Price Trends Research Articles

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.

Price trends in the global electricity market

The relevance of the research topic is determined by the need to adapt to rapid global changes in the electric power industry, which is associated with a change in the structure of the energy balance, the strengthening of geopo­litical factors, the impact of climate change, economic globalization, new regulatory require­ments, and the rapid development of energy storage technologies and smart networks. These changes lead, on the one hand, to challenges in ensuring energy security, and on the other hand, contribute to significant price volatility in the electricity market. As a result of the influence of global price trends and world energy develop­ment priorities on the domestic electricity sector, the study of these trends is of great importance for Ukraine. The aim of the research is to establish the price trends of the global electricity market with the determination of the key factors of price growth in the context of challenges for Ukraine regarding the stabilization of electricity prices. The research is based on the hypothesis that the factors that determine the trajectory of price changes on the international electricity market are also characteristic of the Ukrainian electricity market. The research was conducted using the methods of statistical analysis, syn­thesis, grouping, tabular and graphical display of results. A retrospective analysis of price dyna­mics on the global energy market was carried out, which showed a faster pace of price changes for energy products than for other products, with a faster increase in oil prices than for coal. The current trends in the development of changes in electricity prices were analyzed, the reasons for their rapid growth in 2021 were identified, and it was noted that in some countries, such as the USA and China, prices remained stable. Using the example of the EU countries, it has been analytically proven that electricity tariffs depend on the price structure. The factors that affect pricing in the field of electricity in the world are determined: energy prices, global crisis pheno­mena, the amount of available supply, weather conditions, supply chain problems, grid infra­struc­ture and distribution, the structure of the electricity market, the share occupied by taxes and fees in electricity prices and the purchasing power of the population. The current state of the electricity market of Ukraine is analyzed, which is characterized by a price imbalance, especially in the retail sector, a settlement crisis between the main market participants, a lack of real competition, limited transparency of the market and opportunities for manipulation on it, and the imperfection of the legislative framework. These factors are exacerbated by external negative influences in the form of a full-scale Russian invasion due to the destruction of electric power infrastructure and loss of capacity

Enhancing bargaining power for energy communities in renewable power purchase agreements using Gaussian learning and fixed price bargaining

Power purchase agreements aim to secure long-term contracts between sellers and buyers, particularly in renewable energy transactions. However, successful negotiations for a fixed long-term price and energy volume while ensuring maximum utility for stakeholders remains a significant challenge. This paper introduces a comprehensive model for negotiating 24/7 power purchase agreements, focusing on hourly pricing to address deficits and surpluses throughout each day of the contract timeline. The model incorporates demand flexibility through battery storage, settling on the strike price using Nash Bargaining theory and optimal management of energy consumption relative to market price fluctuations. A soft margin support vector machine classification model determines the buyer’s maximum acceptable price. Moreover, Gaussian process classification is employed to calculate a probabilistic, risk-adjusted strike price, enabling a data-driven approach to power purchase negotiations. The proposed model’s performance is demonstrated through a detailed case study of Norway, illustrating how demand flexibility can significantly lower long-term power purchase agreement contract prices. The analysis of yearly price trends indicates that incorporating flexibility resources in long-term energy contracts may lead to a reduction in strike prices by around 25%. Moreover, such flexibility enhances demand-generation matching, thereby increasing renewable energy transactions within such agreements.

Stock Market Prediction in Nepali Stock Market using Machine Learning Model

The stock market, a key driver of the global economy, presents a formidable challenge for accurate prediction due to its intricate, chaotic, and dynamic nature. This study explores machine learning approaches, specifically comparing three prediction models—Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The analysis incorporates historical stock trading data, including open, high, low, and close prices, and technical analysis indicators such as moving average, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Commodity Channel Index (CCI) so on. The evaluation focuses on the performance of these models in predicting future trends in stock prices, utilizing data from the Nepal Stock market spanning from 2013 to 2023. The selected stocks for analysis include two stock NIC Asia Bank Limited and, NMB Bank Limited, two index NEPSE and Hydropower index. Through rigorous assessment, it is determined that LSTM demonstrates superior overall performance compared to GRU and BPNN. This research contributes valuable insights into the effectiveness of machine learning models in forecasting stock market trends, with implications for investors and financial practitioners.

Machine Learning Models for Gold Price Prediction: A Comparative Analysis and Evaluation

The prediction of gold prices is meaningful for multiple industries. Gold is a relatively stable store of value, and its price is closely linked to stocks, exchange rates, and monetary policy. Observing the trend of gold prices can promote the stable development of financial businesses. In this article, the daily opening and closing prices of gold over the past ten years were chosen as the data support. Seven different models were trained to predict prices on a yearly basis with a half-year interval. These predictions were then compared to actual data, and evaluations and analyses were conducted to identify the most suitable model for gold price forecasting. The machine learning models used included Ridge Regression, Linear Regression, Decision Tree Random Forest, Lasso Regression, Support Vector Machine, and K-Nearest Neighbors. In the evaluation process, Mean Squared Error (MSE) was used as the criterion to assess the accuracy of the model predictions.

Improved exponential smoothing grey-holt models for electricity price forecasting using whale optimization

This study introduces a ground-breaking approach, the Whale Optimization Algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model, designed for electricity price forecasting. Key features of the proposed WOA-GMHES(1,N) model include leveraging historical data to comprehend the underlying trends in electricity prices and utilizing the WOA algorithm for adaptive optimization of model parameters to capture evolving market dynamics. Evaluating the model on authentic high- and low-voltage electricity price data from Cameroon demonstrates its superiority over competing models. The WOA-GMHES(1,N) model achieves remarkable performance with RMSE and SMAPE scores of 12.63 and 0.01 %, respectively, showcasing its accuracy and reliability. Notably, the model proves to be computationally efficient, generating forecasts in <1.3 s. Three key aspects of customization distinguish this novel approach:•The WOA algorithm dynamically adjusts model parameters based on evolving electricity market dynamics.•The model employs a sophisticated GMHES approach, considering multiple factors for a comprehensive understanding of price trends.•The WOA-GMHES(1,N) model stands out for its computational efficiency, providing rapid and precise forecasts, making it a valuable tool for time-sensitive decision-making in the energy sector.

Trends in area, production, productivity and market prices of maize in Telangana state

Trends in area, production, productivity and market prices of maize in Telangana state

Predictive Modeling of Stock Price Trends Using Machine Learning and Deep Learning Techniques

Predictive Modeling of Stock Price Trends Using Machine Learning and Deep Learning Techniques

The impact of changing disclosure requirements, competition, and private capital on firm exit methods and premiums

AbstractChanging disclosure requirements and the evolution of US markets in the 21st century have created historic shifts in the exit strategies and payoffs for private firms. The propensity to sell to an acquirer has dominated firm exits in recent decades, especially for smaller private firms in highly concentrated industries. Exceptions to the merger exit preference are venture capital‐backed firms, which exhibit an enduring preference for IPOs, likely due to the reputation effects associated with this strategy. While the premium for IPO exits has exceeded that for M&amp;A exits in the past, we document a reversal in this pricing trend: in more recent years firms that sell out earn higher risk‐adjusted premiums than firms that conduct IPOs. Our empirical tests examine potential drivers of this effect. We believe we are the first to document this reversal in the economics of the exit decision.

A Time Trend and Persistence Analysis of Sunflower Oil and Olive Oil Prices in the Context of the Russia-Ukraine War

The imbalances between sunflower oil production (Ukraine) and olive oil production (Europe) due to the substitution effects caused by the Russian invasion of Ukraine on 24 February 2022 affected the prices. Given that Ukraine is the largest producer of global sunflower oil and Europe is the largest territory that produces olive oil, this scientific article tries to analyze the global prices of both vegetable oils and understand how the war between Russia and Ukraine has affected them. Advanced statistical and econometric methods to carry out this analysis have been used. It is found that the prices of both variables separately have similar behavior and that the shock caused by the war will be transitory, with the original price trend recovering in the long term using fractional integration methods. In a multivariate analysis, using a causality test in the frequency domain we observe that both variables are related to each other, and the effects of war will have an impact in the long term, with olive oil being the cause. A negative relationship between both variables measured with a wavelet analysis is also observed. Furthermore, if this trend continues, the price of olive oil would prevail over the price of sunflower in the war between Russia and Ukraine. Finally, a 12-month prediction is presented using artificial neural networks, where the price of olive oil will be high for at least 11 more months. The price of sunflower oil is predicted to last for only 5 more months.

Original Research Article Analyzing Price Behaviour and Constraints in Marketing of Small Onion in Tamil Nadu, India

This study examines market trends and seasonal patterns in small onion Markets in Tamil Nadu, while also investigating challenges faced by various stakeholders in the onion supply chain. The various analyses have made use of secondary data that has been gathered from different sources. The NHRDF office in Coimbatore, NHB website and AGMARKNET website are the sources of the monthly data on wholesale market prices and arrivals of small onions f markets of Dindigul, Chennai, Coimbatore and Idukki from 2014 to 2023. Primary data also obtained to fulfill the one of the research objectives by surveying the sample of respondents. Two districts were selected purposefully: Perambalur and Trichy districts, based on the top onion producing districts in Tamil Nadu. The sample size constitutes 180 respondents (80 farmers, 40 retailers, 20 wholesalers and 40 consumers) were randomly interviewed with help of pre structured interview schedule. Trend analysis revealed increasing price trends across all markets, with Idukki experiencing the highest rate of increase. Seasonal index analysis indicated that prices in Tamil Nadu markets are influenced by arrivals, with lower prices during peak arrival periods., while Idukki market in Kerala prices less influenced by arrivals. Farmers struggle primarily with high labor costs (78%) and high cost of fertilizers pesticides and machineries (68.7%), while consumers face problems with price fluctuations (68.8%) and quality concerns (68.6%), retailers face problems with lack of storage facilities (76.27%) and absence of facilities for grading (64.85%), and wholesalers struggle with financial risks (79.2%). The study's findings will aid policymakers to focus on improving market infrastructure, regulating intermediaries, and supporting farmers in managing costs. Leveraging existing labor schemes could help alleviate workforce shortages. Furthermore, customers would gain from initiatives to maintain price stability and raise quality standards.

Generalized visible curvature: An indicator for bubble identification and price trend prediction in cryptocurrencies

We propose a novel curvature-based indicator constructed on log-price time series that captures an interplay between trend, acceleration, and volatility found relevant to quantify risks and improve trading strategies. We apply it to diagnose explosive bubble-like behaviors in cryptocurrency price time series and provide early warning signals of impending market shifts or increased volatility. This improves significantly on standard statistical tests such as the Generalized Supremum Augmented Dickey–Fuller (GSADF) and the Backward SADF tests. Furthermore, the incorporation of our curvature-based indicator as a feature into the Light Gradient Boosting Machine enhances its predictive capabilities, as measured by classification accuracy and trading performance.

Interior-point policy optimization based multi-agent deep reinforcement learning method for secure home energy management under various uncertainties

Improving the efficiency of home energy management (HEM) is of great significance in reducing resource waste and prompting renewable energy consumption. With the development of advanced HEM systems and internet-of-things technology, more residents are willing to participate in the electricity market through a demand response mechanism, which can not only reduce the electricity bill but also stabilize the operation of the main grid through peak shaving and valley filling. However, the uncertainties from household appliance parameters, user behaviors, penetrated renewable energy, and electricity prices render the HEM problem a non-trivial task. Although previous deep reinforcement learning (DRL) methods have no requirement for system dynamics due to their model-free and data-driven merits, the unrestricted action space may violate the physical constraints of various devices, and few works have concentrated on this area before. To solve the above challenges, this paper proposes a novel interior-point policy optimization-based multi-agent DRL algorithm to optimize the home energy scheduling procedure while guaranteeing safety during its operation. Besides, a time-series prediction model based on a non-stationary Transformer neural network is proposed to predict the future trend of solar generation and electricity price, which can provide the agents with abundant information to make better decisions. The superior performance of our proposed predictive-control coupled method is demonstrated through extensive numerical experiments, which achieves more precise prediction results, near-zero constraint violations, and better trade-offs between cost and safety than several benchmark methods.

Water and Sewer Price and Affordability Trends in the United States, 2017–2023

Water and Sewer Price and Affordability Trends in the United States, 2017–2023

The impact of energy supply side on the diffusion of low-carbon transformation on energy demand side under low-carbon policies in China

In order to promote the synergistic green and low-carbon development of energy supply-demand sides, this paper uses the complex network evolutionary game to explore the impact of supply side decision-making on the diffusion of low-carbon transformation on demand-side under low-carbon policies. The result shows that: (1) It is feasible for the government to use carbon trading as a policy tool to promote synergistic low-carbon transformation on supply-demand sides, but it is constrained by the carbon price trend. (2) Relying only on the direct supply of clean energy from the energy supply to demand side is not enough to promote synergistic development, and the government needs to guide the both sides to cooperate in the construction of renewable energy power plants. (3) When carbon price is on an upward trend, the government needs to push forward the low-carbon transformation process on supply side, and form the low-carbon transformation of demand side driven by supply side. (4) When carbon price is on a downward trend, the government needs to guide the supply side to bear part of the transformation cost pressure on the demand side, and at the same time give more transformation subsidies.

Forecasting Stock Market Performance: An Ensemble Learning-Based Approach

There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance’s conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities that represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary “Buy” (1) or “Not Buy” (0) recommendation for each stock. We present approaches for a variety of machine learning models – Logistic Regression, Random Forest Classifiers, and Extreme Gradient Boosting models and do a statistical comparison on the balanced accuracies of each machine learning model. This paper aims to detail our understanding of how one can leverage the public sentiment of a stock to gauge its future value.

The link between abnormal numbers and price movements of financial securities: How does Benford’s law predict stock returns?

This paper studies the potential effect of deviation from Benford’s law on stock return prediction. Departures from the anticipated pattern can act as early indicators of irregular market behavior or possible fraudulent activities, both of which have the potential to impact future price trends. In this study, the deviation is measured by chi-squared test statistics over the first significant digit. Preliminary results indicate no compliance between daily stock returns data from Euronext Paris and Tunisian stock markets with Benford’s distribution. Then, the impact on the returns is explored via several models: linear regression and smooth transition models with various transition variables. Empirical results show the nonlinear effect of Benford’s law on stock returns prediction. We illustrate that this law can detect and predict abnormal returns generated by fraudulent or abnormal activities in both developed and emerging markets. By using Benford’s Law to analyze leading digits, investors and analysts can effectively identify irregularities and gain valuable insights into market dynamics.

Data-Driven Price Trends Prediction of Ethereum: A Hybrid Machine Learning and Signal Processing Approach

Due to the recent fluctuations in cryptocurrency prices, Ethereum has gained recognition as an investment asset. Given its volatile nature, there is a significant demand for accurate predictions to guide investment choices. This paper examines the most influential features of the daily price trends of Ethereum using a novel approach that combines the Random Forest classifier and the ReliefF method. Integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Short-Time Fourier Transform (STFT) resulted in high accuracy and performance metrics for Ethereum price trend predictions. This method stands out from prior research, primarily based on time series analysis, by enhancing pattern recognition across time and frequency domains. This adaptability leads to better prediction capabilities with accuracy reaching 76.56% in a highly chaotic market such as cryptocurrency. The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.

Prediction of Resale Value of Pre-Owned Luxury Cars in the Indian Market Employing Machine Learning Techniques

Abstract: The market for second-hand luxury cars in India is witnessing a significant surge, expected to grow at a rate of 16.30% from 2024 to 2032. This growth is fueled by increased car manufacturing, rising disposable incomes, and a shift in consumer preferences towards luxury brands. However, accurately determining the resale value of these vehicles presents a challenge due to various influencing factors. In this dynamic market, informed decision-making is crucial for luxury car buyers. Digital platforms have revolutionized access to real-time market data, helping both buyers and sellers stay updated on pricing trends. Our research explores the complexities of predicting prices for pre-owned luxury cars and introduces a predictive analytics framework using advanced machine learning algorithms. We collected and preprocessed a comprehensive dataset and conducted an in-depth exploratory data analysis. Various regression techniques, including Linear Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting, were employed to forecast prices. These models were evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to identify the most accurate predictive model. This study offers a systematic solution for price prediction, enhancing the buying process for stakeholders in the second-hand luxury car market

Modeling bitcoin price by using Euler-Maruyama method

&lt;p&gt;In this study, we use Euler-Maruyama method to simulate Bitcoin prices dynamics. We investigate a year-long movement of Bitcoin prices. Daily closing prices were collected over a period starting from May 27th, 2023 and ending on May 27th, 2024. This data provides a comprehensive picture of how Bitcoin behaved on a daily basis throughout that specific year. The Euler-Maruyama method is used as numerical method for solving stochastic differential equations (SDEs). The method involves discretizing the SDE into an iterative process to obtain a simulated price trajectory. The drift term was estimated by the average daily return of Bitcoin prices over the study period. The volatility term was estimated by the standard deviation of daily Bitcoin returns. A Monte Carlo simulation was performed to generate a range of possible price trajectories using the Euler-Maruyama method. The result shows that the Euler-Maruyama method was able to capture the short-term trend of Bitcoin prices effectively. However, the method can also be used to capture the long-term trend of Bitcoin prices.&lt;/p&gt;