Price Information Research Articles

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.

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.

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)

ECONOMIC CHALLENGES AND FINANCIAL OPPORTUNITIES OF ONLINE RETAIL IN UKRAINE DURING THE WAR: ALIGNMENT WITH GLOBAL TRENDS AND INNOVATIVE PERSPECTIVES

This article serves both theoretical and practical purposes contributing to the body of knowledge, expanding our understanding of online retail development, particularly in challenging contexts, like wartime, suggesting further avenues for future studies, as well as providing practical guidance for Ukrainian online retailers, helping them improve their strategies, performance and financial capabilities, by stimulation of the innovation over emerging trends and opportunities. It explores the development and financial capabilities of online retail in Ukraine amidst the ongoing war, conducting a comparative analysis with global trends in consumer sentiments and retail business development. Retail is a fundamental pillar of any economy. It serves as a crucial intermediary between producers and consumers, ensuring the provision of essential goods for daily life. It is the main link in providing the population with everything necessary for everyday life and forming stable financial flows to replenish the country's budget. Consequently, the functionality and growth of retail are contingent upon its ability to swiftly adapt to evolving consumer behaviour, constantly finance investments in innovation, and actively seek new opportunities. The war against Ukraine is characterized by severe combat operations, including massive shelling and constant missile attacks. It has presented unprecedented challenges for retailers, including a significant shift in consumer priorities towards consumption, leisure, and future planning, resulting in a serious decline in purchasing power, disruptions to logistical routes, loss of personnel, fuel shortages, and blackouts. Despite these formidable adversities, over the 28 months of the war, retail, and particularly online retail, has demonstrated remarkable resilience, sustainable development and rapid recovery of financial capabilities.The conducted research is the outcome of a set of comprehensive analyses (trends, performance metrics, clustering) of the financial results of retail, primary challenges faced by Ukrainian retailers and the identification of effective adaptation strategies to ensure financial stability. Meta and content analysis with further synthesis of findings, could help to examine the implementation of service-oriented business models, such as marketplaces, which offer a vast array of products and competitive pricing, as a key adaptation strategy. The study also delves into the popularization of online retail and the trends in the transition to hybrid online sales like omnichannel, enabling a "seamless" customer experience through the synchronization of product, pricing, and order information across all sales channels. Additionally, retailers are actively working to enhance logistics and delivery speed through partnerships with logistics companies, establishing their own courier delivery services, and expanding their physical presence through retail outlets. A comparative analysis with international experience reveals the similarities and differences in the challenges faced by Ukrainian retailers, the factors contributing to success and the methods of transformation. It further explores the adoption of digital technologies and innovative approaches to maintain competitiveness and financial stability even under the challenging conditions of war.

Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty

This study investigates the profitability of portfolios that integrate asymmetric fractality within the Black–Litterman (BL) framework. It predicts 10-day-ahead exchange-traded fund (ETF) prices using recurrent neural networks (RNNs) based on historical price information and technical indicators; these predictions are utilized as BL views. While constructing the BL portfolio, the Hurst exponent obtained from the asymmetric multifractal detrended fluctuation analysis is employed to determine the uncertainty associated with the views. The Hurst exponent describes the long-range persistence in time-series data, which can also be interpreted as the uncertainty in time-series predictions. Additionally, uncertainty is measured using asymmetric fractality to account for the financial time series’ asymmetric characteristics. Then, backtesting is conducted on portfolios comprising 10 countries’ ETFs, rebalanced on a 10-day basis. While benchmarking to a Markowitz portfolio and the MSCI world index, profitability is assessed using the Sharpe ratio, maximum drawdown, and sub-period analysis. The results reveal that the proposed model enhances the overall portfolio return and demonstrates particularly strong performance during negative trends. Moreover, it identifies ongoing investment opportunities, even in recent periods. These findings underscore the potential of fractality in adjusting uncertainty for diverse portfolio optimization applications.

Equilibrium Data Mining and Data Abundance

ABSTRACTWe study theoretically how the proliferation of new data (“data abundance”) affects the allocation of capital between quantitative and nonquantitative asset managers (“data miners” and “experts”), their performance, and price informativeness. Data miners search for predictors of asset payoffs and select those with a sufficiently high precision. Data abundance raises the precision of the best predictors, but it can induce data miners to search less intensively for high‐precision signals. In this case, their performance becomes more dispersed and they receive less capital. Nevertheless, data abundance always raises price informativeness and can therefore reduce asset managers' average performance.

Bayesian estimation of stochastic volatility jump diffusion model parameters using S&P 500 and VIX data

The Stochastic Volatility Jump Diffusion (SVJD) model is a common tool used for option pricing. Existing literature has employed many techniques in order to estimate the parameters of this model, including both Bayesian and frequentist approaches. In 2010, Duan and Yeh [Jump and volatility risk premiums implied by VIX. J Econ Dyn Control. 2010;34(11):2232–2244] used a frequentist approach with maximum likelihood estimation that included the VIX, the Chicago Board Options Exchange (CBOE) Volatility Index, which carries real time S&P 500 option price information in addition to the S&P 500 data. In this paper, we combine the previous Bayesian approach in Cape et al. [Estimating Heston's and Bates' models parameters using Markov Chain Monte Carlo simulation. J Stat Comput Simul. 2015;85(11):2295–2314] with the inclusion of VIX by Duan and Yeh. We provide the derivations of the conditional posterior distributions of the SVJD model's parameters and a guide to a Markov Chain Monte Carlo (MCMC) algorithm used to estimate the parameters. We apply the algorithm to the historical S&P 500 and VIX data and provide the subsequent parameter estimates. We offer these tools for others performing similar research with our R code available upon request.

Predicting smartphone prices using machine learning algorithms

This study investigates the prediction of smartphone prices using machine learning algorithms, motivated by the increasing demand for accurate pricing information in the highly competitive and rapidly evolving mobile phone market. With the proliferation of smartphones and the constant introduction of new models with varying specifications, it is crucial for consumers, retailers, and manufacturers to have a reliable method for estimating prices based on technical features. Accurate price prediction can assist consumers in making informed purchasing decisions, retailers in setting competitive prices, and manufacturers in optimizing production costs and pricing strategies. Utilizing Decision Tree Regression, Support Vector Regression (SVR), Random Forest Regression and Convolution Neural Network (CNN), the research investigates the efficacy of these methods in estimating prices accurately. Principal Component Analysis (PCA) is applied to reduce dimensionality and enhance model performance. Through rigorous experimentation and hyperparameter tuning, Random Forest Regression emerges as the most effective method, achieving the lowest Mean Squared Error (MSE) and the highest R-squared (R) score. The findings highlight the potential of ensemble learning techniques in addressing complex prediction tasks.

ANALISIS HARGA PANGAN POKOK TERHADAP KETAHANAN PANGAN DI INDONESIA

Food security ensures that sufficient, high-quality food is accessible to everyone for optimal consumption. Rising food prices can severely impact food security, especially for economically vulnerable groups. This research investigates the relationship between staple food prices and food security in Indonesia, focusing on factors like rice energy intake, egg protein intake, Gross Regional Domestic Product (GRDP) per capita, and regional conditions on Java, Bali, and other islands. Data from all Indonesian provinces for 2018-2020 is analyzed using the Ordinary Least Squares (OLS) regression method. The Food Security Index (IKP) comes from the National Food Agency’s Food Security Index Book, and food price data is sourced from the National Strategic Food Price Information Center (PIHPS). Control data such as GRDP per capita is from the Central Statistics Agency (BPS), while rice energy and egg protein intake data are from the Ministry of Agriculture’s Food Consumption Directory. The analysis reveals that rising rice prices from 2018 to 2020 significantly negatively affected food security. The study recommends evaluating rice price policies to maintain stability and enhance food accessibility. Similar evaluations for egg prices are also suggested to ensure protein availability for the community.

The African cigarette price data landscape: an overview of gaps and opportunities

IntroductionAfrica has the lowest cigarette taxes of any region. Price data are needed to produce evidence that informs tax policy change.ObjectiveTo locate and describe datasets that provide information on cigarette...

Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory

Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian machine learning framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Developed a Bayesian machine learning framework for inferring the non-linear potential function from observed cryptocurrency prices. (iii) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes in the literature. (iv) Analysis of cryptocurrency market during various Bitcoin crash durations shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (v) The proposed cryptocurrency market indicators (attractors and repellers) was used to improve the prediction performance of state-of-art deep learning price prediction models.

Does industry co-location improve corporate information environment?

This study examines how the co-location of firms within the same industry enhances their information environments. We hypothesize that benefits arise from two effects: the industry peer effect, involving intra-industry information spillovers, and the geographic peer effect, related to shared local economic conditions with nearby non-industry peers. Our analysis evaluates how these effects, both separately and jointly, impact analysts’ forecast properties and stock price informativeness regarding future earnings. We find that the industry peer effect is negatively related to forecast dispersion and positively related to forecast accuracy, while the geographic peer effect shows no significant correlation. In addition, stock price informativeness about future earnings is positively associated with the industry peer effect but not with the geographic peer effect. We also find that greater proximity to industry or local peers enables analysts to incorporate more private (relative to public) information into their earnings forecasts, thereby enhancing the firm’s information environment. JEL CLASSIFICATION: M41, D80, G10

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.

Can long-term institutional owners improve market efficiency in parsing complex legal disputes?

Long-horizon institutional investors can help mitigate information asymmetries around securities class action (SCA) lawsuits. We find that the machine readability of SCA complaint filings can predict the outcome and duration of class actions. Long-term institutional investor ownership leads to a more positive post-SCA announcement price reaction and increases the volatility ratio of prices as a measure of price informativeness. Furthermore, there is a significant interaction effect between long-term institutional ownership and SCA complexity on price informativeness consistent with a superior information processing ability about complex corporate events affecting portfolio firms.

Momentum mechanisms under heterogeneous beliefs

We establish a continuous-time heterogeneous beliefs model to discuss the mechanisms of Momentum and Reversal. Price learning, information transmission and extrapolative expectation are incorporated into a unified framework for the Momentum and Reversal. The calibration results from SP500 show that the presence of Extrapolators and Information-driven Traders are important influences of Momentum and Reversal in all phases. We also find that momentum and reversal become significantly stronger as belief weights approach true belief weights.

The Controversy over Price Transparency: Patients and Plastic Surgeons Disagree.

The No Surprises Act (NSA) went into effect on January 1, 2022. This federal law is designed to minimize surprise bills for emergency care, out of network (OON) care, and care for uninsured or self-pay individuals. To achieve these goals, the NSA relies heavily on physicians providing cost estimates to patients prior to administering care. Because healthcare is often price-opaque, this study was conducted to assess physician and patient attitudes toward price transparency in plastic surgery and aesthetic/self-pay medicine. Two cohorts of providers received a survey asking whether and how their practice offers pricing information prior to a consultation and their views regarding the NSA. Patients who had previously used an online price estimator tool also received a survey regarding their experience and accuracy of estimates received online. Most surveyed providers do not share the costs of their services online, whereas patients had a positive experience when receiving estimates of medical services before consultation. Although many providers cited the inaccuracy of price estimator tools as a reason not to incorporate them into their website, most patients reported that their final cost of service was close to (or even less than) the estimate received online prior to the consultation. This study suggests that price transparency is a patient satisfier. Therefore, plastic surgeons should exercise price transparency to be compliant with federal law and to gain a competitive advantage in the market, as the decision to implement price transparency can be mutually beneficial to patients and providers.