Market Uncertainty Research Articles

Analysis of Aggregate Herding Behavior in the Capital Market: Evidence from Indonesia and Singapore

<p>The high uncertainty in the capital market due to some crises that hit the world over the last few decades has the potential to cause herding behavior in the aggregate capital market, both in developed and emerging capital markets. The main objective of this study is to detect the existence of herding behavior, including asymmetric herding and global factor drives (oil prices and fed fund rates) on aggregate herding behavior in the Indonesian and Singapore capital markets during the period Jan 2015 to December 2020. This study employs a cross-sectional dispersion approach to achieve study goals. Research findings denote aggregate herding behavior occurs only in the Singapore capital market, while in Indonesia no herding behavior is detected. Asymmetric herding testing for both capital markets revealed no herding tendency in up and down market conditions. This condition implies that low volatility cannot ensure the absence of aggregate herding behavior. Global factors have proven to significantly drive herding behavior in the Singapore capital market, while in Indonesia it is only the oil price. The findings of this study will provide information that policymakers can use to maintain capital market stability in both countries.</p>

What affects China's green finance? Evidence from cryptocurrency market, oil market, and economic policy uncertainty.

This paper discusses the dynamic long- and short-term effects of bitcoin price (BTP), crude oil price (COP), and uncertainty of economic policy (EPU) on China's green bond (CGB) market, separately. Depending on the quantile autoregressive distributed lag method, the empirical results are shown that BTP and EPU exert substantial positive and negative effects on the CGB market in the long term for most circumstances, while their effects reflect not prominent in the short term. The main contributions can be summarized as follows. Given that China is the largest bitcoin mining state and a major green bond issuer, this study first explores the linkages between them. Furthermore, both long- and short-term effects are investigated from BTP, COP, and EPU to CGB, and long-term effects are dominated in the interrelationships among variables, indicating that the CGB market is mainly driven by permanent shocks. In addition, the mentioned long-term effects are deeply discussed from time- and quantile-varying aspects. This approach considers diverse situations in the bond market and various incidents that occur at various durations of time. The results underscore the significance of market participants gaining a deeper comprehension of how BTP, COP, and EPU impact green bond within varying market conditions. Implementing specific policies, such as establishing a cohesive and efficient bond market and making careful adjustments to economic policies, can be advantageous in maintaining stability within the CGB market.

Beyond the dollar: A global perspective on exchange rate dynamics via currency factors

This paper investigates the dynamics of currency exchange rates from a global perspective, by expanding on the concept of currency factors. Through the analysis of G10 currencies, we disclose the interconnections and patterns driving exchange rates, shedding light on the central role of the euro and Swiss franc in shock propagation. Furthermore, we observe that pound and yen factors have acted as net absorbers of spillovers from other currencies. Our findings suggest that currency factors provide valuable insights for international diversification, risk sharing strategies, and real-time indicators of foreign exchange market uncertainty.

Learning research: theory building and theory testing in educational technology innovation and beyond

This study draws on extensive ethnographic data and a multiple case study design to develop new theory about innovation processes and techniques. By contrasting the experiences and outcomes of five innovation projects in an educational technology company, the study considers two distinct, if often interrelated, strategies to navigating market uncertainty: theory building and theory testing. The paper theorizes how the appropriate balance between these two strategies is driven by the multiplicity, or number of and relationships between relevant stakeholders and stakeholder interests, in the innovation context. The data suggest that multiplicity increases the value of rigorous – and potentially more time-consuming – theory building, while simultaneously increasing the potential risks and costs of poorly grounded theory testing. These insights challenge contemporary views on innovation teaching and practice, which have increasingly emphasized action over analysis.

Systematic Literature Review in Reshoring Strategies 4.0

Background: With global changes in markets and government regulations, most organizations, which previously opted for offshore production, began to return to their country of origin—a process called reshoring. The new strategies for locating units and business models, which involve decisions to outsource production and organizational capacity, now face a new context of Industry 4.0, a market for technologies with the potential to transform entire supply chains in addition to directly influencing organizational aspects with innovative solutions and resource efficiency. Methods: This systematic literature review (SLR) found 43 articles relating to reshoring and Industry 4.0; there was an increase in publications in the last few years. We divided the studies according to 9 aspects: market uncertainties and labor supply; support for managerial decision making; competitive priorities analysis (costs and efficiency); business strategies; information knowledge and collaboration management; political, social and economic changes; risk security and privacy management; supply chain management; and investment market and private equity funds. Results: This article presents a content analysis of articles in the field of administration, focusing on location strategies, technology adoption, and organizational changes. The study identifies key aspects such as research quality, publication trends, regional focus, and strategy topics. The findings emphasize the importance of research objectives and problem-solving for managerial decision-making and highlight the need for further exploration in the literature. Conclusions: Reshoring, Industry 4.0, and location strategies are relevant topics for global management. The supply chain plays a role in decision-making for location and technology adoption, suggesting the need for more research in supply chain management.

CVaR‐based method for optimizing the contract bidding strategy of PV power stations

AbstractWith the rapid development of the new power system, the proportion of renewable energy participating in the electricity market is constantly increasing, and the uncertainty of renewable energy power output and market clearing price brings risks to its participation in long‐term contract bidding. Therefore, a method of contract bidding involving bidding amount and bidding price of PV power station is proposed. Firstly, the multi‐order weighted Markov chain model is used to forecast the distribution of weather states in the trading cycle, and the fuzzy C‐means (FCM) method is used to cluster the joint samples of power output indicators and market clearing price indicators, and then Latin hypercube sampling is used to generate power output scenarios and market clearing price scenarios. Then, an optimization method for long‐term contract bidding strategy of PV power station considering conditional value at risk (CVaR) and transaction probability is established, and the model is divided into two stages and solved in an alternate iterative way. Finally, an example is used to verify that the constructed model enables PV power stations to effectively avoid risks and obtain more stable revenue.

Hybrid robust-stochastic energy scheduling of multi-carrier systems integrated with ice storage and heat storage systems: A multi-solver approach

This paper focuses on the short-term operation management of small-scale energy systems. The proposed model integrates the cooling, heating, and electrical sections to provide more flexibility, efficiency, and economic benefits for the system. To this end, an optimization framework is performed to minimize the total costs of different sectors considering the marginal costs of local resources, emission costs, incentive cost for demand response programs, and cost of purchasing energy from the main grid. We apply the stochastic, robust, and hybrid robust-stochastic approaches to consider the uncertainty of non-controllable energy resources, loads, and market prices to get more accurate results. In the proposed model, the probabilistic behaviors of photovoltaic and wind energy are considered by stochastic optimization. Also, the load and market prices are modeled by a robust approach to consider the worst condition in the operation and ensure robustness. To get the best operation scheduling, the proposed model is integrated with multiple solvers such as CPLEX, CBC, LINDO, MOSEK, GUROBI, and BDLMP. The proposed multi-solver model ensures that the solution found is optimal and robust. The proposed model is tested on a general case study and results show that demand response programs and electric vehicles can reduce short-term costs by $ 37.88. Also, the sensitivity analysis shows that the electrical demand response program and wind energy can reduce the operation costs of the system by 6.22 % and 19.17 %, respectively. Besides, the thermal demand response program can reduce the operation cost of the system by $ 12.52.

Making sense of uncertainty: An application to the Scandinavian banking sector

AbstractWe study the impact of different sources of global, regional and local uncertainty on the seven largest Scandinavian banks over the period 2005–2018. Using a spillover approach and network analysis, we find that Swedish banks are the main source of contagion in the region and spillovers tend to increase in times of heightened uncertainty. Global economic policy uncertainty, global financial uncertainty and local housing market uncertainty affect the Scandinavian banking sector the most. By contrast, geopolitical risk spillovers are limited.

S&P 500 volatility, volatility regimes, and economic uncertainty

AbstractWe assess the relationship between regime‐dependent volatility in S&P 500, economic policy uncertainty, the S&P 500 bull and bear sentiment spread (bb_sp), as well as the Chicago Board Options Exchange's VIX over the period 2000–2018. Our findings from two‐covariate GARCH–MIDAS (GM) methodology, regime switching Markov Chain, and quantile regressions suggest that the association of realized volatility and sentiment varies across high‐ and low‐volatility regimes and depends on investors’ sensitivity toward incidents of market uncertainties under these regimes. The findings suggest that these indicators may not be useful in volatility forecasting, especially under high‐volatility regimes.

Understanding the role of aggregate analyst attention in resolving stock market uncertainty

This paper investigates whether sell-side analyst attention effect extends to the aggregate stock market. Our findings demonstrate that: (i) aggregate analyst coverage negatively predict future stock market returns while controlling for a rich set of predictors related to economic fundamentals and investor sentiment. (ii) higher aggregate analyst coverage also precede lower market-wide cash-flow volatility, cash holdings, absolute magnitude of unexpected earnings, investor information-search demand, and greater capital expenditures, long-term debt, indicating that the negative return premium stems from less compensation for lower expected market uncertainty. (iii) long-short profits of overreaction-related and lottery-related anomalies significantly decrease following high levels of aggregate analyst attention. Our results highlight the important role of aggregate analyst attention in reducing stock market uncertainty and enhancing investor rationality, thereby mitigating anomalies induced by psychological biases.

Extreme dependence and spillovers between uncertainty indices and stock markets: Does the US market play a major role?

This study investigates the spillovers and connectedness between uncertainty indices of oil gold, and stock (VIX), the economic policy uncertainty (EPU) and international stock markets (US, EU, UK, Japan, China, and Vietnam) under bearish, normal, and bullish market conditions. We employ a cross-quantilogram and quantile connectedness approach to investigate the contemporaneous linkages and asymmetries among stock markets under various financial market uncertainty. Using the cross-quantilogram approach, we find strong cross-quantilogram dependencies from the US stock market to other markets, even after controlling for the uncertainties. We find greater spillovers under volatile market conditions—bearish and bullish—than under stable market conditions using the quantile connectedness approach. The financial volatility or uncertainty indices are net transmitter (receiver) of spillover in the stock markets, especially in the bearish and tranquil (bullish) market status. Furthermore, all markets and the uncertainty indices except the US, Europe, and the UK are net receivers of spillovers in the lower quantile, while VIX uncertainty index shifts to being a net transmitter of spillovers in the lower and median quantiles. In the upper quantile, Japanese stock market and the uncertainty indices are net receivers of spillovers. VIX is strongly linked to the US (Chinese) market in tranquil (bullish) market conditions. In addition, the maximum amount of spillovers was attained during the beginning of 2020, coinciding with the onset of the COVID-19 pandemic's first wave.

Organizational value and participatory leadership for sustaining the competitive advantages of hospitality and tourism companies

In light of the market uncertainty for hospitality and tourism companies caused by COVID-19, which even led to the legal closure of borders, a study model is proposed. Our model considers market uncertainty as a moderator of the principles of business management. This moderator leads to a sustainable competitive advantage through organizational values, participative leadership, market orientation and innovative intensity. The viability of the proposed model has been analyzed with a mixed methodology using the Smart-PLS tool, on the one hand, and two fsQCA-based samples of hospitality and tourism companies located in Spain and South Korea, on the other hand. The results show the relevance of participative leadership supported by organizational values in obtaining a sustainable competitive advantage. This will help the tourism sector to adapt to future health pandemics. Market uncertainty has not affected participative leadership in achieving this sustainable competitive advantage.

A knowledge graph–GCN–community detection integrated model for large-scale stock price prediction

Owing to uncertainty in the stock market, stock price prediction has always been a challenging research hotspot. In recent years, many stock prediction methods have used stock price series and technical indicators as inputs and the time series algorithm to predict, but they often ignore the influence of deeper factors such as the situation of the stock company and current situation of the stock industry. In addition, most of them predict based on small-scale stock datasets with limited characteristics and have certain defects such as bias, poor stability of prediction results, and lack of statistical significance tests on experimental results. To solve these problems, we propose a new method for stock price prediction based on knowledge graph (KG) and graph convolution neural network (GCN) models. First, stock KG is constructed, and the semantic relationships between stocks are described in the form of triples. Second, the correlations between stocks are quantified by fully utilizing their explicit/implicit relationships in the KG. Third, K-means, community detection (CD), and GCNs are merged to obtain accurate clustering results for similar stocks. Finally, the historical prices of similar stocks are used as the input characteristics of the time series models to predict stock price trends. We collect 4684 A-share market stocks in China from 2013 to 2019 and predicted the stock price trends for 762 of them. The experimental results and significance test show that the proposed method achieve the best accuracy, precision, and F1-measure on large-scale stock datasets and have the best stability, proving that the overall prediction effect outperforms that by state-of-the-art methods.

Twitter‐based uncertainty and stock market returns: Evidence from G7 countries

AbstractThe aim of this study is to investigate the impact of Twitter‐based economic uncertainty (TEU) and Twitter‐based market uncertainty (TMU) on G7 stock returns in the challenging year in which the COVID‐19 pandemic began (2020) under different stock market conditions (bearish, normal, and bullish). To this aim, this study applies novel quantile‐based approaches, namely Quantile autoregression unit root test, Quantile‐on‐quantile approach, and Quantile Granger‐causality test covering the period from 01 January 2020 to 15 September 2020. Main findings of the study are (1) G7 stock return series are stationary for all quantiles of the conditional distributions with minor exceptions meaning that shocks have temporary effects on stock returns of G7 markets. (2) The impact of Twitter‐based uncertainty strongly depends on the market condition, whether it is bullish or bearish for all G7 markets. A heterogeneous association exists between variables caused by different market conditions. (3) A bi‐directional causal association exists between stock returns‐TEU and stock returns‐TMU. This result confirms the existence of feedback hypothesis between G7 stock returns and TEU, TMU, respectively. This study provides important policy implications and recommendations for policy makers and investors on the nexus between Twitter‐based uncertainties and stock returns.

Environmental optimization model for the European batteries industry based on prospective life cycle assessment and material flow analysis

The market of batteries for electric mobility is expected to greatly expand in Europe during the next decades. The most sustainable batteries to be produced in the future depend on the primary and secondary resources available and on the capacity to produce and recycle battery cells. This paper proposes an optimization model based on Material Flow Analysis and prospective Life Cycle Assessment which allows us to suggest the types of batteries which should be produced and recycled, the primary and secondary resources to be used, and in which quantities. Several scenarios are proposed to address the uncertainty of the future batteries market. In the Reference Scenario, designed to reflect the European policies, the results show that Europe would achieve high self-sufficiency from external producers (72%), but this percentage could decrease drastically in the case of alternative scenarios (up to 35%). Furthermore, in the Reference Scenario, lithium-iron-phosphates (40%) and lithium-air (23%) batteries result to be the best options. To cut the consumption of lithium and cobalt, sodium-ion cells (3%) could also be considered for manufacturing or even nickel-cobalt-manganese (6%) type to exploit secondary cobalt. In alternative scenarios instead, the proposed algorithm would suggest different choices as a function of the boundary conditions. For instance, in case the demand and the re-use rate of the cells are higher than in the Reference Scenario, the environmental impact of batteries for mobility turns out to increase because of the lower self-sufficiency of the European industry.

FedEE: A Federated Graph Learning Solution for Extended Enterprise Collaboration

Today's business environment is characterized by uncertainty and competition, so the capability to adapt to the evolving era and unforeseen challenges is essential in business strategies. Recent studies on extended enterprise indicate that collaboration among different stakeholders is beneficial for surviving these unexpected changes. However, the barriers such as market uncertainty, privacy and trust concerns, and individual contribution evaluation limit the implementation and application of the extended enterprise concept. Federated learning (FL), in which multiple enterprise entities can use a shared model while retaining all training data locally, has emerged as a promising AI solution for accumulating insights from multiple stakeholders and providing collaborative decision-making. Furthermore, the enhanced privacy-protection benefits of FL remove the barriers to implementing extended enterprise collaboration. In particular, an FL central server manages the local updates of multiple enterprise entities (FL clients) and aggregates their contributions to improve the global model training. Meanwhile, to address the time-series graph learning problem in most business environments, we incorporate TCN (Temporal Convolutional Network), GCN (Graph Convolutional Neural Network) and GRU (Gated Recurrent Unit) architecture into FL to capture the temporal-spatial dependencies in individual data sources. Furthermore, we use traffic flow forecasting as the use case of our proposed framework to verify its effectiveness. Finally, the experimental results on a real traffic flow dataset and the comparison results with the state-of-the-art baseline methods show that our proposed solution achieves superior performance.

Make, buy, or ally?—A configurational approach to governance choices in vertical expansion

AbstractFirms expand vertical boundaries by obtaining ownership of relevant resources. There are three means by which firms can achieve this goal: internal development, mergers and acquisitions (M&As), and equity joint ventures. From transaction cost theory and resource‐based view, this study analyzed how resource characteristics, environmental conditions, and firms' capabilities influence their governance choices by studying 30 cases involving architectural, engineering, and construction firms and employing a fuzzy‐set qualitative comparative analysis method. The findings reveal that all the antecedents jointly influenced firms' governance choices; however, they did so in a way that led to the firms' rejection as opposed to the implementation of one strategy. Firms avoided choosing internal development to circumvent high internal transaction costs or expand into new business rapidly. Firms excluded M&As when the similarity between new and existing resources was high to avoid resource redundancy. When market uncertainty was high, they did so to avoid investment risks. Moreover, when high external transaction costs were evident due to interactions and interdependency between firms and their partners, firms tended to choose a more integrated strategy and excluded equity joint ventures. The research indicates that firms' relational capabilities help them reduce external transaction costs and identify strategies with the lowest efficiency.

통화정책 발표가 외환선물 거래에 미치는 영향

This study analyses the impact of monetary policy announcements by the Bank of Korea and the U.S. central bank on the prices, volumes, and open interest of four foreign exchange futures listed and traded on the Korea Exchange: dollar futures, yen futures, euro futures, and yuan futures. The study examines whether the prices, volumes, and open interest of the foreign exchange futures differ from other periods in even-numbered weeks (weeks 0, 2, 4, and 6) beginning on the day before, day of, and week of the monetary policy announcements by the Financial Services Commission and the Federal Open Market Committee and the regular meeting dates of the Federal Reserve from 2005 to 2019. The main findings of this study are as follows First, I find that returns on foreign exchange futures slow down around the time of monetary policy announcements. This reflects the tendency of investors to hold off on trading before the information following the monetary policy announcement is released. Second, I observe an increase in foreign exchange futures trading volume on monetary policy announcement days. This could be interpreted as investors resuming trading once the information is released after the monetary policy announcement. Third, I observe a decrease in open interest in foreign exchange futures around monetary policy announcements. This suggests that when there is uncertainty about the monetary policy announcement, investors reduce their exposure to manage their risk on foreign exchange futures. Finally, I compare the effects before and after the financial crisis and do not find any significant differences. I find that investors reduce their directional investments in foreign exchange futures during monetary policy announcements and resume trading after the uncertainty is resolved through the policy announcement, thus confirming the significant impact of monetary policy announcements in the Korean foreign exchange futures market. This study has both theoretical and practical implications as it supports that monetary policy announcements play a significant role in increasing and resolving uncertainty in the foreign exchange market.

Universities as a Source of Precarious Employment

Introduction. Rapid social changes and new challenges are increasingly transforming all spheres of human life. These factors affect not only the labor market and the general nature of professional activities, but also educational approaches to training specialists in the higher education system. Modern university graduates should possess competencies that meet diverse employer expectations. At the same time, universities are becoming “factories” for the production of graduates focused on precarious employment, since the uncertainty in the type of employment caused by global transformations in the labor market is reorienting the higher education system, thereby changing the vector of educational activity. Aim – analysis of studentsʼ orientations towards precarious employment and identification of the role of universities in their development. Materials and Methods. In 2021, an empirical sociological survey was conducted. The survey sample included 716 learners studying at Yekaterinburg universities, who were interviewed using a questionnaire. In order to elucidate the process of forming studentsʼ orientation towards precarious employment and the role of universities therein, we used semi-formalized interviews with young people whose activities in the labor market could be characterized as atypical employment, as well as with heads of educational programs and curators of projectoriented programs at the Ural Federal University. In addition, the results of a study conducted in 2020–2021 among university graduates in the Ural region were used. Results. Uncertainty in the labor market, secondary employment and involvement of university students in project-oriented activities form their following work preferences: the desire for atypical forms of employment, irregular working hours, and free work schedules. Discussion and Conclusion. The conclusions made by the authors expand the understanding of the needs of young people in the labor market and the transformation of university education in conditions of uncertainty. The materials of the article contribute to the development of sociological knowledge and can be useful to management and administrative structures of Russian universities.

Assessment of consumers' preference for orange-fleshed sweet potato puree chapati: a case of rural and urban consumers in Kenya.

Orange-fleshed sweet potato (OFSP) is bio-fortified with vitamin A precursors through breeding and is thus ideal for combating vitamin A deficiency (VAD). One of the potential mechanisms to promote the consumption of OFSP is to enhance its availability through processing into products with a longer shelf life and more desirable to consumers. However, very few farmers and agro-processors practice value addition due to market uncertainties; there is little information on market availability for OFSP products. This study examined consumers' preference for OFSP puree chapati in rural and urban areas of Kenya, using the contingent valuation method. Data was collected from a random sample of 411 sweet potato consumers and a double-bounded logit model was applied to analyze consumers' willingness to pay (WTP) for OFSP puree chapati. Consumers were willing to pay a price of Kenya shillings (KES) 19 (USD 0.14) and 35 (USD 0.26) for OFSP puree chapati in Homa Bay and Nairobi counties, respectively. The presence of children under 5 years in a household, consumers' awareness of OFSP products and benefits of consuming OFSP, and level of education had a positive and significant influence on WTP for OFSP puree chapati in both regions. The study demonstrated that consumers exhibited a positive preference for OFSP puree chapati. In order to increase the consumption of OFSP and its value-added products, it is important to promote consumer awareness of OFSP puree chapati and other such nutritious products through cooking demonstrations and other nudging approaches using attractive illustrations and social media platforms that engage mothers and care-givers of children under 5 years old as well as the youth. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.