Price Information Research Articles

A physics-informed graph learning approach for citywide electric vehicle charging demand prediction and pricing

A growing number of electric vehicles (EVs) is putting pressure on smart charging services. As a foundation of informing drivers of vacant charging facilities and rationalizing pricing, an effective approach for predicting citywide spatio-temporal EV charging demand that incorporates pricing information is required. Although many deep learning models have been carried out with the orientation of improving prediction accuracy and achieved good results, there is still a lack of prediction models that are economically interpretable and can consider the spillover effect of price adjustments on EV charging demand. To fill the research gaps, we propose a learning approach for accurate EV charging demand prediction and reasonable pricing, named PIAST, which enables the integration of convolutional feature engineering, spatio-temporal dual attention mechanism and physics-informed neural network training. On a dataset containing 18,061 EV charging piles, we demonstrate the state-of-the-art performance of the proposed model. The results empirically showed that the proposed approach achieved an average improvement of 14.78% in accuracy, compared to other representative models. Moreover, the model can uncover the price elasticity of demand during training, making the model interpretable. Finally, price adjustment experiments are conducted to demonstrate the self-influence and spillover effects of price.

Analysis of stock forecasting based on LSTM

The use of the long short-term memory (LSTM) machine learning algorithm for stock prediction is widely used contemporarily. This study the feasibility of price prediction based on LSTM. This study first collected extensive data on stock prices, including the closing prices, volumes, and other relevant indicators, for several companies. Then, it implemented an LSTM-based model using this data, which was trained on a dataset containing historical stock price information over a fixed period. The models performance was evaluated using a separate test set. Based on the analysis, LSTM is successful in forecasting stock a remarkable degree of precision, rendering it an invaluable asset for investors aiming to make well-informed investment choices. Its ability to capture long-term dependencies and patterns within time series data makes it an ideal choice for forecasting future trends in financial markets. By leveraging this model, investors can gain valuable insights into potential market movements and adjust their investment strategies accordingly. Overall, the promising results obtained from applying LSTM-based models highlight their potential value in supporting investors' decision-making processes and ultimately improving investment outcomes.

Climate change disclosure and stock price informativeness, evidence from China

ABSTRACT This study examines whether and how corporate climate change disclosure in the Management Discussion and Answer section of annual reports affects its stock price informativeness. The empirical findings of the study suggest that firm climate change disclosure could significantly increase stock price informativeness by reducing stock price synchronicity. Further channel tests suggest that corporate climate change disclosure significantly increases stock price informativeness through higher media attention, larger institutional ownership percentages, and more corporate site visits. Further analysis shows that the positive impact of climate change exposure on stock price informativeness is more pronounced for more readable and negative annual reports. Finally, we find that opportunity shock information within the MD&A section is an important component of climate change disclosure.

Interrelationships Among the Rice Export Prices of Major Exporting Countries: In Search of the Leader and Followers

AbstractBy examining the econometric interrelationships of rice export prices of India, Thailand, Vietnam, Pakistan, the U.S., Argentina, Brazil, and Uruguay, this study examines the most influential rice-exporting countries in the international rice export market. The sampled countries are the source of more than 80% of the global rice exports. In the process, this study relies on online data portals of the United Nations and country-level sources for aromatic and non-aromatic rice export price information from India, Pakistan, and Thailand. This study employed the Prais-Winsten and Autoregressive Distributive Lag (ARDL) estimation processes with the error correction procedure. Combining the findings from both models, this study concludes that in terms of interactions, the non-aromatic long-grain rice price of the U.S., and the aromatic rice price of Pakistan are the most correlated prices in the international rice market. Considering the number of countries influenced by the price of a given country, our results indicate that Vietnam is the price leader in the non-aromatic long-grain market, followed by Argentina, Brazil, and India. India, the largest exporter, is the fourth top price leader. In the case of aromatic rice, Thailand is the absolute leader. Many countries in Asia and Africa rely on rice imports to meet domestic demand, where the food security situation is already precarious. Based on the findings, this study suggests closely monitoring rice production and export trends of Vietnam, Argentina, Brazil, India and Thailand to provide early warning when necessary to better manage supply shocks in the international rice market.

CEO overconfidence and the informativeness of bank stock prices

This study examines the effect of the overconfidence of chief executive officers (CEOs) on the informativeness of banks' stock prices. Using an option-based overconfidence measure, we demonstrate that banks with overconfident CEOs have less informative stock prices. This finding is robust to alternative measures of overconfident CEOs and to addressing endogeneity with propensity score matching, an instrumental variable approach, and difference-in-differences analysis. Furthermore, banking and financial market crises strengthen the negative effect of overconfident CEOs on stock price informativeness, especially in larger banks. Finally, better corporate governance or higher capital mitigates the negative impact of overconfident CEOs on banks' stock price informativeness. Policymakers should consider implementing supervisory strategies that require banks to provide accurate information and encourage private-sector monitoring of banks.

Dynamics of cereal product price transmission between markets in Burkina Faso

PurposeEffective agricultural product price regulation policies depend on market integration and the degree of symmetry in the transmission of agricultural product price signals. This study analyzes the transmission and asymmetry of the price series between the Ouagadougou consumer market and assembly markets considering three primary cereal products in Burkina Faso.Design/methodology/approachThis study applies the nonlinear autoregressive distributed lag (NARDL) econometric model, which is an asymmetric extension of the ARDL cointegration model. The price series examined covers the period extending from January 2005 to December 2020.FindingsOur analysis provides novel insights regarding short- and long-term asymmetric effects in the transmission of price signals between assembly markets and the consumer market. We also determine that the effects of negative shocks are more persistent than those of positive shocks in several markets.Research limitations/implicationsFor markets that exhibit symmetrical responses of assembly market prices to consumer market prices, the results could reflect the continuous efforts of market players, particularly the government, to eliminate market failures and ensure the long-term efficiency of cereal markets. To this end, an agricultural market information system can have a crucial role in easing information access for all market players.Originality/valueThis study provides new evidence regarding the nature of the transmission and asymmetry of price information on primary cereal products in the largest markets in Burkina Faso. Applying the NARDL model makes it possible to simultaneously estimate short- and long-term asymmetry.

Corporate green innovation and stock price non-synchronicity: Evidence from China

Leveraging a large sample of publicly listed companies in China from 2012 to 2021, the present study investigates the impact of corporate green innovation on stock price synchronicity. We provide compelling evidence that green innovation facilitates the incorporation of firm-specific information into stock prices, measured by idiosyncratic volatility. In addition, we find that analyst coverage and institutional ownership are two possible economic mechanisms underlying the relationship we identified. The price efficiency implications of corporate green innovation are more evident among small firms, manufacturing firms, and firms with weak corporate governance. Our key findings are robust to a battery of identification strategies. Overall, our study sheds light on the unintended consequences of corporate green innovation (i.e., stock price informativeness), thereby enriching the literature on green innovation and stock price efficiency.

Economic supply chain modelling of industrial insect production in the Netherlands

Abstract Defatted larvae meal (dLM), specifically from black soldier flies, could help overcome the animal protein gap. As insect production is an emerging sector, current economic research is scarce and very diverse. Thus, the aim of this research was to develop a simulation model that enables the analysis of full industrial scale costs of producing dLM and to provide insight in the distribution of these costs in the insect supply chain. The deterministic supply chain model is built on three modules, technical, transition and economic module, which all follow a previously defined supply chain structure and allow to extract quantity and price information for intermediate or final products. The model was parameterized and checked for plausibility in multiple consultation rounds with the INSECTFEED consortium and business partners. Additionally, model behaviour was checked with scenario, sensitivity, and break-even price analyses. In the default situation 5.57 tDM raw substrate and 26.7 million neonates are required to produce 1 tDM dLM for a price of €5,116/tDM. Most costs are added in the raw substrate delivery (€1,952/tDM) and production and collection (€821/tDM) step. Important cost factors are the raw substrate (€1,939/tDM) and building and inventory (€1,459/tDM). Parameters with high relative response rate towards the price of dLM are the feed conversion rate, dry matter percentage of larvae, raw substrate price, larvae density, labour wage and growth rate. To reach break-even prices for substituting fish meal with alive grown larvae (AGL) (€1,318/tDM AGL), improving production parameters is not sufficient. Just changing prices of raw substrate to −€78/tDM or frass to €1,175/tDM would enable a profitable operation. However, these are not deemed as realistic in mass production. Although there is some insecurity in data, the model results are the most realistic representation of industrial scale production amounts and costs.

Market Synchronicity Among African Markets: is IFRS Adoption an Augmentor or Inhibitor?

Abstract This paper investigates the dynamics of market synchronicity and International Financial Reporting Standards (IFRS) adoption in Africa. It seeks to determine whether or not IFRS is an augmentor or inhibitor of market synchronicity in Africa. This study is not only a foundational study that unfolds the dynamics of market synchronicity and IFRS adoption in Africa but also extends the scope of synchronicity from firm-level assessment to country-level market synchronicity. It is one of the few studies which have shifted the literature on synchronicity from one largely limited to firm-level synchronicity to country-level synchronicity through analyses of cross-border market synchronisation. It employs Dynamic Ordinary Least Square (DOLS) and Generalised Method of Moments (GMM) to investigate the market synchronicity-IFRS nexus. The study uses monthly data from March 2005 to November 2021 in five African markets: Botswana, Ghana, Namibia, Nigeria, and South Africa generating 1005 observations in an 18-year data span. The paper reveals a significant negative consequence of IFRS on market synchronicity. Thus, IFRS exhibits an inhibition role in market synchronicity (reduces synchronicity) and improves price informativeness. A decreasing pattern was also observed immediately after IFRS was adopted by the stock markets and persisted for about five years. It implies that the price informativeness of stock markets through IFRS makes them good candidates for intra-Africa portfolio diversification due to reduced investors’ risk exposure to market synchronicity.

Optimal channel selection considering price competition and information sharing under demand uncertainty

PurposeThe rapid development of the Internet has led to an increasingly significant role for E-commerce business. This study examines how the green supply chain (GSC) operates on the E-commerce online channel (resell mode and agency mode) and the traditional offline channel with information sharing under demand uncertainty.Design/methodology/approachThis study builds a multistage game model that considers the manufacturer selling green products through different channels. On the traditional offline channel, the competing retailers decide whether to share demand signals. Regarding the resale mode of E-commerce online channel, just E-tailer 1 determines whether to share information and decides the retail price. In the agency mode, the manufacturer decides the retail price directly, and E-tailer 2 sets the platform rate.FindingsThis study reveals that information accuracy is conducive to information value and profits on both channels. Interestingly, the platform fee rate in agency mode will inhibit the effect of a positive demand signal. Information sharing will cause double marginal effects, and price competition behavior will mitigate such effects. Additionally, when the platform fee rate is low, the manufacturer will select the E-commerce online channel for operation, but the retailers' profit is the highest in the traditional channel.Originality/valueThis research explores the interplay between different channel structures and information sharing in a GSC, considering price competition and demand uncertainty. Besides, we also considered what behaviors and factors will amplify or transfer the effect of double marginalization.

Top–down and bottom–up information acquisition: Application to financial markets

Recent studies confirm the main prediction of the rational inattention framework that the perceived value of information (top–down) as a key driver of attention. However, these investigations also report stimulus-driven salience effects (bottom–up) that counteract the framework’s predictions. In this manuscript, I propose an extension to the standard rational inattention model by incorporating bottom–up attention processes such as salience-effects through varying information processing costs. Applied to asset pricing with a representative agent a higher information salience generally reduces the cost of information processing (attention) needed for the same level of uncertainty reduction. Due to a substitution effect in the attention allocation across information, an attention-maximizing salience emerges. In general, a higher information salience consistently enhances asset price informativeness.

Development of AI Modeling for Implementation of Agricultural Product Price Information Prediction System

Development of AI Modeling for Implementation of Agricultural Product Price Information Prediction System

The role of total value added to be distributed to reduce the negative effects of the pandemic on the share price

This research analyzes the relationship between the total value added to be distributed and the share price in the before, during, and post-pandemic periods. The data were collected on the Securities and Exchange Commission (CVM) website and Refinitiv Eikon®, resulting in 1,910 observations of Brazilian companies from 2010 to 2022 from all sectors, except Government Activity (due to lack of data). The data were treated using descriptive statistics and the Generalized Least Squares (GLS) regression model. The results revealed that i) the disclosure of the TVAD is relevant to explain the share price; ii) there is a negative relationship between the pandemic period and the share price; and iii) the interaction between the TVAD and the pandemic has a positive relationship with the share price. The results contribute to investors, suggesting that the Value Added Statement (VAS) information can increase the share price in periods of more significant uncertainty. With managers, it shows that the generation of more wealth can increase the price of shares, which can help them obtain better returns since part of the remuneration is usually linked to the performance of the shares. With Brazilian regulators, the results may indicate that the mandatory disclosure of VAS during the pandemic is relevant to assisting shareholders in decision-making. The gap explored in this research is the lack of analysis of the relationship between TVAD and the pandemic. The study's relevance is to highlight to the shareholders what information from the VAS contributes to an increase in the share price in periods of greater instability. Therefore, the impact of the study is that users can use the VAS information for share pricing, especially in periods of greater economic instability.

Choosing to Disagree: Endogenous Dismissiveness and Overconfidence in Financial Markets

ABSTRACTThe psychology literature documents that individuals derive current utility from their beliefs about future events. We show that, as a result, investors in financial markets choose to disagree about both private information and price information. When objective price informativeness is low, each investor dismisses the private signals of others and ignores price information. In contrast, when prices are sufficiently informative, heterogeneous interpretations arise endogenously: most investors ignore prices, while the rest condition on it. Our analysis demonstrates how observed deviations from rational expectations (e.g., dismissiveness, overconfidence) arise endogenously, interact with each other, and vary with economic conditions.

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPBiGRU that integrates candlestick patterns and a sparrow search algorithm (SSA). The incorporation of candlestick patterns endows the input data with structural characteristics and time series relationships. Moreover, the hyperparameters of the CPBiGRU model are optimized using an SSA. Subsequently, the optimized hyperparameters are employed within the network model to conduct predictions. We selected six stocks from different industries in the Chinese stock market for experimentation. The experimental results demonstrate that the model proposed in this paper can effectively enhance the prediction accuracy and has universal applicability. In comparison to the LSTM model, the proposed model produces an average of 31.13%, 24.92%, and 30.42% less test loss in terms of MAPE, RMSE and MAE, respectively. Moreover, it achieves an average improvement of 2.05% in R2.

Compliance With Federal Price Transparency Rules and Cost Estimation at United States Hospitals With Neurosurgical Training Programs.

The Centers for Medicare & Medicaid Services implemented federal requirements on January 1, 2021, under the Public Health Service Act that require hospitals to provide a list of payer-negotiated prices or "standard charges" in a machine-readable file and in a patient-friendly online estimator for standard services. We sought to assess compliance by United States hospitals associated with neurosurgical training programs with these federal requirements for 11 common neurosurgical procedures. We performed a cross-sectional analysis in March 2023 of 116 United States hospitals associated with a neurosurgical training program to assess compliance with the new federal requirements to have a machine-readable, downloadable file with standard charges and a patient-friendly online estimator for two spinal procedures. A total of 110/114 (96.5%) hospitals were compliant with the requirement for a machine-readable file with payer-negotiated prices. A total of 47/110 hospitals (42.7%) were compliant with downloadable machine-readable files and reported at least one payer-negotiated price for 1 of the 11 common neurosurgical procedures. A total of 45/110 (40.9%) used bundled Diagnosis-Related Group codes, and 18/110 (16.4%) did not contain any price information for neurosurgical procedures. For neurosurgical procedures, the percent difference between the average negotiated private insurance and Medicare price per procedure ranged from 17.5% to 77.6%. Medicare and private insurance data for each procedure were available on average for 10.3 states (SD = 3.8) and 15.6 states (SD = 4.8), respectively. While hospital compliance with federal requirements for machine-readable files with payer-negotiated prices was high, availability of payer-negotiated prices for 4 major insurance types across 11 common neurosurgical procedures based on Current Procedural Terminology codes was sparce. Consequently, meaningful conclusions on procedure-related charges for elective procedures are difficult for patients to make because of the unintelligible format of data and a lack of reporting of charges per Current Procedural Terminology code in a comprehensive manner.

Contagion mechanisms under heterogeneous beliefs

ABSTRACT A continuous-time heterogeneous belief model is developed to discuss the mechanisms of the formation of contagion phenomena. Various mechanisms of contagion formation, such as price learning, information diffusion and extrapolative expectations, are incorporated into a unified framework. Calibration results from Hong Kong and the UK show that changes in the weights of the four types of traders affect the contagion phenomenon at different time intervals over time.

Stock price crash and information environment: Do CEO gender and financial expertise matter?

Stock price crash and information environment: Do CEO gender and financial expertise matter?

Development strategy of candlenut oil business toward a new paradigm for the utilization of non-timber forest products

In an effort to realize productive and sustainable forest management in a unified and comprehensive forestry development unit so that the economic (Sustainable Production), social (Sustainable Social and economic) and environmental (Ecological Sustainable) benefits for all parties based on the identification of the potential superiority of regional commodities, Forest Management Unit (KPH) has compiled a business development plan to improve the economic community in and around the forest, through the development of various productive businesses utilizing timber and non-timber forest products (NTFPs). Therefore, it is necessary to develop a business strategy to overcome the weaknesses and threats by utilizing internal and external strengths and opportunities in efforts to develop sustainable rural agro-industry. The study was conducted at a pilot location of the KPH Kulawi candlenut oil processing business, Central Sulawesi Province. Communication and observation techniques were carried out using a questionnaire through individual interviews and group interviews. While the business development strategy is carried out through focus group discussions (FGDs) involving stakeholders, namely the KPH, raw material producing farmers, processing farmers, target village governments, local governments, the business world and academics with a total of 15 respondents. Based on the SWOT Matrix, The choice of strategies that can be applied are resource optimization, increasing product quality at competitive prices, increasing managerial skills and HR skills and efficiency in production costs. While policies that can support business development include easy access to capital, facilitating export bureaucracy mechanisms and the existence of business associations as a source of market and price information.

A Comparative Analysis of Online and Offline Shopping

The advancement of technology in modern times has led to a highly varied purchasing path for consumers. The internet has transformed consumer and business communication. In India, the rate of increase in internet usage is extremely swift. Online shopping, sometimes referred to as "E-Shopping," enables consumers to make direct purchases of goods and services from sellers via websites or mobile applications. Websites for online shopping provide a wide range of goods and services to users. Customers have greater opportunities to compare prices across many websites and find products at reduced costs because to this. The practice of purchasing goods from marketplaces, shops, and stores dates back to the beginning of civilization. The Internet has supplanted physical stores as the primary means of shopping these days. Still, a lot of customers choose to buy from markets since they would rather inspect the item before making a purchase. Indian youth make up more than 75% of the country's internet users. More products and information are easily accessible when shopping online, seven days a week, around the clock. Young people who have used the internet can search and obtain information fast, and since most of them don't have time to go shopping in person, they try to make purchases online. Customers are influenced to shop online by a number of variables, including price selection, privacy, reviews, convenience, and information. There are a lot of portals available online. The websites Amazon.in, flipkart.com, snapdeal.com, jabong.com, and myntra.com are among the Top 5 Ranking Online Shopping Sites.