Market Conditions Research Articles

Traces of Herd Behaviour Over a Decade in Indian Stock Markets

This study investigates herd behaviour in the Indian Stock Market from 2011 to 2020, analysing daily returns of stocks listed in the S&P BSE 500 index. By employing the Chang, Cheng and Khorana model, the research assesses the presence and impact of herding behaviour across various market conditions, industry sectors and timeframes. The findings reveal nuanced patterns of investor behaviour, with evidence of herd behaviour particularly pronounced during down-market conditions, as indicated by negative and statistically significant coefficients of cross-sectional absolute dispersion returns. Sector-specific analysis unveils varying degrees of herd behaviour, with strong evidence observed in the capital goods and IT sectors, while banking displays a propensity for independent decision-making. The study identifies market capitalization and year-wise variations in herd behaviour, highlighting the dynamic nature of investor sentiment and its implications for market functioning and stability. This comprehensive empirical analysis contributes to a deeper understanding of herd behaviour in the Indian Stock Market, offering valuable insights for investors, policymakers and market participants. The study underscores the importance of considering contextual factors, such as market conditions and industry dynamics, in analysing investor behaviour and its impact on market dynamics. The findings have practical implications for informed decision-making and regulatory interventions aimed at promoting market efficiency and resilience. By elucidating the drivers and consequences of herd behaviour across different dimensions, this research enhances our knowledge of investor behaviour and lays the groundwork for further studies in this area, emphasizing the need for ongoing research to navigate the complexities of financial markets and ensure their stability and integrity.

Insurance for Green Produce: Ensuring Agricultural Sustainability

This study examines the importance of the insurance in the agricultural area in Albania by emphasizing the importance of it in agriculture sustainability. There are a lot of risks that Albanian farmers face, including the weather changes and market conditions. Implementing an effective insurance strategy is essential for mitigating these risks and fostering financial stability among farmers and stakeholders in the agricultural sector. The research evaluates the current insurance framework and its influence on agricultural practices in Albania, offering recommendations for enhancing insurance coverage tailored to the needs of environmentally friendly products. Additionally, it analyzes the advantages and challenges associated with these insurance policies. The goal of this study is to enhance our understanding of how insurance can facilitate sustainable agricultural practices and contribute to food security in Albania. Received: 19 August 2024 / Accepted: 29 October 2024 / Published: 3 December 2024

Factors Shaping Organizational Contingency Plans: Insights into Risk Management and Preparedness

Contingency planning is vital for organizations to sustain operations amidst unforeseen challenges, particularly in the volatile and uncertain business landscape of today. This study explores the factors influencing the contingency planning process, focusing on internal elements such as leadership and organizational culture, alongside external factors like technological advancements and market conditions. Using a quantitative methodology, data were collected from 139 participants in IT companies based in Erbil, Kurdistan. The findings reveal significant relationships between organizational culture, leadership, risk management strategies, and external factors in shaping effective contingency plans. Practical recommendations are provided to enhance preparedness and resilience in the IT sector. This research contributes to the growing body of knowledge on risk management and business continuity, offering actionable insights for organizations striving to navigate complexities in a dynamic environment.

Investment Strategies for Risk-Averse Individuals

This study aims to explore investment strategies tailored for risk-averse individuals, who prioritize capital preservation over potential gains. Investing is vital for achieving financial independence and countering inflation, with a strong portfolio often emphasized by thought leaders like Warren Buffett. While high-risk investments can yield significant returns, low-risk options are essential for financial stability, especially for those averse to risk. The paper delves into risk aversion, highlighting a preference for security. Risk-averse investors typically opt for low- risk investments such as savings accounts, fixed deposits, municipal bonds, and gold, which provide steady returns but limited growth potential. Strategies like diversification and income investing are recommended to achieve a balance between stability and returns in conservative portfolios. The benefits of risk aversion include lower financial loss and stable income, but drawbacks may include reduced long-term returns and eroded purchasing power due to inflation. Factors influencing risk tolerance, such as age and income level, are also discussed. This study will evaluate various risk-averse investment techniques, including fixed-income investments, diversification, index funds, and conservative mutual funds. It will analyze their historical performance and adaptability to changing market conditions, aiming to empower risk-averse investors to make informed decisions that align with their financial goals. Keywords: ETFs, risk aversion, capital preservation

THEORETICAL APPROACHES TO THE FORMATION OF BUSINESS PROCESSES IN THE SPHERE OF INFORMATION TECHNOLOGY IN THE CONTEXT OF DIGITALISATION

THE PURPOSE OF THE ARTICLE is to study theoretical approaches to the formation of business processes in the field of information technology in the context of digitalization. RESEARCH METHODS. The following methods were used in the article: SWOT-analysis; PESTLE-analysis; benchmarking; market and competitor research; data analysis and BI (Business Intelligence); project management; analysis and synthesis, graphical and tabular methods. PRESENTING MAIN MATERIAL. Modern development of society, digitalization of the economy, globalization and digitalization of economic processes determine fundamentally new approaches to the formation of business processes in the field of information technology in the context of digitalization. The formation of business processes in the IT sector is determined by the rapid pace of digitalization and its impact on all aspects of modern enterprises. Digitalization is a key factor in achieving competitive advantages in the global market, as it allows optimizing business processes, automating business operations, improving communication and data-driven decision-making. The role of business processes in the context of digitalization is to increase the efficiency and speed of enterprises, which allows them to respond quickly to new challenges, minimize costs and improve the quality of products and services. In the context of IT enterprises, digitalization not only accelerates processes but also radically changes the very nature of business, forcing enterprises to adapt their models, strategies, and business processes. The study of theoretical approaches to the formation of business processes in the context of digital transformation has shown that the main approaches are: classical approaches (process-oriented, Input-Output model, Project-Based Approach); modern approaches (Agile methodologies, Lean Management, DevOps). The following features of IT business processes were identified: dynamism and flexibility; innovation; project-oriented structure. The following tools are used to model and optimize business processes in the IT sector in the context of digitalization: BPMN; ERP systems; project management tools. It is proved that business processes in the IT sector cannot function without a synergistic combination of the main components of digitalization: automation, integration, analytics, flexibility and adaptability, artificial intelligence, cloud technologies. CONCLUSIONS. It is established that the digitalization of IT enterprises provides new opportunities for optimizing business processes and achieving competitive advantages. It is proved that the introduction of digital technologies contributes to the automation, integration, efficiency and flexibility of business processes. It is determined that IT enterprises should actively use innovative approaches to adapt to the latest market conditions, taking into account theoretical variations and functional aspects of digitalization. It is proved that the formation of business processes in the IT sector requires taking into account theoretical and practical aspects, where classical approaches (process-oriented, project approach) remain basic, and modern approaches (Agile, Lean and DevOps) open up new opportunities for optimizing processes and increasing their efficiency. It has been established that technological innovations, in particular artificial intelligence and cloud solutions, play a synergistic role in transforming business processes in the IT sector, helping enterprises to remain competitive in the face of rapid change and globalization. KEYWORDS: business processes; information technologies; digitalization; theoretical approaches; IT enterprises; competitive advantages; digital technologies; automation; integration; adaptation; classical approaches; modern approaches; artificial intelligence; transformation.

Machine Learning-Driven Stock Selection System: A Proposed Multi-Factor Model-Based Navigation Tool for the Chinese Financial Market

In view of the constantly changing and volatile nature of the Chinese financial landscape, characterized by the introduction of high-tech and huge amounts of market data, there is a need for an improved method for stock selection. Conventional stock methods are, however, inadequate due to their tendency to ignore the high volatility of the market and the nonlinear relationship between many financial variables which are at play. They have not been able to deliver predictions that are strong and accurate. This research, therefore, aims to explore how machine learning algorithms can be utilized to improve the accuracy and adaptability of stock selection models, thereby addressing these limitations. Through empirical results, it can be claimed that ML models, especially tree-based algorithms like LightGBM and XGBoost, are superior to traditional models in predicting stock returns. In this case, rolling windows are very well adapted to machine learning methods, which stand out as superior in terms of the adaptability to the changing stock market conditions. In addition, the research reveals the necessity of integrating the market-driven data, for instance, the trading volume and the momentum, into the stock selection models can help to better capture short-term pricing dynamics. The dissertation concludes with a machine learning-based stock selection system that is optimized for the Chinese financial market. The results of this work will increase the knowledge of quantitative finance by showing the machine learning algorithms can be more accurate in stock selection, and at the same time, providing practical solutions to the investors and financial institutions. The research emphasizes the necessity of combining modern computational methods and traditional financial theories to devise more effective and adaptive investment strategies in a complicated financial environment.

News‐Driven Household Macroeconomic Expectations: Regional vs. National Telecast Information

AbstractUsing variations in the human‐annotated topic content of national and regional telecasts, I explore the link between aggregated daily news and household unemployment, inflation, and economic situation expectations in France. Beyond the information included in national statistics, household expectations embody news information on topics related to the state of the economy, such as deficit, living cost, and economic crisis. Inflation expectations additionally incorporate news on taxes, energy, and especially oil. Distinguishing regional from national television broadcasts reveals that household expectations incorporate both regional and national news, with news on local labor market conditions having a prominent role.

Information‐Sharing Strategies Based on Trust Risks Under a Governmental Incentive Mechanism

ABSTRACTDue to swift shifts in market demand and variable production capacities within the chip industry, trust risks between upstream and downstream entities can readily develop, leading to challenges in information sharing. To tackle this challenge, this paper develops a two‐level evolutionary game model between vehicle manufacturers and chip suppliers. It examines the effects of government rewards and penalties, trust risks, and spillover benefits from information sharing on the strategic decisions of both parties. Furthermore, the paper investigates system evolution strategies under three distinct government policies: forced, fair, and favored. The findings show that as trust risks escalate, both entities in the supply chain tend to withhold information due to concerns over losing their unique competitive advantages. However, enhancing spillover benefits through information sharing positively impacts the mitigation of trust risks. Numerical simulations demonstrate that while a government‐forced policy with increased penalties may boost information sharing in the short term, it proves less effective over the long term. Conversely, preferred rewards and subsidies under equitable policies can significantly boost one party's readiness to share information, thus enhancing cooperation between the two parties. The paper also offers countermeasures and recommendations. For instance, it suggests that upstream and downstream enterprises should not only foster mutual trust but also vigilantly track government policy directions to develop effective information‐sharing strategies. Furthermore, government industrial policies should fully consider the actual market conditions to alleviate trust risks between upstream and downstream entities and promote information sharing among enterprises.

The role of entrepreneurial leadership in influencing the performance of SMMEs in the Vhembe district Municipality

SMMEs in the Vhembe district encounter distinct problems, such as restricted access to financing, inadequate infrastructure, and insufficient support services. This study examines the impact of entrepreneurial leadership traits, including innovation, risk-taking, and proactive decision-making, on corporate outcomes such as growth, competitiveness, and sustainability. The study examined the correlation between leadership types and business success to provide actionable recommendations for governments and business leaders in promoting entrepreneurial leadership. The research utilised a qualitative methodology to investigate the influence of entrepreneurial leadership on small, medium, and micro enterprises (SMMEs) in the Vhembe district. Data were collected via semi-structured interviews with 16 leaders of small, medium, and micro enterprises (SMMEs), offering a varied perspective across multiple industries and business scales. The findings indicate that entrepreneurial leadership substantially influences SMMEs in the Vhembe District, with transformational and collaborative approaches improving financial and non-financial performance, innovation, and customer satisfaction. The study indicates that implementing transformational and collaborative leadership styles can markedly enhance financial performance, innovation, and customer happiness for SMMEs in the Vhembe District. This research provides novel perspectives on entrepreneurial leadership by examining the distinct setting of the Vhembe District. It contests prevailing ideas by emphasising the necessity for a leadership strategy adapted to local dynamics and market conditions. The study's novelty and significance stem from its distinctive emphasis on the Vhembe District, providing a novel viewpoint on entrepreneurial leadership.

Anomaly Pattern Detection in High-Frequency Trading Using Graph Neural Networks

This paper presents a new method for detecting abnormal patterns in high-frequency trading (HFT) using graph neural networks (GNNs). The increasing sophistication of trading algorithms and the large volume of data have often created unprecedented challenges for traditional market analysis. Our framework addresses these challenges by introducing a GNN-based architecture that takes advantage of the physical and structural properties of business data. The proposed method transforms HFT data into graphical models where the nodes represent market conditions and the edges capture their physical and price relationships. A specialized GNN architecture, incorporating attention mechanisms and temporal convolution modules, is developed to learn complex trading patterns and identify potential anomalies. The model is evaluated on high-frequency trading data from five major stocks listed on NASDAQ, spanning six months of trading activity with over 10 million events. Experimental results demonstrate superior performance compared to existing approaches, achieving a 15% improvement in detection accuracy and maintaining robust performance across different market conditions. The framework exhibits particular strength in identifying complex manipulation patterns while maintaining low false positive rates. Our approach processes large volumes of trading data in real time with significantly reduced computational requirements compared to traditional methods. This research contributes to the development of more effective market surveillance systems and provides valuable insights for regulatory authorities in maintaining market integrity.

Adaptive Hybrid Optimized LSTM Models: A Novel Computational Framework for Algorithmic Financial Trading System

In our modern society, with the development of Internet and information system, pre-programing algorithmic trading strategies for online automatic trading has also flourished, especially in the rapidly fluctuating trading market. Using quantitative trading programs that can automatically trade in response to market conditions has attracted the attention of major financial institutions and governments in the financial market. How to use self-adaptive programs to automatically trade in the ever-changing financial market has become a popular research topic for the development and pursuit of all financial markets in recent years. This research proposes an online self-adaptive trading algorithm that can be applied to financial markets such as stock market, currency markets, cryptocurrency markets, futures markets, etc. In the first part of the algorithm, the simplified swarm optimization will be used to optimize the parameters of the newly proposed flexible grid in this research. Then the data will be imported into the artificial neural network model for training in the latter part, helping the trading model automatically select the appropriate parameters for construction flexible grid corresponding to the market conditions. The greatest contribution of the research is to provide a whole new trading algorithm that can adapt to the dynamic trading market, automatically make suitable adjustments to current trading strategy and place real-time orders. The algorithm controlling both profit and risk, which can seem like a balanced trading algorithm that are robust and profitable.

Techno-economic performance analysis of biomass-to-methanol with solid oxide electrolyzer for sustainable bio-methanol production

The analysis of the technical and economic performance of an integrated biomass to methanol and solid oxide electrolysis process (BtM-SOEC) is studied to find more sustainable process of bio-methanol production. The oil palm empty fruit branch (EFB) which is abundant in Thailand is used as biomass feedstock. Modeling of the BtM-SOEC is done using Aspen Plus. For technical aspects, the production rate of oxygen and hydrogen from the SOEC can be enhanced through an appropriate adjustment of the number of cells and cell temperature. The BtM-SOEC offers higher methanol yield and overall efficiency, while consumes less energy than the conventional biomass to methanol process (BtM). The maximum methanol production rate of 0.4995 kmol h-1 derived from BtM-SOEC is achieved at a number of cells of 325 cells and a cell temperature of 700 oC, at this condition the overall efficiency is 64.79 %. The economic assessment indicates that the conventional BtM and BtM-SOEC are still not economically feasible. However, the conventional BtM is more economically feasible than the BtM-SOEC. The methanol cost of BtM-SOEC can turn out to be economically feasible when renewable electricity cost and SOEC cost decrease substantially. The methanol cost of the BtM-SOEC (620 USD ton-1) can be competitive to that of the BtM (703 USD ton-1) when the cost of input renewable electricity decreases by 80%. Consequently, this research highlights the potential of BtM-SOEC from agricultural residues for sustainable bio-methanol production in the future market condition that the cost of renewable electricity tends to continuously decrease with the technology development and increased technology adoption and the carbon policy tends to be tightened to relieve global warming.

Study on Household Investment Decision of Household Photovoltaic Project Promotion -- Based on Inclusive Finance Perspective

Considering the lack of research on households' investment decisions in promoting photovoltaic projects in existing studies, this study constructs a four-party evolutionary game model based on local governments, financing institutions, photovoltaic enterprises and households from the perspective of inclusive finance. In addition, according to the influence of irrational psychological factors on the decision-making outcome of each interested party in decision-making, this study innovatively introduces regret theory to improve the evolutionary game model. Then, each subject' evolutionary stabilisation strategies and system equilibrium points are analysed explicitly using the perceived benefit function and the replicated dynamic differential equation. Finally, combined with the system dynamics method, the theoretical derivation results are verified by parameter assignment, and the sensitivity of each subject to multiple influencing factors is explored. The results show that: (1) under the current market condition, the system is stabilised in the strategy combination of Case I (subsidy, conventional finance, non-promotion, loan to build), but the ideal state can be achieved through the refinement and adjustment of policies. And there is a mutual influence between the subjects. (2) The regret-elation perception psychology of each subject has a non-negligible influence on their decision-making behaviour. The combination of strategies in Case I is a conservative decision by the subjects. (3) A variety of factors influences the behavioural decisions of each subject, and the amount of rent that photovoltaic enterprises can provide is a crucial factor affecting households' investment decisions. (4) Local governments incentives and penalties play a key incentive role in promoting the active participation of financing institutions and photovoltaic enterprises in the promotion endeavour. This study provides practical guidance and policy insights for promoting the diffuse use of renewable energy and the promotion of household photovoltaic projects while enriching the applied research by combining regret theory and evolutionary game theory.

Legal Study in Takeover of Shares (Acquisition) in Limited Liability Companies in View of Law Number 40 of 2007

By owning shares, a person or company has the right to a portion of the company's assets and profits. When investors sell or buy shares of a particular company, they change their shareholdings. This condition is caused by market conditions, business performance and investors' strategic decisions. Information recorded on the stock exchange when a transaction occurs can influence the share price and ownership structure of the company. Changes in share ownership can impact market conditions and business performance. First, if shares of a business pass to more experienced or better-connected owners, this can improve management strategies and operational efficiency, which in turn can lead to better results for the company

Programming Model for Enterprise Multi-Process Decision Optimization

This paper addresses critical decision-making challenges faced by enterprises during the production process, particularly focusing on efficient quality inspection, inspection and disassembly decisions, defective rate control, and cost minimization. In the current dynamic market environment, existing algorithms demonstrate insufficient adaptability, hindering their effectiveness in addressing complex production scenarios. To overcome these limitations, we propose a novel genetic algorithm that incorporates dynamic change adjustment through a 0-1 programming model. This approach enhances the algorithm's flexibility, enabling enterprises to develop scientifically sound and rational production decision schemes tailored to varying market conditions. Our empirical analysis reveals that the average production cost for enterprises utilizing this method is 32.7, indicating significant potential for cost reduction while maintaining product quality. The findings contribute to the existing literature by providing a robust theoretical framework that underscores the importance of adaptive algorithms in improving production efficiency and decision-making processes in an ever-evolving market landscape.

Adaptive demand planning models: Utilizing reinforcement learning to address contingencies and dynamic market conditions in supply chains

There are remarkable opportunities in supply chain management associated with using the reinforcement learning (RL) approach in demand planning. As opposed to numerous other techniques, such as modeling and forecasting and applying them over a certain period, RL allows for the making and adapting these decisions in real-time, depending on the current demand and other conditions in the market. Supplementary to this, RL-based models enable supply chains to constantly adapt to shifts since they directly update knowledge from incoming data, making them less susceptible to economic shocks or other supply uncertainties. This flexibility is particularly important for contemporary supply chains in uncertain global environments where conventional, deterministic demand planning techniques cannot address changing needs. In this research, we discover how RL-based models could reduce demand volatility and react to contingencies. As such, it is best suited for industries that are required to respond flexibly and faster. This paper enlightens RL on the benefits of demand planning. It provides live examples that give insight into how it may be used to improve inventory, reduce cost, and improve decision-making. Regarding the specific research questions, this study helps investigate the methodologies, evaluation metrics, and case outcomes that shape RL's potential for changing demand planning and improving supply chain adaptability for future research on adaptive supply chain management.

The effect of rare events on information-leading role: evidence from real estate investment trusts and overall stock markets

The information relationship between real estate investment trusts and stock markets has garnered significant interest in finance; however, the time-varying information flow between the two can vary due to different market conditions. This study scrutinizes the information-leading roles between the real estate investment trust sector and overall stock markets under various market conditions. Shannonian transfer entropy, used for identifying information-leading roles, can reveal nonlinear interactions between two markets; however, the results can be hindered by rare events, such as sporadically occurring episodes. Therefore, this study introduces Rényian transfer entropy to examine the role of rare events in the information flow between the two markets. This conjecture is confirmed using the COVID-19 pandemic as a natural experiment setting of rare events. The results reveal notable findings. (i) Rényian transfer entropy can accurately capture the impact of rare events regardless of the sample period, including the changes in information-leading roles when volatility clustering strengthens. Furthermore, (ii) the significance of liquidity and market efficiency produces different information flows depending on market status. The findings suggest that investors can use Rényian transfer entropy to accurately identify information-leading roles between assets under various market conditions, facilitating optimal portfolio allocation. Moreover, policymakers can benchmark this approach to identify financial risk contagion paths, enabling regulators to implement timely policy actions to mitigate market risks.

From exploration to production: Understanding the development dynamics of lithium mining projects

Recently, there has been considerable recent controversy whether current and new lithium mines will be able to supply the rapidly growing needs of the electromobility transition. Mineral exploration projects are typically active for many years, and only some become operational mines. From exploration to production, the projects go through several stages of characterisation and evaluation. At each stage, decisions are made by companies and stakeholders to advance, continue or stop the project. This is a complex process, and even projects with very similar geological and technical characteristics may take very different trajectories, depending on external factors such as global market conditions and local regulatory environments. The present study investigates the dynamics of this process for lithium exploration projects. A global database of 397 lithium projects was compiled, covering their progression through major development stages between 2004 and 2022. Ordinal logistic regression was used for the statistical analysis of this data. Different explanatory variables were tested, including economic, geological, technical, and geographic factors, to identify the best predictors for project progress at each development stage. The results suggest an essential role for lithium carbonate prices, and a variable role for other factors at each stage. Critically, the already elapsed lead time and project economics, which are traditionally considered important for the prediction of the start-up of individual mines, do not appear to be relevant in all cases. The results provide important insights into the dynamics of lithium supply and may eventually allow more realistic forecasts to be made for future lithium market dynamics.

Monitoring food prices in the context of improving the mechanism for supplying products for government needs

The calculation of protection coefficients for producers of agricultural products supplied for republican needs, reflecting the price seizure of income of agrarians until 2022 has been carried out. Within the framework of the mechanism of formation of competitive prices for each type of food, the priority of monitoring of international trade data is justified, which implies the creation of a forecast model of price formation for agricultural raw materials or systemic adjustment taking into account the trends of external market conditions. A modular architecture of a digital platform for monitoring food prices for the subsequent calculation of the cost of products supplied for public needs is proposed.

Impact of market classifications on the innovative use of financing and dividend techniques: a global review of survey papers

Purpose This study aims to investigate the belief that the innovative/skilled use of financing and dividend policy decision techniques depends on the firm’s host market classifications (frontier, emerging and developed markets). Design/methodology/approach Using survey studies with similar questions, the authors reviewed, tallied and compared firm characteristic subgroup mean values of 2.4 and above (≥ 60% usage rate) per technique per market classification for the financing decisions analysis. In the dividend policy analysis, the authors tabulated existing rank results per market classification. Findings Managers in Malaysia significantly issue stock based on whether the firm's recent profits have been sufficient to fund their activities; this technique is of low value to US and Ghanaian managers. Managers in Ghana significantly limit their debt so their customers/suppliers are not worried about the firm going out of business; this technique has low value for Malaysian and US managers. Managers in Malaysia significantly issue debt when it gives investors a better impression of their firm prospects than issuing stock; this technique is not valuable to Ghanaian and US managers. On dividends, Ghanaian and sanctioned Iranian managers significantly consider cash availability before a dividend decision; this has low value to emerging and developed market managers. Practical implications These findings suggest that managers must customise their sets of valuable financing and dividend techniques to reflect the business risks and hurdles per their firm’s host market classification – as markets may determine a technique’s usefulness. Also, the innovative/skilful use of financing and dividend techniques decreases as managers move from developed to frontier markets, possibly due to degrading market conditions. Originality/value A global comparative study of survey literature covering frontier, emerging and developed markets is rare in the literature.