Enhance Market Efficiency Research Articles

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.

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.

Index futures mispricing: a multi-regime approach to the NIFTY 50 Index futures

PurposeThis study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.Design/methodology/approachThe study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.FindingsThe study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.Practical implicationsThese findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.Originality/valueThe Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.Research highlights (1)This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.(2)We highlight how factors like volatility, futures volume, and open interest vary in their impact.(3)The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.(4)We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

An Overview of the Roles and Functions of Selected Financial Role Players to Protect Financial Stability and Promote Market Integrity in South Africa

The South African Reserve Bank (SARB) and other financial role players such as the Prudential Authority (PA), the Financial Sector Conduct Authority (FSCA), the National Credit Regulator (NCR), the Financial Intelligence Centre (FIC), the Financial Stability Oversight Committee (FSOC), the Financial Sector Contingency Forum (FSCF), the Financial Sector Council of Regulators (FSCR) and the Financial Sector Inter-Ministerial Council (FSIC) play a significant role in the protection and promotion of financial stability and market integrity in South Africa. The SARB has the primary financial stability mandate, whilst the FSCA was established to primarily promote market integrity and enhance market efficiency through protecting and promoting the fair treatment of financial customers and promoting financial literacy programs. As technology advances and new players enter the market, traditional financial institutions are faced with increased competition and the need to adapt and innovate to stay relevant. In this regard, regulatory frameworks and the functions of regulatory bodies should be constantly reviewed to conform to financial technology (fintech) developments and safeguard consumer interests. This article examines the roles and functions of the aforementioned financial role players as regulated by the Financial Sector Regulation Act (FSR Act) and other relevant financial sector laws. This is done to evaluate the effectiveness of their roles and functions to protect and promote financial stability and market integrity in line with fintech and Artificial Intelligence (AI) developments. The paper also evaluates the importance of cooperation and collaboration between the financial role players to fulfil their roles and functions.

Research on Agricultural Product Price Prediction Based on Improved PSO-GA

The accurate prediction of scallion prices can not only optimize supply chain management and help related practitioners and consumers to make more reasonable purchasing decisions, but also provide guidance for farmers’ planting choices, thus enhancing market efficiency and promoting the sustainable development of the whole industry. This study adopts the idea of decomposition–denoising–aggregation, using three decomposition and denoising techniques combined with three single prediction models to form a base model. Various base models are divided into different combinations based on whether the computational structure is the same or not, and the optimal weights of the combinations are determined by using the improved particle swarm optimization–genetic algorithm (PSO-GA) optimization algorithm in different combinations. The experimental results show that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of “high in winter and low in summer”; the semi-heterogeneous-PSO-GA model reduces the MAPE by 49.03% and improves the directional accuracy by 41.52%, compared to the optimal single prediction model, ARIMA. In summary, the combined model has the most accurate prediction and strong robustness, which can provide ideas and references for the difficult problem of determining the optimal weights of the combined model in the field of predicting the prices of agricultural products.

PREDICTION OF SELLING PRICE OF VARIOUS CARS

Abstract – Predicting the selling price of cars is a complex problem that involves analyzing various factors, including the car’s make, model, year, mileage, condition, and other relevant attributes. This study aims to develop a predictive model that accurately estimates the selling prices of cars using machine learning techniques. By leveraging a dataset containing historical data on car sales, we employ multiple regression analysis and other advanced algorithms to identify patterns and key determinants that influence car prices. The proposed model is validated using a separate test dataset to ensure its accuracy and reliability. The results demonstrate that our model can effectively predict car prices, providing valuable insights for buyers, sellers, and automotive industry stakeholders. This predictive tool can assist in making informed decisions, enhancing market efficiency, and ultimately contributing to a more transparent automotive market. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Advanced AI Solutions for Securities Trading: Building Scalable and Optimized Systems for Global Financial Markets

Integrating Artificial Intelligence (AI) into securities trading software has revolutionized the financial markets by enhancing scalability, efficiency, and optimization. This paper explores the historical evolution and advancements in AI-driven trading systems, emphasizing their impact on global financial markets. The study investigates how machine learning, deep learning, and other AI technologies enable sophisticated trading strategies, improve market liquidity, and reduce transaction costs. It also addresses the challenges AI integration poses, including decision-making opacity, bias, increased market volatility, and data privacy concerns. The paper argues for the need for scalable architectures, optimization algorithms, and transparent governance to mitigate these issues. Additionally, human oversight remains crucial for evaluating AI outputs and maintaining accountability. This research aims to provide a comprehensive analysis of advanced AI systems in securities trading, highlighting their potential to enhance market efficiency and stability.

Navigating the Nexus of Urbanization, Technology, and Global Economics in Real Estate Market Dynamics

This article provides a comprehensive analysis of the key factors shaping real estate markets, focusing on urbanization trends, technological advancements, and global economic shifts. Urban migration is fueling demand in metropolitan areas, leading to significant infrastructural developments and a reevaluation of property values. Concurrently, technological innovations like artificial intelligence, virtual reality, and blockchain are transforming real estate transactions and management, enhancing market efficiency and transparency. Additionally, the paper examines the impact of global economic conditions, including trade wars and policy changes, on real estate investment and market stability. By integrating these elements, the article forecasts potential market trends and offers strategic insights for navigating the complexities of the real estate sector. This synthesis not only highlights current influences but also projects future changes in the landscape of real estate, suggesting adaptive strategies for industry stakeholders.

Navigating the Nexus of Urbanization, Technology, and Global Economics in Real Estate Market Dynamics

This article provides a comprehensive analysis of the key factors shaping real estate markets, focusing on urbanization trends, technological advancements, and global economic shifts. Urban migration is fueling demand in metropolitan areas, leading to significant infrastructural developments and a reevaluation of property values. Concurrently, technological innovations like artificial intelligence, virtual reality, and blockchain are transforming real estate transactions and management, enhancing market efficiency and transparency. Additionally, the paper examines the impact of global economic conditions, including trade wars and policy changes, on real estate investment and market stability. By integrating these elements, the article forecasts potential market trends and offers strategic insights for navigating the complexities of the real estate sector. This synthesis not only highlights current influences but also projects future changes in the landscape of real estate, suggesting adaptive strategies for industry stakeholders.

The Principle of Proportionality: Unraveling the Practical Application of Proportionality in the EU Regulations and the Solvency II Directive for Insurance Undertakings

Proportionality, pivotal to EU regulations and Solvency II, tailors rules to insurers’ size and complexity. Inconsistent application by supervisory authorities (NSAs) necessitates clarity to prevent undue costs. This study examines the issue via a review of the literature and industry discussions, emphasizing Solvency II’s introduction of proportionality and the varied interpretations it evokes. Transparent communication is crucial, and regulatory evolution must align with market dynamics, with the European Insurance and Occupational Pensions Authority (EIOPA) fostering convergence. Assessing proportionality mandates a comprehensive evaluation of an insurer’s nature, scale, and complexity. Regulatory distinctions between first-party and third-party risks could enhance market efficiency. Ultimately, a holistic, market-oriented approach is essential for proportionate regulation in the insurance sector, requiring concerted efforts to elucidate frameworks, foster transparency, and align regulatory evolution with market dynamics.

A Framework for Assessing Electricity Market Performance under Different Bidding Zone Configurations

Improper configuration of bidding zones can lead to market efficiency losses, hinder the integration of renewable energy sources (RESs), and reduce grid security. To evaluate the impact of different bidding zone configurations on market performance, we developed a multi-dimensional evaluation framework containing a series of indicators covering aspects of market efficiency, grid security, and sustainability. These indicators facilitate the comparisons among different market dispatch mechanisms. To validate the proposed framework, the reconfiguration of the Italian bidding zones has been applied to a simplified Italian grid model to compare the market performance under different bidding zone configurations. The simulation results indicate that the implemented reconfiguration has led to enhanced market efficiency and security in the Italian power system. However, the reconfiguration shows a comparatively lower reduction in greenhouse gas (GHG) emissions, suggesting a weaker sustainable performance.

MARKET EFFICIENCY AND STABILITY IN THE ERA OF HIGH-FREQUENCY TRADING: A COMPREHENSIVE REVIEW

This comprehensive review analyzes the impact of high-frequency trading (HFT) on market efficiency and stability, synthesizing insights from 50 peer-reviewed articles, industry reports, and regulatory documents. High-frequency trading, which leverages sophisticated algorithms and high-speed data networks, has significantly transformed financial markets. The review confirms that HFT enhances market efficiency by providing liquidity and facilitating rapid price discovery, contributing to tighter bid-ask spreads and lower transaction costs. However, it also highlights several challenges, including market fragmentation, increased volatility, and potential for market manipulation. The review examines how HFT can exacerbate market instability and systemic risks, as demonstrated by incidents like the 2010 Flash Crash. It underscores the importance of robust risk management practices and regulatory measures to mitigate these risks and enhance market resilience. While current regulatory frameworks have had some success, continuous adaptation is necessary to keep pace with rapid technological advancements. Additionally, the review points to the potential of AI and machine learning in improving market surveillance and risk management. Ultimately, the findings suggest that a balanced approach to regulation and innovation is crucial to maximizing the benefits of HFT while ensuring market integrity and stability.

Impact of ownership concentration on stock returns: a case of developed economies

PurposeThe primary objective of this study is to determine how concentrated ownership affects stock returns by country and scale (by market capitalization), like large, medium, and small-cap firms in selected developed economies of the world.Design/methodology/approachUsing a dataset comprising 12,751 annual observations from 850 listed companies from developed economies from 2004 to 2018, the study employs panel data models and instrumental variable estimation to mitigate endogeneity bias.FindingsThe findings reveal a significant and positive correlation between ownership concentration and expected returns on corporate equities in developed economies. Furthermore, the study categorizes firms into distinct size categories and finds nuanced differences in the relationship between ownership concentration and stock returns across large, medium, and small-cap enterprises. The results of the study reveal that ownership concentration (by country) and scale (Large, medium, and small) have a significant and positive impact on the stock returns of firms in developed economies.Practical implicationsthe practical implications of this study extend to investors, firms, policymakers, regulators, and other stakeholders involved in the financial markets. By considering these implications, stakeholders can make informed decisions to enhance market efficiency, investor protection, and overall market integrity.Originality/valueTo the authors' understanding, this study is the first to examine the impact of concentrated ownership on excessive stock returns across countries and scales, with an explicit focus on large, medium, and small companies in select developed economies worldwide.

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.