Changing Market Conditions Research Articles

Internal determinants of bank deposit flows under different market conditions in Ghana

This study analyzes the effect of internal factors of bank performance on deposit flows, considering the changes in the stock market conditions in Ghana. A panel dataset covering 2007 to 2021 of 18 banks in Ghana is applied in a dynamic panel model for the analysis. The results show that the lagged deposit growth exerts an impressive influence of 0.68 percent on the future deposit flows of banks, thus positing a favorable implication for their trading momentum. Also, liquidity was found to have a negative relationship of –0.64 percent with deposit growth. This implies that the holding of excess liquidity diminishes investors’ confidence in a bank’s ability to generate more revenue to enhance the value of their funds, as high liquidity ratios reduce the bank’s capacity to grant more loans for profit. Furthermore, the analysis revealed a positive effect of 1.93 percent by expenditure on deposit growth, which suggests that depositors recognize a rise in operational costs as an indication of a bank’s potential for growth and rapid expansion. Moreover, the analysis found the existence of a negative effect of –0.88 percent by the stock market conditions on deposit growth, which implies that bullish market conditions reduce bank deposits. This verifies that the determinants of deposit flows adapt to the changes in market conditions. Policy strategies should include non-performance metrics such as an increase in the interest paid on customers’ deposits, product promotions, and targeted advertisements to sustain the inflow of depositors’ funds under changing market conditions.

Види цифрового бізнесу у сфері рітейлу

Analyzing various aspects of digital business in the context of European integration processes, it is substantiated that digitalization plays a key role in the modern economy, facilitating the transformation of traditional business approaches. It has been proven that European integration processes provide a powerful impetus for the development of digital business, opening new markets and promoting the establishment of unified standards in e-commerce, data protection, and consumer protection. This creates conditions for seamless cross-border interaction between companies and consumers, ensuring high competitiveness and innovative development. It is argued that the use of digital technologies and online platforms allows businesses to adapt to changing market conditions, optimize internal processes, and enhance customer satisfaction through personalized offers and high-quality service. At the same time, digital businesses face challenges related to the need to comply with regulatory requirements, ensure cybersecurity, and protect personal data. However, these challenges also encourage companies to innovate and seek new solutions for effective risk management. Overall, European integration processes significantly contribute to the development of digital business in Europe, opening up broad prospects for growth and international cooperation.

Financial Modeling and Forecasting in Corporate Finance Management

"The ARIMA, VAR, and GARCH models are examined at in this corporate finance study to see how they affect strategy planning, risk management, and choosing investments. The work explains about how statistical models can be used to guess important financial factors like how the stock market will do, interest rates, and market changes. By adding predicted future cash flows to the equation for how stock prices change, our method makes models better at predicting the future and gives businesses more accurate financial predictions. The review of the study showed that the ARIMA model is very good at guessing how much a stock will return. The VAR model fit past data very well, which means it can be used to make accurate financial forecasts. There are some doubts about how accurate the GARCH model is, but it is still useful for assessing risk because it is very good at predicting market instability. Including expected cash flows in our models improved our research by giving us a clearer picture of how the changes would impact our future investment and financial plans. The results show that mixing ARIMA, VAR, and GARCH models might help in figuring out how well a company will do financially. However, there is not a single model that works perfectly for everyone. This could help decide what decisions to make, come up with plans, and lower the risks. The results of the study helped corporate finance experts to choose better strategies. Based on the data, we need to find new ways to predict the future that can adapt to changing market conditions and help businesses succeed in the long run in an always changing circumstances."

Conditions and possibilities of using e-logistics in manufacturing enterprises

Progressing globalisation, competitiveness and implemented technological innovations constitute important challenges for enterprises operating in such a turbulent environment. Organisations are forced to adapt much faster and more flexibly to rapidly changing market conditions. Manufacturing companies realise that it is impossible to meet market requirements by constantly using conventional methods of operation, especially in the sphere of physical flows of goods, information and financial resources, for which logistics is responsible. One of the solutions that can support manufacturing companies is e-logistics, which enriches logistics phases and processes in the sphere of information technology (IT) and information and communication technologies (ICT). The aim of the study is to identify the conditions and possibilities of using e-logistics in Polish manufacturing enterprises.

Trade-offs? The Impact of WTO Accession on Intimate Partner Violence in Cambodia

Abstract We study the impact of trade-induced changes in labor market conditions on violence within the household. We exploit the local labor demand shocks generated by Cambodia’s WTO accession to assess how changes in the employment of women relative to men affected the risk of intimate partner violence. We document that men in districts facing larger tariff reductions experienced a significant decline in paid employment, whereas women in harder-hit districts increased their entry into the labor force. These changes in employment patterns triggered backlash effects by increasing intimate partner violence, without changes in marriage, fertility, psychological distress, or household consumption.

Does COVID-19 decrease price dispersion? Recent evidence from the airline industry

COVID-19 has caused substantial disruptions to the airline industry. This paper analyzes the impact of the pandemic on price dispersion in airfares. The sample includes ticket information from the DB1B database between 2018Q1 and 2021Q4. The fixed-effect panel instrument variable (IV) estimation finds evidence of decreased price dispersion during COVID-19. These results are robust to alternative measures of dispersion and subsamples. Furthermore, the subsample analyes reveal that, as the infection rate rises, the dispersion decreases more in markets where competition is more intense. Specifically, dispersion is lower on routes with the presence of low-cost carriers (LCCs) than those exclusively served by legacy carriers and on short-haul routes (≤500 miles) than long-haul routes (>500 miles). My analysis adds to the literature by exploiting the impact of changes in market conditions (i.e., demand shocks triggered by the COVID-19 recession) on price and price dispersion using the latest data.

Patterns of Resource Allocation and Divisional Budgets: The CEO's Strategic Lens Across Various Industries

This paper explores how Chief Executive Officers (CEOs) across various industries adapt their resource allocation and divisional budgeting strategies in response to contemporary business challenges. It aims to shed light on the evolving role of CEOs in steering organizational success through strategic initiatives, highlighting the shift from traditional, efficiency-focused models to flexible, value-driven approaches. By conducting a qualitative analysis of recent literature, the study examines patterns in strategic decision-making, focusing on the balance between operational needs and strategic goals amidst changing market conditions. The research emphasizes the impact of industry-specific factors, such as market volatility and organizational size, on these strategic adaptations. Findings reveal a significant trend towards more adaptable and strategic resource management practices among CEOs, moving beyond short-term operational efficiency to support long-term goals. This shift is evident across different sectors, including healthcare, manufacturing, and e-commerce, though the extent and nature of its implementation vary according to each industry's unique challenges and opportunities. The study underscores the importance of strategic adaptability for organizational resilience and success in a rapidly evolving business landscape.

АДАПТАЦИЯ МОДЕЛЕЙ УПРАВЛЕНИЯ КАДРАМИ ДЛЯ СТИМУЛИРОВАНИЯ РАЗВИТИЯ ГОРНОДОБЫВАЮЩИХ ПРЕДПРИЯТИЙ

This article examines the issue of adapting HR management models in the context of mining companies in order to stimulate their development. In today's dynamic economic environment, mining companies face a number of challenges that require effective personnel management to ensure competitiveness and sustainable growth.
 The author analyzes the features of personnel management in the mining industries, highlighting key aspects such as the specifics of the production process, the characteristics of the workforce and the risks associated with occupational safety. Then modern models of personnel management are considered, including approaches to the selection, development and motivation of employees.
 The concept of adapting these models to the specifics of mining companies, taking into account their unique needs and challenges, is also proposed, which includes the development of specialized training and development programs, the introduction of motivation systems that take into account the dangers and difficulties of the working environment, as well as the creation of flexible management structures for rapid adaptation to changing market conditions.
 In conclusion, the importance of continuous improvement of human resources management models in mining companies in a rapidly changing economic environment is emphasized, which will allow them not only to survive, but also to thrive in modern competitive conditions.

Towards audience-centric film funding: New strategies for commissioning youth films in Denmark

This article investigates the changed market conditions for production of films for young audiences in the on-demand age. More precisely, I analyse how the public funding of films for children and youth in Denmark has undergone significant change in response to intensified competition and waning audience interest. From the outset of the Commissioner’s Scheme for films for children and youth at the Danish Film Institute (DFI), the state subsidized main financier in the national industry, the institution has changed away from selective funding based on cultural evaluation criteria towards audience-centric funding based on market orientation and data-driven audience insights. The DFI’s objective is to reach young audiences more precisely with films that are relevant to them. Yet, one of the implications so far of this practice has been that funds earmarked by law for films for children and youth are spent on films for adults, with significant ramifications for domestic film culture for young audiences.

Covid-19 Impact to Retail, Hospitality, and Office Space in Malaysia

Abstract The COVID-19 pandemic has disrupted economies and industries worldwide, including the real estate sector. This study aims to assess the effects of the pandemic on commercial real estate prices in the Malaysian market. By examining variations in property types and considering key factors influencing pricing, the research contributes to a better understanding of the pandemic’s impact on the real estate market. To analyze the effects of the COVID-19 pandemic on commercial real estate prices, a mixed-method approach was employed. The study combines data from direct real estate indices, which provide insights into property prices based on transaction data, and listed real estate, which includes publicly traded real estate investment trusts (REITs). By utilizing both sources, a comprehensive analysis of the market is achieved. The sample for this study consists of commercial real estate properties in the Malaysian market. It includes properties from various sectors, such as retail, hospitality, and office buildings. The sample is representative of the overall market and captures the different property types affected by the pandemic. The analysis begins by comparing direct real estate indices to highlight the limitations and potential biases associated with using these indices. It then examines the variations in commercial real estate prices during the COVID-19 outbreak, focusing on the different property types. Statistical techniques, such as regression analysis and trend analysis, are employed to identify patterns and quantify the impact on commercial real estate prices. The study’s main findings reveal that the retail and hospitality sectors experienced the most significant impact on commercial real estate prices during the COVID-19 pandemic. These sectors witnessed a substantial decline in property values due to restrictions, lockdown measures, and reduced consumer demand. Office buildings, although moderately affected, also experienced some decline in prices. This research contributes to the existing literature on the effects of the COVID-19 pandemic on commercial real estate prices, specifically in the Malaysian market. By combining data from direct and listed real estate sources, the study provides a comprehensive understanding of the variations in property prices across different sectors. The findings offer valuable insights for real estate investors, policymakers, and industry professionals in adapting to the changing market conditions and making informed decisions regarding commercial real estate investments. In conclusion, this article sheds light on the effects of the COVID-19 pandemic on commercial real estate prices in the Malaysian market. The research methodology, which combines data from direct and listed real estate, allows for a comprehensive analysis of property variations among different sectors. The findings emphasize the significant impact on the retail and hospitality sectors, while showing office buildings to have been moderately affected. It also provides important insights for stakeholders in the real estate industry, enabling them to make informed decisions and develop appropriate strategies.

INNOVATIVE BUSINESS MODEL FOR FOOD INDUSTRY ENTERPRISES OPERATING IN CONDITIONS OF GLOBAL DESTRUCTIVE PROCESSES IN THE ECONOMY

This research presents the development of strategic management foundations and crisis management mechanisms within the entrepreneurial structures of the food industry, including methods for overcoming crisis phenomena and ways of adapting to changing market conditions. This involves examining the impact of economic crises on entrepreneurial activity and developing effective anti-crisis management strategies that contribute to the preservation and development of economic stability under new conditions, taking into account innovative approaches in the economy. The study aims to identify key elements that should be integrated into business models to enhance their resilience to external shocks and market environment changes. Special attention is given to analyzing the role of innovation, digital transformation, and social responsibility in forming sustainable and adaptive business strategies capable of withstanding the challenges of global economic instability. An important aspect is the examination of enterprise behavior in response to potential types of threats to its economic activity, the risk of which increases during economic crisis periods. The process of implementing effective crisis management methods for food industry enterprises, facilitating early detection and neutralization of potential risks, is considered as a key factor in maintaining operational flexibility and strategic adaptability. This provides enterprises with the necessary foundation for making timely decisions, preventing losses, and seizing new market opportunities, which in turn contributes to strengthening their competitive positions in the long-term perspective. The implementation of these methods forms the basis for constructing an innovative business model for the operation of food industry enterprises in conditions of global destructive processes in the economy, taking into account the identification of key elements that should be integrated into the business model to enhance the resilience of the economic activity of the aforementioned entities under these conditions.

MODERN APPROACHES TO FORECASTING CORPORATE INCOME TAX REVENUES IN THE RUSSIAN FEDERATION

The purpose of the study. The purpose of the study is to determine the optimal model for forecasting corporate income tax revenues. The subject of the study is modern methods of forecasting income tax revenues to the budgets of the subjects of the Russian Federation, based on mathematical and statistical, regression and autoregressive models with a moving average. The article identifies the advantages and disadvantages of existing approaches to forecasting tax revenues, as well as defines the conditions for the applicability of the considered models in conditions of high dynamics of changes in market conditions and macroeconomic uncertainty. The results of the study are presented, which make it possible to establish the optimal methodology for forecasting corporate income tax receipts, taking into account the statistical limitations of the planning horizon, as well as to identify a forecasting mechanism available for use in long-term planning. Conclusions. Based on the results of the study, it was concluded that it is advisable to combine tax forecasting models to achieve optimal predictive and analytical indicators, as well as to apply this approach only within the framework of short-term planning. The final forecast indicator can be identified on the basis of expert assessments, the average value of the indicators obtained, or through a combination of these approaches: an expert assessment of the weighting coefficients to calculate the weighted average value of the forecast indicator.

Artificial Intelligence in ESG investing: Enhancing portfolio management and performance

Artificial Intelligence (AI) has emerged as a transformative force in Environmental, Social, and Governance (ESG) investing, significantly enhancing portfolio management and performance. This paper investigates the integration of AI technologies within ESG investment strategies, elucidating their profound impact on decision-making processes and financial outcomes. By leveraging advanced data analytics and machine learning algorithms, AI empowers investors to analyze extensive ESG-related datasets, extract actionable insights, and identify investment opportunities aligned with sustainability objectives. The application of AI-driven ESG analysis enables investors to construct well-structured portfolios that not only aim for financial success but also adhere to ethical and sustainable principles. Through the utilization of AI, investors can systematically evaluate the environmental impact, social responsibility, and corporate governance practices of potential investments. This approach facilitates the identification of ESG risks and opportunities with greater precision and efficiency, leading to more informed investment decisions. Moreover, AI enables investors to dynamically adjust their portfolios in response to changing market conditions and emerging sustainability trends. By continuously monitoring ESG factors and leveraging predictive analytics, investors can proactively manage risks and seize opportunities to enhance portfolio performance over the long term. This proactive approach not only mitigates potential ESG-related risks but also positions investors to capitalize on emerging market trends and shifts in consumer preferences. Furthermore, the integration of AI in ESG investing fosters transparency, accountability, and stakeholder engagement within the investment ecosystem. AI-powered tools facilitate the dissemination of ESG-related information, enabling investors to make informed decisions that align with their values and sustainability goals. By harnessing the capabilities of AI, investors can drive positive environmental and social impact while achieving competitive financial returns. In conclusion, the utilization of AI in ESG investing represents a paradigm shift in portfolio management, offering investors unprecedented opportunities to navigate complex ESG challenges and achieve sustainable financial success.

Environmental sustainability and the time-varying changing dynamics of green and brown energy ETFs

Over the last decade, increased awareness about environmental sustainability raised additional focus on green assets as a popular investment vehicle for reaching a carbon-free world. In this study, we analyze whether the dynamics of interconnectedness between ecologically sustainable, green energy, and brown ETFs have changed with the changing market conditions induced by the pandemic and a war scenario using an empirical network model based on TVP-VAR. The study findings report that sustainable and green energy ETFs are the primary givers, and brown energy ETFs are net receivers. The information transmission heightens during the first two waves of the COVID-19 pandemic, followed by a cooling phase. The results fully comprehend the effect of shifts in information transmission between environmentally sustainable, green, and brown ETFs, which aids policy-making, asset allocation, and pricing.

An effective design freeze strategy for establishing design rules in modular product development

In rapidly changing market conditions, the timing of a product release is crucial for its success. Design iteration, especially between modules, poses a significant challenge in the development of modular products. This study proposes an effective design freeze strategy as design rules aimed at reducing such iterations by finalising specific tasks before others. However, applying a design freeze without considering dependencies among tasks may lead to the unfreezing of previously frozen tasks in the subsequent design and result in the restart of the design process. In response to this issue, this paper presents a methodology that identifies a set of dependent tasks to be frozen simultaneously, constituting freeze alternatives. Furthermore, the methodology evaluates the reduction of rework due to design freeze for each alternative and determines the effective design freeze strategy that can most efficiently eliminate design iterations. The proposed approach employs a network representation of the Design Structure Matrix (DSM) and network analysis techniques to determine the optimal alternative. A case study on a formula car is used to demonstrate the effectiveness of the suggested strategy.

A microfoundational view of the interplay between open innovation and a firm's strategic agility

Open innovation can support firms looking to deploy strategic agility through product innovations during periods of market and technological change. However, existing research lacks a comprehensive understanding of the microfoundations that underlie strategic agility in the context of open innovation. We address this gap using an in-depth analysis of a firm's open innovation activities in support of new product development (NPD). Our analysis reveals that open innovation can help leverage NPD processes to drive technological innovations in response to changing market conditions. Under such circumstances, open innovation enables firms to deploy strategic agility by continually developing the product portfolio. Our study reveals six mechanisms that enable three mutually complementary practices of agility: knowledge-based agility at the firm-environment interface, behavioural agility in the firm's decision-making process, and organisational agility in the internal NPD process. We theorise the interplay between the mechanisms that constitute each practice and, in doing so, shed light on how they contribute to firm-level strategic agility.

A strategic approach to the development of automotive dealerships in a changing market environment

The activities of automotive dealerships need to be strategically adapted to changing market conditions. The article examines the role played by the automotive industry in the Russian economy, presents an analysis of changes in the structure of new car sales in the period from 2021 to 2023.The strategic factors that dealerships should consider when choosing a brand are described. The necessity of analyzing the competitive environment is justified, the existing marketing tactics of manufacturers are presented, the importance of the availability of spare parts and opportunities for revenue growth from after sales-service are described. The article emphasizes the need to invest in the reconstruction of dealerships, despite the inevitable financial losses, to ensure the future profitability of the business. The study provides information for players in the Russian automotive industry, helping them to change their strategy for the future development of the company in a changing market environment.

Financialisation of the European Union Emissions Trading System and its influencing factors in quantiles

AbstractThis study analyses the financialisation of the carbon market and its possible external shocks, with a focus on the European Union Emissions Trading System (EU ETS), by investigating its quantile dependence and influence paths from stage three onwards. To achieve this, we construct a theoretical model of five factors related to the financialisation of the carbon market and empirically investigate the significant influencing factors and their influence paths under different quantiles using quantile group Least Absolute Shrinkage and Selection Operator (LASSO) and quantile regression models. We find that the price of WTI crude oil and the market risk‐aversion index have a significant effect on the financialisation of the EU ETS at extremely high quantiles. Factors such as the WTI crude oil price, precipitation, average share price of thermal power companies, and the federal funds rate have a statistically significant impact on the medium quantiles. However, we find no significant influence at extremely low quantiles, indicating that policy instruments are necessary to effectively regulate the operation of the carbon market. Therefore, it is crucial for carbon market stakeholders to pay close attention to these factors and adapt to changing market conditions.

Machine Learning-Based Risk Management of Credit Sales in Small and Midsize Business

Sustaining and expanding the finances of small and midsize companies (SMBs) depends on efficient credit risk management. This study redefines credit risk assessment for SMBs via the use of machine learning (ML), hence introducing a disruptive methodology. The all-inclusive approach includes feature selection, preprocessing, data collecting, and the use of ML models, with an emphasis on behavioral insights integration and real-world applicability. The results imply that Random Forests and other machine learning models are superior at predicting credit risk, which may lead to a sea shift in the way SMBs handle credit risk. Improving the research's practical implications involves applying models to actual credit risk management systems and incorporating insights from behavioral economics. Possible future research directions include studying how models adapt dynamically, using different types of data, enhancing explain ability via XAI, and fostering collaborative efforts to develop industry-specific best practices. By outlining the ins and outs of credit sales, this research helps small and medium-sized companies (SMBs) adjust and remain resilient in the face of changing market conditions.

Portfolio Optimization: Application and Comparison of Markowitz Model and Single Index Model on LQ45 Stocks in Indonesia Stock Exchange

This paper examines the optimization of an Indonesian stock portfolio using two models: the Markowitz Model (Mean-Variance Model) and the Single Index Model. The data comprises historical returns of LQ 45 stocks from January 2016 to December 2021. The focus is on selecting stocks for the portfolio and determining their weights based on the two models. The study compares the performance of both optimized portfolios with the LQ45 Index benchmark, IHSG market, and each other using Sharpe and Treynor measurements. The paper tests whether the stock composition of the optimized portfolio from both models successfully and consistently generates a better performance in the future (1 January 2022 – 31 December 2022) and (1 January 2023 – 31 December 2023) compared to both LQ45 and IHSG. The results reveal a notable contrast in portfolio performance between 2022 and 2023. In 2022, both the Markowitz and Single Index portfolios exhibited remarkable returns, surpassing LQ 45 and IHSG. However, in 2023, both portfolios experienced substantial underperformance, with negative returns and unfavorable risk-adjusted metrics. These findings underscore the dynamic nature of financial markets and the need for continuous portfolio monitoring and adaptation. Investors are encouraged to reevaluate their portfolio strategies in response to changing market conditions. The study contributes valuable insights into the temporal variability of optimized portfolios and their sensitivity to evolving market dynamics.