چکیده مقاله
This study aims to investigate the impact of various financial variables on the prediction of profitability and loss in public companies Utilizing financial data from an Iranian company listed on the Tehran Stock Exchange, the study applies machine learning techniques, specifically Artificial Neural Networks ANN , Long Short Term Memory LSTM , and Convolutional Neural Networks CNN , to assess the prediction accuracy of these models The key financial variables considered include initial asset price, interest rate, volatility, and others The novelty of this research lies in replacing the LSTM algorithm with ANN, building upon Funahashi's method for analyzing financial data, and providing empirical insights from a real world dataset By implementing Quasi Process Correction QPC to minimize errors, the study improves the generalization of the models, enhancing prediction accuracy The study's results demonstrate the effectiveness of machine learning algorithms in forecasting financial outcomes, with ANN outperforming both LSTM and CNN in terms of minimizing mean squared error MSE and mean absolute error MAE This research contributes to the field of financial data analysis by offering a comparative evaluation of machine learning models in predicting financial performance, and underscores the importance of accurate predictions for informed investment decisions, risk management, and financial sustainability The findings also highlight the potential of deep learning techniques to improve the accuracy and reliability of financial forecasts, ultimately supporting better decision making in capital markets
کلیدواژهها
نویسندگان
Mahrokh Sahraei
PhD Student, Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
شیوه ارجاع
Sahraei, Mahrokh,1403,Valuation of Financial Variables in Iranian Companies Using Quasi-Process Correction with LSTM,The 14th ECDC2025 international e-commerce conference with the approach of artificial intelligence, Internet of Things, business and metaverse,Shiraz
ارائهشده در
مجموعه مقالات چهاردهمین کنفرانس بین المللی تجارت الکترونیک ECDC۲۰۲۵ با رویکرد هوش مصنوعی، اینترنت اشیاء، کسب و کار و متاورس25 بهمن 1403 · شیراز