random forest algorithm

The purpose of this study is to predict short-term stock returns in initial public offerings using random bat and forest algorithms. In this study, companies that were listed on the OTC market of Iran for the first time during the period 1394 to 1399 were selected as a statistical sample. MATLAB software was used to analyze the data. Two scenarios were proposed to test the hypotheses. The first scenario was considered as annual and the second scenario as 6 years. Financial data with 11 factors: short-term market return, short-term return on new stock, market trends, company age, company size, annual sales, return on assets, return on equity, initial public offering price, operating profit, Cash flow from operations as influential factors and excess return of the offered share relative to the influential operating market entered the algorithms as input assumptions to predict the optimal amount. The results obtained from the bat algorithm indicate that the bat algorithm was able to provide better performance in predicting short-term stock returns in initial public offering in both scenarios and is not much different. While the results of accuracy in predicting the random forest algorithm in the second scenario compared to the first scenario has increased by about 12%. It can be concluded that the use of emerging bat and jungle algorithms in predicting short-term returns can help investors in predicting maximum returns and selecting the best stocks based on a precise and accurate pattern.

The purpose of this study is to optimally select stocks using the bat and random forest algorithm. In this study, based on the analysis of 6 variables: stock price to earnings per share ratio, annual earnings growth rate, annual sales growth rate, return on assets, return on equity and free float shares extracted from 181 companies listed on the Tehran Stock Exchange, It has been used during the period of 1394 to 1398. Six scenarios are considered to estimate the accuracy of the two algorithms, so that for scenarios 1 to 6, the algorithms are asked to participate 5, 10, 15, 20, 25 and 30, respectively. The results show that the nature of the random forest algorithm requires training and selection of features, which makes the algorithm faster and increases the convergence time. One of the main reasons for the higher accuracy of the random forest algorithm in scenarios 1 to 3 could be this. In scenarios 4 to 6, due to the increasing complexity of the problem, the accuracy of the random forest algorithm decreases, but due to the random nature of the bat algorithm, its accuracy does not differ much and it can maintain stability in its selection.

Stock market activists are the acquiring and using methods to predict future stock prices, increasing their capital gains. Therefore, it seems necessary that appropriate, correct, and scientific principles are used to determine the future price of the stock of investor stock options.stock price prediction is an important part of investment, and in most cases it is the field of research for researchers, because it ultimately leads to the choice of appropriate investment. Different methods have now been developed to achieve this goal. Have been introduced that are often statistical methods and artificial intelligence. In this research, using a randomized approach approach that is among artificial intelligence classification methods, along with technical indicators that include: power index Relative Price, Stochastic, Equilibrium Balance, Williams R%, Daily Returns, and Mac.d Series Markets, are looking for stock price trends. This model is compared with logistic regression method and completely randomized method (dice throw). The results of the research on daily data of Tehran Stock Exchange Index from 1393 to 1395 indicate that the accuracy of the proposed method in estimating market trend is 64%, which is more than two methods of logistic regressionand completely randomized method of accuracy Has a higher rate.