Price Information Research Articles

Empirical Comparison of Facebook Prophet and Traditional Models for Tomato Price Forecasting in Greece

Agricultural product prices are crucial to the income and livelihoods of millions worldwide, making accurate forecasting essential for market participants. The European Union regularly reports agricultural product prices through an online databank, ensuring up-to-date price information is widely available. This study evaluates the effectiveness of Facebook Prophet, a modern forecasting tool, in predicting tomato prices in Greece from 2013 to 2024.The results show that Facebook Prophet outperforms traditional forecasting models, providing more accurate predictions with lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) values. Notably, the model exhibited superior performance in forecasting tomato prices over a six-month horizon compared to conventional seasonally adjusted models. This demonstrates Facebook Prophet’s potential to significantly improve decision-making in agricultural markets, offering reliable price forecasts to stakeholders such as farmers, traders, and policymakers. Furthermore, the study incorporated feedback from market participants, which provided valuable insights into market practices and conditions. This integration of practical knowledge with advanced forecasting techniques enhanced the interpretation of the results, making them more applicable to real-world scenarios. Overall, the findings suggest that Facebook Prophet holds considerable promise for future agricultural price forecasting, with potential applications across various commodities. These insights pave the way for more precise agricultural forecasts, benefiting all market participants by supporting more informed and timely decision-making.

Stock Price Forecasting for Xiaomi Auto: Application of ARIMA and GARCH Models

Since Xiaomi Corporation entered the market for electric vehicles, the volatility of its stock price has attracted a lot of attention from investors. This study's objective is to foresee the stock price of Xiaomi Auto by constructing GARCH and ARIMA models in R. By conducting a stationarity test and differencing the previous stock price information of Xiaomi Auto, and then the stock price for the upcoming 30 days is predicted using an ARIMA (0,1,0) model. The findings demonstrate that while the ARIMA model predicts a short-term phase of relatively stable stock price, the widening prediction intervals suggest increasing uncertainty. The construction of a GARCH (1,1) model aims to more accurately represent the fluctuations in the stock price. Predictions from the GARCH model point to higher price volatility in coming 30 days. Based on these model outcomes, this paper proposes short-term and medium- to long-term investment strategies for investors and discusses the limitations of the models as well as potential improvement for future research. This research improves the understanding of stock price trends and volatility in the electric vehicle market. It provides practical insights for better investment decisions. The study also advances financial forecasting methods, making them more applicable to real-world market behavior.

An IGDT-WDRCC based optimal bidding strategy of VPP aggregators in new energy market considering multiple uncertainties

This study addresses the volatility and uncertainty challenges in managing renewable energy within electricity markets, particularly focusing on the role of Virtual Power Plant (VPP) aggregators. Recognizing the risks these uncertainties pose to the revenue and stability of power systems, the paper presents a novel information gap decision theory (IGDT)-Wasserstein metric based distributionally robust chance constraint (WDRCC) approach to devise an optimal bidding strategy for VPP operators. It involves a data-driven distributionally robust optimization framework, leveraging the worst-case scenario from the distributed resource uncertainties, guided by an ambiguity set rooted in the Wasserstein metric. Furthermore, the distributionally robust chance constraint modeling is introduced ensuring that uncertainty constraints of distributed resources meet a predefined risk level. Although this method shows promising out-of-sample performance, it relies on forecasted energy prices, a notable limitation given the price volatility and information inadequacy in the newly-opened market. To address this, the risk-averse bidding strategy, grounded in IGDT, is proposed simulataneously to safeguard the operator’s expected returns against price uncertainties, implementing an advanced piecewise linear approximation technique, ”nf4l,” for linearizing the bi-linear term from IGDT. The effectiveness of this approach is empirically validated through a comprehensive case study and sensitivity analysis.

Do financial markets value corporate culture?

This paper examines how financial market participants incorporate corporate culture, an important value-relevant information, into their investment decisions. Utilizing firm-level corporate culture measures derived from the earnings conference call transcripts, we find that firms with stronger cultural values are associated with higher stock liquidity. We identify three channels through which corporate culture affects stock liquidity: reducing information risk, enhancing trust, and increasing investor recognition. In addition, we find that stronger corporate culture is significantly associated with higher stock price informativeness and future stock returns, and lower level of default risk and informed trading. Overall, our findings highlight the importance of corporate culture in enhancing financial market quality.

Reassessing the information transmission and pricing influence of Shanghai crude oil futures: A time-varying perspective

The Shanghai crude oil futures market, known as INE, has achieved significant success in trading volume and is increasingly recognized as a nascent crucial oil futures contract. As INE aims to serve as a pivotal global pricing reference, evaluating the dynamic characteristics of its pricing capability is essential for comprehending the evolving market landscape. This study initiates with foundational insights from a time-varying perspective. We uncover the time-varying information transmission of INE within the crude oil futures system and reveal its time-varying predictive causality on crude oil spot prices. The empirical findings yield several significant observations. Firstly, the Shanghai crude oil futures consistently functioned as the net receiver of price information transmission from mainstream international crude oil futures. Secondly, during the initial months of post-INE listing, and amid the global spread of COVID-19, Shanghai crude oil futures displayed less information received. Thirdly, the significant predictive causal influence of INE on the crude oil spot markets is predominantly observed to be valid post-2021, with its predictive capabilities exhibiting an ongoing enhancement. Our findings indicate that INE is gradually solidifying and strengthening its role as an influential player in the global crude oil market as it matures.

Trends and Drivers of Avocado Production in Ethiopia: A Review

Global avocado production has seen a significant increase over the past few decades. The area harvested and production quantities have grown substantially, reflecting the rising demand for avocados worldwide. In Ethiopia, avocado production has been experiencing significant growth and the Ethiopian government, in collaboration with international organizations has been working intensively for avocado crop development in the country. These efforts have led to an increase in both the area under cultivation, production and productivity. The data from Food and Agricultural Organization indicated that the area under avocado cultivation in Ethiopia was 9,754 ha in 2000 and through up and downward fluctuations, substantial jump were seen in 2020 hitting 30,588 ha. For about more than a decade from 2000-2013, the volume of avocado production reveals a steady increase and the most significant growth were observed from 2016-2020, with production rising to 81,431.76 metric tons in 2017 and reaching 245,335.63 metric tons in 2020. The productivity of avocado in Ethiopia has experienced notable fluctuations in the last two decades. Even though there was a slight decline in subsequent years, the current productivity 5.8tons per hectare remained relatively high compared to the early 2010s. Favorable agro-ecological conditions, growing domestic demand, and government initiatives to promote and produce, high-yield, disease-resistant varieties, nutritional value, development of export markets, establishment of different processing factories in different parts of Ethiopia, government initiative and international support are factors responsible for expansion and development of avocado crop in Ethiopia. However, challenges such as limited access to quality seeds and planting materials, insufficient irrigation systems, very weak market chains, climate change, root rot and pests, low agronomic practices, lack of market and price information, perishability nature of the crop, lack of proper storage, weak access to transportation and extension services still need to be addressed. Addressing the issues, the future of avocado production and productivity in Ethiopia look promising and positive trends, with the potential to contribute significantly to the food security and economic development.

Substantive innovation or strategic catering: Capital market pressure and corporate green innovation structure

Green innovation requires a market environment willing to take on more risk and accept failure. Capital markets are essential in green innovation because they can spread out the risks associated with innovation. Compared to existing research, this paper focuses on the impact and processes of the short-selling mechanism on corporate green technology innovation structure. Specifically, this study utilizes a difference-in-difference model to examine the effects and underlying mechanisms based on the listed firms from 2011 to 2022. The results suggest that the pressure from the capital market might encourage the development of green technological innovation in corporations. Still, it also hinders the establishment of a structure for corporate green innovation and fosters the expansion of strategic patent behavior. The influence is achieved through enhancing managerial performance, monitoring external pressures, and transmitting stock price information. Heterogeneous analysis confirms that independent directors ratio and CEO duality play a critical role. The findings demonstrate how capital market pressure can affect corporate green innovation structure, support the critical role of capital market and contribute to further engagement in corporate green technology innovation.

Predictive Analytics for Energy Efficiency: Leveraging Machine Learning to Optimize Household Energy Consumption

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented.

ChemPrice, a Python Package for Automated Chemical Price Search

AbstractThe open‐access movement in chemistry has led to a surge in publicly accessible data repositories such as PubChem and ChemSpider, housing more than 100 million chemical compounds. These repositories are invaluable for large‐scale virtual screening but lack crucial pricing information, a vital economic aspect influencing commercial decisions. Currently, these databases merely provide hyperlinks to vendor websites for pricing details, necessitating manual exploration due to the diverse structures of vendor websites, making automated data extraction impractical. To tackle this challenge, we introduce ChemPrice, a Python package that streamlines the collection of pricing data through the integration of ChemSpace, Mcule, and Molport platforms that provide up‐to‐date price information from over 100 suppliers. ChemPrice standardizes pricing units, offering consolidated and best‐pricing information for specified compounds. Its modular design facilitates integration into automated virtual screening workflows, and a user‐friendly web interface caters to those with limited coding skills, enabling easy compound list uploading and pricing data retrieval. This innovation addresses the complexity of collecting compound prices and enables integration into the virtual screening code workflows, providing a valuable resource for a wide community of users seeking comprehensive and up‐to‐date pricing information to enhance their decision‐making processes.

A Study of the Impact of Equity Incentives on Pricing Efficiency in Capital Markets

Equity incentives are crucial for talent acquisition and fostering corporate innovation; yet, the current literature predominantly focuses on the business level, with less research on the correlation between equity incentives and the capital market. This study empirically investigates the influence of equity incentive schemes on capital market pricing efficiency, utilizing data from Chinese A-share listed businesses between 2010 and 2023, focusing on stock price synchronization and post-surplus announcement drift as analytical viewpoints. The findings indicate that the execution of corporate stock incentive plans can significantly enhance the price efficiency of capital markets. The mechanism test indicates that corporate stock incentive plans enhance the efficiency of capital market pricing. Regarding various incentive mechanisms, both stock options and restricted shares influence capital market pricing efficiency. However, when the share price approaches the exercise price (grant price), restricted shares adversely affect pricing efficiency, whereas stock options positively impact it. Subsequent research indicates that the beneficial impact of equity incentives on capital market pricing efficiency is more pronounced in enterprises with high stock price information content and in those where incentive recipients comprise key technical people. The study's results elucidate the theoretical debate over the practical effects of equity incentive schemes and offer recommendations on the appropriate types of equity incentives for various enterprises to enhance capital market pricing efficiency.

Using social media for agricultural information dissemination: the role of informant-recipient homogeneity

ABSTRACT This study uses a randomised experiment targeting wheat producers in Ethiopia to examine the impact of providing market price information through Facebook on farmers’ sales behaviour. To identify the effect of informant homogeneity, we distinguished the informants’ nationality as either Ethiopian or foreign. Our findings reveal that when wheat selling prices were provided immediately after the harvest – when prices are typically at their lowest – only the information provided by Ethiopian informants led to a reduction in sales decisions and sales volume during that period. This information did not affect selling prices during the low-price period. However, analysis of data collected six months post-harvest shows that the selling price of wheat increased by approximately 14% due to information from Ethiopian informants. This suggests that farmers used the price information to delay sales until prices were higher, rather than negotiating with traders during the low-price period. Additionally, our heterogeneity analysis reveals that older, poorer, more socially isolated, and female farmers benefited more from domestic informant information, likely due to their previously limited access to information. These findings demonstrate the potential of social media for efficiently disseminating price information and highlight the importance of informant homogeneity in the effectiveness of such interventions.

Adaptive peak price with lazy updates for short-term portfolio optimization

This paper introduces the novel Adaptive Peak Price with Lazy Updates (APPLU) approach for short-term portfolio optimization (SPO), a method that innovatively combines the Radial Basis Function (RBF) and a new lazy update approach to address the unique challenges of SPO. Our approach is tailored to balance the dual objectives of maximizing returns and minimizing transaction costs, which are critical concerns in dynamically allocating wealth among various assets over time. Unlike conventional methods that primarily rely directly on peak price information, APPLU introduces an adaptive peak price using RBFs to capture continuous depreciation information of assets, thereby alleviating the aggressive nature of traditional peak price strategies and avoiding frequent trades. Furthermore, we propose a new lazy update approach that employs unsquared l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l^{2}$$\\end{document}-norm regularization to represent portfolio changes. This approach contrasts with squared l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l^{2}$$\\end{document}-norm regularization, which disproportionately penalizes larger portfolio changes while being more lenient towards smaller changes. Thereby, our methodology offers a more balanced and effective approach to portfolio adjustment. Extensive experiments conducted on seven real datasets demonstrate that APPLU outperforms existing strategies in terms of cumulative return and risk-adjusted return, while effectively controlling transaction costs and maintaining a moderate wealth accumulation strategy.

Corporate social responsibility and stock price informativeness: evidence from the global banking industry

ABSTRACT This study explores the impact of corporate social responsibility (CSR) performance on stock price informativeness (SPI) in the global banking industry. Drawing on a dataset of 217 banks from 45 countries between 2009 and 2020, we find that CSR performance significantly enhances SPI by ensuring that more bank-specific information is reflected in stock prices. These results align with signalling theory, which suggests that CSR performance reduces information asymmetry by signalling a firm’s commitment to long-term sustainability and ethical practices, thereby improving market efficiency. The effect is stronger in countries with mandatory CSR disclosure regulations, underscoring the role of regulatory frameworks in amplifying the benefits of CSR activities. Furthermore, we find that the relationship between CSR performance and SPI is more pronounced in investment banks than in commercial banks, reflecting the greater market sensitivity to CSR performance in institutions with higher risk profiles. Our findings provide evidence of a critical link between CSR performance and improved market transparency, highlighting the role of CSR initiatives in fostering more accurate stock price reflections.

Do stock prices affect corporate tax behavior?

Purpose This study aims to examine how firm-specific information in stock prices influences corporate tax behavior. Design/methodology/approach This study uses ordinary least squares (OLS) regressions to examine the association between stock price informativeness and effective tax rate (ETR) volatility. Findings Stock price informativeness is positively associated with short-term tax avoidance as measured by ETR volatility. Originality/value While prior research examines the association between market activity and the level of ETRs, this study examines other various aspects of tax behavior.

Option Pricing Based on REGARCH Model with High-Frequency Information

As a prevalent tool for hedging risk, the trading volume of options has been growing increasingly in the derivatives market. Precision in the estimation of volatility leads to accurate option pricing. Since volatility is time-varying and has a clustering effect, GARCH class of volatility models is effective in modeling volatility precisely. This paper utilizes the realized EGARCH (REGARCH) model combined with Monte Carlo simulation to investigate the role of high-frequency information in option pricing. The parameter estimates of the REGARCH model are obtained via joint maximum likelihood estimation using observations on returns and realized measure. Applying the model to S&P options market, the empirical results show that the REGARCH model that using high-frequency data is more efficient than the model that only use daily closing prices, including the EGARCH, NGARCH and GJR-GARCH models. This paper demonstrates that incorporating realized measures into volatility models can improve the accuracy of option pricing. The REGARCH model contained more intraday trading information from high-frequency data, can measure the additional risk premiums and specific volatility shocks.

Effect of ESG rating disagreement on stock price informativeness: Empirical evidence from China's capital market

We assess the level of environmental, social, and governance (ESG) rating disagreement among Chinese listed companies using ESG data from eight ESG rating providers and examine the impact of ESG rating disagreement on stock price informativeness. Our results indicate a significant and positive correlation between ESG rating disagreement and stock price synchronicity. This suggests that different ESG ratings impede the integration of company-specific details into stock prices, thereby reducing the stock price's informativeness. The opacity of corporate information and agency costs can both be used to interpret this result. We also explore the transmission channels through which ESG rating disagreement affects stock price informativeness. Our findings reveal that such disagreement lowers the quality of analysts' predictions and impedes investors' access to information, affecting the informativeness of stock prices. Furthermore, we observe that ESG rating disagreement has a greater impact on stock price synchronicity for companies with high ESG ratings and those operating in highly competitive industries. Increased media coverage would mitigate the effect of ESG rating disagreement on stock price synchronicity. Overall, our findings contribute to the literature on ESG rating disagreement and demonstrate the critical role of ESG rating disagreement in the informativeness of stock prices.

Machine learning approaches for modelling water futures

Modelling water futures is challenging due to the dynamics of several variables and non-linearity. The traditional models are often inefficient to capture such hidden patterns. Thus, utilizing datasets that include daily price and volume information of water futures, this study evaluates the performance of SVM, Random Forest Regressor, LSTM, GBM, and XGBoost models. The findings indicate that XGBoost outperforms other models in accuracy; however, all models tested perform well and provide accurate predictions. Consequently, it makes valuable contributions to the field of modelling financial products based on water-adjacent entities. Furthermore, it underscores the potential for machine learning in enhancing market predictions for emerging and niche sectors that have thus far been overlooked by market participants but will increasingly become more important as climate change progresses. Lastly, as futures markets for water as a commodity mature (with higher trading volume of futures contracts), this study will serve as a benchmark for better modelling of water futures.

Asymmetric Uncertainty Around Earnings Announcements: Evidence from Options Markets

We use the Indian stock options market to study the evolution of uncertainty and asymmetric uncertainty around earnings announcements (EAs). We find that uncertainty (implied volatility) and asymmetric uncertainty (options skew) increase monotonically before the EA day and decrease after EA. Options volume (relative to spot and to futures) also exhibits similar behavior, suggesting that informed investors prefer options markets to spot and futures markets. Both options skew and put-to-call volume ratio can predict the sign of the EA surprise one day before EA, indicating that price discovery and information assimilation happen in the options market.

Price Sensitivity and Information Barriers to the Take-Up of Naloxone

We conducted a field experiment that randomized advertisements, advertisement content, and prices across 2,204 counties in the United States to study the impacts on online purchases of naloxone, an opioid overdose reversal drug. Advertising increased web-site users but only impacted purchases when combined with a price reduction. Messages emphasizing the discreet nature of online sales had no additional impact on purchases. Comparing counties with advertisements featuring a highly discounted price to those featuring the full price, we estimate a price elasticity of demand for online naloxone of −1.3. Price is a significant barrier to online purchases of this lifesaving medication. (JEL C93, H75, I12, I18, L65, M37)

Short selling and product market competition

We empirically investigate how short selling affects firms’ product market performance via a managerial monitoring channel. Using both historical data and exogenous shocks to short selling, we find robust evidence that short interest negatively impacts market shares, especially in large firms. Our Reg SHO results are stronger in concentrated industries and industries where firms compete in strategic substitutes. Further tests show that these effects are driven by low ex-ante stock price informativeness. The evidence suggests that the interaction between market power and price opacity generates incentives for overproduction, which short selling attenuates. Our results support policies that facilitate price discovery in the presence of market power.