Real Pricing Research Articles

Measuring Impact of Market Sentiment on Stock Prices

This study considers the impact of investor sentiment on stock market prices, it is when something beyond traditional economic factors, such as emotions or perceptions that play dominating role in driving psychological and real pricing. This behaviour in investor sentiment often leads to irrational decision making, always influenced by news, social media and the movements of markets. This will make stocks price more volatile. Through sentiment analysis, historical stock data and behavioural finance models the study examines times when there were significant movements in the market. These trends suggest that happy days can cause major prices to rise (along with the sun) and bad news can punch holes in a report even if business hasn't really looked any worse. The regulators and the investors should understand that sentiment can improve the efficiency of the stock price-prediction algorithms as well as bring anomalies to light in the market. The study of market sentiment and its effect on the stock price. In the given paper of research, it is easy to notice the impact of interaction between investor sentiment and stock price.

Research on the Influencing Factors of House Prices in Chicago

This study provides a comprehensive analysis of the factors that influence housing prices in suburban Chicago. Using detailed regression analysis, several key variables were identified as significant predictors of property value. The study included variables such as the number of bedrooms, square footage, number of rooms, size of the lot, annual taxes, number of bathrooms, number of garages, and the condition of the house. The R-squared value of the model is 0.732, indicating that approximately 73.2% of the house price variance can be explained by the selected variable. Houses with more rooms and more square footage tend to cost more. In addition, characteristics such as the number of garages and bathrooms can also have a significant impact on house prices. These findings provide valuable insights for real estate professionals, buyers and sellers, enabling them to make informed decisions regarding real estate investment and pricing strategies. By understanding the key drivers of house prices, stakeholders can better navigate the suburban housing market and ensure more accurate assessments and strategic planning.

Will the real carbon pricing please stand up?

Will the real carbon pricing please stand up?

Impact of variable electricity price on heat pump operated buildings

Background Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.

Modified genetic algorithm for unit commitment of grid connected microgrids under real time pricing conditions

Modified genetic algorithm for unit commitment of grid connected microgrids under real time pricing conditions

Influence of daylight on real estate housing prices. A multiple regression model application in Turin

Daylight is crucial in architecture, influencing human health, well-being, energy efficiency, and the post-COVID-19 perception of residential spaces. People often consider daylight among the most important features when buying a home, which potentially affects real estate asset pricing. Within this framework, this study explored the impact of daylight on real estate asset pricing, particularly focusing on its role in the Italian market. The research employed two approaches: (i) simulations of 100 units to determine a large set of daylight metrics and (ii) a statistical approach, applying Hedonic Analysis principles to investigate how house attributes influence pricing. A data sample of 100 housing units was selected in Turin (Italy), including variables such as location, floor area, construction year, façade type, and daylight metrics. By using Multiple Regression Analysis, after Exploratory Analysis and outlier management, we identified significant variables influencing housing listing prices, such as energy class, conservation status, elevator presence, terraces/balconies, and frame type. Notably, two daylight metrics (annual sunlight exposure and useful daylight illuminance) were found to be significant, while others, like average daylight factor and spatial daylight autonomy, were not. The final model was validated by means of a control sample, demonstrating the relevance of daylight in real estate pricing. This research contributes to the literature, providing an in-depth exploration of the impact of daylight on hedonic analysis in a relatively underexplored research space. Precisely, the work contributes to bridging the literature gap about the detection of the influence of daylight on real estate market pricing processes by means of regression analysis.

STIMULATING THE DEVELOPMENT OF RESIDENTIAL DEVELOPMENT THROUGH MORTGAGE LENDING

The article is devoted to stimulating the development of housing construction through mortgage lending. The purpose of the article is to determine the role of mortgage lending in the development of the housing sector, in particular through its impact on supply and demand in the real estate market and pricing in this sector. In the course of the research, data analysis, correlation analysis, and methods of forecasting economic trends were used. Graphical methods were also used to provide a clear understanding of how changes in the mortgage market affect the development of the real estate sector. The results of the study show that mortgage lending is a key element in stimulating the development of the housing sector, as it not only directly facilitates access to finance for potential property buyers, but also indirectly affects the pricing and investment attractiveness of the housing sector. The paper shows that fluctuations in mortgage rates have a significant impact on the dynamics of supply and demand in the residential real estate market, as well as on price trends. In particular, rising mortgage rates tend to reduce demand for housing, as households expect better investment opportunities. There is also a strong correlation between mortgage rate increases and slower price growth. The expansion of the mortgage lending market and, consequently, a reduction in mortgage rates boosts supply in the real estate market. Nevertheless, it is found that such changes can have complex and ambiguous consequences, including the risks of market overheating and the formation of price "bubbles" that threaten the stability of the sector in the long run. In addition, the study found that mortgage lending facilitates the implementation of new construction projects and the expansion of the housing stock, which is an important factor in stimulating economic growth. At the same time, the analysis showed that the impact of mortgage rates on the real estate market depends on a wide range of factors, including the economic situation, central bank policy, consumer confidence and other macroeconomic indicators. The practical significance of the publication is to provide recommendations for the development of a balanced policy in the field of mortgage lending aimed at supporting the stable development of the housing sector and preventing potential destabilising factors in the real estate market.

An Online Home Energy Management System using Q-Learning and Deep Q-Learning

The users of home energy management systems schedule their real-time energy consumption thanks to advancements in communication technology and smart metering infrastructures. In this paper, a data-driven strategy is proposed, which is an Online Home Energy Management System (ON-HEM) that uses reinforcement learning algorithms (Q-Learning and Deep Q-Learning) to control the optimal energy consumption of a smart home system. The proposed system comprises power resources (grid, photovoltaic), communication networks, and appliances with their agents classified into four groups: deferrable, non-deferrable, power level controllable, and electric vehicle. The system reduces electricity costs and high peak demands while considering the cost of user dissatisfaction with real-life data. Simulations are performed on the proposed ON-HEM considering different pricing approaches (Real Time Pricing and Time of Use Pricing) with Q-Learning and Deep Q-Learning (DQL) algorithms using PyCharm Professional Edition software. The findings demonstrate both the superiority of DQL over Q-Learning and the efficiency of the proposed ON-HEM in decreasing high peak demand, electricity costs, and customer dissatisfaction costs. The efficiency and dependability of the proposed system were verified by utilizing simulation-based findings with real-life data using IBM SPSS Statistics software.

Techno‐economic assessment of distribution system considering different types of electric vehicles and distributed generators

AbstractThe global warming crisis, together with environmental concerns, has already led governments to replace traditional fossil‐fuel cars with low‐emission Electric Vehicles (EVs). This replacement has resulted in the addition of a large number of EVs capable of connecting to the grid. A coordinated charging method is needed to promote EVs on a large scale while preventing grid overloading. In this work, EVs are categorized based on their battery capacity, as cars, trucks, and buses. A charging/discharging mechanism based on Real Time Pricing for each hour is developed for a 20‐min block that has been formed. The locations of EV charging stations have been identified using Voltage Stability Index.In the present work, the power loss when Distributed Generator (DG) units are deployed after the EVs are located in the system for charging and discharging is analyzed. The DGs are considered as solar‐based or wind‐based generators for studying the economic benefit of such an investment under two different weather scenarios and topologies on standard test systems. The results thus obtained give an insight into the various technical and economic benefits.

Real option pricing under the regime-switching model with jumps on a finite time horizon

We consider an irreversible investment decision problem on a finite time horizon where an instantaneous cash flow process of a firm follows a regime-switching jump-diffusion (RSJD) model. The value of a project can be derived from a partial integro-differential equation (PIDE) and then we obtain a closed-form solution of the PIDE. It is proved that the value of the project converges to the solution on an infinite time horizon problem as the lifetime of the project tends to infinity. Moreover, the value function of a real option with a finite expiration date can be evaluated by solving a linear complementarity problem (LCP) under the RSJD model. We apply a 2-step backward differentiation formula (BDF2 method) combined with an operator splitting method to solve the LCP and then it has the second-order convergence rate in the discrete ℓ2-norm with respect to the time and spatial variables. The stability of the proposed method to solve the LCP is also proved in the discrete ℓ2-norm. Finally, a variety of numerical experiments are carried out to determine the optimal investment time and the numerical results on the finite time horizon are analyzed.

A feasibility analysis of reverse mortgages in Brazil

In this study, we examine the feasibility of reverse mortgages in Brazil from the perspectives of both policyholders and insurers. Reverse mortgage is a financial product where the property's ownership is transferred to an insurance company in exchange for a guaranteed annuity, while the insured retains the right to use such property until their death. Using monthly real state pricing data (2008–2019) provided by FipeZAP, we employ statistical modeling to predict future prices of properties to get the future insurer's expected results, while policyholders' annuity benefits are calculated using actuarial approach. To assess attractiveness, we calculate internal rate of return for different policyholder profiles and conduct profit test for the insurer. Our results indicate that, as an investment strategy, reverse mortgage has little or no attractiveness to the policyholder despite its actuarial fairness. However, it may be a viable option for increasing retirement income in case of a consistent real estate overvaluation. For insurers, the results are unfeasible due to the extended time lag for positive financial results, which only occurs after the policyholder's death. The inclusion of safety loadings can produce more favorable outcomes. Nonetheless, we recommend incorporating reverse mortgages as an elderly policy strategy to reduce reliance on state support during retirement, even if no attractive returns are provided as a financial investment. We also discuss policy implications (e.g., right to the city) to help the elderly stay connected to social support networks and local services.

Machine Learning Solutions for Fast Real Estate Derivatives Pricing

Machine Learning Solutions for Fast Real Estate Derivatives Pricing

Day Ahead Solar Generation Forecasting in Smart Grids to Minimize Electricity Bill

Abstract: In order to meet the increasing growth of energy demand, integration of renewable resources into residential applications appears to be a viable solution. In the proposed model,a residential house is considered where the consumer is able to generate his own energy from a microgrid consisting of solar panels and wind turbines. In this study, an optimization method is employed to minimize the overall electricity bill of a residential home over a periodof 24 hours. In smart grids, energy management is imperative in reducing the electricity cost of consumers under a real time pricing approach. Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is one of the most popular techniques of deep learning. This technique predicts day ahead solar irradiation and wind speed data. We also consider an energy storage system (ESS) for efficient energy utilization. Optimization is done and from the obtained results a substantial reduction in electricity bill is observed

Analysis of distribution systems in the presence of electric vehicles and optimal allocation of distributed generations considering power loss and voltage stability index

AbstractElectric Vehicles (EV) have the potential to completely transform the transportation industry, reduce carbon emissions significantly, and pave the road for significant climate progress. According to surveys, worries regarding range and charging accessibility are a significant barrier to EV customer adoption. The electricity grid will be significantly impacted by EV charging. A coordinated charging approach that facilitates EV charging while avoiding the power grid from overloading is required to manage EV charging. In this work, the EVs are classified based on their charging capacity. This classification includes car, truck, and bus. With this a charging discharging scheme has been established using Real Time Pricing for each hour for a 20‐min block. The Voltage Stability Index (VSI) for the system is calculated for the system and the strongest bus is identified for locating the EVs to charge/discharge.Augmenting this, in the present work, the power loss is analyzed when Distributed Generation (DG) units are installed after the EVs are located in the system for charging and discharging. The DG units are installed in each feeder, considering the VSI and Penetration Level (PL) and the power losses incurred due to the presence of EVs. The Monetary benefit of such an investment is analyzed for different topologies of IEEE‐33 and 69 bus systems and compared with the existing methods where DGs are installed before the EVs are located in the system for charging and discharging.

A four‐stage framework for optimal scheduling strategy of smart prosumers with vehicle‐to‐home capability under real time pricing based on interval optimization

AbstractWith the emergence of the Smart Grid concept, utility companies require more active participation of home users in the power sector. This changing paradigm is enabled by the wide deployment of multiple home assets such as small renewable‐based generators or storage facilities. In this context, consumers are no longer conceived as pure loads but also active agents that can exchange energy with the grid. To promote this active participation, utility companies promote different price‐based demand response programs to change the consumer patterns on pursuing a more efficient and economic system operation. In this regard, home energy management programs are becoming an essential tool for efficiently managing the different home users while addressing multiple demand response goals at minimum cost. In essence, a home energy management system is a computational optimization tool, which has to handle multiple uncertainties brought by weather forecast or energy pricing. This paper tackles this issue by developing a novel robust home energy management program based on interval optimization. In contrast to other related approaches, the proposal avoids the explicit use of interval arithmetic. Instead, the different uncertain parameters are sequentially incorporated into the scheduling task through different stages and interval‐based formulation. The developed methodology incorporates weather, load, energy pricing and plug‐in electric vehicle related uncertainties. A benchmark case study in a smart prosumer layout serves to prove the effectiveness of the new approach.

Educational games in the system of economic education

Relevance. The Monopoly game is a popular board game with a simple concept that is relevant as a tool for enterprises and entrepreneurs in the system of teaching the basics of business, as a tool for studying strategic economics and justifying business strategies. Goals. Substantiation of the importance of the Monopoly game model as a business strategy tool for developing skills in strategic thinking, negotiation, risk management and efficient resource allocation. Exploring the similarities between concepts in the game and real economic concepts, including monopoly, market competition, pricing strategies, and business cycles. Evaluation of the effectiveness of the use of “Monopoly” as an educational tool for teaching the concepts of strategic economics. Methodology. Analysis, synthesis, observation, comparative and statistical methods. Conclusions. The business game “Monopoly” is a useful tool in the system of teaching economic concepts, raising the level of knowledge of the real economy. The use of the game model “Monopoly” in the educational process develops critical thinking and the skills of effective management decisions focused on business development.

A novel optimal framework for scheduling rooftop solar home appliances considering electricity, real pricing and user comfort

With an increase in electrification trends, the energy consumption of households is expected to increase significantly in the near future. To reduce electricity bills in the electrified era, management of the demand side is inevitable. Different demand response programs, such as real-time pricing can be introduced from the grid side. The consumption side needs to adjust its consumption patterns in accordance with the price signals. Therefore, an innovative home appliance scheduling method is proposed considering the rooftop solar panels as part of energy suppliers. First, an optimization problem is formulated considering different objectives such as energy price, consumer satisfaction, and peak-to-average load ratio. Then, home appliances are divided into three major categories (shiftable, flexible, and normally operated) to adjust schedules of different load types. Then, the developed model is solved using the bald eagle search optimization algorithm (BESOA). Finally, the performance of the proposed BESOA-based method is compared with other well-known heuristic/meta-heuristic methods. In addition, an analysis of different equipment’s maximal operation time is conducted for all the methods. In order to provide feedback on user comfort levels, digital twin technology is used allowing the user to adjust the scheduling of the appliances to ensure that they are comfortable. By leveraging digital twin technology, users would be able to optimize the scheduling of their rooftop solar home appliances to maximize efficiency and minimize costs. Simulation results have shown the superiority of the BESOA method over other methods in terms of daily average energy cost, peak-to-average load ratio, and consumer satisfaction.

Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC.

Statement of retraction: A generalized real option pricing method of R&D investments: jump diffusion and external competition

Statement of retraction: A generalized real option pricing method of R&D investments: jump diffusion and external competition

Development of two stage optimization-based demand response technique for smart homes under real time pricing

Residential load management deals with two major objectives viz. minimizing the cost of monthly electricity bill and peak demand of power consumption. Both objectives can be achieved by effective operational scheduling of smart home appliances. These two objectives are conflicting in nature because rescheduling of appliances in order to minimize one objective may result in the rise of another. To achieve both objectives concurrently, an algorithm is suggested in this paper based on artificial intelligent techniques like cuckoo search, hybrid GA-PSO, and adaptive cuckoo search. The proposed algorithm is tested successfully on seven households of different monthly power consumption and real data of dynamic pricing options for electricity applicable in two utilities. To reduce the risk associated with real-time price, a cost function based on inclining block rates (IBR) is also suggested for both the utilities. A novel approach to find the threshold limit of hourly power consumption is also suggested in this paper. The proposed algorithm solves the optimization problem in two stages and validates its performance by successfully achieving both objectives simultaneously.