Market Signals Research Articles

The Bond Market: A Pillar of Risk Management and Economic Stability

This essay investigates the utility of the bond market in risk management, corporate finance, and economic forecasting. It explores how the bond market’s inherent characteristics of safety, stability, and liquidity make it a crucial component of the global financial system. By examining the issuance of debt instruments, such as bonds, and their role in maintaining corporate control while accessing capital, the essay highlights the advantages of debt financing. Moreover, it underscores how these attributes help corporations manage financial risks and optimize their capital structure. Furthermore, the bond market’s predictive power is analyzed through key indicators like the yield curve and credit spreads, which provide early warnings of economic downturns. The yield curve, in particular, is known for its ability to forecast recessions when it inverts. Credit spreads, the difference between yields on corporate bonds and government securities, also serve as a barometer for economic health and investor sentiment. To bring these theoretical insights into a practical context, the essay includes case studies, such as Apple Inc.’s strategic use of debt. Apple's case demonstrates how a company can leverage debt to finance growth while preserving equity and maintaining financial flexibility. These examples illustrate the tangible benefits and strategic importance of the bond market in corporate finance. The essay concludes with suggestions for leveraging the bond market’s predictive capabilities, emphasizing the need for continuous monitoring and analysis of bond market signals to inform economic and financial decision-making. It also explores future research directions in the evolving financial landscape, considering how technological advancements and regulatory changes may impact the bond market's role in global finance.

Two-stage multi-objective distributionally robust operation optimization and benefits equalization of an off-grid type electric-hydrogen-ammonia-methanol coupling system

As the proportion of renewable energy has increased significantly, electric-hydrogen coupling technology has emerged as a pivotal strategy for optimizing the utilization of renewable energy resources. In particular, within the power and chemical sectors, the preparation of ammonia and methanol using electric-hydrogen coupling technology has emerged as the two principal avenues for the utilization of hydrogen energy. However, as a nascent technology, the preparation of ammonia and methanol by electric-hydrogen coupling is confronted with a multitude of challenges, including the considerable uncertainty surrounding the generation of renewable energy, the dearth of economic benefits, and the need to ensure a fair allocation of benefits among the various stakeholders. To enhance the risk resistance of the electric-hydrogen-ammonia-methanol coupling system (EHAMCS), improve the economic benefits, ensure the environmentality and stability of the system, and realize the fair distribution of benefits among the energy participants, this paper constructs a multi-objective distributionally robust optimization (DRO) and multi-dimensional benefits equilibrium model for the EHAMCS and performs a numerical analysis with an off-grid type EHAMCS as an example. The results demonstrate that: 1) The cooperative operation of the EHAMCS offers notable economic advantages. When compared with only pursuing economic benefits, under multi-objective optimization, the economic benefits of the coupling system can be reduced by 1.49 %, but the carbon emission can be reduced by 10.06 %, and the instability is reduced by 0.02, which can create derivative benefits. 2) The two-stage DRO model is an effective means of addressing the impact of system operation uncertainty and the affine strategy enables the rapid adjustment of the day-ahead dispatching plan, thereby balancing the power deviation. 3) The multi-dimensional factor benefit balancing scheme is more conducive to the participation of clean energy and downstream ammonia and methanol energy subsystems in cooperative operation and promotes the consumption of clean energy while improving the energy conversion value of the EHAMCS to increase economic benefits. 4) EHAMCS operators should adjust their operational strategies in time with market and policy signals, and select historical samples reasonably, so as to realize the balance of the DRO model in terms of economy, robustness, and practicality. The effectiveness of the model and the superiority of the EHAMCS are thus verified.

Tipos de consumidores responsables: una perspectiva desde la teoría de la señalización

Contemporary societies need to develop awareness of lifestyles and consumption patterns and understand how they affect the environment, society, and their health in order to adapt to balanced and co-responsible ways of living. In order to contribute to this, this study seeks to relate the market signals of sustainable products to different types of responsible consumers. Initially, a literature review allowed the typification of consumers into three groups: integral, relative and accidental, and the identification of signals that communicate the sustainability of a company or a product. Consequently, data was collected from 160 consumers who were classified into each type using k-means clustering, while relating the most relevant signals to each. Each type of consumer is well-defined and has very different behaviours and values about sustainability from the others, perceives signals differently and pays attention to attributes that relate directly to their self-perception as a responsible consumer.

The evolution of two‐stage production and order equilibrium in a random yield supply chain with demand information updating

AbstractThe mismatch between production, order, and demand seriously affects supply chain performance. However, most research focus on the mismatch between the retailer's order and customer's demand, which ignores the influence of the supplier's random yield on supply chain members' decision‐making. This paper investigates a two‐stage optimization problem within a two‐echelon supply chain, featuring a supplier with random yield and a retailer updating demand information in real‐time. Faced with a long production lead time, the retailer can either place advance orders at the production season's onset (first‐stage advance order) or opt for instant orders at the beginning of the sales season (second‐stage instant order). To ensure timely order fulfillment, the supplier initially employs a cost‐effective regular production mode with random yield during the production season. If yields are insufficient during sales, a pricier emergency production mode with guaranteed output becomes available. Utilizing a dynamic programming approach, we formulate the two‐stage optimization problem to derive optimal production and order decisions. Our analysis uncovers how realized random yield and stochastic market signals influence emergency production and instant order quantities in the second stage. We compare expected profits in scenarios with perfect and imperfect market signals, probing the members' preferences regarding order strategies. An intriguing finding emerges: as instant wholesale prices rising, the supplier's preferred order strategy diverges from the retailer. By strategic adjustments to the instant wholesale price, we demonstrate the potential for unanimous agreement on preferred order strategies among supply chain members — a quality enhancing the chain's flexibility and performance. Moreover, we extend the model to hybrid order strategies and identify conditions for unanimous preference among the three strategies. To bolster our theoretical findings, we provide numerical examples, lending practical support to our study.

Determining optimal component configurations for flexible biogas plants based on power prices of 2020–2022 and the legislation framework in Germany

The aim of this study is to find optimal component configurations for flexible biogas plants considering electricity prices in Germany, for maximum annuities considering costs and revenues. Cases for solely power-price driven operation and cogeneration-led operation were investigated for electricity prices in 2020, 2021, and 2022 in hourly resolution.Additional revenue from flexible operation increases as price volatility increased from 2020 to 2022. Interestingly, the choice of capacity for the combined heat and power unit (CHPU) is determined by the expected service life of the CHPU. Irrespective of electricity market signals, power quotients (PQ) of at least PQ = 4 avoid reinvestment for the CHPU within 20 years.Another result is that the the optimality region for CHPU capacity flattens for 2022 and shift to capacities above PQ = 4. Thus, high price volatility will allow plant operators to amortise even larger CHPUs, which could stimulate the installation of peak load by flexible biogas plants. Considering the day-ahead market can provide clear recommendations for optimal component configurations.The presented Smart-Force-Optimisation (SFO) combines a prefiltering of schedules matching given constraints and a brute force optimisation to the filtered subset. Thus, SFO has an improved performance comparing to bare brute force approaches.

The nature, causes, and effects of skepticism on technology diffusion

Although skepticism is involved in technical change and scientific revolutions, surprisingly, the literature lacks a systematic analysis of the different forms, causes, and roles of skepticism in the diffusion of innovation.This paper defines, identifies and models different types and causes of skepticism and their role in technology adoption. The paper identifies and studies: skepticism involving the characteristics of the technology and the producer; skepticism that induces to disbelieve market signal; and comparative skepticism i.e., skepticism produced by an unbalance relationship between the perceived complexity of the problem and the solution.Among the theoretical findings of the paper and regarding skepticism on market signals, we found that the non-differentiability and oscillation of diffusion rates occur if individuals use the information on diffusion rates as proxy of the probability of technology working and modify this probability according to their skepticism.

Does Dividend Payment Generate a Market Signal to Investors?

In perfect market theory, the information will provide signals that are reflected in stock price movements. In the context of dividend payments, these theories are still debatable. Dividend payments will please shareholders as prosperity increases. But not sharing profits can also give a positive signal. The purpose of this study is to see if dividend payment able to give a positive signal on rising stock prices. The research will also look at whether profitability, leverage, asset growth, and interest rates also signal rising stock prices. Logistic regression analysis is applied to answer the research objectives. Purposive sampling is based on retail companies that specialize in the category of supermarkets and minimarkets that have been listed on the Indonesia Stock Exchange for the period 2013 to 2022. The results of this research conclude that dividend payments give a positive signal on rising stock prices. The chance of a stock price increase is 4.7 times compared to a company that does not pay dividends. Interest rates also inform investors to be careful in making investment decisions. Investors are only willing to bear additional risk if the expected profit is above the risk premium. Information on the company's financial performance such as profitability, leverage, and asset growth is not significant on the increase in stock price. This means that it does not provide signals for investors.

Fostering firm value: Unpacking the effect of circular economy disclosure via social media

AbstractGrowing environmental concerns and the need to address global challenges such as climate change and resource exploitation have highlighted the relevance of the circular economy (CE) and the importance for companies to communicate circular practices externally. While academic literature has examined the dissemination of CE information, the effects of CE disclosure (CED) remain underexplored. This study aims to fill this gap by exploring, through signaling theory, the influence of CED via social media on firm value. The econometric analysis, based on 366 observations (an unbalanced panel of 133 internationally listed companies for the period 2019–2021), demonstrates that CED via Twitter serves as a powerful market signal, positively influencing investor perception and increasing firm value. This study enlarges the literature relating to the association between non‐financial disclosure and firm value, and the effects of CED. Furthermore, it extends the debate on the use of social media as disclosure tools and broadens the scope of signaling theory.

Behavioral Efficiency and Residential Electricity Consumption: A Microdata Study

Sustainability requires that policy makers be able to use market signals to implement energy and environmental policy and that energy consumers respond rationally to these signals. Therefore, it is essential to understand how consumers’ responses to market signals are formed. We propose a new model to measure behavioral efficiency in residential electricity consumption derived from the individual householder indirect utility function. This leads to a pair of simultaneous stochastic demand frontiers for electricity consumption (kWh) and power demand (kVA). Each is a function of power demand (standing) charges and energy demand (running) charges together with the net income after demand charges, the stock of appliances and household characteristics. We estimate the model using two samples of household responses, each of which represents around one percent of the total national population available, and we also pool these samples using pseudo-panel data procedures. We demonstrate how the resulting elasticity and efficiency estimates are related to the theory of behavioral agents from recent developments in behavioral economics. These developments also use the individual indirect utility function to derive propositions based on internality and hyperbolic discounting. The econometric estimates permit the calibration of the individual welfare effects of policy initiatives using carbon tax and price incentives with behavioral agents.

Does the carbon market signal the market efficiency of clean and dirty cryptocurrencies? An analysis of quantile directional dependence

The role of the carbon market in the market efficiency of cryptocurrencies has not received much attention from scholars, despite its particular relevance for integrating environmental externalities into the theory and practice of digital financial markets. We investigate whether and how the performance of the carbon market during busts and booms affects the market efficiency of both clean and dirty cryptocurrencies. Overall, our results underscore the complex quantile dependence of cryptocurrency market efficiency on carbon market performance, highlighting significant variability in how different types of cryptocurrencies respond to carbon market. These results have significant implications for regulatory policies, investment strategies, industry practices, public perceptions, and technological advances in the pursuit of aligning cryptocurrency markets with environmental sustainability goals.

Benchmarking of corporate social responsibility in China

This study explores the underexplored application of benchmarking in Corporate Social Responsibility (CSR) among Chinese A-listed companies from 2010 to 2020. The results reveal a positive relationship between CSR performance and local best practices, indicating benchmarking’s influence on CSR activities. Notably, benefits like increased market value, robust revenue, and heightened analyst attention signify positive market signals, motivating companies to enhance their CSR performance. The impacts on CSR performance varies significantly based on leader companies’ CSR scores, ages, and follower companies’ ownership properties, sizes, and locations. Our findings offer valuable insights into CSR motivations and the performance effects of best practices.

Optimal design of renewable energy communities (RECs) in Italy: Influence of composition, market signals, buildings, location, and incentives

In Europe the concept of Renewable Energy Communities (RECs) renovates the need for decentralized energy generation among citizens and companies. The corresponding legal framework enables the creation of business models that shall support environmental, economic and social benefits, provided that adequate planning accounting for the local conditions is executed. The present work is focused on a thermal-electric co-simulation approach for the analysis of the energy exchanges within an Italian REC, aimed at sizing photovoltaics (PV) and energy storage systems. EnergyPlus is used to estimate the thermal and electric needs of the community, whose results are fed into the “EnergyCommunity.jl” tool for the design of the local power system. The influence of REC composition (prosumers/consumers), the building energy demands for heating, cooling, and appliances, the geography of the REC in Italian regions, the market data, and the total peak power of PV are examined. The self-consumed electricity, the energy shared among the community, and the social costs are evaluated in different scenarios, suggesting significant environmental and economic benefits for Energy Communities.

Variational autoencoder-based dimension reduction of Ichimoku features for improved financial market analysis

Financial markets are complex and dynamic, and accurately predicting market trends is crucial for traders and financial analysts. Ichimoku-based features have gained significant attention in financial market analysis due to their ability to capture essential market signals and patterns. This significant compression retains essential patterns related to trends, support/resistance levels, and trading signals. The reduced dimensionality improves computational efficiency and could allow for more accurate predictive modeling by traders. However, real-world testing is needed because compressing data risks losing useful nuances. In this study, we utilize an autoencoder for the dimensionality reduction of Ichimoku-based features in financial market analysis. The autoencoder, a neural network architecture, compresses high-dimensional data into a lower-dimensional representation by learning important features and patterns. The experiments conducted on a Euro/Dollar market dataset spanning 1990, comprising 16 columns with Ichimoku features, reveal the remarkable reduction of dataset size from 2,269,500 to 756,375, equivalent to a decrease of 66.67 %. These results highlight the efficiency of the proposed approach in reducing the dimensionality of financial market data, suggesting its potential as a valuable tool for traders and financial analysts to predict market trends and make informed decisions in the financial markets.

Further quality analytical investigation on soliton solutions of some nonlinear PDEs with analyses: Bifurcation, sensitivity, and chaotic phenomena

In this investigation, the analytical behavior of two prominent nonlinear wave equations, namely the doubly dispersive equation (DDE) and the Ablowitz-Kaup-Newell-Segur equation (AKNSE), have been scrutinized. Bifurcation analysis, sensitivity as well as chaotic phenomena are also performed for the earlier-mentioned dynamical systems. These analyzes have profuse applications in the field of electrical circuits and control systems, phase transitions in materials, climate patterns, chemical reaction networks, forecasting market trends, signal processing, and quantum mechanics. Using an advanced mathematical technique, the exact solutions of the mentioned two-wave equations with singular bell-shaped soliton, bell-shaped soliton, anti-bell-shaped soliton, singular soliton, and singular periodic soliton have been studied. The technique utilized in the study is dependable for solving complex nonlinear problems in various natural science and engineering disciplines employed by many researchers. This study identified ten general solutions and ten particular solutions for the two mentioned equations that are novel and precise wave solutions. The solutions obtained in our study are significant not only for understanding the mentioned field but also may be used in revealing other interesting phenomena, such as the analysis of seismology, the study of compulsive collapse, and the study of the material effects and inner construction of solids.

Developing a three stage coordinated approach to enhance efficiency and reliability of virtual power plants

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.

The economics of public transport electrification: The charging dilemma

The electrification of public transport has the potential to mitigate greenhouse gas emissions and support the electricity system, however this potential depends on how intraday charging interfaces with grid emissions and wholesale prices. Our paper explores these trade-offs by using boundary analysis to evaluate a least-cost tariff-based aggregate bus fleet charging strategy and comparing it to charging strategies that either minimise indirect greenhouse gas emissions or wholesale price exposure, based on the metropolitan Melbourne bus network. We develop a novel two-state fleet charging optimisation model, that allows us to perform a counterfactual analysis across 5 years of historical (2017–2021) and 5 years of modelled (2022–2026) changes to wholesale electricity prices and emission intensities in Australia's National Electricity Market, in response to the three charging strategies. We find that current Australian commercial tariff structures with high daytime volumetric usage and demand charges mute climate and wholesale market signals. By charging to minimise greenhouse gas emissions over bill savings, the fleet can achieve (on average) a further 33.4% reduction in indirect emissions but incurs a 103% increase in annual electricity bills. Alternatively, by charging to minimise wholesale price exposure, the fleet can achieve (on average) a 13.4% reduction in indirect emissions while incurring a 69% increase in electricity bills. Improving the temporal alignment of commercial electricity tariffs with wholesale prices should thus simultaneously reduce indirect emissions and wholesale electricity costs. This necessitates urgent tariff reforms to allow relevant wholesale price signals to pass through to commercial EV operators.

Demand Planning: Riding Disruptive Wave of AI and Accelerated Computing

Traditional demand planning methods often struggle to keep pace with the complexity, volatility, and vast datasets inherent in modern supply chains. Artificial intelligence (AI) offers a transformative solution, revolutionizing demand planning with its ability to analyze vast amount of data, identify complex patterns, and generate highly accurate forecasts. This paper explores the latest advancements in AI for demand planning, encompassing machine learning, deep learning, and natural language processing (NLP). The focus is on how these techniques enhance demand sensing capabilities, incorporating real-time market signals, external data sources, and unstructured text information. Furthermore, the potential of AI to optimize inventory management, enable scenario planning, and increase supply chain resilience in response to unexpected disruptions are examined. The paper also addresses practical challenges in implementing AI-powered demand planning solutions, and outlines areas for future research. Most importantly, the paper provides the robust methologies to integrate the emerging AI developments in demand planning process.

Beyond Data: Strategically Leveraging Consumer Metrics and Market Signals of Business Prosperity in the Context of Marketing Communications

In the dynamic environment of global markets, understanding the complexity of the relationships between economic indicators and consumer behaviour, which can be deliberately influenced by targeted communication activities, is becoming a decisive competitive advantage in achieving economic prosperity for businesses. The aim of the study is, based on market data correlation, to explain and approach the interrelations of selected economic attributes and metrics of consumer behaviour. The inquiry is conducted in the scope of optimisation determination of the allocation of the purchase gradient of a business unit in the context of maximising the effect of communication and service activities oriented towards achieving economic prosperity of the business entity. The focus of the study reflects the application of the interconnectivity of market predisposition, characteristics of the target group and geographic servicing in the form of localisation of the business unit in the paradoxical situational model of Hotelling’s Law. The result of the study confirms the leveraging effect of transactional increase and the rationale of localised communication effect for influencing consumers’ purchase decision-making processes in the purchase pairing of a business unit.

Evaluating Driving Factors of Environmental Behavior of Red Brick Industry Actors During the COVID-19 Pandemic

The number of COVID-19 cases in Indonesia, with a total death toll of 143 445 people as of November 1, 2021, ranked second highest in Asia, disturbing many livelihoods, including the red brick industry. This study aims to analyze the extent to which environmental behavior manifests and assess the capacity and incentive driving forces that affect the environmental behavior of actors in the red brick industry during the COVID-19 pandemic. This research was conducted through questionnaires, data processing, and analysis of descriptive and inferential data using the Statistical Package for the Social Sciences (SPSS). The results showed that the environmental behavior of actors in the red brick industry was less good (12.12%), pretty good (69.69%), and good (18.18%). This study utilized the capacity and incentive variables as driving forces. The variables that contribute to capacity can be categorized as environmental knowledge, social capital, financial capital, natural capital, and physical capital. On the other hand, the incentive variables specifically encompass market signals and government incentives. Neither of the variables have been effective in motivating environmental behavior toward a better or optimal direction. Moreover, their concurrent influence on environmental behavior was weak (8.2%). The study of human behavior focuses on the causal chain. It is, therefore, multi-dimensional, meaning that control or influence of certain factors over behavior is relatively difficult to achieve. The optimal contribution of society toward environmental protection and control has not been realized.

Machine Learning based Financial Stock Market Trading Strategies with Moving Average, Stochastic Relative Strength Index and Price Volume Actions for Indian and Malaysian Stock Market

The study related to a new and very powerful stock, index and commodities trading strategy based on machine learning with Moving Average (MA), Stochastic Relative Strength Index (SRSI) and Price Volume (PV) analysis to generate three types of financial market or stock trading signals such as buy, sell and no trade. Moving averages and moving average crossover are widely used to smooth price data and identify trends, whereas Stochastic RSI is a powerful oscillator that measures the relative strength of recent stock prices. Combination of multiple indicators in machine learning can improve strategies and provide more accurate decision for investors and traders which generate financial worth. This model is very much useful for Indian and Islamic stock markets.