Price Formation Research Articles

Development of a Theoretical Model for the Price Formation of Agri-Food Products in the Food Supply Chain: A Slovenian Case Study

The main objective of the research was to develop a theoretical model for price construction in the agri-food sector, which should enable better transparency and efficiency of pricing policies in the food supply chain. The central part of the article focuses on the data set, in which the data sources and the economic analysis methodology are presented in detail. Based on this analysis, a theoretical model for pricing was developed, which is presented in more detail in a separate chapter. The model focuses on prices in three sectors (primary production, food processing industry and distribution/retail) of different food and agri-food chains and offers concrete solutions to improve pricing in these chains. The final chapter contains guidelines and recommendations for the practical application of the model with the aim of improving the transparency and effectiveness of pricing policy in the agri-food sector. The overall report represents an important contribution to understanding the behaviour and responsiveness of agri-food systems to specific cost changes and provides useful guidance for policy makers and stakeholders in the agri-food sector.

Deep Learning for Bond Yield Forecasting: The LSTM‐LagLasso

ABSTRACTWe present long short‐term memory (LSTM)‐LagLasso, a novel explainable deep learning approach applied to bond yield forecasting. Our method involves feature selection from a large universe of potential features and forecasts bond yields using dynamic LSTM networks. It examines the internal gating signals of a trained LSTM and explains their dynamics through exogenous variables that may influence bond price formation. By considering these variables at various lags and using the Lasso technique for feature selection, we demonstrate how different hidden units within the LSTM dynamically adjust to make predictions across different temporal regimes and how their evolution is shaped by various external factors. In an empirical study on government bond yield forecasting, we demonstrate the statistical accuracy of LSTM‐LagLasso compared to a multilayer perceptron (MLP) and highlight its explainability.

Identification and Use of Latent Variables in the Analysis of The Effects of Certification on Cocoa Farms in the Transamazon

Objective: The goal of this research was to identify and to analyze the effects of certifications on the value-adding in all links of cocoa supply chain, with special focus on the productive efficiency, training and inclusion of human capital, and the use of good sustainable practices. Theoretical framework: We applied the Inclusive and Sustainable Value Chain concept and the Reverse Asymmetric Information theory in the analysis of certification, premium pricing formation and the management of cocoa farms in the Trans-Amazonian region. Exploratory Factorial Analysis (EFA) was used to identify the latent factors or variables associated with the effect of certification to improve the life quality of cocoa producers. Results and conclusion: Female management of the farms was 20%, which was consistent with the reality of family farming in the state of Pará. The EFA identified four latent variables, which explained 77.059% of the total variance of the data. To explain the reality of the certified cocoa farms of Transamazon region, the latent variables Productive Efficiency, Human Capital Formation, Certification Power and Certified Farm contributed to 22.054%, 21.63%, 17.03% and 16.35% respectively. It is concluded that certification improved productive efficiency and human capital formation in the activity but was unable to prevent deforestation. Research implications: The effects of certification on the cocoa farms analyzed have led to progresses in human capital formation and productive efficiency through good crop management practices and improvements in working conditions. However, limitations on livelihoods, labor recruitment, access to credit, and reduction of deforestation remain. Premium pricing is the variable that must be adjusted to increase the efficiency and effectiveness of certification in differentiating products and inserting them into global food chains. Originality/value: According to 68% of respondents, the premium pricing paid for cocoa does not compensate all social opportunity costs and ecosystem services of the standing forest, which limits the effectiveness of certification in meeting the needs and well-being of producers, the preservation of biodiversity and greenhouse gas emissions.

The money market in transition: from city-based market arbitrage to a central banking system in nineteenth-century Spain

This research supports the singularity of the Spanish case. The lessons we can learn are a product of the short transition in the mid 1880s from a city-based monetary system (supported by private actors) to a central banking system in the absence of a developed banking system with a nationwide scope, unlike what occurred in the rest of Western Europe. Introducing market arbitrage, we provide novel evidence – using new data – of how price formation in city-based money markets was driven by more than one price. Furthermore, factors such as market conditions, political circumstances and the asymmetrical development of market potential in the Spanish economic geography also played an important role. We also show new empirical support that transaction cost reduction was not associated with improving efficiency during the 1875–85 period when city-based money markets were still operating. The inland payment system was struggling even before its takeover by the Bank of Spain.

INTELTRADE: Intelligent Peer-to-Peer Energy Trading System with Dynamic Pricing and Coalition Formation

INTELTRADE: Intelligent Peer-to-Peer Energy Trading System with Dynamic Pricing and Coalition Formation

Price Formation Dynamics and Learning in the Tennis Sports Betting Market

Price Formation Dynamics and Learning in the Tennis Sports Betting Market

The Role of Toko Tani Indonesia (TTI) to Supply Chain and The Establishment of Rice Prices in Sragen District

This research examines the role of Toko Tani Indonesia (TTI) in the rice supply chain and price determination in Sragen Regency, Central Java, Indonesia. Using a descriptive analysis approach, the study focused on Sragen, a major rice production center. The samples for this investigation were selected purposively from datasets provided by eleven Community Food Business Institutions and Indonesian Farmer’s Stores that have been recorded in the Office of Agriculture and Food Security of Sragen Regency. The Food Supply Chain Networking (FSCN) model and rice price formation were analyzed qualitatively, while supply chain performance and value-added (VA) were assessed quantitatively. Results revealed a significant reduction in the rice supply chain stages from 6–7 to just 3, improving efficiency. Margins for partner farmers and TTI were calculated at IDR 1,600 per kilogram (51.13% of total margin share) and IDR 300 per kilogram, respectively. Inventory turnover was 12 times annually, with inventory days of supply for farmer groups at 30 days and a cash-to-cash cycle of 37 days. VA analysis showed farmers contributed 72% of total value (IDR 376,216,168), LUPM 26% (IDR 136,678,955), and TTI 2% (IDR 8,090,000). TTI supports farmers by purchasing grains at fair prices, ensuring effective marketing, maintaining rice availability, and stabilizing prices, thus playing a critical role in the community's food security efforts.

The Driving Relationship of China Carbon Price Based on Different Market Volatility States

The China carbon market is a market-oriented designation for addressing climate issues. Price mechanism is the core of carbon market, studying on the price formation can promote the emission reduction targets. This paper conducts an autoregressive adjusted Markov model to divide the carbon price state, and designs a multiple regression model to test the driving mechanism. The results show the 2 order autoregressive Markov model of MS (2) - AR (2) model can classify the carbon price into high and low volatility states. Furthermore, in high volatility state, carbon price is only significantly positively correlated with macroeconomic factors of China Securities Index 300 (CSI300) and European carbon price of European emission allowance future contract (EUAF), while in low volatility state, carbon price is significantly positively influenced by energy market products JM future (JMF) and Oil, macroeconomic factor of CSI300, and European carbon price of EUAF. Furthermore, the impact strength is weaker than the whole sample regression results. The results provide reference for investors to judge carbon price and reveal price trends.

Pricing in the contemporary electricity market: Theoretical considerations

Subject. This article examines and analyzes the theoretical aspects of the formation of prices for electricity and capacities of power supply companies. Objectives. The article aims to examine fundamental approaches to price formation and consider the systems of intra-company management in the supply and sales sector of the electric power industry in order to identify the areas for developing the pricing concept of energy supply companies. Methods. For the study, we used analysis and synthesis, generalization and comparison, and system reasoning. Results. The article summarizes the classical approaches to pricing and characterizes the theoretical provisions regarding the possibility of their adaptation to the formation of the pricing policy of energy supply companies in present-day conditions. On the basis of streamlining the system of views regarding the formation of pricing policy, the article proposes conceptual provisions for price formation in the electric power industry in the form of a grid of price achievements, which helps energy supply companies modify the applied methods of price formation in current realities. Conclusions and Relevance. The article concludes that it is necessary to synthesize different pricing approaches that integrate the specifics of the activities of energy supply companies in the changing business environment. This will help develop conceptual provisions for pricing in the electric power industry on the basis of a grid of price opportunities that takes into account the basic elements of the pricing system. The results of the study can be used for further theoretical research related to the electric power industry, taking into account the needs of market participants and consumers.

Dynamic model of currency exchange based on investor behavior

In the modern financial environment, cryptocurrencies have gained significant popularity, becoming an important element of the global economy and financial markets. The dynamic development of blockchain technologies and decentralized financial instruments fosters increased interest from both private investors and institutional players. However, the high volatility of cryptocurrencies and the complexity of the mechanisms behind their price formation necessitate a detailed study of these processes. This paper models cryptocurrency exchange operations, analyzing price formation influenced by buying, selling, and introducing new crypto coins to the market. The system simulates investor behavior with individual parameters: initial balances, risk profiles, and profit-driven trading strategies over a specified period. The model takes into account the psychological aspects of investor behavior, their reaction to changing market conditions, and the impact of external factors such as news and regulatory changes. Special attention is paid to analyzing the impact of adding additional quantities of coins to the exchange at a reduced price during peak cryptocurrency price values. This creates conditions for activating trading operations, increasing liquidity, and affecting overall market dynamics, particularly volatility and price fluctuation trends. The study shows how such interventions can be used to stabilize the market or stimulate its further growth. The analysis of the obtained data allows for detailed observation of changes in the cryptocurrency’s value over time, identifying patterns and trends. Using statistical and analytical methods, the impact of different investor strategies on their financial results and the overall market situation was investigated. This enables assessing how investor decisions-timing, trade volume, and market reactions-impact profits and market dynamics. The research emphasizes the importance of a deep understanding of market mechanisms and trading psychology and can serve as a basis for developing effective trading strategies on cryptocurrency exchanges. The obtained results may be useful for traders, financial analysts, and developers of algorithmic trading systems, contributing to increased efficiency and stability of cryptocurrency markets. Moreover, the findings of the work can be applied to improve regulatory approaches and policies regarding cryptocurrencies.

Co-movement of real estate prices: a study of Brazil’s three largest cities

The objective of the paper is to verify the existence of transmission of residential real estate prices among Brazilian cities. The strategy adopted was to analyze the time series of prices and estimate an Autoregressive Vector Model - VAR for panel data and Granger causality tests for the three largest cities in Brazil (São Paulo, Rio de Janeiro and Belo Horizonte) between the period 2009-2017. The results point out that each local market has the determining power in price formation. However, an important result of the study was to verify transmission relationship between cities.

Research on Impact of Equity Costs and Environmental Costs on Supply-Side Classified Water Pricing

The classified water pricing system is an effective measure for promoting the rational utilization of water resources under market mechanisms. Studying the water prices of three different types of water sources, including reservoir water, river water, and wastewater treatment plant effluent, is the basis for promoting the use of reclaimed water. However, there remains a spectrum of viewpoints on how to establish a pricing mechanism for reclaimed water at present. This study primarily focuses on the multi-level quality-separated water supply system in Yiwu City, China. It analyzes the limitations of the current water pricing formation mechanism and the externality of integrating reclaimed water into the unified allocation of multiple water sources. Based on the principles of full-cost water pricing and externality theory, a supply-side classified water pricing permit cost and pricing calculation model, covering the entire process of the social water cycle, is proposed. It focuses on the analysis of the impact of equity costs and environmental costs on supply-side classified water pricing. The Shapley value method is used for the technology of cost allocation among stakeholders to reasonably distribute the calculated water pricing of reclaimed water. The price of reclaimed water varies depending on the user type, with residential users paying 2.93 CNY/t, industrial users 4.00 CNY/t, and government allocations at 8.52 CNY/t. Compared with the classified water prices of various stakeholders on the user side of reservoir water, reclaimed water has a significant price advantage. This research demonstrates that the supply-side classified water pricing model, which includes a permit cost and pricing calculation framework, can encourage the supply of higher-quality water at corresponding higher prices, while also providing the internalization of external costs. Furthermore, the Shapley value method of cost allocation can realize the fair burden of stakeholders on the calculated water price and maintain the competitive advantage of reclaimed water prices.

Pricing blue carbon to promote the protection and restoration of the marine environment: A real option model

Pricing blue carbon will allow the monetization of the implicit carbon sequestration value of socioeconomic activities in marine ecosystems. This will empower those engaged in marine environment protection and restoration to reap economic benefits while imposing costs on those who harm the marine environment or carbon-emitting enterprises. This can significantly increase the enthusiasm of local governments, enterprises, and others for protecting and restoring the marine environment. However, owing to the characteristics of public goods and the externalities of blue carbon, it is difficult to price them through the supply and demand relationship of the traditional market. This paper focuses on how to price blue carbon under government policies and market uncertainties to encourage investors to engage in blue carbon projects involving the ecological protection and restoration of coastal wetlands. First, a universal blue carbon pricing model is established via the real option approach. The mangrove blue carbon project in Zhanjiang, Guangdong, was used as a case study to evaluate the price of mangrove blue carbon. This research provides technical support for the formation of blue carbon prices, and this method can be used not only to promote the protection and restoration of the marine environment but also to optimize the supply of blue carbon, fully leveraging its role in addressing climate change.

Bayesian Belief Networks: Redefining wholesale electricity price modelling in high penetration non-firm renewable generation power systems

The transition of electricity generation from firm to non-firm renewable generation is driving changes in wholesale electricity price dynamics that are increasingly challenging to model due to the stochastic nature of wind and solar as the primary energy source. Robust pricing models are essential for optimising financial performance in liberalised electricity markets. The novelty of this paper is the application of Bayesian Belief Networks in the modelling of wholesale electricity price formation, specifically in power systems with a high penetration of non-firm renewable generation. This paper links the mathematical Bayesian representation to established statistical and computational approaches using a functional supply-side wholesale electricity market pricing model. In addition, the paper introduces a novel validation method employing volatility analysis to assess the case study's performance.The case study revealed that when the proportion of stochastic generation was ≥40 % penetration in a trading interval, the Bayesian Belief Network model's accuracy considerably outperformed the benchmark model with a Mean Absolute Scale Error of 1.43 compared to 1.63 in those intervals. Finally, the volatility analysis results challenged the prevailing notion of a positive causal relationship between non-firm renewable penetration and price volatility.

Monitoring food prices in the context of improving the mechanism for supplying products for government needs

The calculation of protection coefficients for producers of agricultural products supplied for republican needs, reflecting the price seizure of income of agrarians until 2022 has been carried out. Within the framework of the mechanism of formation of competitive prices for each type of food, the priority of monitoring of international trade data is justified, which implies the creation of a forecast model of price formation for agricultural raw materials or systemic adjustment taking into account the trends of external market conditions. A modular architecture of a digital platform for monitoring food prices for the subsequent calculation of the cost of products supplied for public needs is proposed.

Rental and sale prices of agricultural lands under spatial competition

Much of the land economics literature has largely ignored the spatial nature of competition and related differences between farmland rental and sales markets when assessing return rates from farming, the capitalization of agricultural, environmental and energy policy into land values, and climate change impacts. We propose a model for price formation in both markets under a spatial competition framework. We demonstrate that price formation differs, particularly under policy-induced output price shocks.We suggest that using the rent-price ratio as an approximation for expectations in the net returns of farming, based on the net present value model, may produce biased results. In consequence, studies relying on land prices need to control for local land competition, farming structure, and policies.

Bayesian Fictitious Play in Oligopoly: The Case of Risk-Averse Agents

A number of learning models have been suggested to analyze the repeated interaction of boundedly rational agents competing in oligopolistic markets. The agents form a model of the environment that they are competing in, which includes the market demand and price formation process, as well as their expectations of their rivals’ actions. The agents update their model based on the observed output and price realizations and then choose their next period output levels according to an optimization criterion. In previous works, the global dynamics of price movement have been analyzed when risk-neutral agents maximize their expected rewards at each round. However, in many practical settings, agents may be concerned with the risk or uncertainty in their reward stream, in addition to the expected value of the future rewards. Learning in oligopoly models for the case of risk-averse agents has received much less attention. In this paper, we present a novel learning model that extends fictitious play learning to continuous strategy spaces where agents combine their prior beliefs with market price realizations in previous periods to learn the mean and the variance of the aggregate supply function of the rival firms in a Bayesian framework. Next, each firm maximizes a linear combination of the expected value of the profit and a penalty term for the variance of the returns. Specifically, each agent assumes that the aggregate supply of the remaining agents is sampled from a parametric distribution employing a normal-inverse gamma prior. We prove the convergence of the proposed dynamics and present simulation results to compare the proposed learning rule to the traditional best response dynamics.

Interest-bearing capital and monetary policy: a heterogeneous agent Marxian model

PurposeIn terms of understanding the new issues emerging in the practice of monetary policies and how to evaluate the latest theories of monetary policy, this paper proposes referring to Das Kapital and developing a monetary policy theory grounded in Marxist political economy.Design/methodology/approachBased on the discussion of interest-bearing capital in Das Kapital and using a heterogeneous agent model, this paper tries to explain the determining mechanism of interest rate, leverage ratio, and asset price.FindingsThe research finds that if there are differences in the techniques possessed by capital, the resulting disparities in production efficiency will lead to differences in profit rates and further influence the functional choices of capital in the movement of social total capital. Thus, with the formation of lending relationships, interest rates, leverage ratios, and asset prices will be endogenously determined simultaneously. Moreover, as the degree of technological diffusion influences the industrial capitalists’ willingness to take loans as well as the level of profit rates, there may be counter-cyclical changes in the returns on productive investment and financial investment at different stages of the technology life cycle, contributing to diverting funds out of the real economy. Besides, this paper discusses the challenges, tools, and goals of monetary policy within the credit money system.Originality/valueClarify the intrinsic mechanism of the functional differentiation of capital determined by heterogeneous technologies and exogenous capital-labor relation and analyze the impact of capital differentiation on the economy.

Social psychology research in the commercial art market: challenges and opportunities

This study investigates the social psychological dynamics of the commercialized art market, employing a multifaceted approach to uncover the complex interplay of factors influencing artistic valuation and market behavior. Utilizing data from 500 top-selling artists and 1000 major collectors over a decade, we conduct quantitative analyses, including time series analysis, principal component analysis, and network structure examination. Our findings reveal the primacy of artist reputation and market demand in price formation, accounting for 45% of the variance in artwork prices. We identify cyclical market patterns aligned with major auction events and economic trends. Network analysis exposes the critical role of intermediaries and community structures in market interactions. Addressing endogeneity concerns through instrumental variable regression, we establish a robust causal link between artist reputation and market outcomes. This research provides valuable insights for artists, galleries, collectors, and investors, offering a comprehensive framework for understanding the contemporary art market's complexities. Our study contributes to the growing body of literature on art market dynamics, highlighting the intersection of social psychology, economics, and cultural trends in shaping artistic value.

The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.