Future Market Conditions Research Articles

Optimizing Strategy for Property Development Plan Changes: A Multi-Criteria Decision-Making Approach Using AHP Methods (Case Study: Project Bumi Cipta 3 Jakarta)

This study examines the optimization strategy of property development plan changes for Bumi Cipta 3 Jakarta, a project by PT Cipta Bangun Property, one of the largest property companies in Indonesia. The analysis uses the Analytic Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) method. The project is motivated by significant post-pandemic shifts in office space demand within Jakarta’s Central Business District. The study identifies four alternative development options: continuing with the office tower plan, or pivoting to an apartment, data center, or hospital. By interviewing key decision-makers and using the AHP method, the research evaluates financial and non-financial criteria, such as market attractiveness and site analysis. The findings suggest that the hospital alternative offers the highest potential in both financial returns and market alignment, followed by the data center and apartment projects. Strategic recommendations are provided to prioritize the hospital project while keeping the other alternatives in consideration for phased development, thus ensuring flexibility in response to future market conditions.

Artificial intelligence in healthcare forecasting: Enhancing market strategy with predictive analytics

This research examines the transformative role of artificial intelligence (AI) and predictive analytics in healthcare market forecasting, with a specific focus on their application at Eli Lilly. AI-driven insights are becoming critical tools in anticipating market trends, assessing the impact of regulatory changes, and optimizing product positioning in the competitive global healthcare landscape. By leveraging machine learning algorithms and vast datasets, AI can identify patterns and correlations that traditional forecasting methods may overlook, offering companies like Eli Lilly a more comprehensive understanding of future market dynamics. The study explores how AI enhances decision-making by predicting healthcare market trends, particularly in response to regulatory shifts such as the U.S. Inflation Reduction Act. This legislative change, aimed at reducing drug prices and increasing market access, presents significant challenges and opportunities for pharmaceutical companies. AI allows firms to model the potential outcomes of such regulatory changes and adjust their market strategies accordingly. For example, predictive analytics can forecast the pricing pressures on specific drugs, helping companies develop competitive pricing models while maintaining profitability. Moreover, the research highlights the use of AI in mitigating risks associated with market volatility. By continuously analyzing real-time data, AI can detect early signals of potential disruptions—whether from economic downturns, competitor actions, or public health crises—allowing companies to take proactive measures. Additionally, AI-driven forecasting helps identify growth opportunities in emerging healthcare markets by analyzing demographic trends, disease prevalence, and healthcare infrastructure development. In conclusion, AI and predictive analytics are revolutionizing how healthcare companies like Eli Lilly approach market forecasting and strategy development. By offering deeper insights into future market conditions, these technologies not only reduce risks but also optimize product positioning and unlock growth opportunities in the global healthcare market. This research underscores the critical importance of AI in shaping the future of healthcare market forecasting. Keywords: Artificial Intelligence, Predictive Analytics, Market Forecasting, Regulatory Impact, Healthcare Strategy

Economic shocks and skill acquisition: Evidence from a national online learning platform at the onset of COVID-19

We study how large shocks impact individuals’ skilling decisions using data from a large, government-sponsored, online learning platform in Saudi Arabia. The onset of the COVID-19 pandemic brought about a massive increase in online skilling, and demand shifted towards courses that offered skills, such as telework, likely to be immediately valuable during the pandemic. Consistent with a model where individuals trade off reskilling costs with their expectations of future labor market conditions and their duration of work, we find that shifts into telework courses were largest for older workers. In contrast, younger workers increased enrollments in courses related to new skills, such as general, occupation-specific, and computer-related skills. Using national administrative employment data, we provide descriptive evidence that these investments in skills in early 2020 helped users maintain employment over the course of the pandemic.

Strategic Advancements for Competitive Edge in the Food Industry

This study explores the strategic management and enhancement of competitiveness within the food industry. Amidst a backdrop of fluctuating market conditions and a need for modernization, this research identifies a significant gap in the effective utilization of foreign investments and the adaptation of industry practices to contemporary standards. The primary aim of this research is to propose methodologies for fostering investment, modernizing operations, and expanding production capacities to stabilize and grow the market presence of food industry enterprises. Employing a methodological approach that combines theoretical frameworks with empirical data analysis, the findings suggest that increased competition and new production capabilities can significantly improve market supply and price stability. The implications of this research underscore the necessity for strategic interventions that not only respond to current industry demands but also anticipate future market conditions, thereby ensuring sustainable development and competitive advantage in the food industry. Highlights: Foreign Investment: Essential for technology infusion and practice improvement. Operational Modernization: Adapts enterprises to contemporary market demands. Production Expansion: Increases supply and stabilizes prices through new capacities. Keywords: Food Industry, Management Strategy, Market Stability, Production Expansion, Competitive Mechanism

Community Tree Nurseries in the UK: Promise, Potential and Pitfalls

Forest restoration and tree-planting projects initiated as a response to climate change and biodiversity loss are increasingly important around the globe. Small-scale and community-based tree nurseries have been promoted as a potential route to meeting some local or regional supply-side bottlenecks. A study in the UK used a mixed methods approach to assess the potential contribution of community tree nurseries (CTNs) to tree material supply. Semi-structured interviews (SSIs) were undertaken with 16 CTNs across the UK to generate a sector-wide characterisation of CTNs. A UK-wide online survey assessed the total number of CTNs, production methods and volumes, biosecurity practices and benefits of community involvement. Another 13 CTNs receiving support to establish and extend their operations took part in SSIs assessing their development. The results indicate that there are four broad types of CTN (Organisation- and project-based, Community-based, Enterprise and Network CTNs). A significant number of CTNs in Britain are new and establishing. The sustainability of CTNs relies in large part on grant support rather than income from tree sales. Production is almost exclusively native broadleaves and local provenances. There are policy implications concerning: i. the suitability of species being produced to future climatic and market conditions and ii. the tension around financial viability, grant dependence and CTN sustainability for those CTNs focused on social and environmental benefits rather than income generation. Identifying gaps in the provision of trees by commercial nurseries, e.g., uncommon or recalcitrant species, could be a feasible financial strategy. It is likely that financial support and skills development are likely to be required as social innovation in the community-supported tree nursery sector establishes and develops. Judging the success and impact of some types of CTNs in terms of production and finance measures may be unfair, as other social and environmental benefits may be as important and valuable. It is clear that more evaluative research is needed to quantify and better understand these kinds of outcomes and the added value they accrue.

A Valuation Model for the Development of Sinovac Biological Company under COVID-19 Shock

This paper provides an in-depth study of the impact of the COVID-19 pandemic on the global economy and the pharmaceutical and medicine industry, focusing on the Chinese company Biologicals. The financial situation of Sinovac Biologicals before and after the pandemic is analyzed in depth by building a valuation model with data from two different time periods. Before the COVID-19, the company faced cash flow problems and a relatively low market capitalization. This is mainly because at that time, the company may not have been involved in areas related to vaccine production, and the market capitalization was constrained by market demand and industry competition. However, with the outbreak, the company became actively involved in the development and production of the COVID-19 vaccine, leading to a significant improvement in its free cash flow and a rise in its market capitalization. Although the model results emphasize this positive trend, this papermust keep in mind the model's uncertainty and risk factors. Future economic and market conditions are fraught with uncertainty, and Cochrane Biologics may also be exposed to other risks, such as volatility in the vaccine market, policy changes and increased competition. Therefore, a combination of these factors is critical to accurately assessing the company's valuation and prospects.

Optimal design of micro pumped-storage plants in the heart of a city

Growth in renewable energy generation leads to an urgent need of expanding energy storage capacity. While large pumped hydro storage remains the most established and prevalent energy storage method, there is potential for evaluating its applicability on a micro scale in urban areas. This study develops a multi-objective optimisation model in Python to assess the feasibility of micro pumped-storage (MPS) for high-rise buildings up to 300 m in height, considering different future energy market conditions. The findings indicate that increasing electricity tariff values lead to improved solutions from both environmental and financial perspectives across all building heights. However, for buildings 50 m or less, the MPS is currently considered economically unviable. In a world first, this study examines the techno-economic viability of MPS systems as a function of building height and different water storage types, including modular tanks, multilayer green roofs, blue roofs, and nearby streams. The MPS system using modular tanks and nearby streams boasts the shortest payback period (4.8 years) and Net Present Value of A$ 600 k over the MPS lifetime. The study provides insights into feasible flow rates and storage sizes. The findings are valuable for designers, helping them decide if MPS is feasible based on the building's height and features.

벌크해운시장에서 용선계약유형 선정에 영향을 미치는 요인에 관한 연구

Purpose: This study provides to charter companies, including shippers, implications for charter party decision-making by identifying factors that affect the selection of contract types in the dry bulk shipping market.
 Research design, data, and methodology: For empirical analysis, the dependent variable was set as a choice between time charter and voyage charter, and the independent variables were set as company and cargo characteristic factors, company internal environmental change factors, and company external environmental change factors. A hypothesis test was conducted using 111 questionnaires.
 Results: Variables such as cargo volume and ship size were found to have a significant impact on the choice of charter contract. Company characteristics, investment propensity, and risk management factors were significant as a company's internal environmental factors. All variables, including market outlook, volatility, and cost factors, were statistically significant as a company's external environmental factors.
 Conclusions: Investment decisions and the outlook for future shipping market conditions are important factors to consider in decision-making as to the type of charter contract. Therefore, it is important for shippers as charters to obtain quick and accurate information on the shipping market.

Measuring Signal of Skill Mismatch from Online Job Advertisements and HR Roles: A conceptual paper

Skill mismatch remains an ongoing issue due to differences in qualitative interpretation and quantitative measurement. Scholars and practitioners have yet to reach a consensus on a definitive method for measurement, making it challenging to diagnose and solve the problem. This conceptual paper focuses on measuring skill mismatch signals through online job advertisements and the roles of HR in addressing this issue. It reviews relevant literature from journals, articles, and dissertations from previous studies. By enhancing online job advertisements and refining HR roles, this study aims to improve future labour market conditions.

Mathematical procedure for predicting tube metal temperature in the radiant superheaters of a tangentially and front fired utility boilers

The superheaters can be indicated as one of the most critical components of the utility boilers experiencing highest temperatures. The local overheating of the heat exchangers material is a significant operational issue. The current and future market conditions implicate new unfavourable regimes of boiler operating conditions bringing the need for prediction capability of tube wall temperature in either design or retrofit process. There is a need to obtain a realistic metal temperature distribution based on uneven heat flux absorbed and complex steam flow. The current paper demonstrates a computational methodology of predicting tube metal temperature of radiant superheaters platens. It couples a full scale 3-D boiler simulations based on CFD technique on the combustion side with an in-house 1-D heated pipe flow model of the steam side. The methodology was applied to two different boilers with different firing strategy (front fired and tangentially fired) to demonstrate the model flexibility. The model is capable of identifying the exact location of specific platen in which the metal maximum temperature occurs. A validation study was conducted by utilising the available measurements. The computations are in qualitative and quantitative agreement with measurement. The modelling approach can be easily extended to boilers utilising all types of fuels as well as heat recovery steam generators.

Quantitative models in asset management: A review of efficacy and limitations

This review provides a comprehensive overview of the efficacy and limitations associated with quantitative models in the field of asset management. Over the past few decades, the financial industry has witnessed a significant shift towards algorithmic and data-driven approaches in investment decision-making. Quantitative models, ranging from traditional risk-return frameworks to sophisticated machine learning algorithms, play a crucial role in shaping investment strategies and portfolio management. The review begins by examining the strengths of quantitative models, highlighting their ability to process vast amounts of financial data efficiently and objectively. These models enable investors to make data-driven decisions, manage risk, and optimize portfolio allocations. Furthermore, quantitative models facilitate the identification of patterns and trends in market behavior, allowing for timely adjustments to investment strategies. However, the efficacy of quantitative models is not without its limitations. The review explores challenges such as model risk, data quality issues, and the inherent complexity of financial markets. Model risk refers to the possibility of errors or inaccuracies in the mathematical models used, leading to suboptimal investment decisions. Additionally, the reliance on historical data assumes that future market conditions will resemble the past, a presumption that may be invalidated during unforeseen events or structural shifts. The review also delves into the ongoing debate surrounding the balance between human expertise and algorithmic decision-making. While quantitative models offer objectivity and systematic processes, the human element remains crucial for interpreting results, adapting to dynamic market conditions, and exercising judgment in situations where models may fall short. In conclusion, this review provides valuable insights into the evolving landscape of asset management through the lens of quantitative models. Recognizing their efficacy in processing vast datasets and identifying patterns, it also underscores the importance of acknowledging and addressing the limitations inherent in these models. Achieving a harmonious integration of human judgment and quantitative methodologies is crucial for enhancing the overall effectiveness of asset management strategies in a rapidly changing financial environment.

АНАЛІЗ МЕТОДІВ ЦІНОУТВОРЕННЯ ТА ОСОБЛИВОСТЕЙ ЇХ ЗАСТОСУВАННЯ НА ЕЛЕКТРОПОСТАЧАЛЬНИХ ПІДПРИЄМСТВАХ

The article examines the peculiarities of the liberal model of the electricity market in Ukraine. The dynamics of the number of enterprises that perform the functions of electricity suppliers on the domestic electricity market during 2019–2022 were determined. It is justified that special attention should be paid to the systematization of existing pricing methods in order to determine the possibility of their application at electricity supply enterprises in the conditions of market liberalization. The specific features of the application of pricing methods at electricity supply enterprises are characterized. The division into three groups of pricing methods, including the following: cost, parametric and market. The specifics of the electricity market products were determined in order to choose pricing methods that are suitable directly for electricity supply enterprises. A critical analysis of the following pricing methods was carried out: the full cost method, the direct cost method, the target pricing method, the specific rate method, the marginal cost method, the break-even method, the method of three estimates, the method of following market prices, the method of following firm prices – market leader, method of calculation based on accepted prices, method of regression analysis, method of price premium, method of prestigious prices, method of competition, method of targeting competitors, tender method (sealed envelope method) of pricing, method of estimating the maximum acceptable price, method of experience curve, method of zonal pricing, method of exchange pricing. It was determined that not all pricing methods can be applied at electricity supply enterprises, taking into account the difference in the specifics of the work of each of their types. It was established that the least number of recommended methods are available to electricity suppliers who sell electricity at free prices, which is due to the specifics of their work in terms of sensitivity to price changes on the electricity market, therefore, when pricing, they cannot afford to neglect forecasting of future periods and market conditions.

Expectation-based consumer purchase decisions: behavioral modeling and observations.

The online version contains supplementary material available at 10.1007/s11002-022-09650-7.

Effects of Monetary Policy on Household Expectations: The Role of Homeownership

We study the role of homeownership in the effectiveness of monetary policy on households' expectations. Empirically, we find that homeowners revise down their near-term inflation expectations and their optimism about future labor market conditions in response to a rise in mortgage rates, while renters are less likely to do so. We further show that the monetary-policy component of mortgage-rate changes creates the difference in expectation revisions between homeowners and renters. This result suggests that homeowners are attentive to news on interest rates and adjust their expectations accordingly in a manner consistent with the intended effect of monetary policy. We characterize these findings using a rational inattention model with two types of households---homeowners and renters.

Unconventional monetary policy, funding expectations, and firm decisions

We study the transmission of (unconventional) monetary policy to the real sector when firm decisions depend on both current and future credit market conditions. For a given level of current credit access, investment and employment increase more for firms expecting bank credit to improve in the future. Three separate unconventional policies by the ECB – the OMT, the introduction of negative rates, and the CSPP – improved expectations of future credit access for SMEs borrowing from banks that were expected to increase SME lending due to the policy. Our results enhance our understanding of the bank balance sheet channel of monetary policy.

Dynamic planning of Power-to-Gas integrated energy hub considering demand response programs and future market conditions

Power-to-gas (P2G) technology and renewable energy are widely developed and are at the edge of a mass roll-out. Still, regulatory and economic barriers are the foremost challenge facing P2G technology and renewable energy. This paper investigates the economic and technical feasibility of designing P2G within the energy hub systems under future market conditions. The design of the P2G integrated energy hub (P2G-EH) system is performed with a stochastic dynamic planning method. The proposed planning method is divided into several sub-horizon times, and the optimal size of the system components is determined in each sub-horizons. The proposed P2G system is equipped with a carbon capture unit recovering CO2 emissions from the flue gases of the boiler and combined heat and power system (CHP). In the planning problem, the reliability constraint is considered to evaluate the quality of supplying loads. Moreover, system uncertainty parameters are modeled through the scenario-based stochastic method. The obtained results exhibit that the P2G system has positive effects on mitigating emissions by about 17.7 %, reducing operating costs by 14.6 %, and improving the reliability of thermal loads. With the future expected development of components' capital costs, the P2G system can become a profitable case for integration with the energy hub system. In addition, the implementation of the demand response programs (DRPs) besides the P2G system has a beneficial impact on system performance and reduces the operation and investment costs of the P2G-EH system by 4 % and 24 %, respectively.

Stock Market Prediction

Researchers have been investigating various approaches to accurately forecast stock market prices. Trading professionals can gain better insights regarding data, such as potential trends, by using useful prediction tools. Additionally, since the study predicts future market conditions, investors stand to gain significantly. Using machine learning algorithms for predicting is one such approach. The goal of this study is to increase the accuracy of stock market predictions made using stock valuation. Many academics have developed various approaches to address this issue, primarily using conventional approaches up to this point, like artificial neural networks, which are ways to identify hidden patterns in data and classify it for use in stock market prediction. This initiative suggests a fresh approach to stock forecasting

Electricity Market and Energy Policies Uncertainties for Investment in Life Time Operation of Nuclear Power Plants

In the electricity sector, market participants must make decisions about capacity choice in a situation of radical uncertainty about future market conditions. Electricity sector is characterized by non-storability and periodic and stochastic demand fluctuations. Capacity determination is a decision for the long term, whereas production is adjusted in the short run. Today decisions pertaining to investment in new capacity or life time extension are surrounded by considerable uncertainties about the future economics of the projects. One reason is that in a deregulated market private investors typically have to bear a greater portion of the investment risk compared to a monopoly utility in a regulated market. This favours flexible investment alternatives with short-lead times and low capital requirements. Moreover, energy and climate policy – with feed-in tariffs for RES or green certificate system and the EU CO2 ETS may add to investment uncertainties. From the economic point of view, the costs of LTO are usually lower than the construction of any other source of electricity. But in the aftermath of the Fukushima accident, policies towards nuclear energy in some countries were changed. Because of that economic life decisions are plant specific. In evaluating the future economic prospects of existing plant, the owners/utility focus on the unique circumstances of that plant and its cost and performance, and the future demand for electricity, and value of electricity. Nevertheless, quantification of the LTO costs is not an easy task. It is recognized that LTO costs are highly dependent on specific conditions related to each NPP, such as: design of the plant; NPP operating history including ageing conditions; regulatory requirements; full or partial replacement of components; refurbishment for LTO; accounting methodologies; etc. The risks that may have an impact on the economic case for the long term operation of NPP should be identified with pre mitigation impact and probability assessment.

From market uncertainty to policy uncertainty for investment in power generation: real options for NPP on electricity market

In the electricity sector, market participants must make decisions about capacity choice in a situation of radical uncertainty about future market conditions. Sector is characterized by non-storability and periodic and stochastic demand fluctuations. Capacity determination is a decision for the long term, whereas production is adjusted in the short run. Paper looks on the main contributions in investment planning under uncertainty, in particular in the electricity market for capital intensive investments like NPP. The relationship between market and nonmarket factors (recent UK policy example) in determining investment signals in competitive electricity markets was analysed. Paper analyse the ability of competitive electricity markets to deliver the desired quantity and type of generation capacity and also investigates the variety of market imperfections operating in electricity generation and their impact on long-term dynamics for generation capacity. Paper analyses how price formation influences investment signals. Number of factors (including market power, wholesale price volatility, lack of liquidity in the wholesale and financial market, policy and regulatory risks etc.) contribute to polluting the price signal and generating sub-optimal behaviour.

A distributionally ambiguous two-stage stochastic approach for investment in renewable generation

The optimal expansion of a power system with reduced carbon footprint entails dealing with uncertainty about the distribution of the random variables involved in the decision process. Optimisation under ambiguity sets provides a mechanism to suitably deal with such a setting. For two-stage stochastic linear programs, we propose a new model that is between the optimistic and pessimistic paradigms in distributionally robust stochastic optimisation. When using Wasserstein balls as ambiguity sets, the resulting optimisation problem has nonsmooth convex constraints depending on the number of scenarios and a bilinear objective function. We propose a decomposition method along scenarios that converges to a solution, provided a global optimisation solver for bilinear programs with polyhedral feasible sets is available. The solution procedure is applied to a case study on expansion of energy generation that takes into account sustainability goals for 2050 in Europe, under uncertain future market conditions.