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The main reason for people investing in the stock market is to make a profit, which requires accurate information about the stock market, price changes and predicting its future trend. Therefore, investors need powerful and reliable tools for forecasting Stock prices in the future. The main purpose of this study is to present a method based on wavelet denoising and dynamic time warping to identify the stock price pattern in the Tehran Stock Exchange. In this regard, first, using the wavelet denoising preprocessing step, noise is removed from the stock price time series, and then the extracted data is used as input to the dynamic time warping prediction model. MATLAB software version 9.11 was used to analyze the research data. The statistical population of the present study includes 3 shares among the shares of steel industry companies of Tehran Stock Exchange. The research was conducted in the period 1395 to 1398. The results show that the predictions obtained from the dynamic time warping method equipped with the wavelet denoising preprocessing step in comparison with the predictions obtained from the dynamic time warping method without the wavelet denoising preprocessing step in all three shares studied, have been associated with much less accuracy and error.

EXTENDED ABSTRACTINTRODUCTION With the increase in population growth and the problems of the planet, researchers have paid more attention to the phenomenon and behavior of greenwashing. Greenwashing behavior is a situation according to which companies and businesses present unhealthy products and services as green and organic (Wang et al., 2019). This study generates new insights for emerging markets and provides potentially important information for academics and practitioners on greenwashing behavior. It can be shown that almost half of people's income is spent on buying food products (Mishra & Sharma, 2012), so the quality, safety and performance of such products have attracted the attention of customers, consumers and shareholders (Bhattacharya, al. , 2012).), in addition, environmental protection has faced problems in the country due to the limitations of greenwashing regulations and their unclear and imprecise implementation. In the field of food products, national regulations on organic and green labels have not been determined, so many companies and food industries can use greenwashing behavior well and show themselves to be environmentally friendly (Chen & Chang, 2012). With these explanations, it is possible that customers are skeptical about green claims and the purchase of green products in society will decrease, which in turn will have harm to people in terms of safety and health, and its consequences for the environment include It will be pollution and destruction of ecosystems (Hai et al., 2017). Due to having industrial companies, Khuzestan province is exposed to industrial and environmental pollution (Guo et al., 2014). In the past years, suggestions for minimizing the environmental effects of waste, access to green life, and maximizing the capacity of using green food industry products, which can reduce their potential effects by using the phenomenon of greenwashing, have not been presented by researchers. As a result, it is vital for consumers to review their product consumption practices and move from non-organic products to green products. By observing these cases, in this research, we will seek to investigate the phenomenon of greenwashing and explain the mechanism of its effect on the intention to buy green. METHODOLOGY The current research is in the field of descriptive and survey research from the methodological point of view. Also, from the point of view of the implementation method, the current study has two phases: library and field. In the library phase, the researchers presented the research model by using the literature review and the existing experimental background. The authors' tool for this has been the use of library resources. In the field phase, the authors distributed questionnaires in order to test the fit of the model and research hypotheses. Finally, 276 healthy questionnaires were collected and analyzed using the structural equation modeling method based on the partial least squares approach. FINDINGS The authors present the findings of this study in two basic steps. The first step of the findings is related to the fit and validity of the research model. The second group of findings of this research includes the confirmation of all research hypotheses, which are summarized in Tabel 1: Tabel 1. The results of the test of research hypotheseshypothesesPathPath coefficientT StatisticResultH1greenwash  Green Purchase Intention-0/3166/045supportedH2greenwash  Green Word of Mouth-0/81940/944supportedH3greenwash  Green confusion0/82740/476supportedH4greenwash  Green Skepticism0/77129/208supportedH5Green confusion  Green Purchase Intention-0/2695/833supportedH6Green Skepticism  Green Purchase Intention-0/1353/053supportedH7Green Word of Mouth  Green Purchase Intention0/2545/218supportedH8greenwash  Green confusion  Green Purchase Intention-0/1042/985supportedH9greenwash  Green Word of Mouth  Green Purchase Intention-0/2085/596supported  CONCLUSION The current research encourages and supports food industry marketers to create credible green claims that not only improve their business status, but also enhance their environmental programs. The results showed that some food industries have a high risk due to consumers' pessimism towards the greenness of their activities and services. Managers should focus on several environmentally friendly methods to reduce consumer cynicism. Managers should increase the transparency of environmental performance, and in addition, non-governmental organizations can audit companies' environmental reports and fine companies that engage in greenwashing. With these words, it is expected to be able to help control greenwashing behavior and green doubts to some extent.

Financial statements are critical to users, as the increasing fraud cases have left behind irreversible impacts. Hence, this study aims to identify the appropriate financial ratios for fraud risk prediction in the financial statements of companies listed on the Tehran Stock Exchange within the 2014–2021 period. The study is based on data from 180 companies listed on the Tehran Stock Exchange, encompassing a total of 1440 financial statements. To select the most appropriate ratios for fraud risk prediction, all financial ratios were tested by three metaheuristic algorithms, i.e., genetic algorithm, grey wolf optimization, and particle swarm optimization. Metaheuristic and data mining methods were employed for data analysis, and these analyses were conducted using MATLAB R2020a (MATLAB 9.8). According to the research results, the fitness function yielded 0.2708 in particle swarm optimization (PSO). With an accuracy of 72.92% after 19 iterations, PSO was more accurate and converged faster than the other algorithms. It also extracted 11 financial ratios: total debts to total assets, working capital to total assets, stock to current asset, accounts receivables to sales, accounts receivables to total assets, gross income to total assets, net income to gross income, current assets to current debt, cash balance to current debt, retained earnings and loss to equity, and long-term debt to equity. The support vector machine (SVM) classifier was then employed for fraud risk detection at companies through the ratios extracted by the proposed algorithms. The accuracy and precision of financial ratios extracted by PSO and SVM were reported at 80,60% and 71,20%, respectively, which indicates the superiority of the proposed model to other models. Considering that the results obtained from the performance evaluation of financial ratios provided by PSO-SVM demonstrate the capability of this method in predicting the likelihood of fraud in financial statements, it can assist financial statement users. By incorporating these ratios about the performance of the target companies and comparing them with those of other companies, users can make more informed decisions in economic decision-making, investments, credit assessments, and more, ultimately minimizing potential losses and risks.

Sanctions increase managers' motivations to manage profits and the accumulation of bad news due to their negative impact on corporate profitability and cash flow. According to Jin and Myers (2006), this issue increases companies' stock price crash risk. The higher probability of stock price crashes indicates a stock price overvaluation. As a result, the expected return on such shares will be low. The current paper used the softmax model to calculate the probability of stock price crashes and the expected return calculated by the Fama-French three-factor model. The sample used in this paper includes 80 import- and export-oriented exchange companies from 2008-2021. The results of this paper indicate a positive and significant relationship between the sanctions variables and the probability of stock crashes. So, sanctions cause an increase in the accumulation of bad news and information asymmetry between managers and investors. The second part of the paper's results indicates a negative relationship between the probability of stock crashes and the expected return on the stocks in the Iranian capital market. Therefore, investors have relatively good analytical skills in the Iranian capital market due to its shallow depth and infrastructure problems. The results of this paper can be used in portfolio management to select stocks with a lower probability of crash and higher return.

The selection of knowledge workers in the organization is one of the main tasks of human resource management in the new era, and the lack of criteria and a coherent list of factors influencing the selection of these workers has been raised as a challenging issue in this field. The following article seeks to examine these influencing factors on the selection of Knowledge workers using a systematic review. The process of conducting a systematic research review is derived from the model of Okoli and Shabram (2017) and the selection steps of Silva (2015). Based on this, the number of 1976 articles published without time limit from the databases of Science Direct, Google Scholar, Normags, a comprehensive portal of humanities, the specialized database of Noor magazines, and the scientific information center of academic jihad were examined, and analyzed. In the final step, effective factors were extracted from 16 articles. The findings of the research were presented in the form of three categories of knowledge, skill, and ability, and finally, the key characteristics of knowledge workers were divided into two parts: some characteristics are non-developable and fixed, and others are developable and variable.