Pricing Model Research Articles

A Predictive Model for Digital Currencies Prices using Geometric Brownian Motion Stochastic Differential Equation: A Case Study of the Bitcoin

In this research work, we developed a predictive model for digital currency prices, involving daily closing price as a function of time. We used the Geometric Brownian motion stochastic differential equation which was solved using inbuild functions in Microsoft Excel. While we used the Bitcoin as our case study, our model was able to predict the daily closing prices of Bitcoin to a reasonable degree of accuracy. We equally observe that the time dependent Geometric Brownian motion stochastic differential equation cannot give digital currency traders and investors a clue on when to trade off their digital assets. Thus, it become very risky using our model to make well informed trading decisions. We therefore, recommend that for minimum risk, trades and investors in digital currencies should consider a combination of other signal tools to take more informed and less risky trading decisions.

The two-stage pricing of preannouncement for platform introducing self-operated logistics under consideration of the first- or second-mover advantage

PurposeThe optimization of transport efficiency by self-operated logistics has brought competitive advantages to platform, who is gradually developing self-operated logistics and adopting the preannouncement to announce the related information in advance. The purpose of this paper is to explore the development order of self-operated logistics on platform under consideration of preannounce behavior.Design/methodology/approachThis paper considers the sequence of platform constructing the self-operated logistics and constructs the two-stage pricing models to analyze the optimal pricing of platforms under different preannounce strategies, including four scenarios: {no-preannounce, first mover}, {no-preannounce, second mover}, {preannounce, first mover} and {preannounce, second mover}.FindingsThe authors receive several conclusions: First, under no-preannounce scenario, regardless of the sequence of entry into self-operated logistics market, when the quality differentiation of two platforms’ self-operated logistics is moderate, the ratio pricing of two platforms at competition stage is positively correlated with quality differentiation of their self-operated logistics. Additionally, there exists the substitution effect between preannouncement and quality differentiation under no-preannounce condition, and the first-mover platform should increase the pricing of the monopoly phase until it is twice as high as its pricing during the competition phase. Interestingly, the pricing of platform and the strategy for developing self-operated logistics are symmetric between first- and second-mover scenarios.Originality/valueFirst, this study analyzes the pricing and self-operated logistics construction under different preannounce strategies, enriching the interdisciplinary research on corporate marketing and providing scientific suggestions on how to use preannouncement to acquire competitive advantages. Second, this paper also considers the sequence of platform developing self-operated logistics and analyzes how platform develops self-operated logistics as well as pricing to gain first-mover and second-mover advantages. Third, this paper develops the two-stage pricing models that consider the continuity of pricing in different cycles, enriching the relevant theories and models.

House Price Pridiction Using Machine Learning

This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This proposed model considers as the most accurate model used for calculating the house price and provides a most accurate prediction. This provides a brief introduction which will be needed to predict the house price. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Predicting the price of a house helps for determine the selling price of the house in a particular region and it help people to find the correct time to buy a home. In this task on House Price Prediction using machine learning, our task is to use data to create a machine learning model to predict house prices in the given region. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to improve the efficiency of the model. for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset

Unveiling housing preferences amidst flood risks: an empirical study in an emerging market

Purpose This study investigates the interplay between flood risk, financial constraints and buyer behavior in the housing market. It aims to discern how flood experience influences housing choices, particularly among financially constrained buyers. Design/methodology/approach Using hedonic pricing models and probit analysis, this research examines the implicit prices of house characteristics and evaluates the determinants of housing choices, considering factors such as flood risk, financial status and past flood experience. Findings The study underscores that lower house prices incentivize buyers to opt for homes in flood-prone areas, thereby increasing their vulnerability to flood risks. Consequently, financial constraints exacerbate this situation, compelling low-income buyers to accept higher flood risks. However, flood experience fosters risk awareness among buyers, encouraging them to steer clear of flood-prone areas, albeit with lesser efficacy among financially constrained individuals. Originality/value This research contributes to the understanding of how flood risk and financial limitations shape housing decisions. It highlights the role of flood experience in influencing buyer behavior and emphasizes the need for targeted policies to mitigate flood-related risks in the housing market, particularly among economically vulnerable populations.

THE EDGEWORTH AND GRAM–CHARLIER DENSITIES

This paper is the first to define the Edgeworth density and comprehensively compare it to the Gram–Charlier density in the context of option pricing. The two densities allow additional cumulants to the normal distribution; although similar, they are not the same when truncated. Many academics have misidentified the two. This paper clearly distinguishes the two, presents the derivation of both, and develops a general option pricing model which can be used for both densities with an arbitrary number of additional cumulants. The option pricing formula for each density is also calibrated and compared to more typical models with the most advanced being the affine jump-diffusion model (stochastic volatility with double jumps).

Importance sampling for option pricing with feedforward neural networks

AbstractWe study the problem of reducing the variance of Monte Carlo estimators through performing suitable changes of the sampling measure computed by feedforward neural networks. To this end, building on the concept of vector stochastic integration, we characterise the Cameron–Martin spaces of a large class of Gaussian measures induced by vector-valued continuous local martingales with deterministic covariation. We prove that feedforward neural networks enjoy, up to an isometry, the universal approximation property in these topological spaces. We then prove that sampling measures generated by feedforward neural networks can approximate the optimal sampling measure arbitrarily well. We conclude with a comprehensive numerical study pricing path-dependent European options for asset price models that incorporate factors such as changing business activity, knock-out barriers, dynamic correlations and high-dimensional baskets.

Hybrid deep learning model for vegetable price forecasting based on principal component analysis and attention mechanism

Abstract With the aim of enhancing the accuracy of current models for forecasting vegetable prices and improving market structures, this study focuses on the prices of bell peppers at the Nanhuanqiao Market in Suzhou. In this paper, we propose a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model for vegetable price forecasting based on Principal Component Analysis (PCA) and Attention Mechanism (ATT). Initially, we utilized the Pearson correlation coefficient to filter out the factors impacting prices. Then, we applied PCA to reduce dimensionality, extracting key price features. Next, we captured local sequence patterns with CNN, while handling time-series features with GRU. Finally, these outputs were integrated via ATT to generate the final prediction. Our results indicate that the hybrid CNN-GRU model, enhanced by PCA and ATT, achieved a Root Mean Square Error (RMSE) as low as 0.16. This performance is 11.11%, 11.11%, and 15.79% better than that of the PCA-CNN, PCA-GRU, and CNN-GRU-ATT models, respectively. Furthermore, in order to prove the effectiveness of our proposed model, the proposed model is compared with the state-of-the-art models and classical machine learning algorithms under the same dataset, the results indicate that our proposed hybrid deep learning model based on PCA and ATT shows the best performance. Consequently, our model offers a valuable reference for vegetable price prediction.

An Intertemporal Risk Factor Model

Prominent factor models are based on tradable factors that do not represent theoretically relevant risks. To address this issue, we develop a factor model that captures the risks to long-term investors present in the intertemporal capital asset pricing model (ICAPM). Empirically, we construct intertemporal risk factors as long-short portfolios based on stock exposures to dividend yield and realized variance. These tradable factors mimic news to long-term expected returns and volatility, and they offset part of the marginal utility increase in recessions induced by wealth declines. Our intertemporal factor model estimation implies significant risk prices that are consistent with the ICAPM restrictions under moderate risk aversion. Moreover, our model performs well relative to previous factor models in terms of its tangency Sharpe ratio and its pricing of key test assets, including single stocks, industry portfolios, and portfolios sorted on risk exposures and lagged anomalies. This paper was accepted by Will Cong, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00261 .

Probability weighting and equity premium prediction: Investing with optimism

AbstractEmpirically motivated theoretical models of probability weighting which overweight tail events are finding many applications in finance. However, probability weighting has not yet been applied to equity premium prediction or to constructing optimal market timing investment strategies. We show that a measure of market optimism from a representative agent asset pricing model with probability weighting can be used to construct optimal dynamic investment strategies that outperform the buy‐and‐hold strategy and strategies generated by 17 leading equity premium predictors. We further show that this theory‐based measure of market optimism predicts the equity premium and market Sharpe ratio in‐sample and out‐of‐sample. The predictability is not subsumed by disaster probabilities, market sentiment, or market skewness. Our results indicate that our theory‐based measure provides a distinct channel for predicting aggregate stock returns.

A random forests-based hedonic price model accounting for spatial autocorrelation

A random forests-based hedonic price model accounting for spatial autocorrelation

DATA MODELING OF GOLD PRICES USING SUPPORT VECTOR REGRESSION WITH LINEAR, POLYNOMIAL, AND RADIAL BASIS FUNCTION KERNELS

Investment is a commitment of funds or other resources made with the goal of obtaining profits in the future. Gold is one of the commodities favored by investors because of its relatively stable value. Although relatively stable, gold prices experience fluctuations, which introduce risks associated with gold investments. The pattern of gold prices can be mathematically modeled using Support Vector Regression (SVR). SVR finds a function as a hyperplane (separating line) in the form of a regression function that fits all input data with an error and minimizes it as much as possible. SVR enables a balance between data fitting and overfitting through the use of kernel functions. The pattern of gold prices and SVR is the main focus of this research, aiming to model gold prices effectively. Furthermore, SVR seeks to obtain a hyperplane (regression function) that fits all input data by minimizing errors. The gold price modeling results using SVR indicated that the best model was obtained with a linear kernel, showing an error of 0.73% with parameters C = 10^(-4) and ? = 10^(-4). This implies the model's strong ability to follow data patterns accurately, resulting in highly reliable forecasting.

Transport development performance with hazard-inducing variables: A frontier-based shadow pricing approach

While economic growth in a country can be spurred by the expansion of the transportation sector, traffic accidents resulting from an increase in vehicle numbers negatively impact social wellbeing. To integrate both the beneftis and costs in evaluating the transportation sector, this study incorporates traffic accidents as a risk factor (undesirable output) in analyzing the performance of road transportation across Chinese provinces. We propose a shadow price model to estimate the revenue generated and the losses incurred from the addition of each vehicle. The potential improvements in desirable outputs (freight and passenger traffic) and the possible reduction in risks (traffic accident losses) are assessed using a nonparametric approach. Our findings indicate that the overall performance of the Chinese transportation sector improved between 2001 and 2018. During this period, the shadow revenue of vehicles increased, while the shadow loss from traffic accidents showed a declining trend, attributed to enhanced transportation infrastructure and effective government regulations. However, we observe regional disparities in performance and offer targeted policy recommendations.

Drug pricing models, no 'one-size-fits-all' approach: a systematic review and critical evaluation of pricing models in an evolving pharmaceutical landscape.

Access to new medicines is crucial for patients but increasingly sparks discussion due to high prices. Simultaneously, the growing emphasis on specialized products and uncertainty surrounding the long-term effectiveness of new drug classes brought to the market underscore the need for innovative pricing approaches. A systematic literature review of pharmaceutical pricing models, accompanied by a critical appraisal, was conducted to offer insights contributing to novel approaches balancing sustainable pharmaceutical innovation with affordability and accessibility for patients. Six different pricing models are identified: value based pricing, basic cost-based pricing, and four more comprehensive pricing models incorporating numerous elements: the cancer-drug-pricing model, AIM model, (Nuijtens) discounted cash flow, and the real-option rate of return method. Although there are many similarities among the models, each has unique assumptions for implementation. For instance, all models except for the standard incremental cost-effectiveness ratio and basic cost-based pricing consider the number of eligible patients and the remaining patent period. Only the AIM model and the Nuijtens discounted cash flow model use lump sums. Both the latter and the real-option rate of return method explicitly include the cost of capital as a major cost-based component. Recognizing the diverse applications of each model highlights the need for more differential and dynamic pricing tailored to the characteristics and therapeutic areas of each drug. Additionally, the study underscores the importance of cost transparency in achieving this goal. Consequently, these findings can help stakeholders develop sustainable and affordable drug pricing mechanisms that address the complexities of the ever-changing pharmaceutical landscape.

Estimating Asset Pricing Models in the Presence of Cross-Sectionally Correlated Pricing Errors

In this study, we propose an adversarial learning approach to the asset pricing model estimation problem which aims to find estimates of factors and loadings that capture time-series covariations while minimizing the worst-case cross-sectional pricing errors. The proposed estimator is defined by a novel min-max optimization problem in which finding a solution is known to be difficult. This contrasts with other related estimators that admit a well-defined analytic solution but do not effectively account for correlations among the pricing errors. To this end, we propose an approximate algorithm based on the alternating optimization procedure and empirically demonstrate that our proposed adversarial estimation framework outperforms other existing factor models, especially when the explanatory power of the pricing model is limited.

On the Distribution of the Mixed-Lognormal-Weibull Option Pricing Model

This paper intends to test whether the Mixed-Lognormal-Weibull Distribution (MLWD) option pricing model comes from the same distribution and whether the model is a good fit in Black-Scholes option pricing model. The data for this study were obtained from Australian Clearing House of Australian Securities Exchange (ASX) which consist of 50 enlisted stock as products of monthly market summary for long term options collected from January, 3rd 2017 to December, 31st 2017, comprising 720 trading days arranged in accordance to 25, 27, 28, 29 and 30 maturity days. Maximum Likelihood Estimate was used to obtain the parameters of both the lognormal and Weibull distributions which were applied in Black-Scholes model. The data were test Wilcoxon Rank Sum test since the mixture model became distribution and Chi-square goodness-of-fit test for the model fit, and the result shows that the mixture model does not follow any of the lognormal or Weibull distributions. The result also shows that the mixture model is a good fit in Black-Scholes option pricing model with the P-value>0.05 when they are shorter maturity days with small sample sizes than longer maturity days with larger sample sizes. Hence, the model is recommended to be used for financial practitioners who are interested in modeling option pricing.

Dynamic pricing and inventory model for perishable products with reference price and reference quality

Reference price (quality) is a benchmark point used by consumers to make price (quality) judgements. Combining reference price and reference quality with dynamic pricing and inventory management, this paper applies differential equations theory to construct a optimal control model for perishable products when the quality of products declines exponentially. The demand of products depends on price, quality, reference price and reference quality, among which reference price and reference quality are influenced by consumers’ past memory. The aim is to maximize the retailer’s profit during the period. The conclusions are as follows. Firstly, a optimal dynamic pricing and inventory model for perishable products with reference price and reference quality is constructed, and the model is extended to dual decision variables, stochastic demand, time-dependent effects, competitive and infinite planing horizon setups. Secondly, through the Pontryagin maximum principle, the analytical expression of the optimal dynamic price is derived. Thirdly, a linear search method to solve the optimal static price is proposed. Finally, the sensitivity of the main parameters is analyzed and the corresponding management enlightenments are given. By comparison of dynamic and static pricing, we find that dynamic pricing can achieve more profits and take a shorter selling period. In addition, for the sales problem of perishable products affected by both reference price and reference quality, retailers should adopt skimming pricing strategy (the optimal price decreases with time). Furthermore, to obtain more profit, retails should strive to increase the sensitivity coefficients of two types of reference, and the reference quality memory coefficient, while to decrease the reference price memory coefficient.

Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%.

Dynamic pricing with multiple consumers and alternating offers under retailer competition: theory and experiment

AbstractWe introduce and test a stylized model of dynamic pricing under duopolistic competition. In our model, a consumer receives alternating price offers between two retailers over an indefinite number of periods so that the game or “season” terminates with a fixed probability after each period. The two retailers do not know the valuation of the consumer for the good they are competing to sell to the consumer, but they have common knowledge about the probability distribution of the valuation. Our equilibrium analysis suggests that price offers decrease exponentially across periods over the season. Moreover, when there are multiple consumers in the game, as long as their valuations are ex ante independently and identically distributed, the equilibrium predictions are the same regardless of the number of consumers. An experiment on the model showed that subjects acting as retailers often overpriced relative to equilibrium predictions. In addition, the theoretical invariance with respect to the number of consumers did not hold: consumers seemed to be more prone to strategic waiting in the first period of the season when there were multiple consumers (compared with when there was only a single consumer), leading to a decrease in the per-consumer payoff of the retailer who made the price offer in the first period and a corresponding increase in per-consumer payoff of the other retailer. There is also evidence of within-session evolution that led to lower retailer prices that were closer to equilibrium predictions, and higher tendency for consumer strategic waiting, as the session progressed.

Common investor coverage and excess return comovement: Evidence from Seeking Alpha

Investors often cover a limited number of stocks, leading to a certain level of correlation in their information sets. Despite its importance, the role of this correlation in determining the return comovement between stock pairs has been less explored. In this study, we introduce a novel measure of common investor coverage, calculated using the number of co-commentators normalized by the geometric average number of commentators for each stock pair on Seeking Alpha. This measure aims to capture the underlying correlation in investors’ information sets. Our findings reveal a positive relationship between common investor coverage and excess comovement, which cannot be explained by traditional asset pricing models. Moreover, we observe a stronger relationship between common investor coverage and excess return comovement for stocks favored by retail investors, particularly after the implementation of the reward policy in 2011. This suggests that retail investors may play a crucial role in driving this relationship.

Asset pricing in African frontier equity markets

This paper undertakes a horse races style comparison of the efficacy of a range of multifactor asset pricing models in explaining the cross section of stock returns in African securities markets. Valuation factors used include size, book-to-market value, momentum, operating profit, asset growth or investment, liquidity and investor protection. Using monthly returns of 375 blue chip firms from 8 African equity markets over 23 years, we undertake a horse-race style comparison of various classes of augmented CAPM models. We show that both the Fama & French (2015) five factor and Fama & French (2018) six factor framework yield the highest explanatory power. Analysis of costs of equity and optimized portfolio opportunity set simulations reveal substantial differences arising and borne by practitioners from the contrasting application of different asset pricing models underscoring the timely importance of our study.