Market Uncertainty Research Articles

Analisis Strategi Pemasaran Beras Dalam Meningkatkan Penjualan

In 2023, the Indonesian government projects rice production of 54 million tons, while the need for rice is projected to be 32 million tons. This fact shows that the rice business is still profitable in Indonesia because rice remains the staple food that is most consumed by the community. Although wheat consumption continues to increase, rice remains the main choice. CV. Rahmat Jaya is a company that has long been engaged in the production and sale of rice. Despite facing fierce competition and market uncertainty, this company managed to manage the rice business well. CV. Rahmat Jaya is able to produce 40,000 kilograms of rice per month and sell 60,000 kilograms of rice per month. This research aims to analyze the marketing strategy carried out by CV. Rahmat Jaya in increasing sales. The research uses the SWOT Analysis approach to classify a company's position and determine marketing strategies that suit its environment. Data analysis is carried out with a SWOT matrix, which consists of strength, weakness, opportunity, and threat factors. From the results of the analysis, four alternative strategies were produced that can be considered by company management.

Financial performativity and financial security

Subject. This article analyzes the direct and feedback relationships between financial knowledge and financial practice in the context of solving financial security problems. Objectives. The article aims to analyze the impact of financial knowledge, ideas and concepts on society and thereby improve the effectiveness of their use in solving issues related to financial security. Methods. For the study, we used analysis and synthesis, induction and deduction, the method of evolutionary dynamics of finance development, and an actor-oriented approach. Results. The article defines the main conditions for improving the efficiency of socio-financial performativity, develops approaches to analyzing the impact of risk theory in the face of growing market uncertainty, and presents some recommendations for the introduction of new financial structures, in particular, the use of models based on both the hypothesis of market efficiency and behavioral finance. Conclusions. Given the idea of the performative function of financial science, models, and analytical procedures, it is necessary to design new research programmes and include this topic in the educational process.

Adaptation and Innovation Strategies in Facing Business Challenges: A Case Study in the Digital Industry

The digital industry has witnessed rapid growth, powered by innovative start-ups that drive change and transformation. In the bustling digital landscape of Bandung City, these start-ups face a multitude of challenges ranging from market uncertainties to resource limitations. This qualitative study explores how digital start-ups in Bandung City navigate these challenges through adaptation and innovation strategies. Through in-depth interviews and thematic analysis, this research sheds light on the dynamic strategies that enable these start-ups to thrive and remain competitive in the ever-evolving digital ecosystem. The findings offer practical insights for founders and decision-makers while enriching the theoretical understanding of adaptation and innovation within a localized context.

Implications of information sharing on blockchain adoption in reducing carbon emissions: A mean–variance analysis

Considering market uncertainties and risks, this study examines the implications of demand information sharing (DIS) on blockchain adoption (BA) in reducing carbon emissions, and further analyzes the impacts of DIS and BA on the welfare of consumers and the whole society. In this paper, we first develop analytical models based on the mean–variance theory to investigate a two-level supply chain’s optimal decisions in four scenarios (i.e., without and with DIS and BA). Furthermore, we examine the impacts of supply chain members’ DIS and BA decisions and risk attitude on consumer surplus and social welfare. Lastly, we extend our models to explore a general scenario, i.e., we consider the presence of imperfect information sharing, privacy-conscious consumers, consumer carbon sensitivity differences, carbon cap-and-trade regulations, and inflexible blockchain deployments. We find that retailers should consider more crucial factors (e.g., demand uncertainties and the risk sensitivity of supply chain members) rather than simply focus on the type (i.e., high- or low-type) of demand before sharing demand information. Moreover, DIS and BA practices can not only improve the business performance of supply chain members but also contribute to the circular economy and sustainability. Then, DIS practices can minimize the manufacturers’ misjudgments about the timing of BA. Finally, the welfare of consumers and the whole society can be improved by sharing demand information and using blockchain technology.

Motivators and impediments to mushroom commercialisation in Ghana: Implication for policymakers, development partners and extension agents

Waste (organic waste) management is a major challenge for urban and peri-urban areas all over the world. This is because landfilling raises greenhouse gas emissions, whereas indiscriminate refuse dumping wastes valuable nutrients and energy that are still present in the refuse. Fungi, such as mushrooms, have the unique ability to decompose and transform obstinate organic substances into protein. The government, private sector, and foreign development partners in Ghana's Greater Accra region have invested in mushroom (oyster) production to boost commercialization for economic development. However, commercialization remains low, and significant income gains from mushroom commercialization have yet to be realised. Psychosocial factors are imperative for agricultural commercialization because they create a thriving environment and incentive for agricultural commercialization in general, and mushroom commercialization in specific. To determine the motivations and impediments to mushroom commercialization, we used a questionnaire to survey 153 oyster mushroom producers censused from the Ga East and the Adentan municipalities in the Greater Accra region. The data was analyzed using Principal Component Analysis (PCA), means and standard deviations. Our results showed three [3] motivators: social awareness, economic value, and market incentives, and three [3] impediments: institutional and personal weakness, market uncertainty, and value chain challenges. The major motivators to mushroom commercialization are social awareness (M = 3.94) and economic value (M = 3.93) whereas the major impediments comprised market uncertainty (M = 3.59) and institutional and personal weaknesses (M = 3.29). We recommend that policy effort to create enabling institutional environment and social awareness of mushroom coupled with development partners' and extension agents' effort to train farmers on mushroom production management are key to enhancing commercial mushroom production in Ghana.NB: M=Mean

Global energy market connectedness and inflation at risk

After the outbreak of COVID-19 and the Russia–Ukraine war, historically high inflation has become a problem that plagues many countries, leading researchers to model and explore inflation dynamics from a conditional mean-based perspective to a conditional quantile-based perspective and examine the inflation distribution’s tail characteristics, also known as inflation-at-risk. Given the rising uncertainty in the global energy market during COVID-19 and large-scale energy supply shocks brought about by the Russia–Ukraine war, this paper first extracts the global energy connectedness index (GECI) based on the MSCI energy index of 29 countries, then incorporates GECI into the quantile regression to characterize the dynamic distribution of inflation. The results show that the predictive effect of GECI on short-, medium-, and long-term inflation dynamics is mainly reflected in upper quantiles of inflation, with heterogeneity across countries. We find that the dynamic distributions of inflation under the impact of major global events are skewed, and there are significant changes in inflation risks. The out-of-sample forecast verifies the validity and forward-looking property of GECI. The results provide practical regulatory tools and decision-making suggestions for policymakers to prevent and respond to high and low inflation risks.

Female CEOs and investment efficiency in the Vietnamese market

This paper proposes female CEOs’ overconfidence and risky behavior stem from gender stereotype threats. Using two subsamples from Vietnam—firms in the Northern and Southern regions—we empirically show that female CEOs in the North, where there is less gender stereotyping, tend to overinvest relative to male CEOs. However, in the South, they are indifferent. Additional analysis reinforces the main finding that female CEOs in the North tend to take more risks even when dealing with market volatility and uncertainty (e.g., the COVID-19 pandemic). Such risky behaviors do not deteriorate firm value but, instead, possibly improve firm performance.

Border Games: A Game Theoretic Model of Undocumented Immigration

We consider a game-theoretic model of undocumented immigration with the aim of emphasizing the strategic interaction between the different forces affecting the issue. Specifically, we analyze the strategic interaction between firms in the destination country, native labor, the elected officials in the destination country, and the undocumented immigrants from the Source Country. We show that the impact of border enforcement is dampened because strategic interaction between the players will tend to mute any unilateral changes. We also study the effect of uncertainty in the labor market on migration issues and analyze the two cases where policymakers have to make their decisions before (ex-ante) or after (ex-post) the market state is realized.

FII Profitability and Market Uncertainty: An Emerging Economy Context

This study aims to uncover differences in profitability and market timing skills of Foreign Institutional Investors (FIIs) against benchmarks. Using a Markov Regime Switching Model, the article comments on the extent of information utilization by FIIs during periods of low and high economic uncertainty across global (NASDAQ-Composite 3000) and local markets (NIFTY-500 index). The results show significant over-performance (under-performance) in FIIs against benchmarks (NIFTY-50 and NIFTY-500 indices) during low (high) market uncertainty for local uncertainty. However, there is no significant difference in the overall profitability of FIIs during events of global economic uncertainty. The analysis utilizes precise estimates by taking all companies within the index individually and maintaining holdings for all. The article utilizes profitability measures of cash flows, Modified Internal Rate of Return and Buy-and-Hold-returns. The results of this study are relevant to institutional investors, policymakers and investors, especially during local and global uncertainty events.

Uncertainty in the platform market: The information asymmetry perspective

The platform market, where a platform serves as both market maker and regulator, has received increasing attention in recent years. Despite great success, the inherent uncertainty of the platform market has hindered its overall development. Previous papers have studied uncertainty by focusing on either sellers or products, ignoring a platform's significant effects. To fill this void, we propose and validate a new dimension of uncertainty in the platform market: platform uncertainty. To examine the systematic dimensions of uncertainty in the platform market and reveal its consequences and antecedents, we utilize the perspective of information asymmetry. Structural equation modeling is used to build a theoretical framework, and we then conduct a survey among 352 consumers of a leading commerce platform in China to collect our data. We use the partial least squares (PLS) method to validate our hypotheses. Our empirical analysis provides the results that follow: First, we confirm that platform uncertainty is not only distinct from seller uncertainty and product uncertainty but also impacts both of these aspects. Consumers' perceived platform uncertainty positively influences their perceived seller uncertainty and product uncertainty. Consequently, these three dimensions of uncertainty negatively influence consumers' purchase intention. We also find that platform uncertainty negatively and indirectly influences consumers' purchase intention by impacting seller uncertainty. We demonstrate that consumers' concerns about a platform's hidden characteristics and opportunistic behavior contribute to platform uncertainty. These two significant antecedents explain much of the variance in platform uncertainty.

Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction

The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid development of data engineering, a situation has arisen wherein extensive amounts of information must be processed at finer time intervals. Addressing the prevalent issues of difficulty in handling multivariate high-frequency time-series data owing to multicollinearity, resource problems in computing hardware, and the gradient vanishing problem due to the layer stacking in recurrent neural network (RNN) series, a novel algorithm is developed in this study. For financial market index prediction with these highly complex data, the algorithm combines ResNet and a variable-wise attention mechanism. To verify the superior performance of the proposed model, RNN, long short-term memory, and ResNet18 models were designed and compared with and without the attention mechanism. As per the results, the proposed model demonstrated a suitable synergistic effect with the time-series data and excellent classification performance, in addition to overcoming the data structure constraints that the other models exhibit. Having successfully presented multivariate high-frequency time-series data analysis, this study enables effective investment decision making based on the market signals.

Clarification or Confusion: A Textual Analysis of ASC 842 Lease Transition Disclosures

We study the transition disclosures in firms’ 10-K filings preceding the mandatory adoption of Accounting Standards Codification 842 on leases. We find that ASC 842 transition disclosures become more unreadable and dissimilar the closer to adoption, potentially because the SEC guidance on transition disclosures emphasizes detail on the specifics of the standard and whether it has material effects on future financial statements. As a result, firms’ ASC 842 transition disclosures reflect an increasing amount of technical and complex language over the transition period. Firms’ increasing use of technical and complex language may not benefit all investors and the market as a whole. Based on tests of the change in analysts’ earnings forecast delay and market uncertainty in stock returns, we find that ASC 842 transition disclosures mostly favor investors with superior information processing skills. This is contrary to the statutory goal of the SEC to increase transparency for all investors who may prefer firms’ use of clear and straightforward language to describe the effects of future accounting changes.

Exploring the time-varying dependence between Bitcoin and the global stock market: Evidence from a TVP-VAR approach

This study examines the return dependence between Bitcoin and major international stock markets from January 1, 2018, to September 30, 2021. Utilizing a time-varying parameter VAR (TVP-VAR) model with stochastic volatility, we analyze the dynamics of the dependence structure while considering market uncertainty indices (UCRY Policy, UCRY Price and VIX) before and after the outbreak of COVID-19 pandemic. Our findings indicate that Bitcoin's role as a safe haven for stock markets has yet to be observed during the post-COVID-19 period. Furthermore, we observe varying impacts of the return correlation between different stock markets and Bitcoin, suggesting an asymmetric dependence. These results contribute to the ongoing discussion regarding Bitcoin's role as a safe haven asset and provide valuable insight for investors and policymakers.

INTERVENTION ANNOUNCEMENTS AND NAIRA MANAGEMENT: EVIDENCE FROM THE NIGERIAN FOREIGN EXCHANGE MARKET

Many studies establish how foreign exchange intervention affects the exchange rates. Intervention announcement do also have impact different for the actual financial involvement. Recent evidence has tested this for some countries but none has investigated Nigeria, despite volume of interventions and its announcements made via press circulars by the central bank. The paper applies daily data, from January 02, 2001 to May 15, 2023, to verify the impact of intervention announcements on the Nigerian exchange rate. The paper evaluates the relationship based on an event driven baseline specification, which measure the impact of announcement period windows on the exchange rate. The paper finds conclusive evidence of highly significant impacts that past, contemporaneous and future intervention announcements cause appreciation shocks. The naira is revealed to appreciate by 3.5% upon the intervention announcement, and this further increases to 4.49%, 4.55% and 5.22%, on one day, two day, three days after, but subsequently slow down on fourth day (5.21%) and fifth day (3.45%) after the intervention announcements. Robustness test based using alternative data frequency for the estimation yields close (different) result for the monthly (quarterly) periodicity, therefore supposes that the data frequency matters. The result has implications for future conduct of interventions and conventional monetary policies. Amongst others, higher market uncertainty, low credibility of transmission mechanism and possible predominance of global over the national factors may contribute to influences the effectiveness of interventions. The paper’s major limitation is that it excludes the influence of actual intervention, via sales and purchases of dollar, by the central bank.

Decentralized energy management system of distributed energy resources as virtual power plant: Economic risk analysis via downside risk constraints technique

A virtual power plant (VPP), as a decentralized energy management system (EMS), can be a handy application to aggregate and schedule distributed energy resources (DER). The DERs can be dispatchable and non-dispatchable energy resources wishing to participate in the day-ahead (DA) electricity market. Therefore, they can provide the grid with higher reliability and safety margins. However, there are uncertainties in the market that VPP should manage and the associated risks to obtain robust scheduling in such a probabilistic environment. In this paper, risk-oriented stochastic optimization is proposed to investigate the market's uncertainties and obtain the maximum profit while ensuring the safety margins of the network. Downside risk constraints (DRC) are integrated into the model to cover the risks. So, the risk-based scheduling of VPP is obtained. The proposed risk-based framework is formulated as the mixed-integer linear programming (MILP) model, solved by the CPLEX solver of GAMS software. The proposed model is applied to an 18-bus test system to investigate the proposed risk-oriented stochastic optimization of VPP. Finally, the costs, revenues, and total profit are illustrated, compared, and analyzed in the model's risk-neutral (RN) and risk-averse (RA) states so that an appropriate decision can be made. Moving toward the RA strategy lowers the amount of generated power of the DG and the purchased power from the upstream grid, consequently leading to a lower amount of sold power by the VPP within the studied power grid. The analysis shows that the RA strategy decides with low risk by decreasing the cost and revenue of the VPP simultaneously. In this regard, the results indicate that the average profit of the VPP decreased by 8.62%, from $15407 to $14079 when decision-maker tends to take risk-averse decisions with zero risk.

Do peers’ negative earnings surprises Stifle corporate social responsibility?

This study examines the effects of rival firms' negative earnings surprises on focal firm's corporate social responsibility (CSR) investment. I theorize that rival firms' negative earnings surprises attract stronger scrutiny by shareholders in the focal firm, which exposes short-term pressure on the focal firm and makes it limit investments in socially responsible activities. I find that rival firms' negative earnings surprises decrease a firm's CSR engagement, and the effects are stronger when the focal firm has poorer financial performance, when market uncertainty is lower, and when rival firms that experience negative earnings surprises receive a greater amount of media coverage and more consistent news tone.

Exploring Coinvestment Partner Selection Strategies Using Exponential Random Graph Models

State-owned private equity funds in China currently oversee assets worth more than 12 trillion RMB. Due to the uncertainty in the private equity market and the presence of information asymmetry, these state-owned private equity firms frequently engage in coinvestments with other private equity firms. The coinvestment strategy allows them to mitigate risks and exchange valuable information and resources. Which types of partners do state-owned private equity firms typically collaborate with? The existing literature built coinvestment partner selection models based on the traditional regression models and ignored the complexity of the network structure. This research analyzes cooperative relationships using exponential random graph models, considering both structural effects and node attributes. The empirical study of 4645 private equity firms operating in the Chinese private equity market shows that state-owned private equities are more likely to collaborate with foreign private equities and domestic private-owned private equities compared to collaborating with other state-owned private equities. Furthermore, in markets characterized by high marketization indexes, state-owned private equities demonstrate greater inclinations to partner with foreign and domestic private-owned private equities. When state-owned private equities allocate their investments to high-tech industries, their likelihood of collaborating with foreign private equities increases.

Geopolitical threats, equity returns, and optimal hedging

In this paper, we demonstrate that the U.S. equity market and a few specific sectors produce significantly positive returns during high geopolitical threats, even with the presence of standard controls, whereas other major markets around the world fail to exhibit such results. We use the geopolitical threats (GPT) index of Caldara and Iacoviello (2022). We extend our study by examining the equity returns during extremely high geopolitical threats and find the results significantly positive for the U.S. equity market and two specific sectors- information technology and financials. The results of our investigation are likewise supported by the lead-lag regression and the Markov regime-switching model. Our results are robust in the presence of various alternative measures of market uncertainty indices, for instance, economic policy uncertainty, economic uncertainty, macroeconomic uncertainty etc., on a daily basis. However, the return on equity was not robust when conditional volatility and monthly frequency were considered. We also investigate and find the optimal hedging implications for investors during the presence of geopolitical threats. We find a considerable hedge alternative between the US market and gold and further explore how Geopolitical threats affect Gold and different US sectoral Exchange-traded funds (ETFs).

TimeGAN based distributionally robust optimization for biomass-photovoltaic-hydrogen scheduling under source-load-market uncertainties

The Biomass-Photovoltaic-Hydrogen Integrated Energy System (BPH-IES) is receiving increasing attentions due to the utilization of hydrogen storage for renewables accommodation. However, owing to its internal components and capability for participating in electricity and carbon markets, source-load-market uncertainties such as the random photovoltaic output, loads, as well as electricity and carbon prices are non-negligible, which seriously hinders the stable operation and realization of economic and environmental benefits for BPH-IES. In this paper, in view of the insufficient samples and the time-series attribute for the uncertainties, a novel Time-series Generative Adversarial Network (TimeGAN) based Distributionally Robust Optimization (DRO) model is proposed for BPH-IES scheduling. Based on TimeGAN learning the dynamics of the limited samples of uncertainties, sufficient generated samples are obtained with approximating the real distributions. Then, the temporal covariance conditions are introduced in the ambiguity set construction, aiming at getting rid of those distributions unmatching the temporal correlations of the enlarged samples. Finally, DRO model for scheduling is established, which jointly optimizes multi-energy operation and electricity-carbon trading strategy for BPH-IES considering the “worst-case” distributions within the narrowed ambiguity set. The solution procedure is developed according to the duality and semi-definite programming theories. Simulations have verified that for BPH-IES, the proposed model: 1) improves the economic and environmental benefits by 10.04% and 7.79% respectively, due to the TimeGAN and temporal covariance based modifications; 2) maintains operation stability and 100% renewables accommodation under the source-load uncertainties; 3) reduces the fluctuation of benefits via considering the market uncertainties.

Regulatory Uncertainty in Nascent Markets and Entrepreneurial Strategy

Regulatory Uncertainty in Nascent Markets and Entrepreneurial Strategy