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In the past year, a set of energy laws was adopted in the Federation of Bosnia and Herzegovina (FB&H), namely the Law on Energy and Regulation of Energy Activities in the FB&H, the Law on Electricity in the FB&H and the Law on the Use of Renewable Energy Sources and Efficient Cogeneration. The new set of energy laws also defines a new method of incitement, which is provided for large plants for the production of electricity from RES and efficient cogeneration through the auction system. In this sense, in addition to the establishment of the auction system, the correct calculation of the replacement market price is also very important. During the writing of this paper, an analysis of the surrounding organized electricity markets was performed, and a methodology was proposed that would, in the best sense, give a correct calculation of the replacement market price. In this regard, the replacement market price would at the same time mirror the actual electricity trade in the Federation of Bosnia and Herzegovina for a period of one month, and would also take into account the price movement on the surrounding organized electricity markets.

The scope of the present investigation is to provide a description of final electricity prices development in the context of deregulated electricity markets in EU28, up to 2020. We introduce a new methodology to predict long-term electricity market prices consisting of two parts: (1) a self-developed form of Porter’s five forces analysis (PFFA) determining that electricity markets are characterized by a fairly steady price increase. Dominant driving factors come out to be: (i) uncertainty of future electricity prices; (ii) regulatory complexity; and (iii) generation overcapacities. Similar conclusions derive from (2) a self-developed form of multiple-criteria decision analysis (MCDA). In this case, we find that the electricity market particularly depends on (i) market liberalization and (ii) the European Union (EU)’s economy growth. The applied methodologies provide a novel contribution in forecasting electricity price trends, by analyzing the sentiments, expectations, and knowledge of industry experts, through an assessment of factors influencing the market price and goals of key market participants. An extensive survey was conducted, interviewing experts all over Europe showed that the electricity market is subject to a future slight price increase.

This study dynamically investigates the mathematical Ivancevic option pricing governing system in terms of conformable fractional derivative, which illustrates a confined Brownian motion identified with a non-linear Schrödinger type equation. This model describes the controlled Brownian motion that comes with a non-linear Schrödinger type equation. The solution to comprehend the market price fluctuations for the suggested model is developed through the application of a mathematical strategy. The modified Kudryashov analytical method is applied to find the fractional analytical exact soliton solution. The restrictions on the parameters required for these solutions to exist were also the result of this approach. The dynamical insights are examined and significant aspects of the phenomenon under study are discussed through the use of the bifurcation analysis. In the related dynamical system, the phase portraits of market price fluctuations are displayed at equilibrium points and for different parameter values. Additionally, the chaos analysis was carried out to show the quasi-periodic and periodic chaotic patterns. In order to track changes in market price, the sensitivity analysis of the studied model is also looked at and presented at different initial conditions. It was discovered that the model experienced price fluctuations as a result of minute changes in initial conditions.

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage.

The stock market is changing quickly, and its nonlinear characteristics make stock price prediction difficult. Predicting stock prices is challenging due to several factors, including a company’s financial performance, unforeseen circumstances, general economic conditions, politics, current assets, global situation, etc. Despite these terms, sufficient data are available to identify stock price movement trends using the different technical approaches. In this research, we empirically analyzed long short-term memory (LSTM) networks in the context of time-series prediction. Our investigation leveraged a diverse set of real-world datasets and provided quantitative insights into the performance of LSTMs. Across a spectrum of time-series forecasting tasks, LSTM models demonstrated an impressive mean absolute error (MAE) reduction of 23.4% compared to traditional forecasting methods. Specifically, LSTM achieved an average prediction accuracy of 89.7% in financial market predictions, outperforming baseline models by a significant margin. The aim is to obtain a value that can be compared to the present price of an asset to determine whether it is overvalued or undervalued, which anticipates the price patterns by analyzing previous market information, such as price and volume, compared to this stock analysis approach.

\"To study the effect of MSP on price and arrivals, Madhya Pradesh was purposively chosen with its major crops, paddy, wheat, soybean, bengal gram, and black gram. With the help of data collected from Agmarknet portal for period 2010-2020 study was carried out. The data was analysed by calculating Weighted averages, Percentages along with Linear trend analysis, Tabular analysis, Correlation analysis, and Seemingly Unrelated Regression. The study found that MSP for selected crops had growth rates ranging from 4.5 to 8.2 percent per annum. Share of arrivals sold below MSP ranged from 15 to 68 percent. MSP had positive relationship with price of commodities but had negative relationship with share of arrivals sold below MSP and price difference from MSP. Thus, MSP had negative effect on arrivals and price reported below MSP. So, procurement should be done by the government for the commodities where 50% of the arrivals are sold below MSP. Government should also provide the facilities (grading, processing, storage etc.) that will be helpful for the farmers to sell their commodities at MSP in the market.\"

In this paper, the authors take Hubei carbon trading market prices as a sample, and select 27 variables from five aspects: International carbon market prices, energy prices, the macroeconomic situation, exchange rate factors and climate environment. The authors construct the elliptical approximate factor model and use a robust two step method based on multivariate Kendall’s Tau matrix to extract common factors, identify the influencing factors of carbon prices, make out-of-sample forecasting of carbon prices, and compare with the prediction based on the historical mean of carbon trading market prices. The results show that the prediction of carbon trading market prices using elliptical approximate factor model is more accurate than the prediction based on the historical mean of carbon trading market prices. Among them, fossil energy prices, international carbon prices and climate environment are important influencing factors of carbon trading prices.

Stock market price prediction is vital for investment decision amid difficulties with effective price predictions. The paper aims to analyse the rate of effectiveness in actual stock market price prediction using the open, high and low prices. The paper draws insight from diverse prior research with assorted models such as Markov Chain, time series and computer aided stock price prediction. The paper’s approach is quantitative with forty-three days stock market price data from S&P500 and Shanghai Composite Index. Data was analysed with the regression statistics. Results show that the open, high and low prices can significantly predict the actual market price at probability level of P<0.0001 for both the S&P500 Index and the Shanghai Composite Index. Prediction rates exceed 70% for S&P500 and over 80% for Shanghai Composite Index. The model was verified by using data other observation periods (during the COVID-19 and during the financial crisis). The implication therefore is that in the absence of other expensive market information, an average investor may use the open, high and low prices to make a useful prediction of actual stock market price. The findings present a useful case reading for academics in business schools and offer an agender for future research to apply this model in other stock markets. The paper offers a novel value from the finding by demonstrating that the showing that application of open, high and low prices with regression may give a prediction accuracy rate of over eighty percent, which is higher than reported seventy percent prediction rate in prior work that used other models.

To address the challenges posed by ore grade fluctuations, extraction cost variations, and market price instability in open-pit ore production strategies, an optimization model integrating a solution algorithm integrating dynamic simulation is proposed. First, Monte Carlo simulation is used to generate random variables, simulating ore grade and extraction cost fluctuations at different extraction points. Then, the Non-dominated Sorting Genetic Algorithm II with Grey Relational Analysis (NSGA-II-GRA) is employed for an initial solution to obtain the preliminary Pareto-optimal solution set. Furthermore, a market price fluctuation constraint equation is introduced during the optimization process to conduct a second iterative optimization of the initial solution set, generating optimized plans under different price fluctuation intervals. Finally, by integrating subjective and objective weights, the weighted grey relational analysis method is applied to select the optimal Ore Production Scheduling Strategy. The optimization results indicate that, while achieving the optimization objectives, calcium oxide grade and production target achievement rate increased by 7% and 7.17%, respectively, while ore output increased by 16.7% and 20.5% under different price fluctuation intervals.

Studies of the dynamics of linear and nonlinear differential equations with delays described by mathematical models play a crucial role in various scientific domains, including economics and biology. In this article, the Lambert function method, which is applied in the research of control systems with delays, is proposed to be newly applied to the study of price stability by describing it as a differential equation with a delay. Unlike the previous work of Jankauskienė and Miliūnas “Analysis of market price stability using the Lambert function method” in 2020 which focuses on the study of the characteristic equation in a complex space for stability, this study extends the application of this method by presenting a new solution for the study of price dynamics of linear and nonlinear differential equation with delay used in economic and biological research. When examining the dynamics of market prices, it is necessary to take into account the fact that goods or services are usually supplied with a delay. The authors propose to perform the analysis using the Lambert W function method because it is close to exact mathematical methods. In addition, the article presents examples illustrating the applied theory, including the results of the study of the dynamics of the nonlinear Kalecki’s business cycle model, which was not addressed in the previous work, when the linearized Kalecki’s business cycle model is studied as a nonhomogeneous differential equation with a delay.