Market Efficiency Research Articles

A Study of Market Efficiency and Volatility of Jeera Future Trading

In 2004, the Jeera futures contract was launched on the National Commodity & Derivatives Exchange Limited (NCDEX) platform. Since Jeera was launched on the NCDEX it has witnessed volatility, and in the last 10 years, the Jeera rates have doubled on the spot markets. This was due to a fall in production – especially in Gujarat. To examine the performance of the Jeera futures market and its efficiency, econometric models, specifically Johansen’s Co-integration, the Vector Error Correction Model, the Impulse Response and the Variance Decomposition, are used on data from 2005 to 2019 from the NCDEX. The analysis revealed a longterm correlation between spot and futures prices, demonstrating that futures contracts are a viable hedging tool. Information flows unidirectionally from spot markets to future markets. Both markets undergo error correction to restore market equilibrium. The Granger causality test is utilised to assess the causal connection between future and spot prices. The augmented GARCH model was utilised to examine the contemporaneous relationship between spot volatility and unexpected futures trading activity, and the Granger Causality test was utilised to determine causality. The finding supports regulators’, real hedgers’, and other traders’ concerns.

Exploring the factors influencing farmers' purchase behavior towards submersible pumps: A PLS-structural equation modeling study in Coimbatore district

Submersible pumps are increasingly being utilised to facilitate irrigation, providing farmers with a dependable mean to access groundwater. Efficient water management is crucial for enhancing agricultural productivity, with irrigation playing an important role in crop development. The purpose of this research is to examine the factors influencing farmers’ purchasing behaviour for submersible pumps in the Coimbatore region. Data were collected from 380 farmers using a convenience sampling method, employing a well-prepared interview schedule. The study aims to gain a better understanding of how various attributes such as price (P), durability (D), brand loyalty (BL), dealer recommendation (DR), warranty (W), product quality (PQ), spare parts availability (SPA), and after-sales service (AS) influence farmers' purchasing decisions for submersible pumps. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyse the data. The results revealed that factors such as AS, BL, DR, P, and PQ significantly influenced farmers’ purchasing decisions for submersible pumps. The findings underscore the need for regulations that enhance the availability and accessibility of high-quality submersible pumps, along with the development of efficient marketing methods that address the specific needs of farmers.

The Current State and Future Development Directions of Camera Supply Chain Management

Modern camera supply chain management is constantly evolving in the context of globalization, digitalization, and rapid technological advancements, becoming a key factor for camera manufacturers to improve efficiency, reduce costs, and tackle market competition. This study delves into the current state of the camera supply chain, exploring the many challenges and potential opportunities within it. The research shows that while a globalized supply chain brings significant cost benefits to manufacturers, it also presents risks of supply chain disruptions, rising cost pressures, and increasing management complexity. By introducing advanced technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain, manufacturers can enhance the transparency, flexibility, and sustainability of the supply chain. Furthermore, promoting green supply chain management practices will help companies achieve more efficient supply chain management while reducing their environmental footprint. This study ultimately suggests diversification of supply chains, technological innovation, and sustainable development as the core strategies for future camera supply chain optimization to address the complex and volatile market environment.

Problems and Solutions in US Stock Market

This research explores critical challenges facing the U.S. stock market, specifically focusing on the impacts of algorithmic trading-induced market volatility and cybersecurity threats. Algorithmic trading, which utilizes high-speed computer systems to make trades, has increased market efficiency but also introduced risks of extreme price fluctuations and flash crashes. In parallel, the digitalization of financial markets has heightened exposure to cybersecurity threats, posing risks to trading integrity and data security. To address these issues, this study proposes two targeted solutions. For the issue of market volatility driven by algorithmic trading, the research recommends implementing circuit breakers and trading halts. These mechanisms can prevent severe market disruptions by temporarily pausing trading during extreme price movements, allowing the market to stabilize and preventing panic-driven selloffs. For the cybersecurity challenges, the study suggests enhancing information sharing and fostering collaborative defense strategies among financial institutions and regulatory bodies. By encouraging greater collaboration, the financial sector can more effectively identify and counteract cybersecurity threats, reducing vulnerabilities across the market.

Overconfidence, managerial accounting competence and dividend smoothing: evidence from emerging markets

PurposeThe purpose of this research is to review the dividend smoothing effectiveness from the perspective of managers' overconfidence and accounting competence. Accounting competence is considered as an important factor in recognizing management’s ability to override internal controls as an opportunity to distort financial reports.Design/methodology/approachThe current study applies multivariable linear regression method estimator to investigate the relationship between Overconfidence, Managerial Accounting Competence and Dividend Smoothing of 1,320 firm-year observations in Iran for the period of 2012–2022.FindingsThe result show that manager overconfidence leads to dividend smoothing. Moreover, this relation is stronger in low information quality and not driven by high information quality or by others measures. This research show that accounting competence has led to positive change in the efficiency of the manager performance and reduce the self-interesting motives of manager.Practical implicationsIn the present study, the weaknesses caused by the ambiguity of capital market efficiency in market performance-based statistical models are compensated and partially covered by classifying the relationships and implementing models in each group. Results obtained from this study will aid market practitioners to evaluate the firms’ dividend smoothing. The results provide evidence and information for policymakers and investors about the theoretical gap and the factors affecting to it. It also informs policymakers to the dividend smoothing associated with the manager characteristics.Originality/valueThe previous researches emphasize on limiting agency costs by creating limits for the optimistic actions of managers, while framing and standardizing a major part of management behaviors is not possible. In this research, the manager’s executive ability has been examined in the form of accounting competence at the same time as excessive self-confidence, in order to control part of the inherent limitations caused by the managers' behavior. This study also considers the positive aspect of managers' ability in the form of accounting competence.

ANALYSIS OF MACHINE LEARNING AND AI TO ENHANCE MARKETING NEEDS AND CUSTOMER SATISFACTION

The development of Machine Learning (ML) and Artificial Intelligence (AI) technologies has revolutionized various industries, including marketing and customer satisfaction. In the modern competitive business era, companies are increasingly relying on this technology to improve operational efficiency and effectiveness, especially in answering marketing needs and customer satisfaction. This study aims to analyze the role of ML and AI in strengthening aspects of marketing and customer satisfaction in the business sector. The research method uses a qualitative approach with data collection techniques through literature studies. After the data is collected, it is then analyzed by the stages of filtering relevant data, presenting key information, and answering the research objectives in the conclusion. The results of the study show that the application of ML and AI can significantly improve the marketing effectiveness of companies through personalization of products and services, conducting more accurate customer segmentation, predicting consumer behavior, and optimizing various aspects of marketing. On the other hand, the application of ML and AI also plays an important role in improving customer satisfaction. For example, with the use of intelligent chatbots and customer feedback analysis, companies can understand the shortcomings that need to be fixed, then improve the quality of customer service. So, by utilizing this technology, companies can increase efficiency in marketing, drive increased sales, and build more solid relationships with customers, which ultimately contributes to increased customer satisfaction.

THE INFLUENCE OF PRICE EARNINGS RATIO, PRICE TO BOOK VALUE, AND RETURN ON EQUITY ON STOCK RETURNS IN COMPANIES CONDUCTING INITIAL PUBLIC OFFERINGS FOR THE 2022-2023 PERIOD

This study aims to analyze the effect of financial ratios such as PER, PBV, and ROE on stock returns in companies that have just conducted an IPO in the 2022-2023 period. The research population consists of companies that conducted IPOs on the Indonesia Stock Exchange (IDX) in the period July 2022 to July 2023. The study population was taken using the purposive random sampling method, which combines purposively sampling with a randomization process, to ensure valid representation. This study uses data on the financial statements of companies listed on the IDX during the period 2022-23. Based on the results of the research that has been conducted, it can be concluded that there is a significant influence between financial ratio and stock returns. The regression model developed was able to explain about 42.5% of the total variation in stock returns, showing that the independent variables used made a significantly contribution in explaining the fluctuations in stock return. This research could offer valuable implications for investors in evaluating IPO companies and assist companies in maintaining high profitability and good market reputation before going public. Moreover, findings could inform capital market regulators in developing policies aimed at enhancing investor protection and market efficiency.

The Potential of AI Technology in Marketing

With the rapid development and increasing popularity of artificial intelligence technology, its influence has penetrated into every corner of the social economy. The marketing field has also ushered in unprecedented changes. The introduction of AI technology has not only reshaped the strategies and execution methods of traditional marketing, but also opened up new paths for the future development of marketing. This paper conducts a case study on Amazon to explore the wide application direction of AI technology in the field of marketing, especially focusing on how AI learning systems play a key role in various core processes of marketing, and the emerging development trends emerging in this process. Through research, it is found that AI technology still has great development potential in marketing in many aspects such as market and customers. At the same time, the challenges that come with it cannot be ignored. It requires joint efforts from both inside and outside the industry to ensure that AI technology can develop healthily and sustainably in the field of marketing. From a practical perspective, this study helps companies better understand and apply AI technology to improve the efficiency and effectiveness of marketing, so as to formulate more scientific and effective marketing strategies. From a theoretical perspective, this study helps enrich marketing theory and promote the development of marketing disciplines. The introduction of AI technology provides new tools and methods for marketing, but also brings new challenges and problems. In-depth research on these issues will help improve the marketing theory system and provide theoretical support for future marketing practices.

The Influence, Selection, and Management Strategies of AI Empowered Platform KOLs: A Case Study of Xiaohongshu

In the era of self-media, the influence of Key Opinion Leaders (KOLs) on social platforms is growing and becoming a key driver of marketing. Compared to the traditional media era, KOL marketing demonstrates greater interactivity and precision. This article takes the Xiaohongshu platform as an example. Firstly, the differences in the current situation and characteristics of KOL marketing between the traditional media era and the current era are introduced, followed by an exploration of how AI technology can empower the influence of platform KOLs. It also analyses the important role of different levels of KOLs in AI technology-enabled management strategies. Artificial Intelligence (AI) technology, utilizing refined content creation optimisation, personalised audience targeting, enhanced effective interaction mechanisms, real-time data monitoring and analysis, and improved operational management efficiency. It builds a more efficient and precise marketing ecosystem for KOL groups. By analysing, this article believes that AI technology has a significant impact on enhancing the influence and commercial value of KOLs.

Analysis of New Media Marketing Strategies for Sports Brands - A Case Study of Nike

This study explores the significant changes in Nike's marketing approach in today's rapidly changing new media environment. With the rapid development of digital technology, traditional marketing models have gradually been replaced by more accurate, interactive, and efficient digital marketing strategies. Against this background, this paper deeply analyzes the opportunities and challenges faced by Nike in the digital transformation process, as well as the evolution and status of its business model. This study draws the following main conclusions through this in-depth and detailed analysis. First, although Nike has achieved remarkable results in digital transformation, especially the innovation and implementation of its digital marketing strategy, which has greatly enhanced its brand influence and market penetration, its over-reliance on the direct sales business model has, to a certain extent, limited the further growth of the company's overall revenue. Based on this, this paper puts forward targeted suggestions for the sustainable development of Nike, aiming to continuously optimize its marketing strategy and adjust its business structure to better adapt to market changes in the new media environment.

Assessing the efficiency of pacific Asia Islamic stock markets using multifractal detrended fluctuation analysis

This paper aims to assess the informational efficiency of six Islamic stock markets in the Asia-Pacific region. To achieve this, the study pursues two specific objectives: first, to capture and quantify the degree of multifractality, and second, to identify the sources of this multifractality within these markets. Utilizing the Multifractal Detrended Fluctuation Analysis (MF-DFA) method, the multifractal behavior of these markets is analyzed. The empirical findings, derived from the application of generalized Hurst exponents, Rényi exponents, and the singularity spectrum, indicate that the six Islamic markets studied display multifractal properties, suggesting a lack of market efficiency. Furthermore, by employing shuffling and phase randomization techniques, it is determined that long-term correlations and heavy-tailed distributions jointly contribute to the observed multifractality. The results of this study have several practical implications for various market stakeholders. Investors should incorporate multifractal analysis into their strategies, focusing on long-term correlations and extreme events to enhance predictive accuracy and risk management. Diversification can help mitigate risk, while remaining vigilant about long-term trends and market patterns. Policymakers should improve market transparency, address inefficiencies, and implement targeted regulations to enhance market stability, such as stress-testing for extreme events and long-term trends. Future research should explore the factors driving multifractality to improve financial models.

An empirical study on maize value chain in Tamil Nadu

Maize is one of the leading staple cereals in the world in terms of production and it is termed as a most versatile and multipurpose industrial and energy crop. Due to varied number of value-added products evolving in the recent years, maize has a major shift towards indirect consumption in Tamil Nadu. Therefore, the study is selected to analyse market efficiency of different marketing channels in the maize value chain in Tamil Nadu. The primary data on cultivation of maize is collected from 60 maize producers of Perambalur and Salem districts of Tamil Nadu. Value chain analysis of maize includes producers, traders, commission agents, processors (poultry feed units), wholesalers, retailers and the consumers (livestock farmers). Price spread and marketing efficiency of different marketing channels is calculated. Among the three existing marketing channels of maize in Tamil Nadu, Channel III is considered to be the best channel by the maize producers and it is confirmed by the Acharya’s approach which have higher marketing efficiency. The farmers share in consumer rupee in the marketing channel III was high compared to other marketing channels. The study is intended to help the maize farmers in adopting a better marketing channel where they can get better profit in maize cultivation by adopting less intervention of market intermediaries.

Financial intermediation development model as a factor of growth of population welfare in post-Soviet countries

The economy of post-Soviet countries has its own specifics, including those related to the development of the financial system and the financial intermediation market. Based on the results of a study, the author has identified a problem associated with the lack of sound tools that could be used to improve the efficiency of the financial intermediary market. The main directions of such a process are mobilization of savings, distribution of financial assets, payment system, risk management and control over market agents involved in financial transactions. The digital or cyber-social approach proposed by the author involves the use of innovations in the financial market, expanding the accumulated pool of knowledge and increasing the intellectual capital of financial intermediaries, and the active use of information and communication networks to perform various financial transactions and provide financial services. The unique characteristics of financial intermediation development in former USSR countries are determined by the emerging type of economy, characteristic of many states, which causes an insufficient degree of development of traditional forms of financial intermediation, the use of specific tools, such as Islamic finance, and poor penetration of digital technologies into the economy.

Using the wisdom of crowds in sports: how performance analysis in football can benefit from the information enclosed in betting odds

ABSTRACT Betting odds have proven to contain highly accurate information on football matches, which researchers have used to study sports integrity, human behaviour, economic theories or forecasting, but widely neglected in performance analysis to date. The paper intends to stimulate changing this as betting odds contain a wealth of information usable to control for the inherent playing strength of teams as a situational variable and thus to better distinguish between strength, performance and success. Crowd wisdom and market efficiency are discussed as theoretical concepts driving the accuracy of betting odds. Moreover, based on a dataset of nearly 100 000 matches from 17 seasons in 17 European men’s football leagues, this study demonstrates that odds outperform methods related to points or goals in predicting the match winner as well as the anticipated number of goals by both teams. The code used in this study is made available to public in order to provide performance analysts with tools to extract information from odds. Finally, examples on how to benefit from betting odds in performance analysis are highlighted. Results of these use cases suggest that the relationship between performance and success can indeed be confounded by team strength, emphasising the need for further investigation.

Measuring the Systemic Risk of Clean Energy Markets Based on the Dynamic Factor Copula Model

This study is based on the stock returns of 11 subindustry markets in the international clean energy market from 2010 to 2024 and constructs a skewed t distribution dynamic factor copula model. The time-varying load factor is used to characterize the correlation between a single subindustry market and the entire system, and the joint probability of distress is calculated as a measure of the overall level of systemic risk. Two indicators, Systemic Vulnerability Degree and Systemic Importance Degree, are introduced to evaluate the vulnerability of a single subindustry market in systemic risk and its contribution to systemic risk. A conditional risk-spillover index is constructed to measure the risk-spillover level between subindustry markets. This method fully considers the individual differences and inherent correlations of the international clean energy market subsectors, as well as the fat tail and asymmetry of returns, thus capturing more information and more timely information. This study found that the correlation between subindustry markets changes over time, and during the crisis, the market correlation shows a significant upward trend. In the measurement of the overall level of systemic risk, the joint probability of distress can identify the changes in systemic risk in the international clean energy market. The systemic risk of the international clean energy market presents the characteristics of rapid and multiple outbreaks, and the joint default risk probability of the whole system can exceed 0.6. The outbreak of systemic risk is closely related to a series of major international events, showing a strong correlation. In addition, the systemic vulnerability analysis found that the biofuel market has the lowest systemic vulnerability, and the advanced materials market has the highest vulnerability. The energy efficiency market is considered to be the most important market in the system. The advanced materials market and renewable energy market play a dominant role in the risk contribution to other markets, while the geothermal market, solar market, and wind energy market are net risk overflow parties in the tail risk impact, and the developer market and fuel cell market are net risk receivers. This study provides a theoretical basis for systemic risk management and ensuring the stability of the international clean energy market.

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.

The Disappearing Index Effect

ABSTRACTThe abnormal return associated with a stock being added to the S&P 500 has fallen from an average of 7.4% in the 1990s to less than 1% over the past decade. This has occurred despite a significant increase in the share of stock market assets linked to the index. A similar pattern has occurred for index deletions, with large negative abnormal returns during the 1990s but an average return of only 0.1% between 2010 and 2020. We investigate the drivers of this phenomenon and discuss implications for market efficiency. We document a similar decline in the index effect among other families of indices.

Equity and efficiency effects of flat premiums.

This paper analyzes the impact of flat premiums on equity and efficiency within a regulated market. We examine the consequences of shifting from a risk-adjusted premium model to a flat premium system, particularly focusing on how this shift affects different income groups and market efficiency. Using a combination of theoretical modeling and empirical analysis, we find that flat premiums may lead to increased cross-subsidization among participants, with notable effects on both equity and efficiency. Our results suggest that while flat premiums simplify the regulatory framework, they also introduce trade-offs that policymakers must carefully consider.

The Effect of Underwriter's Reputation, Auditor's Reputation, and Leverage on Underpricing with Concentrated Ownership As a Moderation Variable

The phenomenon of underpricing in Initial Public Offerings (IPOs) is still a major concern in the Indonesian capital market, especially in ensuring market efficiency and optimal investment decision-making. This study aims to analyze the influence of underwriter reputation, auditor reputation, and leverage on stock underpricing, with concentrated ownership as a moderation variable. This study uses a quantitative method with a descriptive and causal approach. The sample consisted of 363 companies that conducted IPOs on the Indonesia Stock Exchange (IDX) in 2011-2021, which were selected using the purposive sampling method. Secondary data is obtained through IPO prospectus documentation and stock price information from the official portals of IDX, TICMI, and e-bursa.com. Data analysis was carried out using moderation regression, with hypothesis testing through t-test at a significance level of 5%. The results show that the underwriter's reputation and leverage do not have a significant effect on underpricing, while the auditor's reputation has a significant negative influence. Concentrated ownership does not function as a moderating variable in the relationship between independent variables and underpricing. The implications of this study provide insights for companies, underwriters, and regulators to pay more attention to auditor quality factors in the IPO process to improve transparency and efficiency of the capital market. This research also opens up opportunities for further exploration of other factors that affect underpricing in the Indonesian capital market.

In the light of dynamic competition: should we make merger remedies more flexible?

ABSTRACT Mergers and acquisitions shape industry competition. Effective merger remedies are important for market efficiency and consumer welfare. This paper explores the need for more flexible remedies to address changing markets after mergers. While the EU permits some flexibility with less restrictive remedies, we conceptually advance the design elements of a dual-phase, bifurcated merger control system. This system integrates ex-ante processes with more systematic and comprehensive ex-post measures. Such an approach can address the shortcomings of the current system and, consequently, holds the potential to enhance merger control in dynamic markets.