Open Market Research Articles

The Relationship between the Wheat Market and the Financial Market in Malaysia Using a Dynamic Conditional Correlation Model (DCC-GARCH)

The research aims to identify the nature of the dynamic relationship between the returns of the wheat market (RPW) and the returns of the general index of the Malaysia financial market (RISX), and to verify the transmission of shocks and volatility between the returns of both markets during the monthly period from (1/1/2004) to (31/10/2024). Using the Dynamic Conditional Correlation GARCH (DCC-GARCH) model, the results showed an inverse relationship between the returns of both markets, with the correlation sensitivity reaching (–0.08%). This indicates a weak ratio, suggesting that diversifying the portfolio in both markets may increase gains to some extent. The predictive results indicated a positive correlation, which is also weak. As it refers to sensitivity to returns in both markets for dynamic changes that occur over time, there may not be an opportunity to achieve diversification when prices are imbalanced and to achieve returns in these markets. Therefore, the weak correlation between the returns of both markets indicates a decrease in their integration. The research recommended the need to increase the openness of the Malaysian securities market and to enhance its informational efficiency and transparency to increase the capacity and smoothness of the flow of information to and from the financial market.

Dam break in Japan’s immigration policy: the 2018 reform in a long-term perspective

Abstract The 2018 reform is a dam break in Japan’s immigration policy. Previously, for decades, the opening of the Japanese labour market for lower-qualified foreign workers was discussed without any comprehensive reform despite far-reaching proposals. This article discusses this change from a persistent standstill to comprehensive reform by analysing comparatively the frames and institutional setting in earlier immigration debates around 1970, around 1990, and around 2005 with the debate in the late 2010s. It argues that the persistent stalemate on the most hotly debated issue of immigration policy was due to the diversity of frames with very different policy implications and an institutional fragmentation in policy-making without any pivotal policy entrepreneur. The comprehensive reform of 2018 is the result of a window of opportunity by the conjuncture of a declining security frame as the main counterargument and by the centralization of decision-making in the core executive in the later years of the second term of Prime Minister Abe Shinzō (2012–20) on the level of institutional setting. Abe and his entourage were reluctant policy entrepreneurs who only realized the 2018 reform because of pressure and the absence of any other policy option. Still, they fundamentally changed the framework in Japanese immigration policy.

Postcards from Italy – The Art of Controlling Images of Cultural Goods Better Than Copyright Could

Abstract Italian provisions on images of cultural goods may be reminiscent of copyright, in particular because they allow production and uses of such reproductions upon an authorisation, they impose fees for creating or exploiting them, and point out to the notion of integrity. However, the two sets of rules remain independent and autonomous, with a different purpose, subject matter and constitutional ground. National provisions are designed and interpreted in such a way that their outreach is broader than copyright. Therefore, they introduce a para- or quasi-copyright system, i.e. rules that are not necessarily accountable to third parties, affecting in this way the producers, users and reusers of potential images, as well as the material that is supposed to stay in the public domain. This is the main reason why it is crucial to study their legitimacy. This work studies Arts. 106–108 of the Italian Code on Cultural Goods and Landscape (ICCGL) limiting the ability to reproduce cultural goods, including when they embed a work of art, and this even when the latter is in the public domain. It explains why these written rules, based on a public property paradigm, may be criticized in a political and economic perspective, but are still compliant with higher norms, including EU rules on copyright and open data. In addition to that, the paper analyses the case law related to these provisions, it focuses in particular on the personality right on cultural good emerged in recent cases, and it explains why this prerogative raises concerns as to its consistency and compliance with higher norms. Finally, the article explains why a potential extraterritorial outreach of the provisions on images of cultural goods, as stated by an Italian Court, has to be challenged in different perspectives. It also clarifies costs and benefits of this solution, concluding on the operational implications on market operators and on the principle of non-discrimination.

Financial Openness, Trade Openness, and Economic Growth Nexus: A Dynamic Panel Analysis for Emerging and Developing Economies

International market openness has long been regarded as critical for economic development, and recent evidence highlights the distinct roles of financial and trade openness, particularly in emerging and developing economies. This study examines the impact of financial and trade openness on economic growth in ten emerging and developing countries from 1970 to 2023. It employs a dynamic panel generalized method of moments (GMM) model, which is selected for its ability to address potential endogeneity and dynamic relationships within panel data. The analysis finds that both financial and trade openness positively influence economic growth and that stable macroeconomic conditions and political stability enhance these growth-promoting effects. In the context of growing geo-economic tensions, trade fairness, and national security concerns, the study underscores the need for policies that balance global integration with national interests. These findings suggest the importance of designing policies that promote greater integration into global financial and trading systems while ensuring sound macroeconomic fundamentals and supportive institutions. The study recommends that policymakers pursue strategic liberalization and strengthen governance structures to achieve sustained and inclusive growth.

The Impact of International Trade on Emerging Economies: A Study of India

Historical and contemporary research suggests that India stands as one of the most rapidly expanding and appealing economies globally, having established itself as a sought-after locale for foreign investment. Since 1991, India has adhered to stringent policies that have constrained the economy's engagement in international trade, resulting in a greater emphasis on protectionism rather than fostering an open market environment. Since 1991, the Indian government has implemented a series of economic reform measures aimed at invigorating the economy. The proposed reform measures encompass fiscal adjustments, a reduction in tariff levels concerning a substantial array of imports, the management of exchange rates, and the strategic utilisation of exchange rates as a mechanism for promoting exports and shaping trade policies. Nevertheless, the reforms enacted in India bore a striking resemblance to those pursued by other developing nations; the primary distinction lay in the pace at which they were executed. India possesses a notable advantage in the form of a stable political climate. The prevailing government and ruling party have maintained a firm grip on the political landscape, thereby fostering an environment conducive to investment. India has asserted its influence within the global economy, as evidenced by its position in international institutions such as the G-8 and G-20, as well as its free trade agreements with ASEAN and the EU.

Scaling Influencer Marketing Operations: Legal and Financial Challenges In Managing Large Scale Influencer Network

This study examines the intersection of legal compliance and the use of artificial intelligence (AI) in influencer marketing campaigns. With the growing popularity and reach of influencer marketing, brands are increasingly relying on AI tools to manage large-scale campaigns more efficiently. The research explores the role of legal regulations in influencer marketing and how AI can enhance operational efficiency while ensuring adherence to legal standards. A survey of 647 respondents revealed that legal compliance is viewed as essential for maintaining brand reputation, consumer trust, and avoiding regulatory penalties. The findings show that brands that adhere to legal guidelines in influencer marketing are perceived more favorably by consumers, fostering long-term loyalty. Furthermore, AI’s role in automating the influencer selection process, optimizing campaign performance, and improving decision-making was highlighted as a key driver of efficiency. AI-enabled tools allow organizations to manage influencer networks effectively, reduce time spent on manual tasks, and improve overall campaign outcomes. The study concludes that integrating AI with a strong focus on legal compliance offers a strategic advantage for brands, enabling them to manage influencer marketing campaigns with greater precision and impact. This balance between technological innovation and ethical standards is crucial for the sustained success of influencer marketing in a rapidly evolving digital landscape. The research contributes valuable insights for marketers and organizations aiming to optimize their influencer marketing strategies while ensuring legal and ethical responsibility.

Operation Optimization Model of Regional Power Grid Considering Congestion Management and Security Check in Complex Market Operation Environment

Security checks are essential for ensuring the safe operation of the regional power grid (RPG) and the smooth functioning of the electricity spot market (ESM). Currently, China’s RPG operating environment encompasses a complex mix of centralized ESM, decentralized ESM, and planned power generation. This complexity has led to increasingly severe RPG congestion issues. To address this, this paper introduces a security check mechanism design and operational optimization approach tailored for RPGs in complex markets, with a focus on congestion management. Firstly, the paper elaborates on the practical foundations, unique constraints, and requirements for security checks and congestion management during the RPG’s operational mode transitions. Secondly, it outlines the principles underlying the security check mechanism and presents a framework for RPG security checks and congestion management. Through a comparative analysis of three different programs, including their advantages, disadvantages, and applicable scenarios, the paper provides an optimal program recommendation. Building on this, the paper develops an operational optimization method that incorporates congestion management for each of the three security check and congestion management programs. Lastly, an IEEE-39 node test system is simulated to validate the effectiveness of the proposed programs. The mechanism and simulation analysis results show that Program 3, based on market mechanisms, has theoretical and practical advantages over Program 1 (based on multiple adjustments) and Program 2 (based on dispatch plans) for congestion management. Under the same line congestion situation, Program 1 requires two adjustments to relieve the line congestion, while Program 2 and Program 3 can solve the problem with just one optimization adjustment, and the congestion management effect of Program 3 is more obvious and superior.

Do market-based environmental regulations always promote enterprise green innovation commercialization?

Motivated by increasingly strengthened market-based environmental regulations, enterprises intend to pursue both pollution reduction and competitive edge through developing green innovation. Although the volume of green innovation has increased substantially, some of them are not successfully commercialized to pay rewards for enterprises. Hence, how market-based environmental regulations affect enterprise green innovation commercialization is urgently to be explored. Using Chinese manufacturing enterprise panel data, this paper finds that market-based environmental regulations impact enterprise green innovation commercialization in an inverted U shape relationship. Regarding influencing factors, on the technical dimensions, market-based environmental regulation can improve the green innovation commercialization through improving the green innovation quality. Concerning non-technical factors, market development level, openness level and regional green innovation capacity can positively moderate the inverted U shape relationship between market-based environmental regulation and enterprise green innovation commercialization. Finally, it is found the impact of market-based environmental regulation on enterprise green innovation commercialization varies both at the regional and enterprise level. Based on the results, policymakers should optimize the use of market-based environmental regulation by considering its nonlinear impact on green innovation commercialization. Policymakers can also improve non-technical factors, including market development level, openness level and regional green innovation capacity, to increase the positive impact of market-based environmental regulation on enterprise green innovation commercialization.

Online forecasting using neighbor-based incremental learning for electricity markets

Abstract Electricity market forecasting is very useful for the different actors involved in the energy sector to plan both the supply chain and market operation. Nowadays, energy demand data are data coming from smart meters and have to be processed in real-time for more efficient demand management. In addition, electricity prices data can present changes over time such as new patterns and new trends. Therefore, real-time forecasting algorithms for both demand and prices have to adapt and adjust to online data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand and prices forecasting in real-time. The proposed algorithm generates a prediction model based on the k-nearest neighbors algorithm, which is incrementally updated in an online scenario considering both changes to existing patterns and adding new detected patterns to the model. Both time-frequency and error threshold based model updates have been evaluated. Results using energy demand from 2007 to 2016 and prices data for different time periods from the Spanish electricity market are reported and compared with other benchmark algorithms.

Research on the Decision-Making Model of Carbon Quota Trading Based on Deep Reinforcement Learning

This study addresses the complexities of carbon quota trading markets amidst global warming concerns, proposing a deep reinforcement learning (DRL)-based decision-making model to enhance trading strategies. Acknowledging the limitations of conventional methods in navigating volatile carbon prices, policy shifts, and informational disparities, the research integrates DRL's advanced capabilities. It commences with an overview of DRL principles and its successful applications, followed by an analysis of market dynamics and trading nuances. A DRL model is then formulated, delineating state-action spaces and a tailored reward function for optimized learning within the carbon trading context. Model refinement involves hyperparameter tuning for superior performance. The summary concludes with an evaluation of the model's efficacy, highlighting its adaptability and computational demands, while outlining avenues for further enhancement and real-world implementation to combat climate change through improved carbon market operations.

Cooking Oil Price Volatility in the Consumer Market and Wholesalers Market in Indonesia

This study analyzes the price fluctuations of cooking oil in the Indonesian consumer and wholesale markets using the Autoregressive Conditional Heteroskedasticity (ARCH) model across January 2018 to January 2024 period that encompasses the period of study. This study covered the period measuring from January 2018 to January 2024. As per conclusions drawn upon the tests for stationarity, the series has become non-stationary after a second difference. An analysis of the results indicates substantial and even large amounts. Consumer price’s ARCH (α) and GARCH (β) coefficients are 0.569707, and the coefficients for wholesale prices are 1.29 and -0.13 respectively. This indicates that there is the presence of persistent volatility (α + β > 0) in the market. In view of these results, it is very clear that certain policy measures need to be put in place. Such measures should include the stabilization of the delivery mechanism through proper stock management and strategic price mechanisms to avoid wide fluctuations in price. Two other factors that may help in price variations stability are increasing market openness and promoting location diversification of supply sources.

Smell-waking as a cognitive mapping tool: investigating the smellscape of Syrian food for placemaking in Cairo’s outskirts

Abstract Cognitive mapping is central to urban design. As a pioneer of cognitive mapping, Kevin Lynch focused his work on tangible visual elements while only acknowledging other intangible attributes. In an attempt to bridge the gap between Lynch’s cognitive mapping tools and sensorial studies, this paper explores smell-walking as a method of cognitive mapping in the context of Cairo. Through a designed smell walk experiment, 25 participants documented their immediate sensations, past experiences, and memories evoked by the smells of Syrian food practices in an open market situated at the edge of El-Rehab, a gated community on the outskirts of Cairo. This pilot study is a step forward in renewing The image of the city by displaying a smell map, participants’ cognitive image, smellscape, and placemaking narratives. It concludes with the collective cognitive perception of a hybrid smellscape that triggers hunger, happiness, and nostalgia.

A Master–Slave Game-Based Strategy for Trading and Allocation of Virtual Power Plants in the Electricity Spot Market

With the transformation of the energy structure, the integration of numerous small-scale, widely distributed renewable energy sources into the power grid has introduced operational safety challenges. To enhance the operational competitiveness, the virtual power plant (VPP) has emerged to aggregate and manage these distributed energy resources (DERs). However, current research on the VPP’s frequency modulation performance and bidding strategy remains insufficient in the joint market of electrical energy and frequency modulation (FM) ancillary services, with inadequate coordination of internally distributed resources. To fully leverage the flexibility of VPPs and incentivize their participation in electricity market operations, this paper investigates game-based bidding strategies and internal distributed resources allocation methods for VPPs in the joint market for electrical energy and frequency ancillary services. Firstly, the regulatory performance indicators of VPPs participating in the joint market and develops the corresponding market-clearing model. Secondly, to address the competition among distributed resources within VPPs, a master-slave game approach is innovatively employed to optimize the VPP’s trading strategies. This method ensures the rational allocation of electricity consumption among distributed energy resources within the VPP and derives the optimized bidding prices and quantities for both the VPP and its internal members. Finally, the case study shows that the proposed trading strategy provides effective bidding strategies for distributed energy resources participating in the joint market for energy and frequency regulation ancillary services. It enhances the regulatory performance of VPPs in the energy-frequency regulation market, ensures the profitability of distributed energy resources, and contributes to the economically stable operation of the market.

The Volcker Tightening Cycle: Explaining the 1982 Course Reversal

This article studies the factors that led former Federal Reserve Chairman Paul Volcker to stop and then reverse course in the most famous monetary tightening cycle in U.S. history. I explain how the Fed began cutting its policy rate target, thus ending the tightening cycle, in July of 1982. Although the Fed had gained some ground in its fight against inflation, in mid-1982, inflation was running above 7 percent, well above the 2 percent inflation rate that the U.S. enjoyed before the Great Inflation. Beyond the Federal Open Market Committee’s (FOMC) partial success at taming inflation, I describe how economic pain and financial market stress were two practical and related considerations in the summer of 1982 that likely contributed to the monetary policy pivot. Finally, I discuss the political pressure facing the FOMC at that time.

Spatial Configuration of Market Spaces: Accessibility and Management Challenges in Bamenda City, Cameroon

Market spaces are fundamental to urban life in Cameroon, serving as key centers for commerce, social interaction, and cultural exchange. However, rapid urbanization has intensified challenges related to the spatial placement and organization of markets, particularly in Bamenda. Poor infrastructure, congestion, spatial disorganization, and inadequate parking facilities hinder accessibility and affect the efficiency of market operations. These challenges not only limit economic activities but also create difficulties for vendors, customers, and other stakeholders who rely on these markets for daily sustenance and business. The research problem centers on the inefficient spatial arrangement and poor management of market spaces in Bamenda, which contribute to accessibility constraints, safety concerns, and overall dissatisfaction among market participants. This study, therefore, aims to examine the spatial location, layout, and design of major markets in Bamenda, including Bamenda Main Market, Nkwen Market, Food Market, and Ntarikon Market. It seeks to analyze how these spatial characteristics impact accessibility and functionality, as well as to identify the major challenges faced by market participants. To achieve these objectives, the study employs both qualitative and quantitative methods, including informant interviews and the administration of 324 questionnaires to vendors, customers, and shop owners. Spatial analysis mapping is utilized to assess market locations, layouts, and proximities, providing a comprehensive understanding of their spatial organization. The findings indicate that congestion, narrow pathways, inadequate infrastructure, and unregulated market expansion significantly impede accessibility and market efficiency. Given these challenges, the study strongly advocates for the effective implementation of Bamenda’s proposed Master Plan to regulate urban spaces, particularly market infrastructure. Additionally, the proper execution of the Plan of Soil Occupation (POS) is recommended to address the haphazard arrangement of market stalls and control illegal market operations.

Challenges of the yuan to become international monetary standard

Throughout the 21st century so far, despite of worldwide economic crisis, the drive, strength, willpower, dynamics and duration of the Chinese growth has aroused the attention of the academy, businessmen and politicians. The development of China in the global economy has brought to the idea if its currency – called renminbi, also known as yuan – could replace the American dollar as a global currency. This paper is a descriptive bibliographic review, whose explanations are based on research about the possibility of a new global currency. It is also based on international official statistics. The aim is to analyze if yuan has real economic conditions of global protagonism, however not forgetting the political determinants. This paper solidly observes that, despite of the size of Chinese economy, the relevance of its international trade and stability of prices and currency, this country has not a deep and open financial market that would favour this substitution. Delicate political circumstances also work in defiance of it.

Adaptive learning engine for driving marketing channel performance: a machine learning approach

PurposeThis paper aims to develop and implement a machine learning recommendation engine – an adaptive learning engine that drives business revenue through the ranking and recommendation of offers at a granular customer level across the inbound marketing channels.Design/methodology/approachA data set of over 300,000 unique sample of mobile customers was extracted and prepared. The gradient boosting machine (GBM) algorithm was developed, consolidated, deployed and experimented on two inbound marketing channels.FindingsResearch examining machine learning implementation and operationalisation within the large consumer base is seemingly silent. This paper bridges this gap by developing and implementing a machine learning adaptive engine across two inbound marketing channels. The performance of the inbound channels revealed the significant importance of digital campaigns that are driven by machine learning algorithms. Machine learning techniques can be well positioned as an integral part of a large consumer base marketing operations with real-time one-to-one marketing capability.Research limitations/implicationsThe study explores the use of machine learning, a cutting-edge subset of artificial intelligence (AI), to drive consumer business revenue across different marketing channels. Further research should explore these marketing channels in greater depth by considering other branches of AI in driving consumer business revenue.Practical implicationsThis study demonstrates the value, ease and application of a machine learning deployment in a consumer business with a large customer base in driving business revenue. It also shows customers' practical response to offerings across channels and the importance of the digital channel to firms with a large customer base.Originality/valueThe paper defines how machine learning extracts can be deployed and operationalised by marketers to drive business revenue. This approach is unique, realistic, easy to deploy and will guide future research in this space.

Capital Market Opening and Corporate Innovation: Mediating Role of ESG Performance and Financing Constraints

ABSTRACTThis study uses a quasi‐natural experiment of the Shanghai‐Hong Kong and Shenzhen‐Hong Kong Connect Trading Systems to investigate the role of the capital market opening in fostering corporate innovation. We use panel data for A‐listed corporations covering 2009–2021 to construct empirical analysis, and corroborate that the capital market opening can exert significantly positive influences on corporate innovation. The robustness of the research is verified. Furthermore, we conduct quantile analysis to investigate asymmetric effects, and employ multi‐phase difference‐in‐difference‐in‐difference model, which reveals that the positive effect of capital market opening is more distinctly potent among the heavy polluters and the new‐high‐tech corporations. Moreover, the results also reflect that the capital market opening can enhance ESG performance and ease financing constraints, which in turn enhances corporate innovation. This study extends the researches on the topic regarding the relationship between capital market opening and corporate innovation, and outlines the theoretical and practical implications of helping foster corporate innovation.

The Implementation Strategy of Cost Control and the Construction of a Guarantee Model of Financial BPO in the Cloud Computing Environment

Cloud computing platform is a main direction of computer information technology development at present. In this paper, financial SMOTE algorithm, business process outsourcing, hardware, software, data and other resources such as integrated network storage infrastructure, data resources, management platform, servers and various financial service applications are adopted. According to the service functions and service modes provided by commercial cloud computing, a financial information system architecture based on cloud computing is constructed. The proposed method can control the cost by 60%,Improve the financial management level and efficiency of enterprises in an all-round way, provide important decisions for the development of enterprises' market operation, avoid market operation risks in time, and provide more opportunities for enterprises' development.

Analisis Aspek Pasar dalam Pengembangan Bisnis di Era Digital

Market aspects analyze market potential, competition intensity, achievable market share, and marketing strategies that can be implemented to reach the desired market share. Through this analysis, business ideas can be effectively channeled to align with market needs and demands. The success of financial performance often depends on market and marketing capabilities, operations, accounting, human resources, and other functions. In the past, before marketing knowledge developed and became widely recognized as it is today, companies prioritized producing as much as possible before attempting to sell their products.In such circumstances, they ignored existing demand conditions, resulting in many producers experiencing failures and ongoing losses because production quantities did not align with market demand.