This paper is focused on the prediction of a stock market price behavior by an innovative model with combination of artificial neural network (ANN). For this purpose, three types of data that reports daily in Iranian stock market have been used. The structure of this hybrid model consists of two-levels: base predictors in the first level, are responsible for forecasting daily data with different characteristics of a stock i.e. three independent neural networks for prediction of stock price, option volume and rate of return are used. On the second level, the other networks, as a Combinator, the final prediction and analysis of predictive information of the first level will be done. Experimental results on one set of Iran’s stock data showed the superior performance of the suggested model in comparison with current predict model.

This study recognizes and analyzes the basic main factors influencing demand and supply of petroleum; also in addition, with the use of analysis of supply surplus impact on global petroleum market, designs a model for simulation and prediction of oil price.In this model, through the use of modification method of Dynamic Disequilibrium Adjustment Model (DDAM), natural gas price variables, global gross domestic product (GDP), GDP of oil producing countries, production capacity of petroleum, oil supply surplus in market and global oil price, demand and supply of petroleum are being presented. The behavior of natural gas market is transferred meaningfully to the oil market and cause changes in oil price.In the dynamic simulations of model, the impacts of various shocks on supply and demand of oil prices are investigated and it will be predicted for the years 2008 till 2010. So, the designed model indicates that is has proved to have the ability to analyze political shocks and to predict oil prices and can be used in policy making and oil price prediction too.As a result, the model prediction shows that in the years 2008-2010 the oil price will partly go downward, frontier 100$, however this reduction will not reach to the price fall in the 1990 decade.

Analyzing and examining the price trend of an asset is a fundamental step in managing investment risk on that asset. Therefore, in markets, predicting the price trend of an asset is of special interest to traders and even plays a crucial role in a country's monetary policies. Based on this, in this paper, we will try to use the concept of fractal interpolation to predict the price trend of gold, given its price fluctuations and greater importance compared to other metals in markets. By analyzing the gold’s price trend using time series data with a fractal structure, we aim to determine the pattern of price trend to predict the price trend of gold ounces. Such an approach can provide the necessary tool to help investment decision-making in different time periods (short-term, medium-term, and possibly long-term). To achieve this, we first identify the presence of long-term memory in gold's price trend using the Hurst exponent. After confirming stability, we generate fractal data by calling the fractal interpolation algorithm and then predict the behavior of the corresponding time series data using a neural network algorithm based on fractal data. Finally, we compare the results obtained from calling the algorithms present in the literature on gold data. 1- IntroductionThe financial market data is unstable and irregular, and it sometimes contains missing data. To address these issues, researchers have developed different approaches, such as the fractal interpolation method. The aim of this study is to investigate the time series related to the gold market and determine whether it exhibits fractal characteristics. To generate fractal data, we can use the improved fractal interpolation algorithm (IFI). Then, we can use the support vector regression (SVR) algorithm, which is a type of machine learning method (SVM), to predict the price trend of gold in a specific time period. The price of an asset is directly proportional to its risk or fluctuation. Therefore, in the first phase, business owners and investors can determine the appropriate fee rate by analyzing the time series of data with fractal structure, and in the second phase, in the asset management phase, they can control losses caused by big fluctuations in returns and investment. The fluctuation of asset prices is a significant topic that has been studied by many researchers. They use linear or non-linear methods to predict and make appropriate use of these price fluctuations. In economic and social fields, fractal interpolation is often used to fit missing data and predict short-term trends due to the abundance of unstable and irregular data. In time series, data are collected at regular intervals according to a certain rule, and by analyzing the obtained data and with the help of different methods, the behavior of the series in the future can be approximately predicted. There are methods to determine whether a system is fractal or not, and thus, to calculate its fractal dimension. The method of calculating the dimension of the system depends on the type of its fractal structure. By determining the fractal dimension, the stability of the system can be investigated. Stability is a key factor in time series analysis, and the Hurst exponent is one of the criteria used to assess stability. Therefore, the fractal interpolation method can be implemented when the time series is stable. Hurst's exponent is a measure that identifies the long-term memory in time series. The R/S analysis criterion is one of the methods used to calculate the Hurst exponent. This criterion was first proposed by Hurst in his studies of natural phenomena such as the hydrological characteristics of the Nile Basin in 1951. In financial markets, the R/S analysis criterion is used to distinguish fractal from non-fractal systems, to identify the stability of trends, and to determine the length of life-time cycles. The range value of R in this index is equal to the difference between the lowest and highest deviation values from the cumulative average of the time series. Hurst normalized the value of the R range by using the standard deviation of the time series, relative to the fluctuations of the inputs of different time series, and defined the analysis criterion in a certain period of time. In this study, we aim to evaluate the performance of the fractal interpolation algorithm in predicting the trend of gold price based on time series data. The global ounce of gold is of great importance in world markets and experiences fluctuations, making it an ideal candidate for this analysis. To achieve this, we first calculate the Hurst exponent to determine the long-term memory of the gold price trend. We then generate fractal data using the fractal interpolation algorithm and apply the support vector regression (SVR) algorithm to predict the gold price trend. We compare the performance of Wang's algorithm and the Fracsion algorithm to determine the best method for predicting the gold price trend. The primary objective of this research is to examine the predictability of the price trend of gold and determine its price pattern. We analyze and evaluate this process by comparing it with past-oriented methods such as Wang's method. 2- Main ResultsWe have analyzed the results of the Wang algorithm and the Fracsion algorithm separately for their ability to predict the final price of gold in 2020, 2021, and 2022 using well-known evaluation criteria.Two algorithms, the Wong and Fracsion methods, are presented below for the purpose of numerical analysis.Wang, et al., in 2018, using the fractal property of the Shanghai stock market and employing the contribution of algorithm of fractal interpolation and Support Vector Machin (SVM), have focused on predicting price patterns [21]. Algorithm 1 Wang Algorithm:Require: Gold closing price.Ensure: prediction of gold price pattern in a short-term time interval.Start1: Examine the stability and fractal structure of gold price data using the Hurst exponent.2: Predict the data using the SVM algorithm.3: Adjust the points obtained from Step 2 using the fractal interpolation algorithm.4: Predict a short-term period based on the corresponding fractal interpolation function of the pointsfrom Step 3.End Algorithm 2 Fracsion Algorithm:Require: Gold closing price in a priod.Ensure: prediction of gold price pattern in a short-term time interval.Start1: Examine the stability and confirm the fractal structure of the data using the Hurst exponent.2: Generate a set of fractal points for data with a fractal structure.3: Call and train the SVR algorithm based on the obtained fractal data.4: Predict the trend of gold price in a time interval based on the regression function obtained in Step 3.End The results for 2022 are presented in the following figure, for instance.The comparison of the results indicates that although both algorithms exhibit errors in price prediction, the adaptive Fracsion algorithm outperforms the Wang algorithm in predicting the price trend of gold in a short term memory. 3- Summary of Proofs/ConclusionsIn this paper, we analyzed the gold price time series in two phases. Firstly, by applying Hurst's method for each year, we investigated the stability of the gold price pattern (fractal structure of the system). Secondly, we utilized existing algorithms on the time series corresponding to the price of gold to predict its price trend, which provides important information to investors who seek to predict the gold market. The fundamental analysis of the gold market with the fractal structure presents a new approach to the analysis of the gold market and a non-linear perspective on this issue. The characteristic of long-term memory as well as the fractal structure of a time series corresponding to gold price data can play an effective role in predicting gold fluctuations and hence, the return based on gold fluctuations. However, predicting the price trend of the gold market in the long term is difficult due to many different factors that affect the global markets. Nevertheless, using algorithms for predicting the behavior of a time series, as a technical analysis tool, in stable economic and political conditions can have satisfactory results in predicting the price trend of gold in the short term. However, when there are conditions that strongly affect the price trend, such as war, global inflation, and in recent years, the global pandemic of Covid-19, algorithms cannot perform well in predicting the price pattern trend, because the structural order of the price pattern in such a situation, it is faced to distribution.

Stock market prediction is considered as a challenging task in the area of forecasting of financial time series. The main reason for this is the lack of certainty about how the stock market moves. Stock price data analysis is difficult due to the nonlinearity and the high noise level. The purpose of this paper is to forecast the capital market using the improved gray prediction pattern in Tehran Stock Exchange. For this purpose, the total stock price index (TEPIX) was used. The obtained results indicated that the improved gray algorithm fitted with minimizing the prediction error is an appropriate algorithm for predicting the fluctuation of the total stock price index. Stock market prediction is considered as a challenging task in the area of forecasting of financial time series. The main reason for this is the lack of certainty about how the stock market moves. Stock price data analysis is difficult due to the nonlinearity and the high noise level. The purpose of this paper is to forecast the capital market using the improved gray prediction pattern in Tehran Stock Exchange. For this purpose, the total stock price index (TEPIX) was used. The obtained results indicated that the improved gray algorithm fitted with minimizing the prediction error is an appropriate algorithm for predicting the fluctuation of the total stock price index.

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this paper, a novel machine learning approach, which works in two phases, is introduced to predict the price of a stock in the next day based on the information extracted from the past 26 days. In the first phase of the method, an automatic clustering algorithm clusters the data points into different clusters, and in the second phase a hybrid regression model, which is a combination of particle swarm optimization and support vector regression, is trained for each cluster. In this hybrid method, particle swarm optimization algorithm is used for parameter tuning and feature selection. Results: The accuracy of the proposed method has been measured by 5 companies’ datasets, which are active in the Tehran Stock Exchange market, through 5 different metrics. On average, the proposed method has shown 82. 6% accuracy in predicting stock price in 1-day ahead. Conclusion: The achieved results demonstrate the capability of the method in detecting the sudden jumps in the price of a stock.

Many economic tools have been proposed and used to reduce climate change. Carbon trading is one of these market-based tools that is recognized as a cost-effective way to change climate and environmental issues. Today, the issue of carbon sequestration and bioenergy production versus fossil fuels is great concern of governments, and many efforts have been made to reduce or control carbon dioxide emissions. The aim of this study is to investigate carbon price fluctuations and predict price trends based on historical carbon price data in the time series 2005-2020. Data were analyzed by regression analysis based on Fuller augmented Dicky after eliminating inflation. The results show that the trend of carbon prices has fluctuated during this period. The average expected price of carbon is 3, 303, 589 Iranian Rials.

In the financial literature, a bubble is a situation in which the market price of a commodity is traded at a significant difference from its intrinsic value. Determining the true value is difficult, usually after a sudden drop in price and the so-called bubble burst,They notice the bubble. The bubble is characterized by its temporary nature. In this case, investors rush into the market without considering the real market value of a commodity, which causes the market price of a commodity to be separated from its real value. Given the importance of this issue,the present study is based on the study of factors affecting the prediction of stock price bubble irregularity. In this study and in order to achieve the objectives of the research,Data of 99 companies were extracted for a period of ten years from the beginning of 2009 to the end of 2020, the research variables were calculated and the necessary statistical tests were performed. The method of this research is descriptive-correlational and its design is Fundamental using post-event approach. The findings indicated that among the proposed variables,Financial performance and Market Performance are effective in predicting stock irregularities