes to the theoretical understanding of carbon trading by illustrating how digital platforms and technological integration enhance resource acquisition and allocation. It emphasizes the significance of standardization and verification processes in building trust among stakeholders, crucial for effective supply chain collaboration. Practically, the study highlights the benefits of these approaches in enhancing market efficiency and transparency. It also underscores the importance of multi-stakeholder engagement strategies and strategic alliances for creating resilient and inclusive carbon trading markets, offering actionable insights for stakeholders and policymakers alike.

AI-Powered Autonomous Trading: Enhancing Market Efficiency Through Predictive Analytics

Abstract: In the realm of financial markets, AI-powered autonomous trading systems are revolutionizing the way trades are executed and decisions are made. This paper presents a formal approach to developing an AI-powered trading system aimed at enhancing market efficiency through predictive analytics. The proposed system integrates machine learning algorithms, including deep learning and reinforcement learning, to analyze vast amounts of historical and real-time market data. Emphasis is placed on improving decision-making speed, accuracy, and risk management through techniques such as algorithmic trading, automated portfolio management, and sentiment analysis. The result is a robust, efficient, and scalable trading system that outperforms traditional models in terms of profitability, adaptability, and computational efficiency in high-frequency trading environments

Vertical Integration of the Rice Market in Lampung Province

Rice prices in Indonesia rose significantly in early 2024, with premium-quality rice reaching IDR 16,410 per kilogram—a 21.3% increase compared to the previous year. These disparities stem from weaknesses in the agribusiness structure, poor market integration, and insufficient market information. This study aims to analyze rice market integration at the farmer-producer and consumer levels in Lampung Province. Secondary time-series data on grain prices at the producer level and retail rice prices at the consumer level from 2018 to 2023 were analyzed using price correlation, price transmission elasticity, and the IMC method. Results indicate a consistent upward trend in both retail rice and farmers’ grain prices, which are positively correlated. However, price transmission elasticity reveals that price changes at the farmer-producer level outpace those at the consumer level. The analysis concludes that rice market integration in Lampung is weak in the short term but demonstrates strong integration over the long term. These findings highlight the need for improved agribusiness structures and better market information systems to enhance market efficiency

Paratransit services and efficiency: The potential of utilizing idle taxis in a strained welfare market

With population aging, demand for paratransit services has increased, leading to higher operating subsidies. This study uses Japan as a case study to examine the substitutive relationship between subsidized noncommercial wheelchair-accessible vehicles (WAVs), primarily owned by elderly care facilities, and taxis, highlighting inefficiencies in the open-entry paratransit market. Using nationwide panel data, we find that a 10% increase in WAVs results in a 1.7% decrease in taxi revenue, reducing taxi drivers' income by approximately 5%. This effect is more pronounced in regions with higher aging rates and welfare spending. We suggest utilizing vacant taxis for paratransit services to enhance market efficiency.

Industry-specific information disclosure regulation and corporate ESG performance: Evidence from China

Finding ways to promote environmental, social, and governance (ESG) development through effective capital market regulation is crucial. China has initiated industry-specific information disclosure regulation since 2013 to enhance market efficiency and drive economic transformation. Using data of listed firms from 2009 to 2020, this study employs a difference-in-differences model to investigate the influence and underlying mechanisms of industry-specific information disclosure regulation on corporate ESG performance. The findings indicate that industry-specific information disclosure regulation can promote corporate ESG performance by enhancing the comparability of accounting information, which fosters industry competition, facilitates external oversight, and reduces internal agency costs. Heterogeneity tests reveal that the influence is more pronounced in enterprises with higher industry fundamentals relevance, state-owned enterprises, and enterprises in regions with high market development. These findings broaden the scope of information disclosure regulation studies and offer tangible regulatory strategies for transitional economies to bolster ESG development.

Analysis of Monopoly Practices and Business Competition in the Limboto Traditional Market

This study analyzes the practices of monopoly and business competition in the traditional market of Limboto, Gorontalo. The Limboto traditional market is a significant trade center in the region, involving various types of goods and services. The study aims to explore how monopoly practices and competition dynamics affect market conditions, commodity prices, and the welfare of both traders and consumers. The research methods include direct observation, interviews with market participants, and secondary data analysis. The findings indicate the presence of monopoly practices by several major traders that impact the prices and accessibility of goods in the market. Additionally, business competition in the Limboto traditional market exhibits unique characteristics, including informal collaboration among traders and the role and influence of the local market on competition. This research is expected to provide insights for policymakers and stakeholders in formulating strategies to enhance market efficiency and local economic welfare. Keywords : Monopoly, Business Competition, Traditional Market

Exploring The Impact Of Machine Learning On Financial Markets: Opportunities, Risks, And Regulatory Challenges

The increasing use of machine learning in finance is creating the potential to transform the financial industry, offering opportunities for improved risk management, fraud detection, trading strategies, and customer experience. However, there are also significant risks associated with the use of machine learning in financial markets, including data privacy concerns, algorithmic bias, and the potential for unintended consequences or "black swan" events. Additionally, there are regulatory challenges in ensuring that the use of AI in finance complies with existing laws and regulations, as well as developing new rules and standards as needed to address emerging issues. This study involved a comprehensive analysis of financial market indices, namely the S&P 500, NASDAQ Composite, and FTSE 100. These indices were chosen as representative benchmarks for the U.S. and UK financial markets. Historical data for these indices was collected and examined, covering a period of five years to capture a significant timeframe for analysis. The research findings indicate several key implications of machine learning for financial markets: The application of machine learning algorithms has the potential to enhance market efficiency by processing vast amounts of data, identifying patterns, and generating insights in real-time; contribute to better risk management strategies by providing advanced risk models and early warning systems; and development of sophisticated trading strategies by analyzing market data, identifying trends, and generating trading signals. However, the findings also underscore the importance of addressing regulatory challenges. The adoption of machine learning in financial markets presents regulatory challenges that require careful consideration. Regulators need to address issues related to algorithmic bias, data privacy, model interpretability, and system stability to ensure the fair and safe implementation of machine learning techniques in finance. This study highlights the significant impact of machine learning on financial markets, showcasing its potential for improving market efficiency, enhancing risk management practices, and generating alpha through advanced trading strategies. By leveraging financial market indices as benchmarks, this research provides valuable insights into the opportunities, risks, and regulatory considerations associated with the adoption of machine learning in financial markets

Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate

In the rapidly evolving real estate industry, integrating automated valuation models (AVMs) has become critical for improving property assessment accuracy and transparency. Although there is some research on the subject, no thorough qualitative systematic review has been done in this field. This paper aims to provide an up-to-date and systematic understanding of the strategic applications of AVMs across various real estate subsectors (i.e., real estate development, real estate investment, land administration, and taxation), shedding light on their broad contributions to value enhancement, decision-making, and market insights. The systematic review is based on 97 papers selected out of 652 search results with an application of the PRISMA-based method. The findings highlight the transformative role of AVMs approaches in streamlining valuation processes, enhancing market efficiency, and supporting data-driven decision-making in the real estate industry, along with developing an original conceptual framework. Key areas of future research, including data integration, ethical implications, and the development of hybrid AVMs approaches are identified to advance the field and address emerging challenges. Ultimately, stakeholders can create new avenues for real estate valuation efficiency, accuracy, and transparency by judiciously utilizing AVMs approaches, leading to more educated real estate investment decisions.

Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets

Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability.

A trust-building model for financial advisory services in Nigeria’s investment sector

The financial advisory sector in Nigeria, while pivotal for guiding investors and enhancing market efficiency, faces significant challenges related to trust. This paper presents a comprehensive trust-building model aimed at addressing the trust deficit within Nigeria’s investment sector. The model proposes a multifaceted approach to enhance trust through improved transparency, accountability, regulatory reforms, and client education. Trust is a crucial element in financial advisory services, influencing investor confidence and market stability. In Nigeria, historical issues such as regulatory inconsistencies, information asymmetry, and instances of financial misconduct have eroded trust between advisors and clients. The proposed trust-building model emphasizes four key areas: enhancing transparency and accountability, implementing regulatory reforms, improving financial literacy among investors, and leveraging technology. Transparency can be achieved through open disclosure of fees, commissions, and potential conflicts of interest, alongside standardized reporting mechanisms. Regulatory reforms are essential for strengthening oversight and enforcing ethical standards. Improving financial literacy involves educating clients about investment options and risks, empowering them to make informed decisions. Technology plays a critical role by offering real-time updates and personalized advice through fintech platforms and AI-driven tools. Case studies from both developed and developing markets highlight successful trust-building strategies, providing valuable insights for the Nigerian context. For instance, countries with robust regulatory frameworks and advanced technological integration offer practical examples of how transparency and accountability can be effectively implemented. Nigerian case studies demonstrate instances where trust-building initiatives have led to increased investor confidence and market stability. Despite the promising aspects of the model, challenges such as institutional resistance, economic instability, and entrenched industry practices may hinder implementation. Overcoming these barriers requires a collaborative effort from regulators, financial advisors, and investors. The model's anticipated impact includes greater investor confidence, market growth, and a more stable investment environment. By addressing the core issues affecting trust and fostering a culture of transparency and accountability, the proposed model aims to transform Nigeria’s financial advisory sector, ultimately contributing to its long-term development and stability. Keywords: Trust-Building, Financial Advisory, Nigeria, Investment Sector

Time series forecasting of price of the agricultural products using data science

Purpose. The purpose of our article is to research and forecast prices for agricultural products using the example of potato prices based on the most effective models using data science techniques. Methodology / approach. Various forecasting models are explored, starting from baseline models like decomposition and exponential smoothing models to more advanced techniques such as ARIMA, SARIMA, as well as deep learning models including neural network. The data is split into training and testing sets, and models are validated using cross-validation techniques and optimised through hyperparameter tuning. Model performance is evaluated using metrics such as MAE, MSE, RMSE, and MAPE. The selected model is then used to generate future price forecasts, with uncertainty quantified through confidence intervals. Results. The study successfully applied advanced data science techniques to forecast potato prices, leveraging a range of effective models. By analysing historical price data and using various forecasting methods, the research identified the most accurate models for predicting future price trends. The results demonstrate that the selected models can provide reliable forecasts. In particular, the results showed that the model could achieve good forecast results when applied to real problems and, thus, can be effectively used for forecasting tasks especially considering seasonality. In addition, it should be noted that the model has a higher prediction accuracy at the time intervals closest to the original data. The obtained results support using both models simultaneously for forecasting, which can compensate for the shortcomings of each of them. The models can be used separately, to more accurately predict the values for the required period, or a combination of them is also possible. Originality / scientific novelty. The study’s originality lies in development of methods for effectively accounting for seasonality in agricultural price data, such as using seasonal decomposition techniques or more advanced techniques that combine statistical and data science approaches. The novelty implies the implementation of real-time data processing and forecasting system allows for the timely prediction of price changes, enabling stakeholders to make more informed decisions. Practical value / implications. Forecasting potato prices holds significant practical value for various stakeholders. For farmers, accurate forecasts enable informed decisions on the optimal times to plant, harvest, and sell their crops, thereby optimising their profits. In the supply chain, distributors and retailers can use these forecasts to manage inventory more effectively and plan contracts, reducing waste and avoiding shortages. Policymakers benefit from forecasts by anticipating market fluctuations and stabilising prices, which supports both consumers and producers. For consumers, stable pricing ensures better budgeting and helps avoid sudden price spikes, making essential foods more affordable. Overall, accurate price forecasting enhances market efficiency by reducing uncertainty and aiding investors in managing risk.

To what extent can prisoners dilemma in game theory be used in pricing strategy?

The title of this dissertation is to what extent can prisoners dilemma in game theory be used in pricing strategy? The purpose of the current study was to determine the extent of prisoner's dilemma used in pricing strategies, and how the prisoner's dilemma is used in pricing strategies. We first comprehensive analysis and evaluation of relevant literature about prisoners dilemma and common pricing strategies in literature review section. Then in discussion part, we use game theory models to describe and analyze the prisoner's dilemma in pricing strategies to predict various situations, make the optimal pricing strategies. In the dissertation, we found the effects of prisoners dilemma strategies on payoffs when pricing and explained why cooperative pricing agreements promote mutual benefits and long-term stability than non-cooperative pricing strategies. Then we analyzed the stability of cooperative pricing strategy, we also developed a pricing model based on Bayes theorem. Our research has important implications for economics, marketing and game theory. By applying prisoner's dilemma theory, firms can better understand the behavioral of competitors and enhance decision-making processes, optimize their pricing strategies, thereby enhancing market efficiency and profitability.

The Integration of Fintech in Energy Markets: Economic Benefits and Policy Considerations

This study investigates the impact of, financial technology (fintech) integration, on the efficiency, cost reduction, and, sustainability of energy markets, with a, particular focus on, Pakistan. Employing, the Technology-Organization-Environment, (TOE) framework, the research, explores how fintech, adoption, investment in, fintech, regulatory support, technological, infrastructure, and market transparency, influence key market, outcomes. A structured questionnaire, was distributed to professionals, and stakeholders in the, energy sector, yielding 320,responses. The analysis, conducted using, Structural Equation Modeling (SEM) ,in Smart PLS, reveals that fintech, adoption significantly, enhances market efficiency, while, investment in fintech and market, transparency contribute to, cost reduction. Regulatory, support and technological, infrastructure are crucial, for market efficiency, and sustainability, respectively. The, study underscores the, need for a balanced approach that, includes supportive regulatory, frameworks and robust, technological infrastructure to fully, leverage fintech's potential. The findings, offer valuable insights for, policymakers and energy, market participants, highlighting, the critical role of fintech in driving, economic benefits and addressing, market inefficiencies. Future, research should expand, to multiple countries to validate these findings and explore additional contextual factors.