Accurate Price Research Articles

Improving the forecasting approach for crude oil sourcing at an oil refining firm: a decision theory perspective

Accurate price forecasting is pivotal for making informed sourcing decisions. This study addresses this need by empowering logistics and supply planning managers at a leading European oil refining company through the innovative use of unstructured big data analytics. By applying the CIMO (Context, Intervention, Mechanism, and Outcome) framework, the research refined the oil price forecasting process, which is crucial for optimising sourcing strategies. A novel approach was employed, combining sentiment analysis via deep learning models with traditional autoregressive integrated moving average (ARIMA) models, introducing sentiment as a vital exogenous variable. Through comprehensive stakeholder interviews, the study identified critical gaps and opportunities, leading to the development of a forecasting solution that significantly enhanced predictive accuracy. This advancement eliminated the need for manual interventions by senior decision-makers, streamlining the decision-making process. This research utilises decision theory and also demonstrates the transformative power of unstructured big data analytics in supply chain management. By integrating time series modelling with advanced sentiment analysis, this study provides a blueprint for leveraging technological advancements in rapidly evolving markets. The originality of this work lies in its practical application, showcasing essential capabilities that are increasingly vital in supporting sourcing practices within a volatile global landscape.

Target price accuracy of sell-side analysts: evidence from India

Target price accuracy of sell-side analysts: evidence from India

Insurance coverage in unstable region: Managing insurance claim risk amid Kashmir's political landscape

This study investigates the impact of prolonged political unrest on insurance claim amounts in Kashmir. It examines the behavior and coverage provided by insurance companies in high-risk areas, focusing on the hazards and perils associated with terrorism and political uncertainty. By analyzing historical data on political violence and terrorism in Kashmir, the study reveals significant patterns and relationships through descriptive statistics. The findings demonstrate that higher levels of political violence and terrorism lead to increased losses for insurers. Moreover, the study underscores the necessity of tailoring insurance coverage to the specific risks in unstable regions. It suggests employing time series analysis for more accurate pricing of insurance policies during periods of political instability. These insights offer practical implications for insurance companies, policymakers, and stakeholders, facilitating more effective risk assessment, pricing, and risk management strategies in volatile areas. In addition to outlining general customer requirements, the study discusses the measures and regulations needed for commercial insurers to provide economical services in such contexts. This comprehensive analysis enhances the understanding of the effects of abnormal situations like prolonged political unrest in Kashmir. Ultimately, the study proposes the integration of time series analysis into insurance pricing models to better navigate periods of political instability.

Advancements in Soybean Price Forecasting: Impact of AI and Critical Research Gaps in Global Markets

Soybeans, a vital source of protein for animal feed and an essential industrial raw material, are the most traded agricultural commodity worldwide. Accurate price forecasting is crucial for maintaining a resilient global food supply chain and has significant implications for agricultural economics and policymaking. This review examines over 100 soybean price forecast models published in the last decade, evaluating them based on the specific markets they target—futures or spot—while highlighting how differences between these markets influence critical model design decisions. The models are also classified into AI-powered and traditional categories, with an initial aim to conduct a statistical analysis comparing the performance of these two groups. This process unveiled a fundamental gap in best practices, particularly regarding the use of common benchmarks and standardised performance metrics, which limits the ability to make meaningful cross-study comparisons. Finally, this study underscores another important research gap: the lack of models forecasting soybean futures prices in Brazil, the world’s largest producer and exporter. These insights provide valuable guidance for researchers, market participants, and policymakers in agricultural economics.

Abstract 4136705: Complexity and Inconsistency of Echocardiogram Prices at Top-Rated Hospitals in the United States

Background: Recent U.S. policies require hospitals and health insurers to post payer-specific negotiated prices for services. Few studies, if any, have thoroughly investigated the accuracy of these payer-specific prices for individual hospitals. Hypothesis: Top-rated medical centers charge consistent prices for echocardiograms, which are reflected in price transparency files. Aim: To evaluate the concordance of prices of echocardiograms using three datasets: 1) hospital price transparency, 2) payer price transparency data, and 3) Electronic Remittance Advice (ERA) claims data, reflecting actual payments. Methods: We extracted prices for transthoracic echocardiograms (CPT 93306) at 22 top-rated hospitals by US News&World Report from 1/1/2022 to 12/31/2022. We used price transparency data aggregated by Turquoise Health and compared with ERA claims data. This analysis included 9,171 insurance claims, matched to either payer or hospital-reported prices. For each claim, concordance between datasets was determined by evaluating whether each claim payment fell within a $10 margin of a publicly reported payer-specific price. Findings: In the payer and hospital price transparency files, reported prices ranged from $1.3 to $34,424. In the ERA claims data, the median payment for the professional fee was $203 (Q1 = $116, Q3 = $245), and the median payment for the facility fee was $2682 (Q1 = 1829, Q3 = $4531). There was a ten-fold difference in median price between the cheapest and most expensive facility. Only 27.4% of claim payments had a concordant price in the payer price transparency dataset, and 11.9% in the hospital dataset. Conclusion: Deriving accurate price estimates for patients requires precise matching of the insurance plan, place of service, and fee type. This process highlights the complexity of insurance contracting, and it is not surprising that there is some disagreement in prices. Regardless, we are hopeful that highlighting variation and complexity in prices is a step towards a better system of healthcare payments in the United States.

PERANCANGAN SISTEM INFORMASI ANALIS BREAK EVEN POINT DAN STOK PADA BUDIDAYA AYAM POTONG BERBASIS WEB

Although broiler chicken farming offers many benefits, there are also risks of losses, especially when production costs are not accurately recorded, leading to inaccurate per-unit pricing that can harm farmers. To assist farmers in accurately determining the Break Even Point (BEP) price for each chicken, a web-based cost and stock management information system for broiler farming has been developed. To provide a clear and sequential software lifecycle approach, this system was built using the waterfall method, which includes the stages of requirements analysis, design specifications, coding, testing, and maintenance. Each stage produces measurable outcomes before proceeding to the next. The development results indicate that the system successfully records the total production costs, including detailed costs for feed, medicine, operational expenses, and chicken stock. By using the formula BEP = total production cost/number of live chickens, the system achieves accurate pricing for broiler chickens. Based on these findings, future research is suggested to improve the user interface for greater appeal, optimize system accessibility on mobile devices, and integrate this system with other systems.

A Study on the Pricing of Convertible Bonds in the Chinese Market Based on the LSMC Model

Convertible bonds, which possess both stock and option characteristics, currently lack a sufficiently effective pricing method for the complex and specific clauses found in Chinese convertible bonds. This study employs structured model pricing and numerical experiment methods for the pricing of Chinese convertible bonds. The structured model selected is the Black-Scholes model, while the numerical experiment methods include the binomial tree pricing model and the Least Squares Monte Carlo (LSMC) model. A proposal is made to introduce an implied volatility model in the LSMC model pricing method, incorporating the Heston model to consider the impact of the implied volatility of the underlying stock in the real market, and further adjusting the LSMC model by estimating the volatility of the underlying stock using the implied volatility model. Empirical results indicate that numerical methods have higher pricing accuracy than structured models, with the LSMC model demonstrating the highest accuracy. It is also found that there is a certain premium phenomenon in the Chinese market for convertible bonds. Furthermore, the accuracy of the model improves after the volatility adjustment, indicating that implied volatility has explanatory power for the specific clauses contained in convertible bonds, and this influence is reflected in pricing.

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.

Enhancing pricing strategies in the aftermarket sector with machine learning

PurposeThis research explores the application of machine learning to optimize pricing strategies in the aftermarket sector, particularly focusing on parts with no assigned values and the detection of outliers. The study emphasizes the need to incorporate technical features to improve pricing accuracy and decision-making.Design/methodology/approachThe methodology involves data collection from web scraping and backend sources, followed by data preprocessing, feature engineering and model selection to capture the technical attributes of parts. A Random Forest Regressor model is chosen and trained to predict prices, achieving a 76.14% accuracy rate.FindingsThe model demonstrates accurate price prediction for parts with no assigned values while remaining within an acceptable price range. Additionally, outliers representing extreme pricing scenarios are successfully identified and predicted within the acceptable range.Originality/valueThis research bridges the gap between industry practice and academic research by demonstrating the effectiveness of machine learning for aftermarket pricing optimization. It offers an approach to address the challenges of pricing parts without assigned values and identifying outliers, potentially leading to increased revenue, sharper pricing tactics and a competitive advantage for aftermarket companies.

Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

NFT price and sales characteristics prediction by transfer learning of visual attributes

Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework.Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point. Our code and NFT datasets are publicly available at https://github.com/seferlab/deep_nft.

Quantum walk option pricing model based on binary tree

In this paper, we present a novel quantum model for financial markets constructed within the risk-neutral framework. We innovatively integrate the theory of quantum walks into the binomial tree model for option pricing, thereby achieving a more precise simulation of asset price dynamics. Furthermore, we explore the multi-step quantum binomial tree model and enhance the traditional approach by incorporating the concept of quantum superposition. This advancement enables our model to simultaneously consider multiple states of asset prices at various nodes, effectively reducing computational complexity as time steps increase while significantly improving option pricing accuracy. The superior performance of our quantum model is demonstrated through simulation experiments.

A Multi-Model Output Fusion Strategy Based on Various Machine Learning Techniques for Product Price Prediction

In the digital era, precise product price prediction becomes crucial for enhancing competitiveness in the online marketplace. This paper presents a hybrid model framework that enhances the accuracy of online product price predictions by integrating several machine learning algorithms, including Linear Regression, Decision Trees, and Gradient Boosting. The objective of this approach is to leverage the distinct advantages of each model to address their individual limitations and create a robust unified predictive model. This integration allows for improved handling of complex data relationships and diverse market dynamics that are typical in online sales environments. The results demonstrate that the hybrid model achieves superior prediction accuracy, as reflected in reduced Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics, and an exceptionally high R ² value compared to single-model approaches. These outcomes underscore the efficacy of combining multiple predictive models to enhance the precision of price forecasts in the highly competitive online marketplace. This model fusion strategy not only provides more accurate pricing predictions but also offers strategic insights into the optimal pricing strategies for businesses looking to enhance their market position.

Research on the Feasibility of Machine Learning Methods in Stock Price Prediction

Abstract: Stock price prediction has always been an important topic in financial research. Accurate price prediction can not only help investors make informed investment decisions but also enhance market stability and reduce systemic risk. In recent years, with advancements in computing technology and data science, machine learning methods have been increasingly applied in the financial field. Compared to traditional methods, machine learning methods can better handle high-dimensional, nonlinear, and large datasets, thus demonstrating higher prediction accuracy and applicability in stock price prediction.This paper reviews relevant literature and selects five models for empirical research: Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), LightGBM, a combination of LSTM and LightGBM, and Convolutional Neural Network (CNN). The effectiveness of these models in predicting the stock price of Meituan-W (3690) was analyzed and compared in detail. The experimental results show that the LightGBM model performs best in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), proving its significant advantages in handling large-scale, high-dimensional, and nonlinear data. By comparing the prediction results of different models, this paper explores the strengths and weaknesses of each model and their feasibility and effectiveness in practical applications. Machine learning methods have significant potential in stock price prediction, model selection needs to comprehensively consider data characteristics, computational resources, and practical application scenarios.

Integrated market scheduling with flexibility options

This work presents a generic optimization framework using advanced mixed-integer programming techniques to integrate day-ahead and balancing markets with distributed energy resources such as storage, electric vehicles, demand response, and Transmission and Distribution System Operators' coordination schemes. The day-ahead model determines optimal initial energy scheduling, while the balancing model optimizes energy and reserve products for three market designs with varying Transmission and Distribution System Operators’ coordination. The framework is applied to the interconnected Greek-Bulgarian-Romanian power system, considering 2030 installed capacities.The model outputs illustrate market coupling scenarios among Romania, Bulgaria, and Greece, highlighting clear price signals and distributed energy resources flexibility. Results reveal the significance of a diverse energy mix for energy security and show that Transmission and Distribution System Operators’ coordination significantly influences ancillary service prices, with reductions of up to 80 % in certain scenarios. Net demand values determine electricity flow direction. Flexibility providers like storage can cover up to 100 % of the upward congestion management requirements and 11 % of upward balancing energy, helping smooth energy allocation from intermittent renewables. The impact of electric vehicle penetration on generation scheduling is minimal. The proposed model offers valuable insights for system operators, market participants, and policymakers, enabling them to provide accurate price signals and optimize resource allocation.The integrated day-ahead and balancing market models support efficient renewable energy integration, emissions reduction, enhanced grid stability, and investment in low-carbon technologies. This aligns with the United Nations Framework Convention on Climate Change goals, contributing to the development of sustainable and resilient energy systems.

Small-Sample Data Pricing Based on Data Augmentation and Meta-Learning

Data trading platforms play a crucial role in facilitating data circulation and promoting the sustainable allocation of data resources. Establishing a transparent, fair, and efficient pricing mechanism is key to ensuring the long-term stability and development of such platforms. However, these platforms face challenges in pricing due to the small sample problem, as traditional machine learning methods typically rely on large amounts of data. To address this issue, this paper proposes a data resource pricing model that combines WGAN-GP data augmentation and the Reptile algorithm. Data augmentation generates related datasets to increase sample size, enhancing the renewability of data resources, while meta-learning transfers knowledge across tasks, improving the model’s ability to quickly adapt to new tasks and efficiently utilize resources. Validation using actual trading data from the data trading platform shows that the proposed model accurately predicts data resource prices under small-sample conditions, outperforming other models. This study addresses the limitations of existing pricing methods in small-sample scenarios, providing a sustainable pricing solution for small-sample data resources and improving the accuracy and long-term stability of data pricing in the market.

Application of the Average Based Fuzzy Time Series Lee Method for Forecasting World Gold Prices

Gold is a investment that investors are interested in because it has relatively low risk and gold investment is not affected by inflation. Gold prices always change from time to time, so it is necessary to forecast gold prices as a basis for investors in making decisions. The forecasting method used in the fuzzy time series lee method. The purpose of this research is determine the world prices and determine the accuracy of the gold price forecasting value ortained using fuzzy time series lee method. The results of this research are forecasting gold prices in the period November 20, 2023 of US$ 63,89/grams and relatively the level of forecasting accuracy based on MAPE value of 0,540091% included in the very good criteria in forecasting gold prices.

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.

Machine learning-based price prediction for thermal insulation materials: A holistic approach integrating thermophysical, technical, and environmental attributes in the Greek construction market

This study underscores the pressing need to accelerate the energy transition in existing housing to meet the EU’s decarbonization targets. It examines the correlation between thermophysical, technical, and environmental properties of thermal insulation materials and their commercial prices in Greece. Utilizing a dataset of 500 products from technical leaflets, Environmental Product Declarations (EPDs), and market surveys, the study developed six prediction models for material retail prices. A novel aspect of this research lies in quantifying the economic ramifications of insulation material properties through supervised machine learning (ML) techniques. Accurate price prediction models can provide practical applications across stakeholders, including cost estimations for designers, customized recommendations for homeowners, pricing tools for traders, optimization of manufacturing processes, support for policymaking, educational resources for construction professionals, and improved energy efficiency certification payback estimations. Feature analysis showed that total Global Warming Potential (GWP) significantly influences material prices. The Ensemble of Trees and Gaussian Process Regression (GPR) models demonstrated robust performance, with high R2 values of 0.95 in validation and 0.95/0.93 in test sets, indicating reliability without overfitting. Recommendations include integrating more predictors, collaborative model approaches, regular updates, and adopting federated learning techniques for enhanced data privacy and stakeholder cooperation.

Pricing of timer volatility-barrier options under Heston’s stochastic volatility model

Timer options are financial instruments that enable investors to exercise their rights on a random maturity date the realized variance reaches the level of variance budget. These options provide a stable investment opportunity for investors under the unpredictable and complex financial markets, such as global financial crisis or COVID-19 pandemic, which can induce the drastic changes of the volatility for the underlying asset. Meanwhile, in the financial markets, investors who invest in standard timer options may face the problems caused by the postponement of the exercising time for too low volatility, compared to standard vanilla options. In this regard, to overcome such disadvantages, we propose timer volatility-barrier options, which are activated and expired when the volatility arrives at a relatively low barrier level, with the original properties of the standard timer options. In this paper, by making use of the method of images, we derive an analytical formulas for the timer volatility-barrier options so that the volatility process can be driven by Heston stochastic volatility model, and verify the pricing accuracy of the timer options by comparing our solutions with those obtained from Monte Carlo simulations. Finally, we conduct numerical studies on the timer volatility-barrier options to examine the pricing sensitivities with respect to the various model parameters, and implement the discussion for pricing formula of double volatility barrier type of the timer options.