Market Uncertainty Research Articles

공동주택 개발사업 사례연구

Currently, the real estate PF market is exposed to uncertainty and volatility due to the high interest rate trend and housing recession. Accordingly, this study examined the real estate development project process and the risks of real estate PF by dividing them into financial, construction, and legal factors through a case study on the development of Ulsan District 2, and analyzed the real estate development project procedure and financing plan. In addition, we would like to consider the importance of indicators such as LTV and exit sales rate. First, through analysis of each major risk, we identified the risks borne by the financial institution and the developer, confirming that the case was a stable business structure with no significant uncertainty during the business period. In the case of real estate PF financing, we analyzed the cash flow within the business period of the case by reflecting cash inflow and cash outflow, and estimated that full PF repayment was possible upon completion. Meanwhile, the trend of LTV reduction when the sales rate increases was identified and the importance of LTV was confirmed in times of increased uncertainty in the housing finance market. The necessary sales rates for each scenario were presented through analysis of the exit sales rates of developers, construction companies, and financial institutions.

부동산 임대료의 현금 흐름에 대한 리스크 분석

Investment decisions in real estate development projects need to be made more flexibly in uncertain environmental changes such as legal and economic conditions in competitive business in the rapidly changing real estate market. In analyzing the economic feasibility of real estate development projects, the valuation method of the investment plans using the traditional NPV method is evaluated based on a fixed standard planned in advance. Therefore, it was not possible to flexibly cope with uncertain changes in the business environment. In the face of growing uncertainty in the real estate development market, a more sophisticated evaluation method that can evaluate investment plans is needed according to changes in the market. Accordingly, the valuation of real estate development projects was analyzed using real options approach. In this study, an analytical framework was presented to evaluate the return value of commercial rental properties from a financial engineering perspective and to analyze the risk of rent cash flows. Real estate rate assessment methods, risk analysis methods, rent derivatives valuation methods, and real estate option valuation methods were analyzed for uncertainty in rent, tenant residence, vacancy, and real estate price using Monte Carlo simulation. In terms of financial engineering probability models, research in the field of valuation and risk analysis of real estate is now in its infancy, and the framework for analysis itself is not sufficiently established. Based on these studies, various analysis methods will be developed, and in this sense, this study can play a fundamental role.

Investor’s Intention to Invest in the Stock Market Amidst the Economic Crisis in Sri Lanka: Using an Extended Theory of Planned Behavior

The primary objective of this research is to investigate the behavior of individual investors in the context of stock market investments during Sri Lanka's economic crisis. The study employs an extended version of the Theory of Planned Behavior (TPB) to comprehensively understand the factors influencing individual investor decisions. Utilizing a quantitative approach, the research aims to determine the better prediction of this expanded TPB in predicting individual investor behavior amid economic challenges. Data was collected through a systematic self-administered questionnaire distributed to both existing and potential individual investors. The relationship between variables was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrated that, amid the economic crisis, the subjective norms towards investing had the largest impact on individuals' intentions to participate in the stock market and Risk avoidance and Uncertainty avoidance had a highly significant influence on investor’s intentions. This research examined only current and prospective Colombo stock market investors' investment intentions during Sri Lanka's economic crisis. Few studies have attempted to include risk avoidance and uncertainty avoidance within a decision-making framework and future studies focus on these diverse market contexts. Owing to the economic downturn in Sri Lanka, the augmented TPB put forth in this study has contributed valuable insights for financial professionals, regulators, as well as existing and prospective investors. It furnishes a substantial knowledge base to aid in making informed investment decisions. Furthermore, it offers an expanded comprehension to existing and potential investors about the intricacies of the relationship between investment intentions, thereby widening their perspective.

Digital Banking dalam Akselerasi Pemberdayaan Ekonomi Womenpreneur Indonesia : Eksplorasi Sosial-Ekonomi dan Peran LPS Berbasis PLS-SEM

ABSTRACT Integrating technology in the digital 5.0 society, including artificial intelligence, cloud computing, and financial services, is creating a new business model: digital finance (FinTech). Indonesia's focus on economic logistics systems, FinTech development, and economic integration has positively impacted the real economy. However, digital financial innovation also brings risks, such as market uncertainty. After the pandemic, digital financial inclusion empowers women to reduce the gender gap. The history of women's economic role, such as in early Islam, highlights their success in this sector. Government measures, such as inclusive programs, show efforts to encourage women's role in the digital economy but improved financial literacy and structural support are still needed. This study aims to analyze the determinants of socio-economic conditions and the role of the Indonesian Deposit Insurance Corporation (LPS RI) in women's economic empowerment through digital banking services. This study chose a quantitative approach by conducting an online survey of 306 Indonesian womenpreneurs. The respondents' data was further analyzed using the SEM PLS analysis technique to test the proposed hypotheses. The results showed that digital banking is significant for womenpreneur empowerment in Indonesia. Financial literacy is strong, infrastructure is solid, and government support is in its favor. LPS RI protects digital banking services. recommendations including literacy campaigns, training, technology infrastructure, fiscal incentives, and government-LPS cooperation to advance womenpreneurs with financial technology.

FinTech-powered integration: Navigating the static and dynamic connectedness between GCC equity markets and renewable energy cryptocurrencies

This study examines the static and dynamic connectedness between renewable energy cryptocurrencies and the GCC stock market using the TVP-VAR and DCC-GARCH approaches. Our findings indicate a significant connectedness between the GCC stock markets and energy cryptocurrencies. Moreover, the total connectedness is higher during COVID-19 than pre-COVID-19, highlighting the heightened sensitivity to extreme events within the financial system. The energy cryptocurrencies act as net receivers of return spillovers, while GCC stock markets predominantly act as net transmitters in the overall interconnectedness framework. Our findings imply that adding energy cryptocurrencies to stock portfolios can mitigate risk and optimize portfolio performance. The findings also indicate that energy cryptocurrencies offer a potential diversification avenue and can act as a hedge during periods of higher market uncertainty. The hedging ratios are low, implying the relatively low cost of hedging risks of portfolio stocks through energy cryptocurrencies. These insights provide valuable guidance for investors and policymakers regarding portfolio management, hedging, and financial markets' stability.

Exploring the future with PRO-SPECT: an evidence-based modular foresight approach

In the face of rapid technological advancements and unforeseen societal events, predicting the future of work proves challenging. Foresight, the exploration of potential futures, emerges as a valuable strategy to navigate labor market uncertainties. However, existing foresight methods often lack alignment with the goals prioritized by sectoral and organizational stakeholders. Addressing this gap, we introduce PRO-SPECT (PROfessional Sectoral perSPECTive), a modular foresight approach crafted through literature review, expert interviews, and workshops. Tailored to meet the needs of policymakers, organizations, and communities, PRO-SPECT consists of four key steps: Scope, Scan, Impact, and Perspective. This evidence-based approach aims to equip stakeholders with actionable insights for the future of work and beyond. This article provides a comprehensive overview of PRO-SPECT, encouraging further adoption and knowledge development in foresight practices.

Risk assessment and optimal scheduling of serial projects

The valuation and planning of complex projects are becoming increasingly challenging with rising market uncertainty and the deregulation of many industries, which have also raised the need for efficient risk management. We take the perspective of a private firm interested in sequential capacity expansion of a project and develop a framework for measuring the downside risk of the serial project and optimising the sequence of the stages. Under general distributional assumptions for the duration of each stage, we present an accurate representation of the project’s net present value (NPV) distribution based on a Pearson curve fit, leading to closed-form expressions for the associated risk measures. We then assess the impact of duration variability on the value at risk and demonstrate its role in stochastic project scheduling. We also account for the trade-off between maximising the expected NPV and minimising the risk exposure, and obtain the optimal schedule for risk-averse decision-makers. It becomes obvious that both the duration variability of each stage and the decision-makers’ risk preferences can significantly affect the optimal sequence of the stages and that high duration variability is not always undesirable, even for risk-averse decision-makers.

The Impact of China-US Trade Conflict on Foreign Investment in China

The trade war between China and the United States, as the two largest global economies, has had a profound impact on both countries. However, China, being a developing nation that has attracted significant interest from investment companies worldwide, has been particularly affected by this conflict. The trade war has presented numerous challenges for companies looking to invest in China, but there have been notable improvements in recent years. This study aims to delve into the examination of the influence of objective changes and subjective changes experienced by investors on the ultimate outcomes of their investments. By analyzing both the tangible and intangible factors affecting investment decisions and outcomes, a comprehensive understanding of the trade war's impact on investment in China can be gained. Objective changes refer to the concrete alterations in trade policies, tariffs, and regulations that have been implemented as a result of the trade war. These changes may include increased tariffs on certain products, revised import and export regulations, or modifications to investment laws. These objective changes can directly impact the costs, profitability, and feasibility of investments in China. On the other hand, subjective changes pertain to the perceptions, attitudes, and sentiments of investors toward investing in China amid the trade war. These subjective changes can be influenced by factors such as geopolitical tensions, market uncertainty, and the perceived risks associated with investing in a country engaged in a trade war.

Consequences of COVID-19 for Currency Markets of US Dollar and RMB

The present research explores the influence of the COVID-19 epidemic on the currency markets of the US dollar and RMB, with the aim of comprehensively grasping the operational mechanisms of global financial markets. Through time series analysis, ARIMA and GARCH models are constructed to capture the short - and long-term effects of currency markets fluctuations. The findings showed that the market uncertainty surged in the early stage of the epidemic, leading investors to turn to the US dollar, making the US dollar/RMB currency markets fluctuate significantly in the short term. This paper emphasizes that exchange rate fluctuations are affected by multiple factors, including economic activity, investor sentiment and government policy intervention. Investors are advised to diversify their portfolios and consider the impact of currency markets fluctuations over immediate and extended periods. Policymakers should also prepare plans to deal with market uncertainty, including intervening in foreign exchange markets, stabilizing the financial system, and adopting targeted monetary policy. In conclusion, this paper provides an in-depth examine how the USD/RMB exchange rate has been influenced by the COVID-19 pandemic. and provides practical advice for investors and policymakers.

Machine Learning in Finance: A Case Study on Forecasting Google's Stock Price

Stock price prediction remains an attractive and essential area in financial markets, with researchers constantly working hard to improve existing models or develop new ones to achieve more accurate predictions. Foreseeing the future direction of stock prices allowing people to plan and formulate effective investment strategies. However, predicting stock prices remains a difficult challenge due to many uncontrollable factors. Traditional forecasting methods rely primarily on economic data analysis and formulation. However, these traditional methods often provide limited information and forecast accuracy due to market uncertainty. Many business organizations and individual investors started to utilize the programmed approaches to improve the accuracy of stock price predictions as machine learning and deep learning capabilities continue to advance. Through an actual case study, this essay examines the innovative use of machine learning methods for researching in the field of predicting stock prices. In the case study, an LSTM model is built to find the transforming trend of the stock price, while Googles stock price is collected to use as the dataset for training the model. The article finally conducts a comparative study on stock price prediction based on LSTM is conducted to clarify its working progress and accuracy of the outcome.

Economic Uncertainty and Bitcoin Volatility: Evidence During COVID-19

This research investigates the predictability of economic uncertainty indexes on the volatility of Bitcoin (BTC) during COVID-19. The economic uncertainty indexes include US economic policy uncertainty (EPU), Twitter economic uncertainty (TEU), Twitter market uncertainty (TMU), geopolitical risk index (GPR), and trade policy uncertainty (TPU) index. The empirical findings show that the Twitter market uncertainty (TMU) and geopolitical risk (GPR) uncertainty index are valuable predictors of BTC volatility. Moreover, the combination forecasts information for all economic uncertainty indexes is useful for BTC volatility forecasting. Also, we find evidence during high and low volatility and the Russia–Ukraine war. Our results show that Twitter market uncertainty and geopolitical risk uncertainty index are effective predictors of Bitcoin volatility during high volatility periods. During the Russia–Ukraine war, economic policy uncertainty (EPU), the Twitter market uncertainty index, and combination forecast information for all uncertainty indexes are effective for Bitcoin volatility prediction. Our findings are robust with the alternative method MCS test.

Predicting the Planting Time of Bird's Eye Chili Based on Environmental Conditions Using Internet of Things (IoT) and Neural Network Method

In Indonesian cuisine, the red Tabasco pepper holds a significant place as a commonly used ingredient. However, the cultivation of this chili variety is not without its challenges, primarily due to the volatile nature of the chili prices. Farmers often struggle with the critical decision of when to plant Tabasco peppers to optimize their yields and income. Understanding the complexities of this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advances in Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study focuses on the development of an IoT-based solution aimed at helping farmers in precisely determining the optimal planting time for Tabasco pepper. It uses five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight (hours) and groundwater usage data (m3) to make data-driven planting decisions. The urgent need for such a system becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Using historical data from 2019, obtained from the Jakarta Provincial Government Open Data DKI, and climate data from the Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The result is a reliable prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market.

Universal coordinates and methods of reformatting an ambiguous humanitarian paradigm

The main problem: In the conditions of market uncertainty, the coronavirus pandemic, the absence of an officially articulated national idea, national interests and ideology in Kazakhstan, the problem of the lack of logically justified and coordinated coordinates of systemic thinking, an unambiguous paradigm of humanitarian thinking (analytical, pedagogical, managerial, economic, etc.) is becoming more and more clearly realized. as the most important conditions for personal, civil and professional self-determination. Purpose: to substantiate the general logical coordinates and methods of reformatting the ambiguous humanitarian paradigm used in the activity. Methods: the ontological principle of genetically meaningful logic, the method of transformation of concrete images of objects based on their abstract essence, the method of ascent from the abstract to the concrete, the speculative language of schematic images of thought and the language of methodological theory of activity. The results and their significance: logically justified universal abstract-concrete coordinates of universal motion are given. In relation to life and the world of activity, they can be concretized by coordinates: "standard of living – efficiency of activity", or "efficiency of activity – time", or "results – costs", or "quality – quantity", etc. The use of motion vectors in the logic of descent from the concrete to the abstract (NCA), ascent from the abstract to the concrete (AAC), as well as in their combination – logic CAC gives certainty to thinking. As an example, the scheme of the qualitative difference between educational (pre-professional) activities from professional ones using these methods and paired dialectical categories is given. The scheme and description of the functional model of the system object, considered as a rubicon of the transformation of educational activity into professional activity, are given. To reformat the indefinite, ambiguous paradigms of the humanities used in the activity into functional-logical, visible, constructive and unambiguous, it is recommended to use abstract-concrete coordinates, the methods given, the language of schematic images and the paradigm of the methodological theory of activity. As an example, the concept of a typical unit of activity development is given. A visible unambiguous paradigm is a necessary, but not sufficient prerequisite for adequate personal, civil and professional self-determination of a person in situations of high uncertainty. A sufficient prerequisite is the acquisition by subjects of appropriate reflexive-thinking abilities for self-determination, self-organization, self-regulation, etc.

Asymmetric multifractality: Comparative efficiency analysis of global technological and renewable energy prices using MFDFA and A-MFDFA approaches

This paper examines renewable and technological prices' asymmetric multifractality and efficiency in international and Chinese marketplaces. The asymmetric multifractal detrended fluctuation analytics (A-MFDFA), multifractal detrended fluctuation analytics (MFDFA), and fractal dimension techniques are employed for multifractality and herding behavior. In addition, market deficiency measures (MDM) and hurst exponents are used to construct the inefficiency index (MLM- Magnitude of long-memory) during and before Covid-19. The empirical outcomes supported the existence of asymmetric multifractality across all renewable and technological marketplaces. This multifractality has been observed in up-and-down trends. Moreover, during the COVID-19 outbreak, inefficiency in CELS and SPGlobal's green energy prices increased, which is more apparent in the descending trends. Fractal dimension outcomes suggest a herding behavior in these markets during pandemic. The SPTSX green energy pricing statistics demonstrate that its upward multifractality is larger than the downward in both phases, signifying the strong efficiency position in the market. The SPIC-SH green energy pricing displayed considerable asymmetric multifractality, higher levels of efficiency, and even low levels of market uncertainty during COVID-19. The findings imply that all financial market players should prioritize various green energy investments depending on its asymmetric efficiency and predictability. It will help in their decision-making and reduces herding behavior in the market.

A Novel Deterministic Probabilistic Forecasting Framework for Gold Price with a New Pandemic Index Based on Quantile Regression Deep Learning and Multi-Objective Optimization

The significance of precise gold price forecasting is accentuated by its financial attributes, mirroring global economic conditions, market uncertainties, and investor risk aversion. However, predicting the gold price is challenging due to its inherent volatility, influenced by multiple factors, such as COVID-19, financial crises, geopolitical issues, and fluctuations in other metals and energy prices. These complexities often lead to non-stationary time series, rendering traditional time series modeling methods inadequate. Our paper presents a multi-objective optimization algorithm that refines the interval prediction framework with quantile regression deep learning in response to this issue. This framework comprehensively responds to gold’s financial market dynamics and uncertainties with a screening process of various factors, including pandemic-related indices, geopolitical indices, the US dollar index, and prices of various commodities. The quantile regression deep-learning models optimized by multi-objective optimization algorithms deliver robust, interpretable, and highly accurate predictions for handling non-linear relationships and complex data structures and enhance the overall predictive performance. The results demonstrate that the QRBiLSTM model, optimized using the MOALO algorithm, delivers excellent forecasting performance. The composite indicator AIS reaches −15.6240 and −11.5581 at 90% and 95% confidence levels, respectively. This underscores the model’s high forecasting accuracy and its potential to provide valuable insights for assessing future trends in gold prices. The deterministic and probabilistic forecasting framework for gold prices captures the market dynamics with the new pandemic index and comprehensively sets a new benchmark for predictive modeling in volatile market commodities like gold.

Investigating the effects of Information and Communication Technology (ICT) on capital market uncertainty by considering its impact on the textile industry: a case study for Iran

Information and Communication Technology (ICT) plays a vital role in sharing information and greater participation in exchanges and trading of investors' shares. Because the use of information and communication technology in financial markets reduces marketing costs, it has a significant role. Therefore, the primary purpose of this research article is to investigate the impact of ICT on capital market uncertainty in Iran. In this study, the GARCH model has been used to estimate the capital market uncertainty index. The results of ARDL model estimation using quarterly data in the period 2011: 1 to 2020: 4 in the short and long term showed the effect of the ratio of online transactions to the total volume of transactions as an ICT indicator on capital market uncertainty in the short term. Long-term are different. In the short run, increasing the ratio of online transactions to the total volume of transactions has increased capital market uncertainty. It has reduced capital market uncertainty in the long run. Other model results showed that inflation, economic growth, and exchange rate significantly affect the performance uncertainty of the total stock exchange index as an indicator of capital market uncertainty. And it takes about 7.5 seasons on average to completely offset the imbalance in capital market uncertainty.

Network evolution of diffusion in enterprise digitalization and intellectualization transformation: A technology-organization-environment framework perspective.

Enterprise digitalization and intellectualization (EDI) is a crucial aspect of China's modernization process. However, uncertainty in market and business decisions hinders the EDI diffusion process in China. Therefore, this research aims to solve the uncertainty problem of EDI diffusion by examining market demand and government policy coordination. First, we utilize complex network game theory and establish a technology-organization-environment framework for the factors that influence the elements of EDI transformation by combining evidence from existing studies. Second, the network game model is constructed to analyze and optimize the updating rules in the network as a diffusion strategy that enterprises under the uncertain market can adopt. Finally, the impact of adjusting government subsidies and different premiums on the diffusion of EDI transformation strategies is examined. The degree of market diffusion and average revenue of EDI are higher after the optimization of network node strategy updating rules compared to before optimization. Further analysis reveals that only the premium effect of product pricing and inverted U-shaped subsidy support from the government affect the degree of market diffusion and the average revenue of EDI, while the other premium effects are not significant. These findings enrich research related to complex networks and nonlinear dynamic strategies. They also indicate recommendations for government policies to enhance diffusion efficiency and reasonable pricing for enterprises to promote returns.

From cryptos to consciousness: Dynamics of return and volatility spillover between green cryptocurrencies and G7 markets

Based on TVP-VAR technique, this study examines the return and volatility spillover between green cryptocurrencies and G7 equity markets. The static connectedness measure posits that green cryptocurrencies function as receivers of return and volatility spillovers from G7 markets. The analysis of dynamic connectedness reflects a spike in total return and volatility spillovers during market stress and uncertainty. Furthermore, we estimate the optimal portfolio weights and hedge ratios for all pairs of green cryptocurrency and stock markets, implying critical insights for policymakers, hedge fund managers, and portfolio management professionals.

Entrepreneurial orientation and product innovativeness: the mediating roles of technology diversity and intellectual property protection

ABSTRACT This study examines two important mediating roles, namely, technology diversity (TD) and intellectual property protection, in the relationship between product innovativeness and entrepreneurial orientation (EO) in small- and medium-sized enterprises (SMEs). Based on survey data from 119 Taiwanese SMEs, EO enhances product innovativeness indirectly through TD and IP protection. In addition, market uncertainty moderates three relationships, as follows: TD/product innovativeness positively, and EO/IP protection and IP protection/product innovativeness negatively. Our results improve knowledge of EO and product innovativeness in SMEs.

Navigating the Double-Edged Sword: Executive hubris and its impact on customer acquisition and retention

This research employs upper echelons theory to examine whether executive hubris augments a firm's customer acquisition while concurrently impairing its customer retention. Drawing on an information processing perspective, we suggest that the influence of executive hubris on customer acquisition and retention is shaped by executives' selective attention to information. This influence is observed to amplify in the presence of market uncertainty and recede with an increase in firm product market experience. We validate these predictions through a mixed-method research design. Study 1 includes an original survey that gathers multi-informant responses, coupled with the subsequent year's ROA data, to assess the role of hubris and the moderating effects of market uncertainty and firm product market experience. Study 2 consists of two experimental studies that manipulate executive hubris to test its causal influence on customer acquisition and retention. Moreover, we utilize an eye-tracking method to examine whether the proposed influence is driven by executives' selective information processing. Our findings enhance the existing literature on upper echelons and marketing strategy, providing practical insights to align executive traits with firms' marketing objectives.