Market Turbulence Research Articles

Business analysis and investment recommendation of Nike Corp

With the development of the economy, people’s demand for their health and leisure activities is constantly increasing, which provides many opportunities for Nike’s international development. However, the fierce competition of competitors and the turbulence of the financial market have caused a certain negative impact on it. The study aims to focus on the brand’s competitiveness in the market, business model and development strategy to evaluate whether it has the potential for sustainable development in today’s market. The method mainly comes from some Nike stocks and analyses the revenue data. The data sources are mainly financial data, market data and consumer data. After this preliminary analysis and data collection, this research will write out the performance of the company, which will be reflected by brand influence, brand management and product innovation. Finally, some relevant investment suggestions will be given in the later paragraph of the article, which are generally divided into long-term, short-term and diversified investments.

EU food price inflation amid global market turbulences during the COVID‐19 pandemic and the Russia–Ukraine War

AbstractSince the COVID‐19 pandemic and the Russia–Ukraine War, global food markets have been in turmoil. Agricultural input and energy prices doubled between 2020 and 2022, with immediate consequences on food accessibility. We examine the drivers of the EU food inflation patterns, and how trade integration shapes these dynamics. We find that food price inflation has been mainly driven by surges in agricultural production costs and, to a lesser extent, by global food price increases. Trade openness has not exacerbated the inflating dynamics during this period. We conclude with reflections on inflationary, production, and trade policies.

Digitalisation of manufacturing and innovation: the role of inbound open innovation and environmental turbulence in enabling Industry 4.0-based innovation

Industry 4.0 (I4.0) technologies have demonstrated their potential to significantly drive innovation and enhance the performance of firms. However, I4.0 implementation is complex, requiring the application of diverse fields of knowledge and skill. Furthermore, although open innovation may contribute to the implementation of I4.0 technologies, the literature is still not clear about which open innovation practices best leverage the adoption of I4.0 technologies or how they might lead to improvements in product and process innovations, particularly when firms are facing turbulent environments. The present article aims to reduce this gap by investigating the relationships between open innovation, the adoption of I4.0 technologies, and product and process innovation performance, considering technological and market turbulence as moderating variables. As a research method, we carried out a quantitative survey of 246 industrial companies in Brazil. The results revealed that inbound open innovation flows can be used to support the adoption of I4.0 technologies, which in turn improves both process and product innovation performance. Our findings also indicate that in more technologically turbulent environments, the relationship between inbound open innovation and the adoption of I4.0 is stronger. Unexpectedly, the moderating role of market turbulence was not confirmed. The discussion arising from these findings addresses theoretical implications for the digitalisation of manufacturing, innovation management and contingency theory, as well as offering practical insights for managers and policymakers interested in performing I4.0-based innovation.

Business continuity management in the sharing economy: Insights from Airbnb online reviews

The aim of this paper is to demonstrate how text mining of online reviews can support business continuity management in the sharing economy during market turbulence. We use topic modelling and sentiment analysis of Airbnb online reviews to identify specific areas where short-term rental hosts can harness the power of the sharing-economy business model to bounce back from market disruption. The dataset comprises 894,686 online Airbnb reviews from the pre- and post-Covid periods. We approach the pandemic as a paradigm example of a high-impact, external market shock and infer insights and recommendations from the dataset that are relevant to the general understanding of business continuity management in the short-term rental, peer-to-peer sharing economy.

Evaluation of the Effect of Commercialization of Technology in Creating Sustainable Competitive Advantage of Knowledge-based Companies Emphasizing the Moderating Role of Environmental Factors

The present study aimed at designing a structural equation model of the effect of technology commercialization performance on creating sustainable competitive advantage of knowledgeable companies with an emphasis on the moderating environmental factors.Research population includes 133 managers of knowledge based companies at the center for the growth and development of science and technology Park of East Azerbaijan University and based on the Cochran formula, the sample size was measured to be 99, and selection by stratified random sampling method, and the standard questionnaire is distributed among them. The research is applied in nature and the method is descriptive and survey. Then, the data were analyzed using smart pls software for partial least squares (PLS) path modeling. The results show that the technology commercialization performance has a positive effect on the competitive sustainable advantage of knowledge-based companies, as well as the role of variable moderating environmental factors (market turbulence, competitive intensity and technological turbulence) in the path between the technology commercialization performance and the sustainable competitive advantage of knowledge-based companies.

Big data analytics managerial skills and organizational agility: a moderated mediation model

PurposeAlthough big data analytics managerial skills (BDAMS) offer opportunities for firms to foster organizational agility, existing studies present inconclusive indications of this impact, with an overlooking of the intermediate pathways involved. This study explored how BDAMS affect organizational agility by investigating the mediation effect of data-driven organizational learning (DDOL) and the moderating roles of technological and market turbulence.Design/methodology/approachThis study employed mediation and moderated mediation analyses to test the hypotheses using data collected from listed Chinese firms. Furthermore, we performed a fuzzy set qualitative comparative analysis (fsQCA) as a supplementary approach to identify the configurations that lead to organizational agility.FindingsThis study shows that DDOL partially mediates the relationship between BDAMS and organizational agility. Besides, technological and market turbulence positively moderate the effect of DDOL on organizational agility and the mediation effect of DDOL. Our additional analyses also reveal several patterns of conditions that facilitate agility.Originality/valueThis study offers a comprehensive exploration of the relationship between BDAMS and organizational agility by verifying the mediating effect of DDOL and moderating effects of technological and market turbulence. In addition, the fsQCA results highlighted the combinatorial effects of key factors in this study, reinforcing and refining the moderated mediation results.

Investing during a Fintech revolution: The hedge and safe haven properties of Bitcoin and Ethereum

Global financial markets have witnessed significant transformations in response to the worldwide spread of the Covid-19 pandemic, resulting in heightened risk and alterations in inter-market relationships. Both cryptocurrency markets and conventional equity markets have been influenced by these changes. This study aims to investigate the potential hedging and safe haven benefits of Bitcoin and Ethereum against risks in Asian emerging stock markets under various Covid-19 pandemic contexts including the onset of the pandemic, varying levels of panic, the extent of media attention, and sentiment analysis. Utilizing the dynamic correlation coefficients derived from the DCC-GARCH methodology, our analysis indicates that, both Bitcoin and Ethereum do not effectively serve as hedges or safe havens for major Asian emerging stock markets during the Covid-19 outbreak. These results remain robust during more extreme periods characterized by high panic, intense media attention, and negative sentiment. Nevertheless, an noteworthy exception is the case where Ethereum exhibits strong hedging properties specifically in the context of the Pakistan equity market. Recognizing these unique hedge and safe haven potential of cryptocurrencies can contribute to enhancing the effectiveness of investment strategies and risk management practices during periods of market turbulence.

Quantile-frequency connectedness among climate change stocks: The roles of climate change attention and global uncertainties

Concerns regarding climate change and the environment have heightened interest in climate-conscious investing, necessitating a deeper understanding of climate-friendly financial products. This study investigates climate change stock market connectedness and spillover effects, focusing on quantile-frequency dependency. The study utilizes a novel quantile-frequency connectedness with seven MSCI climate change market indices and daily data from January 2014 to December 2022. The empirical analysis reveals that climate change stocks are highly interconnected across various market conditions, including bearish and bullish periods. Spillover effects have amplified during increased stock market turbulence, regardless of market directions. Short-term spillover effects have a stronger impact than long-term effects, regardless of market conditions. Climate change stock markets in Japan and the Asia Pacific consistently experience net shocks, while the United States, Europe, Emerging Markets (EM), North America, and the European Economic and Monetary Union (EMU) exhibit variations based on market conditions and timeframes. The study identifies events including the 2015 Paris Agreement, the 2016 US withdrawal from the Paris Agreement, the COVID-19 pandemic, the 2021 US rejoining of the Paris Agreement, and the 2022 Russian-Ukraine War that have influenced the dynamic connectedness and spillover of climate change markets. The analysis also reveals that increased investor attention to climate change leads to higher interconnectedness among climate change stock markets across various market scenarios. Moreover, global financial stress, economic policy uncertainty, geopolitical risk, global carbon emission futures, oil market uncertainty, and stock market uncertainty significantly impact the interdependence of climate change markets. The findings have implications for portfolio managers, emphasizing the need for hedging techniques to mitigate risks in climate change markets.

Safe Havens Characteristics of Islamic Stock Indices During Bearish and Bullish Markets: A Case of Developed and Developing Countries

Purpose — This study examines the performance of Islamic and conventional indices during bullish (positive) and bearish (negative) markets while the economic and financial conditions are different. Design/Methodology/Approach — This study uses GARCH (1,1) and wavelet-assisted cross-spectral, cross-correlation analyses to investigate the performance of Islamic and conventional stock indices and study the presence of safe havens, which reflect low volatility in the indices during times of market turbulence, for investors during bearish and bullish periods from 2019–2021. Findings — The paper finds that there is high and persistent volatility in Islamic indices in bullish markets, while in bearish periods the Islamic indices show low volatility persistence; and conventional indices were highly volatile during crisis periods. Also, the impact of news shocks on Islamic indices was intense before COVID-19, while after that news, shocks had a high impact on the conventional indices. The wavelet coherence analysis shows that there is high coherency between Islamic and conventional indices in developed countries but in developing countries, the opposite prevails. Originality/Value — The study is the first to use both Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and wavelet coherence models for examining Islamic and conventional stock indices during bearish and bullish markets, especially in the COVID-19 pandemic era. Practical Implications — The results conclude that there are safe havens present in Islamic indices in both developing and developed countries, especially the United States (US), the United Kingdom (UK), India, and Indonesia, while in other countries the presence is weak. Both GARCH and wavelet coherence models show the same results regarding the presence of volatility and safe havens for investors in different countries.

Distribution of Rare Earth Elements in Ash from Lignite Combustion in Polish Power Plants.

Rare earth elements are an essential critical raw material in the development of modern technologies and are highly sensitive to both supply chain disruptions and market turbulence. The presented study examines the characteristics of fuel, fly ash, and bottom ash from lignite combustion in power plant units. Also, we attempted to determine the amount of amorphous glass in the ashes and whether and to what extent the glass from the ash samples is bound to REY. The suitability of the ash was assessed as an alternative source of REY. The fuel and ash samples were acquired from power plants in Poland. The tests determined the fuel quality parameters, including the chemical and phase composition, of amorphous glass using ICP-MS and XRD methods, respectively. The study showed that all ash samples dissolved in 4% HF were enriched in REY. The efficiency of REY enrichment varied, and its presence in the residue samples was found to be in similar proportions compared to the raw sample. All ash residue samples were enriched in critical elements. The obtained values of the Coutl prospective coefficient allowed for the classification of some of the analyzed ashes and their residues after dissolution in 4% HF as prospective REY raw materials.

Robustness Investigation of ML Models for Stock Price Prediction During Market Volatility: A Case Study of the Japanese Stock Market Amidst the COVID-19 Pandemic

In the evolving realm of financial markets, precise stock price prediction remains a pivotal challenge. This paper investigates the efficacy of traditional machine learning models for stock price forecasting, particularly during the high volatility phase induced by the COVID-19 pandemic, using the Japanese stock market as a case study. This study utilized a dataset from the JPX Tokyo Stock Exchange Prediction competition, selecting two stocks at random to avoid bias. After standardizing the data for consistency, this study employed linear regression and decision trees to project stock prices, comparing their performance during stable periods and the economic upheaval of the pandemic. The analysis revealed that both models' predictive capabilities were compromised during the pandemic, with the decision tree model particularly underperforming. In normal times, linear regression showed improved accuracy for stock 1301 in 2021, while for stock 6758, the model's performance declined. These findings underscore the necessity for robust models that can withstand market turbulences like those observed during the COVID-19 crisis. The research contributes to both academic discussions on financial forecasting and practical strategies for market participants navigating complex stock environments. Future work will focus on developing more resilient forecasting methods to address these challenges.

Analyzing Stock Price Prediction Models: A Comparative Study of Linear Regression and Decision Trees During the COVID-19 Pandemic

Stock market forecasting plays a vital role in financial decision making. In a volatile and uncertain situation like the COVID-19 pandemic, it is important for us to compare the accuracy of different models for forecasting. This study compares the performance of the Linear Regression (LR) algorithm and the Decision Tree (DT) algorithm in predicting the stock price of Pfizer Inc. before and during a pandemic. The study evaluates the accuracy and stability of the predictions using datasets before and during the pandemic. The results show that the prediction accuracy of LR is better than that of DT in both cases, although both accuracies decrease during the pandemic. The results of the study emphasize the need to consider external factors in the selection of forecasting models and suggest some lessons for future research, leading to the selection of more stable and accurate models under turbulent market conditions. It is worth noting that overfitting occurs when a model learns to capture noise in the data rather than the underlying patterns, leading to reduced generalization performance on unseen data. Future research should address this by exploring techniques such as regularization or cross-validation to mitigate overfitting and enhance the robustness of predictive models in dynamic market environments.

Запровадження проєктного підходу як інструменту забезпечення гнучкості та адаптивності підприємств у турбулентному середовищі

Introducing a project approach as a tool for ensuring the flexibility and adaptability of enterprises in a turbulent market environment is necessary for increasing business sustainability. The project approach allows enterprises to adapt to changes faster, reduce risks, and improve management efficiency. The modern market environment is characterized by high levels of uncertainty and turbulent change, necessitating the search for new business management approaches. Traditional management methods, focused on stability, often need to provide the flexibility and adaptability required for enterprises to survive in turbulent conditions. This article substantiates the role of the project-based approach as an effective tool for enhancing organizations’ ability to respond quickly to changes in the external environment, reduce risks, and improve management efficiency. The analysis demonstrates that the project-based approach promotes organizational flexibility, fosters innovation, and optimizes resource allocation — critical factors for the successful operation of enterprises in unstable market conditions. However, implementing this approach is accompanied by challenges, such as resistance to change and difficulties in project coordination. The article also explores various project management models, including hybrid approaches that combine the strengths of traditional and agile methodologies. The conclusions emphasize the need for further research focused on integrating the project-based approach with other management tools and studying the impact of corporate culture on the success of its implementation. The application of various models within the project approach framework allows organizations to respond more effectively to the challenges of the turbulent market, combining stability and predictability with the necessary flexibility and adaptability. Keywords: project, project-based approach, entrepreneurship, adaptability, flexibility, hybrid project models, turbulent environment.

How Market Turmoil Affects Investors' Investment Psychology

Using bibliometric research methodology, this study provides insights into the impact of market turmoil on investors' investment psychology, revealing the significant influence of financial markets on investment decisions. The findings of the study emphasize that investors mostly exhibit fearful, extremely risk-averse emotions when faced with market downturns. Overall, the study advocates for a diverse approach to investment decision-making that combines different methods of analyzing market situations and controlling emotions to increase the stability of an individual's investment in times of market turbulence and reduce financial losses, leading to a more inclusive and prosperous economic landscape.With every arrival of financial crisis causing bankruptcy or severe financial losses for a large number of investors, finance exists for everyone. There has been a great deal of literature on what factors influence investors' investment psychology, but these studies do not do a good job of linking the influencing factors to a specific context, such as the severe impact of the 2020 New Crown Epidemic on the financial markets. The world landscape is ever-changing, and the crises that investors have to deal with are beginning to be complex, specific, and diverse. This paper collects and analyzes relevant literature to focus on the relationship between specific crisis environments and investor psychology, and divides crisis periods into two categories to illustrate how they affect investors' psychological activities.

Dynamic margin optimization

In response to the Global Financial Crisis of 2007–2009, by now, most of the financial transactions must be cleared through central counterparties operating a dynamic margin setting mechanism. High margin calls can reduce counterparty risk in a turbulent market, but at the same time, increase liquidity risk and escalate systemic risk. In this paper, we construct a theoretical model to address this challenge, deriving an optimal margin setting policy framed as a stochastic control problem. Our analysis reveals that an adaptive, countercyclical approach is superior to a purely risk-sensitive strategy, primarily by minimizing the expected loss for the clearing institution.

Comparative Analysis of GARCH-Based Volatility Models of Financial Market Volatility: A Case of Nairobi Security Market PLC, Kenya

This paper conducted a comprehensive comparative analysis of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models to forecast financial market volatility, with a specific focus on the Nairobi Stock Exchange Market. The examined models include symmetric and asymmetric GARCH types, such as sGARCH, GJR-GARCH, AR (1) GJG-GARCH, among others. The primary objective is to identify the most suitable model for capturing the complex dynamics of financial market volatility. The study employs rigorous evaluation criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Error (ME), and Root Mean Absolute Error (RMAE), to assess the performance of each model. These criteria facilitate the selection of the optimal model for volatility forecasting. The analysis reveals that the GJR-GARCH (1,1) model emerges as the best-fit model, with AIC and BIC values of −5.5008 and −5.4902, respectively. This selection aligns with the consensus in the literature, highlighting the superiority of asymmetric GARCH models in capturing volatility dynamics. The comparison also involves symmetric GARCH models, such as sGARCH (1,1), and other asymmetric models like AR (1) GJG-GARCH. While these models were considered, the GJR-GARCH (1,1) model demonstrated superior forecasting capabilities. The study emphasizes the importance of accurate model selection and the incorporation of asymmetry in volatility modeling. The research provides essential insights into financial market volatility modeling and forecasting using both asymmetric and symmetric GARCH models. These findings have significant implications for government policymakers, financial institutions, and investors, offering improved tools for risk assessment and decision-making during periods of market turbulence.

Announcement effect of tender offer share buyback around turmoil period – evidence from India

The announcement of a buyback informs the market about the company’s decision to repurchase its own shares. This announcement highlights the company’s price valuation and the inefficiencies that exist in the market. This study examines the share buyback announcement effect during the COVID-19 period. The study considered the stocks listed in the National Stock Exchange (NSE) that offered share buyback under tender offer mode during the pre-pandemic period between April 2016 and February 2020 and the post-pandemic period between March 2020 and March 2022. 75 firms in the pre-pandemic period and 43 in the post-pandemic period that announced share buyback under the tender offer method were analyzed. The event study methodology using a market model was employed to determine the presence of abnormal returns during the event period, which consisted of –21 days and +21 days. The findings of the study revealed the existence of abnormal returns in and around the announcement date. Besides, statistically significant cumulative abnormal average returns (CAAR) were also found on the event day, i.e., on Day 0. The study found that the impact of buyback announcements on stock returns significantly differed before and after COVID-19 for 10 and 21-day periods, with no significant differences for shorter periods. These insights can help traders and fund managers make informed portfolio adjustments during turbulent market periods surrounding buyback announcements. AcknowledgementThe authors express their sincere gratitude and special thanks to Dr. Krishna T.A., Assistant Professor, Department of Professional Studies, School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bangalore, India, for encouraging, motivating and providing all the required support throughout this empirical investigation and to accomplish this research task.

The Impact of Market Turbulence on Information Technology Governance and Organizational Agility

The Impact of Market Turbulence on Information Technology Governance and Organizational Agility

Empirical Analysis of Portfolio Allocation During Market Turbulence Based on Efficient Frontier

The global financial markets have experienced considerable turmoil due to the COVID-19 pandemic, in this context, portfolio management becomes critically important. This paper employs Monte Carlo Simulation to determine Efficient Frontier of a portfolio comprising fourteen US technology stocks in the period of COVID-19 pandemic by python, which also allocates portfolios for Minimum Volatility and Maximum Sharpe Ratio, along with their key parameters. According to this study, Maximum Sharpe Ratio portfolio outperforms Minimum Volatility portfolio in terms of cumulative return. However, its important to note that Maximum Sharpe Ratio portfolio also experiences a greater maximum drawdown. Additionally, this paper visually depicts the optimal allocation of asset weights across different levels of risk aversion using a chart. Furthermore, it identifies NVDA as the optimal choice for investors who have low degree of risk-aversion due to its high volatility and expected return. Conversely, IBM and CSCO emerge as the preferred choices for risk-averse investors, as they effectively mitigate the portfolios entire risk.

The Mediating Role of Eco-Innovation between Adaptive Environmental Strategy, Absorptive Capacity, and Environmental Performance

This article delves into the role of adaptive environmental strategies, absorptive capacity, and eco-innovation in enhancing environmental performance, particularly in the context of market turbulence. The study, conducted among 568 companies in Colombia, employs structural equation modeling to evaluate relationships between the studied variables. The findings suggest that adaptive strategies alone do not directly impact environmental performance, emphasizing the need for integration with eco-innovation initiatives. Moreover, organizations with high absorptive capacity can leverage market turbulence to drive eco-innovative initiatives, highlighting the indirect yet significant impact of market turbulence on environmental performance through absorptive capacity. The study underscores the critical role of eco-innovation in directly influencing environmental outcomes, suggesting that the effectiveness of adaptive strategies and absorptive capacities hinges on successful eco-innovation initiatives. These insights offer practical guidance for organizations seeking to enhance their environmental performance in turbulent markets, providing a roadmap for sustainable business practices.