Market Uncertainty Research Articles

Impact of COVID‐19 on Indonesia stock portfolio allocation based on a technical & fundamental approach using a machine learning algorithm

Background: COVID-19 has had a negative impact on economic growth in Indonesia and affected the level of investment uncertainty in the Indonesian capital market. Leading stocks with large market capitalization and included in the LQ45 stock index on the Indonesia Stock Exchange (IDX) were also affected by the COVID-19 pandemic, which was corrected by -15% from the pre-pandemic period. This significant decline has a direct negative impact on capital market investors, so as to avoid the Black Swan event that occurred due to the recession, investors need to diversify their portfolios. Methods: Therefore, this study aims to assist investors in better portfolio diversification, especially in the face of the pandemic by using the clustering method. Clustering was carried out on 35 stocks that are members of LQ45 Index from January 1, 2019 to December 31, 2020 based on business fundamental and technical indicators stocks using the K-Means Clustering algorithm. Results: The clustering found that LQ45 stocks are grouped into 4 clusters, each of which have different unique characteristics. Furthermore, the results are used as a reference in stock portfolio allocation where the stocks which have the best sharpe ratio from each selected cluster will be selected to form a portfolio. Conclusion: Portfolio performance testing throughout the market period in 2021 show that formed portfolios are having excellent performance because it has a high positive return and looks much better compared to the LQ45 index as a macro evaluation. In addition, portfolios have also been well diversified, as evidenced by the low correlation value of its inter-stock correlation.

Volatility forecasting of crude oil futures based on Bi-LSTM-Attention model: The dynamic role of the COVID-19 pandemic and the Russian-Ukrainian conflict

The COVID-19 epidemic and the Russian-Ukrainian conflict have created significant uncertainty in the crude oil market, greatly increasing the difficulty of crude oil futures price volatility forecasting. The objective of this study is to investigate the time-varying nature of the impact of major events on the crude oil market and to examine how to accurately predict crude oil futures price volatility during major shocks. To this end, we propose a novel crude oil futures price volatility forecasting framework based on the Bidirectional Long and Short-Term Memory Neural Network-Attention Mechanism Model (Bi-LSTM-Attention), which contains period division, variable screening, model prediction, and assessment indicators, to further analyze in-depth the impacts of COVID-19 and the Russia-Ukraine conflict on the volatility of crude oil futures price. In addition, we also investigate the impact of external factors on the crude oil market, including economic status, geopolitical events, and the COVID-19 pandemic. Empirical evidence demonstrates that the impact of major events on crude oil futures price volatility is significantly time-varying and differentiated. Particularly during high shock periods, the Fake News Index plays an extremely influential role in driving crude oil futures price volatility. Moreover, our proposed model reliably captures the trend of crude oil futures price volatility during these violent shocks. The contributions of this study are to provide valuable insights into the impact of significant events on crude oil price volatility and contribute to a deeper understanding of crude oil market dynamics.

Macroeconomic impact and stock returns' vulnerability by size, solvency, and financial distress

We examine how firms' equity value, financing and credit quality respond to changing macroeconomic indicators. We provide evidence that the value of small, low-credit quality, and financially distressed companies are more vulnerable to labor market changes. Our findings show that the equity value of U.S. companies deteriorates when the economy experiences increased cost of capital from an inflation hike, a decrease in domestic output, and uncertainty in the labor market. Our findings are robust to changes in the sample period and after controlling for disparities across industries, states, and firms.

Forecasting in Stock Markets Using the Formalism of Statistical Mechanics

The possibility and expediency of forecasting in the stock markets are analyzed analytically using the methods and approaches of statistical mechanics. The apparatus of statistical mechanics is used to analyze and forecast one of the most important indicators of the market – the distribution of its logarithmic profitability. The Lotka-Volterra model used in ecology to describe systems of the "predator-prey" type was used as the initial model. It approximates market dynamics adequately. In the article, its Hamiltonian property is used, which makes it possible to apply the apparatus of statistical mechanics. The apparatus of statistical mechanics (using the principle of maximum entropy) makes it possible to implement a probabilistic approach that is adapted to the conditions of stock market uncertainty. The canonical variables of the Hamiltonian are presented as logarithms of stock and bond prices, the joint probability distribution function of stock and bond prices is obtained as a Gibbs distribution. The Boltzmann factor, included in the Gibbs distribution, allows us to estimate the probability of the occurrence of certain stock and bond prices and obtain an analytical expression for calculating the logarithmic return, which gives more accurate results than the widely used normal (Gaussian) distribution. According to its characteristics, the resulting distribution resembles the Laplace distribution. The main characteristics of the resulting distribution are calculated – the mean value, variance, asymmetry, and kurtosis. Mathematical results are presented graphically. An explanation is given of the cause-and-effect mechanism that causes a change in the profitability of the market. For this, the idea of Theodore Modis about the competition between stocks and bonds for the attention and money of investors is developed (by analogy with the turnover of biomass in models of the "predator-prey" type in biology). The results of the study are of interest to investors, theorists, and practitioners of the stock market. They allow us to make thoughtful and balanced investment decisions due to a more realistic idea of the expected return and a more adequate assessment of investment risk.

The Monetary Market Uncertainty, Reasons and Consequence of Financial Market

The previous Federal Reserve rate hikes have had an important impact on the global financial economy. In this paper, on the overview of the fed rate cycle and the related theory and literature review, by combing the fed in 2022 start rate cycle of high inflation and huge debt deficit pressure province scene and process, analyzes the influence of the fed rate of the world economic development, international trade, monetary policy, financial market stability, especially the fed started in 2022 rate cycle on the stock market, foreign exchange market. Epidemic of the haze to the U. S. economy, rising unemployment, rising prices, and caused inflation, the fed to curb inflation, adopt tightening monetary policy start interest rates cycle, this paper thinks the fed this move also to the global stock market and foreign exchange market caused irreversible negative impact. Based on this, this article suggests that investors act cautiously in uncertain financial environments.

Technological sensing and response capabilities as drivers for radical innovation in the context of apocalyptic uncertainty

This paper introduces a new conceptual model to examine the impact of technological opportunism (technology sensing and response capabilities) on the adoption of incremental and radical technological innovation by small and medium enterprises (SMEs). We tested our hypotheses with data from 228 Indian SMEs using a symmetric method of partial least square structural equation modeling (PLS‐SEM) and the asymmetric method of fuzzy–set qualitative comparative analysis (fsQCA) followed by sensitivity analysis done by an artificial neural network (ANN) modeling. PLS‐SEM results highlighted the roles of technology sensing and response capabilities as key drivers of both incremental and radical product innovations and the strengthening of these relationships by market uncertainty triggered by external events and crises. Apart from this, the results of fsQCA demonstrated multiple configurations of dimensions associated with the adoption of technological innovations by SMEs. This paper extends the current literature by exploring the process by which SMEs may adopt different types of technological innovation. Our findings also have useful implications for SMEs aiming to adopt technological innovations.

Demonstrating clean energy transition scenarios in sector-coupled and renewable-based energy communities

Background Energy communities facilitate several advantages, including energy autonomy, reduced greenhouse gas emissions, poverty mitigation, and regional economic development. They also empower citizens with decision-making and co-ownership prospects in community renewable projects. Integrating renewable energy sources and sector coupling is a crucial strategy for flexible energy systems. However, demonstrating clean energy transition scenarios in these communities presents challenges, including technology integration, flexibility activation, load reduction, grid resilience, and business case development. Methods Based on the system of systems approach, this paper introduces a 4-step funnel approach and a 4-step reverse funnel approach to systematically specify and detail demonstration scenarios for energy community projects. The funnel approach involves four steps. First, it selects demonstration scenarios promoting energy-efficient state-of-the-art renewable technologies and storage systems, flexibility through demand side management techniques, reduced grid dependence, and economic viability. Second, it lists all existing and planned project technologies, analysing energy flows. Third, it plans actions at different levels to implement the demonstration scenarios. Fourth, it validates the strategies using key performance indicators (KPI) to quantify the effectiveness of the planned measures. Furthermore, the reverse funnel approach delves deeper into the demonstration scenarios. The four steps involve identifying stakeholder perspectives, describing scenario scopes, listing conditions for realisation, and outlining business models, including value chains and economic assumptions. Results This approach provides a detailed analysis of the demonstration scenarios, considering actors, objectives, boundary conditions, and business assumptions. The methodologies are exemplified in three diverse European energy communities extending across residential, commercial, tertiary, and industrial establishments, allowing power-to-x and sector coupling opportunities. The paper also suggested thirteen KPIs for validating renewable-focused energy community projects. Conclusions Finally, the paper recommends increased collaboration between energy communities, knowledge sharing, stakeholder engagement, transparent data collection and analysis, continuous feedback, and method improvement to mitigate policy, technology, business, and market uncertainties.

ANALISA LINDUNG NILAI ( HEDGING ) EVALUASI ATAS TRANSAKASI DERIVATIF PADA PERUSAHAAN PT. SURYA SEMESTA INTERNUSA TBK. PERIODE TAHUN 2019

Hedging is an important approach to managing financial risk in a corporate context. The purpose of hedging is to protect the company's value from fluctuations in prices, interest rates or other risks that could negatively impact the company's financial performance. This study aims to analyze hedging practices in the context of the company PT Surya Semesta Internusa Tbk. This research methodology uses secondary data analysis which includes financial reports, risk reports, and company hedging policies. The results show that PT Surya Semesta Internusa Tbk has adopted a proactive hedging approach in managing financial risk. The company uses various hedging instruments, such as futures contracts, options and swaps, to mitigate the impact of commodity price fluctuations and foreign currency risk on their financial performance. PT Surya Semesta Internusa Tbk actively analyzes and identifies the risks it faces and implements appropriate hedging strategies to manage these risks. This study also reveals that hedging management at PT Surya Semesta Internusa Tbk is based on risk management principles that are integrated into the overall corporate strategy. The company has a structured hedging policy and clear standard operating procedures. In addition, PT Surya Semesta Internus Tbk's team is equipped with the necessary knowledge and skills to implement and monitor hedging effectively. However, this study also identified several challenges faced by PT Surya Semesta Internusa Tbk in implementing hedging. These challenges include the complexity of hedging instruments, market uncertainty, and regulatory changes. PT Surya Semesta Internusa Tbk needs to continue to evaluate and adjust their hedging strategy to overcome this challenge. This research provides better insight into hedging practices in the context of the company PT Surya Semesta Internusa. The results of this study can provide guidance for other companies in developing effective and integrated hedging strategies to manage financial risk and protect company value from adverse market fluctuations.

Enhanced safe-haven status of Bitcoin: Evidence from the Silicon Valley Bank collapse

This paper sets out to assess the safe-haven property of Bitcoin by employing a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-based event analysis approach amid the collapse of Silicon Valley Bank. Results reveal Bitcoin's role as a safe-haven amidst the uncertainty permeating the US banking market. In comparison to gold, stock, bond, and foreign exchange market indices, Bitcoin exhibits superior short-term performance and outperforms gold in terms of return and volatility stability in the medium term. Notably, the observation that Bitcoin sustained its safe-haven attribute over a span of 50 days even during periods of relatively diminished market uncertainty further solidifies its status as an improved safe-haven asset.

COVID-19, Stock Liquidity, and Abnormal Returns

This paper examines the relationship between ex-ante stock liquidity and abnormal returns during various phases of COVID-19 led market uncertainties in India. We find that the volume-based liquidity supports stock more significantly during the crisis than in periods of calm. However, contrary to existing empirical evidence, price-based liquidity penalizes stocks during a crisis. Moreover, during periods of calm and recovery, the inverse relationship of liquidity-abnormal return reverses. Further analysis shows that this change of price-based liquidity to abnormal return relationship is more prominent in firms with higher ex-ante liquidity. In contrast, highly illiquid firms appear immune.

A systematic analysis of stakeholder interaction and the barriers towards upscaling urban residential solar PV: The case of Shanghai, China

Political, economic, and social factors that impede the diffusion of residential solar photovoltaics (RSPV) are at the frontline of academic research in renewable energy development because solar PV has rapidly become cost-benefit effective relative to traditional energy. Using Shanghai for our investigation, this study employed a systematic and stakeholder engagement framework to analyze the political, economic, and social actors and their relationships with the residents' acceptance of solar PV. Our results were based on 20 in-depth interviews and rich secondary qualitative research data. We found that several factors that jointly contributed to poor collaboration among actors for upscaling of RSPV development in Shanghai. These included the poor implementation of RSPV policies from political actors, residents’ distrust of the market players, uncertainties and risks faced by small and medium solar companies, and an individualism-collective dilemma and poor community engagement due to rapid social atomization. The development of RSPV is a systematic project that requires joint efforts and collaborative mechanisms among political, market, and community actors. Community engagement is an indispensable measure to promote collaboration among these key stakeholders for the success of RSPV development.

Analyzing Risk Premiums in the Brazilian Power Market: A Quantitative Study

This paper conducts an empirical analysis of risk premiums in the Brazilian electricity market, a critical but understudied field. Employing two distinct methodologies—Average Forward Prices and Last Observed Forward Prices—the study calculates risk premiums between spot and forward electricity prices. Our analysis consistently identifies negative risk premiums, which serve as indicators that the market may be underestimating certain types of risk. These underestimations are potentially influenced by inherent market uncertainties, including volatile demand, unpredictable supply, and frequent regulatory shifts. Additionally, we observe a high volatility in risk premiums, signifying a dynamic and ever-changing market where expectations are continuously recalibrated. Such conditions present possible arbitrage opportunities for market actors and underline the need for policymakers to introduce measures mitigating market unpredictability. By focusing on these nuances, this paper enriches the broader discourse on risk premiums in electricity markets and underscores the necessity for further research aimed at devising effective risk management strategies.

Environmental attention and uncertainties of cryptocurrency market: Examining linkages with crypto-mining stocks

Given public concerns about the negative consequences of environmental consequences of cryptocurrencies, along with their volatilities, we investigate connectedness between cryptocurrency environmental attention (ICEA), cryptocurrency policy uncertainty (UCRY Policy), cryptocurrency price uncertainty (UCRY Price), and crypto-mining stocks. We identify an increase in connectedness over time, indicating that the crypto mining stocks are particularly vulnerable to rising concerns about the environment, prices, and policies surrounding the cryptocurrency market. ICEA and UCRY Policy are net return transmitters for crypto mining stocks. Findings provide implications to global investors and policymakers regarding portfolio management and market stability.

The Effect of Trade Agreements on Korea’s Bilateral Trade Volume: Mitigating the Impact of Economic Uncertainty in Trading Countries

Purpose - This research empirically analyzes the influence of economic policy uncertainty and free trade agreements (FTAs) on bilateral trade volumes between Korea and its trading partners. The study investigates whether fluctuations in the Economic Policy Uncertainty Index (EPUI) for both Korea and its trading partners significantly impact trade volumes and whether the implementation of FTAs mitigates these effects.
 Design/methodology - The study employs dynamic panel data analysis using the system generalized method of moments (system GMM) estimation method to achieve its research objectives. It utilizes country-month-level panel data, including the EPUI, trade volume between Korea and its trading partner countries, and other pertinent variables. The use of system GMM allows for the control of potential endogeneity issues and the incorporation of country-specific and time-specific effects.
 Findings - The analysis yields significant results regarding the impact of economic policy uncertainty on Korea's exports and imports, particularly before the implementation of FTAs. An increase in the EPUI of trading partners leads to a notable increase in Korea's exports to them. Conversely, an increase in Korea's EPUI negatively affects its imports from trading partners. However, post-FTA implemen- tation, the influence of each country's EPUI on trade volume is neutralized, with no significant difference observed.
 Originality/value - This research contributes to the existing literature by providing empirical evidence on the interaction effects between economic policy uncertainty and FTAs on bilateral trade volumes. The study's uniqueness lies in its examination of how FTAs mitigate the impact of economic uncertainty on trade relations between countries. The findings underscore the importance of trade agreements as mechanisms to address economic risks and promote international trade relations. In a world where global market uncertainties persist, these insights can aid policymakers in Korea and other countries in enhancing their trade cooperation strategies and navigating challenges posed by evolving economic landscapes.

Energy, metals, market uncertainties, and ESG stocks: Analysing predictability and safe havens

To address ESG stock susceptibility to episodic shocks in financial markets, we use nonparametric quantile-based techniques applied to the 2014–2022 period. We (i) analyse the ability of traditional assets to predict ESG stocks returns, (ii) explore whether oil or gold serves as a safe haven for ESG stocks, and (iii) ascertain how ESG stocks respond to market sentiment, crypto-based uncertainty, and geopolitical risk (GPR). We find that gold, oil, market sentiment (tracked by the VIX), the implied volatility of crude oil (OVX) and GPR are significant predictors of ESG returns. None of gold or oil serves as a safe haven for ESG stocks, both acting just as diversifiers. In their turn, ESG could stocks hedge against the shocks from GPR and cryptocurrency-triggered market uncertainties in bearish states of the market. These findings are important for asset allocation and risk management, assisting investors in the already ongoing switch from ordinary to sustainable investments.

Shaping sustainable strategies of freight forwarding companies in the environment of the road transport market

Purpose. The paper aims to propose an approach to shape the sustainable strategies of freight forwarders under uncertainty of the stochastic environment of the transportation market. Methodology. The elements of the game theory were used to formalize the conflict situation, where the uncertainty of the transport market is considered as a game with nature. To design the model of the freight transportation market, the principles of the system’s theory were used in the representation of a forwarding company operating as an element within the macro-logistic system of the transport market. The methods of object-oriented programming were applied to develop the dedicated software for computer simulations of the forwarder’s operation within the market of transport services. The regression analysis was used as the main methodology to process the numeric results of experimental studies. The elements of functional analysis were applied to substantiate the sustainable strategy of a forwarding company in the considered example. Findings. The results of the conducted experiment allowed for determining the high-quality dependencies between the number of serviced requests and the number of dispatchers involved in the requests’ servicing for the case when the operators’ decisions are supported by the specialized software and for the case when decisions are made conventionally. Originality. The use of a game-theoretical approach in this study is based on the advanced simulations of a freight servicing process where the demand randomness and the servicing process stochasticity are integrally considered. Practical value. The proposed methodological approach is proposed to be used by forwarding companies to evaluate sustainable strategies when servicing clients within the given market. The use of the developed approach in practice allows forwarders to decrease the operational costs achieving the minimal negative impact on the environment.

Monetary Policy and Exchange Rate in a Large Emerging Economy

This article examines the causal relation between monetary policy and an exchange rate for a large emerging economy, Indonesia. In the analysis, we employ a vector autoregressive (VAR) model comprising of the rupiah exchange rate, monetary policy variable (interest rate or interest rate differential) and inflation rate and use monthly data after the global financial crisis from 2009 to 2022. Our baseline result suggests that the rupiah exchange rate appreciates following monetary policy tightening. We note further that the appreciating effect of monetary policy is more apparent when the interest rate differential is used as an indicator of monetary policy and is robust to the addition of the fourth variable (alternatively the US inflation, market uncertainty and oil price) in the VAR framework. These results likely reflect the importance of central bank independence and inflation-targeting framework in enhancing the credibility of Indonesia’s monetary policy.

Emission reduction technologies for shipping supply chains under carbon tax with knowledge sharing

In response to the gradually increasing pressures of global climate change, governments and enterprises around the world are taking proactive measures to encourage the shipping industry to reduce emissions. Meanwhile, knowledge sharing plays an important role in uncertain market environment and has a profound impact on the development of emission reduction strategies. This paper constructs a shipping supply chain consisting of the port and liner company, and explores the impact of carbon tax policies on carbon emission reduction technologies. By using the Stackelberg game model, the benefits of port and liner company are compared and analyzed in order to explore the interrelationship between knowledge sharing and the use of emission reduction technologies. The results of the study show that: (1) Under the conditions of different emission reduction technologies, the state of knowledge sharing has a significant impact on carbon emission benefits, and the determination of the optimal reduction technology is affected by both knowledge sharing and carbon tax rate. (2) Although the impact of knowledge sharing on total carbon footprint is not obvious, it can effectively reduce the redundant carbon footprint. (3) The inclusion of consumers’ green preferences and knowledge sharing in the model is conducive to the port and liner company to realize mutual benefits and win-win situation. The findings of this paper provide a reference for the adoption of emission reduction technologies by shipping supply chain stakeholders under market uncertainty.

Stochastic modeling of decarbonizing strategy, policy, and market-induced incentives for the US electricity sector

In line with the global pursuit of achieving net-zero carbon emissions, integrating carbon capture and storage (CCS) and renewable energy (RE) technologies is important in power production. This study evaluates the profitability of CCS and RE technologies as alternative ways of achieving climate change goals. While past research focused on costs, technological advancements, and capture methods, there is a need for more studies on assessing the financial feasibility of these climate change solutions under uncertain conditions, alongside specific performance goals and strategies to entice power producers. Using a comprehensive framework featuring deterministic and stochastic modeling approaches, this research explores the impact of policy and market incentives on CCS and RE investments within the U.S. power sector. It analyzes the interactions of variables such as market uncertainties, technical factors, and policy dynamics on the financial viability of adopting CCS and RE for targeted CO2 reductions. The results reveal that, given the status quo of policies, RE and CCS exhibit annualized net present values of $4.62 and $1.76, respectively, for each metric ton (MT) of CO2. Uncertainties in policy incentives emerge as a primary hindrance to achieving cost-effective carbon reduction mandates using CCS, while changes in the green electricity price premium cause high variability in RE returns. The study proposes a hypothetical market, featuring the sale of CCS-linked net-zero electricity at a distinctive premium price of $0.03/kWh. The study's findings underscore the importance of both policy and market incentives to enable power producers to deploy carbon management technologies at a large scale.

Market Structure, Risk Preferences, and Forward Contracting Incentives*

We examine the determinants of the levels of forward contracting preferred by generators and buyers of electricity. Increased forward contracting systematically reduces the variance of a generator's profit, so a generator prefers higher levels of forward contracting as market uncertainty or its aversion to risk increases. In contrast, increased forward contracting can either increase or reduce the variance of a buyer's profit. Consequently, a buyer can prefer either reduced or increased levels of forward contracting as market uncertainty or its aversion to risk increases.