Pricing Model Research Articles

Mapping the Conceptual and Intellectual Structure of the Investor’s Financial Behaviour: A Bibliometric Analysis

Examining the diverse roles played by various kinds of investors in the stock market is crucial, because of their differing behaviours while making investment decisions. This study reviewed the academic literature on investor behaviour to acquire insights into the current status, developments and significant gaps with the help of bibliometric analysis. The research methodology involved selecting articles from the Scopus database and conducting performance analysis using VOSviewer software for science mapping. The study retrieved 313 research articles using the PRISMA protocol. It identified influential articles, journals, authors and five major themes (inefficiency and bubbles in the stock market, technological advancement in the financial market, behavioural aspect in investment decisions, prospect theory and cognitive bias and stock trading behaviour), adding to the body of knowledge on investor behaviour. The findings also revealed that there has been a consistent rise in research in this area. In addition, scientific mapping techniques, such as bibliographic coupling, showed that the majority of the literature is focused on the connection between asset price models, behavioural biases, investor sentiment and portfolio choices. Finally, the research directions identified in this study could serve as a guide for future research.

MODEL INVESTASI POLIS ASURANSI JIWA BERBONUS TIPE DWIGUNA DENGAN AMERICAN PUT OPTION

Insurance is a form of protection against the concept of risk transfer that may occur from an uncertain event. One type of insurance is life insurance. With the changing times, life insurance launched innovative products, one of which is endowment life insurance. Endowment life insurance is a type of life insurance that provides compensation benefits if the policy holder dies during the coverage period, as well as providing cash value and bonuses if the policy holder is still alive until the end of the period. This study aims to determine the fair price of insurance policies and the price of bonus life insurance policies. Determination of the value of the American put option with the binomial method using a numerical approach, the formulation of the portfolio model includes determining the model of the unit price with Geometric Brownian Motion, solving using Black-Scholes, determining the structure of the bonus life insurance policy with the formulation of a single premium payment, bonus rate, and benefits. then analyze the movement of the price of a reasonable bonus life insurance policy with a surrender option based on the age of the insured, technical rate, participation level, and volatility. The surrender value obtained is the difference between the value of the American Put option and the European Call Option. Based on the simulation, the conclusion of this analysis is that the price of the endowment type life insurance policy can be estimated using the binomial method at around 0.78 for a fair policy price and around 0.27 for a policy price with a surrender option. This gives an idea of the relative value of the policy price to the expected benefits under certain conditions, such as the death of the insured in the first year of the contract.

GOLD PRICE PREDICTION IN INDONESIA BASED ON INTEREST RATE USING DISTRIBUTED LAG ALMON TRANSFORMATION

Gold is valued for its safety and profitability, driven by steady price changes and influenced by interest rates. Accurately predicting gold prices is very important to make the right investment decisions. This study aims to build a gold price model in Indonesia using the Almon transformation lag distribution and see gold price predictions based on the model that has been built. We used the data on gold prices and interest rates from January 2016 to December 2023. Based on the results of the analysis, the best Almon transformation model used in this study is the Almon model with a maximum lag length of 16 and the second polynomial degree. The prediction results have a MAPE of 16.49%, which shows that the Almon model can predict gold prices well for one year. This study contributes to the understanding of gold price dynamics amid economic variations. However, limitations in the model assumptions should be considered.

Hedonic Pricing Models in Rural Tourism: Analyzing Factors Influencing Accommodation Pricing in Romania Using Geographically Weighted Regression

This study investigates the factors influencing pricing in Romanian rural tourism using a hedonic pricing model through a hybrid LASSO-OLS regression and geographically weighted regression (GWR). By analyzing data from 5028 unique accommodation units across 1170 local administrative units, we identify some key pricing determinants, including accommodation size, capacity, facilities, and environmental attributes. The results reveal that larger accommodations and those with higher guest capacities command higher prices. Luxurious facilities, such as massage services, pools, and fireplaces, significantly increase pricing, although the impact of such features varies by region, as do accommodation type and natural scenery, with agritouristic boarding houses and proximity to natural attractions like water bodies and forests being more valued in certain regions. These factors can aid rural entrepreneurs in optimizing pricing to enhance competitiveness and profitability.

Smart Grid Technologies and Their Role in Sustainable Energy Management

Purpose: The general objective of this study was to analyze smart grid technologies and their role in sustainable energy management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to smart grid technologies and their role in sustainable energy management. Preliminary empirical review revealed that smart grid technologies significantly transformed energy management by enhancing sustainability, reliability, and efficiency through advanced metering, real-time analytics, and improved communication networks. These technologies enabled better integration of renewable energy sources, reduced greenhouse gas emissions, and empowered consumers with detailed energy usage information, leading to cost savings and increased energy efficiency. However, challenges such as high implementation costs, infrastructure needs, regulatory hurdles, and cybersecurity risks were identified. The study highlighted the importance of continued research, supportive policies, and public engagement to optimize smart grid deployment and achieve a more sustainable energy system. Unique Contribution to Theory, Practice and Policy: The Diffusions of Innovations Theory, Socio- Technical Systems Theory and Actor- Network Theory may be used to anchor future studies on smart grid technologies. The study recommended adopting a multidisciplinary approach to smart grid research, enhancing interoperability through standardized protocols, and investing in workforce training. It emphasized supportive regulatory frameworks, including incentives and dynamic pricing models, to promote smart grid adoption. Public awareness and engagement were deemed crucial, suggesting educational campaigns to increase consumer participation. The study highlighted the importance of international collaboration for knowledge sharing and called for continuous investment in research and development to drive technological advancements and ensure the sustainability and efficiency of smart grid systems.

Harnessding the Power of Machine Learning to Superchange CAPM Forecasts

This paper investigates the improvement of the Capital Asset Pricing Model (CAPM) by incorporating machine learning techniques. The objective is to address the model's conventional constraints in accurately predicting stock returns. The conventional Capital Asset Pricing Model (CAPM), which heavily depends on the beta coefficient to explain returns, frequently proves inadequate in emerging markets and neglects to consider market irregularities such as size and value effects. Our methodology enhances the estimation of beta by refining the Capital Asset Pricing Model (CAPM) to exclude the intercept term and incorporating a dynamic rolling regression method. This approach captures the fluctuations of beta more effectively across different periods, resulting in a more precise estimation. This study utilises ten years of weekly closing price data from companies listed on the NSE Nifty 50 index. It employs rolling regression and two-stage regression analysis to improve the prediction of excess returns above the risk-free rate. The revised CAPM model, which omits the intercept, exhibits a superior level of accuracy, as evidenced by higher adjusted R-squared values and improved F-statistics. In addition, the paper analyses the Fama-French models within the Chinese stock market and explores the potential of advanced machine learning models, such as Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), to improve forecasting methods in unpredictable markets. Integrating LSTM-RNN models presents a promising approach to capturing intricate patterns in financial time series data, demonstrating substantial enhancement in forecasting accuracy compared to conventional methods such as OLS.

Hybrid Prediction Model for International Crude Oil Prices Based on EEMD and SE Reconstruction Methods

Accurate prediction of crude oil prices can provide references for the energy economy and valuable guidance for investors and decision-makers in formulating strategies. However, due to the complexity and volatility of the crude oil market, achieving accurate and reliable crude oil price forecasts is challenging. Therefore, this paper proposes a hybrid forecasting model for crude oil prices that combines Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SE) reconstruction. Firstly, the daily closing price data of WTI crude oil futures from January 2, 2018, to March 21, 2024, is selected. The EEMD method is used to decompose the original data, and the decomposed sub-sequences are reconstructed and merged into high-frequency, mid-frequency, and low-frequency sequences using Sample Entropy (SE). Finally, these sequences are input into six machine learning models for prediction and comparison. The research results show that, firstly, by comparing the R2, RMSE, MAE, and MAPE values, it can be seen that the forecasting ability of the EEMD-SE hybrid forecasting model is significantly better than the EEMD hybrid forecasting model, indicating that the combination of SE methods can effectively improve the model s forecasting ability. Secondly, the EEMD-SE-LSTM model has higher prediction accuracy than other models. Finally, the comparison of models reveals that the BP neural network exhibits the greatest improvement after SE reconstruction. Therefore, it is recommended to improve the crude oil futures price forecasting and risk warning system.

A Pricing Model Study of Shared Parking Area Charge Based on Game Theory

A Pricing Model Study of Shared Parking Area Charge Based on Game Theory

The study of phase portraits, multistability visualization, Lyapunov exponents and chaos identification of coupled nonlinear volatility and option pricing model

The study of phase portraits, multistability visualization, Lyapunov exponents and chaos identification of coupled nonlinear volatility and option pricing model

Deep learning-driven intelligent pricing model in retail: from sales forecasting to dynamic price optimization

Deep learning-driven intelligent pricing model in retail: from sales forecasting to dynamic price optimization

Modeling and analysis of crypto-backed over-collateralized stable derivatives in DeFi

In decentralized finance (DeFi), stablecoins like DAI are designed to offer a stable value amidst the fluctuating nature of cryptocurrencies. We examine the class of crypto-backed stable derivatives, focusing on mechanisms for price stabilization and exemplified by the well-known stablecoin DAI from MakerDAO. For simplicity, we consider a single-collateral setting. We introduce a belief parameter to the simulation model of DAI in a previous work (DAISIM), reflecting market sentiments about the value and stability of DAI, and show that it better matches the expected behavior when this parameter is set within a particular range of values. Our methods include comparing simulated data with real-world data, focusing on monthly correlations between ETH and DAI prices and scatter plots illustrating the relationship of their price trends over time. We also propose a simple mathematical model of DAI price to explain its stability and dependency on ETH price. Finally, we analyze possible risk factors associated with these stable derivatives to provide valuable insights for stakeholders in the DeFi ecosystem.

Strategic Optimization of Conveyor Belt Splicing Solutions: Enhancing Competitive Edge in the Asia-Pacific Market (Case Study: Inspirare Luxiner Group)

The Asia-Pacific region, marked by rapid industrial growth and significant mining activities, offers both opportunities and challenges for companies in the conveyor belt splicing materials market. IL Group, known for its high-quality RARE System, has seen a decline in sales within this competitive environment. The centralization of manufacturing in Atlanta has led to long lead times and high logistics costs, reducing the company’s competitiveness. Moreover, insufficient after-sales support and integration challenges with client systems have further decreased customer satisfaction and retention. This research aims to identify the primary factors behind the RARE System’s decreased sales and to develop strategies for optimizing it to attract and retain clients. Additionally, it seeks to recommend business strategies to enhance IL Group’s competitiveness in the Asia-Pacific market. Data collection involved interviews and surveys with clients and internal stakeholders. The data was analyzed using thematic analysis, root cause analysis, SWOT & TOWS analysis, and Porter’s Five Forces framework. The study found that logistical challenges from centralized manufacturing, inadequate support, intense competition, and integration issues with client systems were key factors driving sales declines. To address these issues, the research recommends that IL Group establish regional manufacturing facilities to reduce costs and lead times. Improving customer support through regional centers and robust CRM systems is also essential. Investing in continuous R&D for product innovation will keep the product relevant and appealing. Implementing flexible pricing models and targeted marketing, along with forming strategic local partnerships, will further enhance competitiveness. By proposing these strategies, this research contributes to strategic business management. It offers practical solutions to enhance operational efficiency and market competitiveness. These strategies provide a structured approach to overcoming key challenges and achieving sustainable growth in the Asia-Pacific market.

Planning with the electricity market One day ahead for a smart home connected to the RES by the MILP method

This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous day’s bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the program’s effectiveness in reducing both net energy costs and peak load on the electricity grid.

Economic valuation of open research data: A conceptual framework and methodological approach

Abstract The economic significance of open research data is widely acknowledged, yet its quantification remains challenging. This paper presents an effective valuation instrument to help stakeholders understand and evaluate the economic benefits of open research data. By conducting a scoping review and prioritizing user engagement, this study introduces a comprehensive conceptual framework for the economic valuation of open research data. The valuation is based on economic value and willingness to pay, employing the Contingent Valuation Method (CVM). It incorporates per-use models (view, download, and request) and periodic subscription models (monthly and yearly). An empirical survey was conducted at the National Basic Science Data Center (NBSDC) in China to verify this framework. Both pricing models, comprising five distinct tactics, was supported by surveyed users. Measuring economic value by views and by year was preferred, while willingness to pay by downloads and by year was considered more reasonable. Overall, the most applicable valuation approach is on a yearly basis. Through this case study at NBSDC, specific pricing tactics were identified, and the total economic value and users’ willingness to pay were assessed. This study is arguably the first to establish a conceptual framework with pricing tactics from a user perspective. This methodological approach for economic valuation of open research data provides evidence and tools for future research, policy formulation, and resource allocation in the context of open science and innovation.

Herd Behaviour and COVID-19 Intervention

The paper attempted to confirm the rational assets pricing model by examining the herd behaviour in extreme market returns and examining the COVID-19 intervention by comparing before COVID-19, during COVID-19 and after COVID-19 period. Cross Sectional Standard Deviation (CSSD) calculated as the index and further regressed by using dummy variables (Extreme upper tail market returns =1, otherwise = 0). For the better inferences stationarity of time series data were checked by both graphically and Augmented Dickey –Fuller test in all three states of COVID-19. Finally, the Cumulative-Sum (CUSUM) test was conducted to check the parameter stability of the regression model. The finding of the study supports the rational asset pricing models, meaning that there is no presence of herding in NEPSE in extreme upper tail returns. But it rejects the rational asset pricing models in case of extreme lower market returns and in COVID-19 period. For better affirmation of rational assets pricing models, future research can extend the work by addressing the market characteristics, such as: trading volume, volatility, and liquidity.

Application of Hedonic Pricing Model and Geographic Information System on Property Valuation in Yogyakarta

This study aims to: 1) identify the differences in housing choices between Generation Z (students) and the previous generation (parents or guardians); 2) determine the factors influencing housing purchase decisions; and 3) map the areas of interest for Universitas Gadjah Mada (UGM) students based on price. The research employs a combination of the Hedonic Pricing Model (HPM) and Geographic Information System (GIS), a method that remains limited in the field of property research. The findings reveal that significant factors affecting property prices are building area and location, with the majority of UGM students choosing to reside in Ngaglik District, Sleman Regency. Sleman Regency is also the predominant area where UGM students purchase housing in Yogyakarta. Key attributes influencing UGM students' housing decisions include a safe neighbourhood, residential environment, and distance from waste centers. This research contributes valuable information to property appraisers regarding property price distribution in Yogyakarta and its influencing factors, and provides crucial insights for effective decision-making and policy implementation in regional economic development planning, especially in less popular areas.

Research on the risk effects of accounting information disclosure

Drawing upon accounting-based asset pricing model and insights from theories of information asymmetry and cognitive psychology, this study investigates the risk implications of accounting information disclosure from dual perspectives: firm-specific risk and market risk. Our findings reveal a positive association between earnings management and both firm-specific and market risk effects of accounting information, underscoring the presence of post-disclosure risk effects. Heterogeneity analysis further uncovers the moderating roles of factors such as accounting firm size, company tenure, and regional economic growth in firm-specific risk effects, while corporate governance structures influence market risk effects. This study enriches the theoretical literature on accounting information and risk effects.

Application of Multi-Factor Financial Models in Asset Pricing

This paper explores the theoretical underpinnings and practical applications of multi-factor models in stock portfolio management. It outlines the theoretical frameworks of multi-factor asset pricing models such as APT theory, Fama-French three-factor, and five-factor models, analyzing how various risk factors influence asset returns. The study then discusses methods for identifying core risk factors empirically, constructing portfolios, optimizing weight allocations, and evaluating investment performance. Empirical testing using data from the CSI 300 and CSI 800 indices confirms that optimized multi-factor portfolios achieve significant excess returns while managing risk effectively, showcasing the practical utility of this quantitative investment framework in the Chinese A-share market.

The effect of avian influenza outbreaks on retail price premiums in the United States poultry market

This study analyzes the effect of avian influenza outbreaks on retail price premiums in the US poultry market. We estimate hedonic price models for eggs, chickens, and turkeys, controlling for quality characteristics, unobserved time, and regional factors. To measure the impact of avian influenza outbreaks we use 2 proxies. The first proxy is a measure of the number of new bird infections at the production level. The second proxy measures online search queries related to the outbreak. The results show that, on average, prices increase across product categories, that is, egg, broiler, and turkey markets, during avian influenza outbreaks. Furthermore, we observe price convergence and reduced dispersion within product categories, which is consistent with the economic theory of asymmetric substitutability between conventional and premium products. Our analysis finds that the HPAI outbreak caused a reduction of the price gap between conventional and premium products.

Unlocking visual data to enhance the accuracy of AI-enabled mass valuation of urban houses: An Australian city case study

Despite earlier praises of hedonic price models (HPMs) in predicting the prices of residential properties, stakeholders in the property market have raised concerns regarding inaccuracy in automated valuation models (AVMs) using conventional HPMs. Furthermore, despite significant advancements in integrating artificial intelligence and image data into AVMs, research in the Australian context is absent. This paper investigates how image data capturing visual features of properties could enhance valuation accuracy using the data of actual sales of 34,399 properties from 2018 to 2020 across 128 urban suburbs in Brisbane, the third largest city in Australia. We develop a convolutional neural network model to extract visual features from large-scale data of 320,000 street-view and aerial-view images. We develop fusion models to integrate these additional visual features in HPMs. Using several experimental designs, our preferred fusion models generate a reduction of 28.35% in root-mean-square errors. Less predictive errors of AI-enabled AVMs through the use of visual data would enhance business confidence for AVMs’ end-users for investment, reporting, risk management, tax estimation, and urban planning purposes.