Efficient Pricing Research Articles

Analysts’ ESG attention and stock pricing efficiency: evidence from machine learning and text analysis

PurposeCorporate ESG performance has attracted widespread attention from various sectors of society. This paper aims to investigate whether analysts’ ESG attention can convey additional information to the market and consequently influence stock pricing efficiency.Design/methodology/approachUsing A-share listed companies from 2014 to 2021 as the research subjects, this paper employs a deep learning algorithm, word2vec, to construct an ESG dictionary. Text analysis is then applied to create an analysts’ ESG attention index, delving into its impact on stock pricing efficiency.FindingsEmpirical research reveals that: (1) Analysts' ESG attention effectively enhances stock pricing efficiency, with a more significant impact from analysts’ attention to environmental (E) and social (S) factors compared to governance (G); (2) Further analysis indicates that this effect becomes more pronounced when there is higher disparity in corporate ESG ratings, greater marketization in the province where the company is located, and a higher institutional ownership percentage and (3) The mechanism by which analysts' ESG attention influences stock pricing efficiency is through an elevation in investor attention and stock liquidity. Additionally, it is observed that analysts prioritize ESG information to enhance their reputation and business capabilities.Originality/valueFrom the perspective of ESG rating divergence, this paper innovatively uses analyst reports to construct ESG attention indicators and analyzes their impact on the efficiency of stock pricing.

Power Shift: Decarbonization and the New Dynamics of Energy Markets

This paper examines the transformative effects of decarbonization on electricity market design, emphasizing the challenges and opportunities posed by the rapid integration of renewable energy sources such as wind and solar. It analyzes the evolution of key wholesale market segments—including day-ahead, real-time, capacity, long-term purchase agreements, ancillary services, and transmission markets—highlighting their critical roles in managing the variability of renewable energy generation through efficient price signals and resource coordination. Variable renewable energy integration introduces significant operational challenges, including overgeneration risks, ramping capacity demands, forecast inaccuracies, and transmission constraints. Addressing these issues requires enhanced market flexibility, dynamic pricing mechanisms, and advanced real-time balancing strategies. This paper assesses these challenges, offering strategies to align generation with demand and optimize market outcomes. As electricity systems evolve, legacy market structures must adapt to incorporate carbon-free resources while maintaining grid reliability and economic sustainability. By exploring case studies such as Chile and California, this paper demonstrates the importance of targeted innovations in market design, regulatory frameworks, and operational technologies. It advocates for a holistic approach to ensure a reliable, affordable, and equitable transition to a decarbonized energy future.

Probing Assembly Models: Exploring Innovations in Models with Perfect Complementarity

This paper introduces and scrutinizes three groundbreaking models aimed at probing the intricacies of duopoly dynamics within perfect complement factors: (1) the spontaneous assembly model, (2) the non-spontaneous assembly model, and (3) the sequential spontaneous assembly model. This study delves into oligopoly with perfect complementarity and makes the following discoveries. First, the renowned and significant quantity competition model, the Cournot model, is equivalent to the price competition models studied in this research, the spontaneous assembly model and the non-spontaneous assembly model using ECPR. Second, the renowned and significant quantity competition model, known as the chain monopoly model, aligns with the price competition model explored in this study, termed the sequential spontaneous assembly model. Third, the three price competition models examined in this study are found to be as follows. Initially, a comprehensive investigation of the spontaneous assembly model lays the basis for a compelling argument, demonstrating that the equilibrium price of the ‘system goods’ in duopoly surpasses that of the monopoly scenario. Subsequently, we meticulously establish the mathematical equivalence between the non-spontaneous assembly model, using the ‘efficient component pricing rule’, and the spontaneous assembly model. Our focus then shifts to the sequential spontaneous assembly model, where a meticulous comparison with the simultaneous model unveils elevated equilibrium prices for the ‘system goods’. In particular, we elucidate that those industrial profits are based on the price elasticity of demand, with this sequential model yielding higher profits under conditions of inelastic demand.

Counting the cost of hospitalised injury using administrative data in Australia: three considerations from a retrospective cohort data linkage study

ObjectivesTo examine the relative differences in injury hospitalisation cost estimates from hospital administrative data when using (1) hospital reimbursement based on National Efficient Price (NEP) versus local hospital costings methods,...

Short of Capital: Stock Market Implications of Short Sellers’ Losses

We provide evidence that losses constrain short sellers but not the transmission of information to prices. Using unique data on U.S. equity lending, we document a negative impact of the mark-to-market losses of a stock’s short sellers, but no impact of their gains, on the future shorting of the stock. Consistent with funding and institutional constraints limiting short selling, we further show that the effect is highly asymmetric across different loss levels and stronger among stocks facing higher margin requirements. However, loss-making short selling has no impact on price efficiency or predictive power for returns, suggesting that these constraints affect mostly uninformed shorting activity. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the University of Melbourne [Faculty Research Grant]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01356 .

Greening or greenwashing? Corporate green bonds and stock pricing efficiency in China

Greening or greenwashing? Corporate green bonds and stock pricing efficiency in China

Are carbon trading markets efficient? A longitudinal investigation

ABSTRACT We examine the evolution of price efficiency in the European Union Carbon Emissions Market and the net cross-correlations between the carbon and commodities markets using Multifractal Detrended Fluctuation Analysis (MFDFA), and Detrended Partial Cross-Correlation Analysis (DPCCA), methods based on dynamic systems derived from econophysics. Our analysis is based on daily values of the S&P GSCI Carbon Emission Allowances (EUA) EUR index and the S&P GSCI index for the period between January 2010 and June 2024 and uses seven-year rolling windows. Our results are supportive of the adaptive market hypothesis and indicate that the efficiency of the EU ETS markets has been improving steadily and that its correlation with the broader commodities market is increasing over time.

Are rare earth stocks efficient? Novel insights using asymmetric MF-DFA

This study investigates the weak-form efficiency and asymmetric multifractal scaling behavior of rare earth stock indices in the global, U.S. and Chinese markets during the trade war and the COVID-19 period. We examine the scaling behavior across overall, upward (bullish), and downward (bearish) market states from 2013 to 2021, employing an asymmetric multifractal detrended fluctuation analysis approach. Our findings indicate asymmetric multifractality in U.S. rare earth stock prices, caused by fat tails and long-range correlations. Weak-form price inefficiency and asymmetry in U.S. rare earth stock prices are prominent during market downturns, such as the trade war and COVID-19 periods. Chinese rare earth stocks demonstrate greater efficiency than U.S. and global stocks; thus, the latter markets provide arbitrage opportunities during upward and downward trends.

Price transmission and asymmetry in a changing seafood supply chain

This study investigates price transmission and asymmetry in the cod value chain between the export market of Norway and the retail market of Portugal. Using monthly data from January 2011 to December 2022, it analyzes the value chains of salted dried cod, salted wet cod, and the relationship between the export price of salted wet cod and the retail price of salted dried cod using a non-linear ARDL model. The findings reveal that price transmission occurs across all investigated value chains, though the timing and extent of adjustments vary. Furthermore, the findings indicate that the export market plays a leading role along the value chains. Notably, no evidence of price asymmetry is observed in any of the product chains, indicating efficient price transmission mechanisms.

Life cycle cost analysis of power-to-fuel from renewable energy

A life-cycle cost model for power-to-fuel technologies, including production, storage, transportation, and refueling processes, was developed to evaluate four types of power-to-fuel options: proton exchange membrane electrolysis hydrogen, single-step methanol synthesis, two-step methanol synthesis, and ammonia synthesis. The study compares cost sensitivity factors (scale, reaction efficiency, and electricity price) based on unit calorific value costs. Results indicate that electricity price significantly affects overall costs, with two-step methanol synthesis being the most cost-effective. Single-step methanol synthesis has the highest life-cycle cost due to efficiency and electricity price limitations, but it becomes the cheapest when efficiency reaches 80% and electricity price drops to 0.05 CNY/kWh. The impact of transport distance on storage and transport costs for methanol and ammonia showed they are significantly lower than hydrogen, suggesting methanol and ammonia can serve as effective hydrogen carriers, reducing costs and improving safety.

High Price Impact Trade Identification and Its Implication for Volatility and Price Efficiency

We include limit order book (LOB) matchedness as a new trade attribute to identify high price impact trades (HPITs). HPITs are trades associated with large price changes relative to their volume proportion. We show that the inclusion of matchedness provides a finer analysis of the relationship between price contribution and trade categories. We further verify that a stronger presence of HPITs leads to a decline in volatility and improves price efficiency, which suggests a link between HPITs and informed trades.

Do Institutional Investor Cliques Undermine Stock Price Efficiency?

AbstractThis study investigates the effects of institutional investor cliques on stock price efficiency. Using a community network algorithm, it identifies cliques of institutional investors and finds that they significantly undermined stock price efficiency in China. The robustness of this finding is demonstrated through a range of methodologies, including the substitution of dependent variables, the alteration of estimation methods, the exclusion of data from extreme market periods, and the application of instrumental variables. The role of institutional investor cliques in diminishing market efficiency is attributed to market speculation. The detrimental effect of cliques on efficiency is exacerbated in environments characterized by low stock liquidity, poor information disclosure, and heightened panic among retail investors. However, regulatory interventions, such as inquiry letters, are shown to mitigate these effects and enhance market efficiency. These findings highlight the unexpected ways institutional investor cliques can influence emerging market economies and underscore the critical importance of effective regulation to safeguard market efficiency.

Pengaruh Penggunaan Plastik LDPE (Low Density Polyethylene) Sebagai Substitusi Aspal dan Serbuk Kaca Sebagai Substitusi Filler pada Campuran LASTON AC-WC

Road deterioration is a common problem in Indonesia, influenced by population growth, the number of vehicles, and environmental factors. LASTON (Asphalt Concrete Layer) is a type of flexible pavement consisting of a mixture of asphalt, aggregate, and filler, which requires innovation to improve its quality. The objectives of this study were to determine the marshall parameters of LASTON AC-WC mixtures added with LDPE plastic and glass powder variations, obtain the KAO (Optimum Asphalt Content) value, and find the price efficiency of Conventional LASTON AC-WC mixtures and Innovative LASTON AC-WC. The research method used was an experimental test at the Civil Engineering Laboratory of Diponegoro University Vocational School, referring to the 2018 Bina Marga standard (Revision 2). This study designed 15 samples to obtain the KAO value so that the KAO obtained from the marshall test calculation was 5.5%, as well as 27 samples to determine the optimum content of LDPE plastic and Glass Powder with variations in LDPE plastic content of 4%, 5%, and 6% by weight of asphalt and Glass Powder content of 25%, 50%, and 75% by weight of fillers. The variations were tested using hot asphalt mixtures' stability and flow test methods with marshall test equipment referring to the 2018 General Specifications. This study obtained the optimum percentage of LDPE 5% and Glass Powder 75%, which met the requirements of the 2018 General Specifications. The marshall test results show the BJ Bulk value of 2.399 T/m3, VIM 4.901%, VMA 14.063%, VFA 70.393%, Stability 2029.455 kg, Flow 2.297 mm, and MQ 868.783 kg/mm. The cost of conventional asphalt mixture material is Rp 1,046,123, while the cost of innovative asphalt mixture material is Rp 987,017; thus, the innovative asphalt mixture material is more economical, with a price difference of Rp 59,105.90/ton.

The Impact of Full Registration System on IPO Pricing Efficiency

Registration system is an important measure of capital marketization reform in China, which has a significant impact on IPO pricing efficiency. Based on the change of main board from approval system to registration system, we find that registration system can significantly reduce IPO underpricing rate and improve IPO pricing efficiency. The registration system has a moderating effect on the level of information disclosure and the pricing utility of intermediaries. The results show that the registration system reform is conducive to alleviating the problem of high IPO underpricing rate in China's capital market Registration system, improve the capital market resource allocation ability has accumulated certain experience.

OPTIMIZING REAL-TIME DYNAMIC PRICING STRATEGIES IN RETAIL AND E-COMMERCE USING MACHINE LEARNING MODELS

This study investigates the application of machine learning models for real-time dynamic pricing strategies in the retail and e-commerce sectors. We employed three prominent supervised machine learning models—Linear Regression, Random Forest, and Gradient Boosting Machines (GBM)—to predict optimal prices using a dataset sourced from Kaggle. The models were trained and evaluated with a 70:30 train-test split, while hyperparameter tuning was performed using grid search and cross-validation. The results indicate that the Gradient Boosting Machines (GBM) model consistently outperformed the other models, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and demonstrating a higher R-squared (R²) value. The comparative analysis highlights GBM's ability to capture complex interactions in dynamic pricing data, making it a robust choice for accurate price forecasting. The Random Forest model also delivered satisfactory results, balancing accuracy and computational efficiency, whereas the Linear Regression model showed higher prediction errors due to its limitations in modeling non-linear relationships. Real-time testing in a simulated environment confirmed the models' adaptability and responsiveness in a dynamic marketplace. These findings provide actionable insights for retail and e-commerce businesses, emphasizing the importance of model selection, hyperparameter optimization, and system integration to implement efficient dynamic pricing strategies. Future work should explore more extensive datasets and real-world applications to address seasonal variations, regional preferences, and consumer behavior, ensuring a more comprehensive and practical deployment of machine learning-driven dynamic pricing models.

Does increased broadband internet penetration improve the quality of investors' information about local firms?

AbstractThis study documents that greater availability of residential high‐speed broadband internet in the area around a firm's headquarters is associated with an improvement in the quality of analysts' information, and the information efficiency of stock prices, for local firms. These results are driven by firms in areas with two and fewer local broadband providers, consistent with these firms benefiting more from additional information dissemination by locals. Increased internet penetration is also positively associated with locals producing more stock tweets about local firms. Our study provides evidence that increased broadband penetration is associated with positive information externalities for local firms.

Deep learning of optimal exercise boundaries for American options

Efficiently determining a price and optimal exercise boundary for an American option is a critical subject in the financial sector. This study introduces a novel application of long short-term memory neural networks to solve a relevant Volterra equation, enhancing the accuracy and efficiency of American option pricing. The proposed approach outperforms traditional numerical techniques, including finite difference methods, binomial trees, and Monte Carlo methods, delivering an impressive speed improvement by a factor of thousands while maintaining industry-accepted accuracy levels. It exhibits computational speeds up to about a hundred times faster than the state-of-the-art method used by Andersen et al. [High-performance American option pricing, J. Comput. Finance 20(1) (2016), pp. 39–87] when evaluating numerous options. The proposed network, trained on a reasonable range of parameters related to the Black–Scholes model, swiftly determines option prices and exercise boundaries, serving as a practical closed-form solution.

ACTIVITY-BASED COSTING (ABC) SYSTEM TO DETERMINE PRODUCTION COST (STUDY ON CV TWIN ALFA)

This study explores the implementation of the Activity-Based Costing (ABC) system at CV Twin Alfa, a company engaged in the production of automotive training equipment for vocational and mechanical engineering students. In an increasingly competitive market, the necessity for accurate cost information is paramount for strategic decision-making and pricing strategies. Traditional cost accounting methods, which allocate manufacturing overhead based solely on production volume, lead to significant inaccuracies in cost calculations, particularly for companies with diverse product offerings. This research demonstrates that the ABC system provides a more precise method for calculating production costs by linking expenses directly to the activities that generate them. Findings indicate that employing the ABC system not only enhances the accuracy of cost information but also supports improved operational efficiency and competitive pricing. The study concludes that adopting the ABC approach is essential for CV Twin Alfa to refine its cost management practices and maintain a competitive edge in the marketplace.

Corporate Financial Performance Following the Opening of China's High‐Speed Rail: A Firm‐Level Perspective on Supply Chain Performance

ABSTRACTIn the context of China's successful high‐speed railway (HSR) construction, this study examines the micro‐mechanisms of financial performance related to the opening of HSR from a firm‐level supply chain perspective. This study's findings are as follows: (1) Supply chain performance significantly increased by 14.71% on average after the HSR opening. (2) After the HSR opened, real earnings management (REM) significantly reduced, especially overproduction and abnormal cash flow manipulation. (3) HSR reduced REM by promoting supply chain performance. (4) With a reduction in REM, HSR improved stock pricing efficiency, which is reflected in the reduction of stock price synchronisation and crash risk. The conclusions continue to hold after controlling for other traditional urban transportation, major urban events and strategic corporate M&A arbitrage events.

On bid and ask pricing of European options via direct discretization of Choquet distorted expectations

We present a numerical approach to compute bid and ask prices of European options within the conic finance paradigm. In particular, we show how discretizing option bid and ask pricing formulae, where the latter are expressed in terms of Choquet integrals, allows to perform efficient pricing and calibration by means of the COS method and the fast Fourier transform technique. Additionally, the approach introduced provides alternative expressions for risk-neutral European option prices by leveraging the relationship between Choquet and Riemann-Stieltjes integrals. These alternative formulations, requiring only the discretization of the cumulative distribution function of the (transformed) terminal value of the underlying asset, enhance computational efficiency under risk-neutral settings. We provide practical illustrations of the methodology when the underlying asset process follows Heston or Variance Gamma dynamics for both pricing and implied liquidity calculations.