[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120943-en":3,"doc-seo-120943-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120943,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Simulation and Assessment of Stock Market Forecasting Using Machine Learning Methodology","This paper explores neural network-based machine learning methodologies for stock market forecasting, motivated by their potential to generate high returns while addressing the market’s uncertainty and volatility. The approach leverages deep learning, with Long Short-Term Memory (LSTM) networks suited to time-series inputs and temporal dependency modeling. Historical stock data (open, close, high, low, and traded volume) is collected, normalized, and reshaped for LSTM training. A multi-layer architecture with dropout reduces overfitting, and prediction quality is assessed using RMSE and MAE, with comparisons to traditional statistics and simpler models.","Simulation and Assessment of Stock Market Forecasting Using Machine Learning Methodology  \nSunil Awasthi1, Dr. Mukesh Kumar2  \n1Research Scholar, Department ofCSE, Rabindra Nath Tagore University, Bhopal, India 2Associate Professor, Department of CSE, Rabindra Nath Tagore University, Bhopal, India  \nAbstract— : This paper explores the application of neural network-based machine learning methodologies for stock market forecasting, an area of significant interest due to its potential to yield high returns. The study employs deep learning models, particularly Long Short-Term Memory (LSTM) networks, recognized for their ability to process time series data and capture temporal dependencies that are crucial in understanding stock market behaviors. The methodology involves collecting extensive historical stock price data, including open, close, high, low prices, and volume traded. This data is preprocessed to normalize the values and convert them into a format suitable for LSTM networks. The neural network architecture is designed with multiple layers, including dropout layers to prevent overfitting, and is trained on a substantial dataset to predict future stock prices based on past patterns. The performance ofthe LSTM model is evaluated using metrics such as root mean squared error (RMSE) and mean absolute error (MAE), comparing its predictive accuracy with traditional statistical methods and simpler machine learning models. The results indicate that LSTM networks can significantly improve the accuracy of stock market forecasts, demonstrating the model's efficacy in capturing complex stock price movements and providing a reliable tool for investors and financial analysts. The study not only confirms the viability of using sophisticated machine learning techniques in financial markets but also opens avenues for further research into neural network optimizations for enhanced predictive performance.  \n.  \nKeywords-Artificial Neural Network, Back-propagation, Forecasting, Stock market, Feed forward, RMSE.  \nI. INTRODUCTION  \nThe growth of stock market has been identified as an economic strength of a country. So accurate prediction of stock market is incredibly vital issue in commerce, mathematics, engineering, finance and science domain because of its prospective investment returns [1]. On the other hand it providesan aid to shareholders to take relevant, timely and felicitous decision. Especially, the persons connected directly with share market may escape nasty astonishments. Appropriate and proper speculates may offer significant and helpful information for achieving financial reliability in India. As we know the stock market is difficult to predict due to its higher rate of uncertainty and volatility. It holds more risk rather than other speculation region. So it is the basis why stock market is so demanding to predict. Thus a soft computing tool i.e., artificial neural networks can be used in stock market prediction. Neural networks have many features as a data analysis tool and relatively efficient implementation scheme in accordance with computation rate and computer memory requirement. ANN model also exhibits complex and non-linear relationship without rigorous assumptions regarding the distribution of samples [2, 3] and can identify new sample even if they have not been in training set.  \n1.1 Motivation  \nForecasting Financial prediction, though controversial, has been the center of attraction for the investors around the earth  \ndue to high return. The controversy is mainly due to the popularity of several well known theories which ultimately concludes that price movement in financial markets can never be predicted. The most important of such theories is Efficient Market Hypothesis (EMH) (Fama, 1964). In EMH, it is assumed that the price of a financial security reflects all available information and that everyone has some degree of access to the information. Fama’s theory further breaks EMH into three  \nforms; weak, semi-stro","cbCaig4ghAEbIVwf","https://ap.wps.com/l/cbCaig4ghAEbIVwf","pdf",462423,1,"English","en",105,"# Introduction\n## Motivation\n# Methodology\n## Data collection and preprocessing\n## LSTM model design\n## Training and evaluation\n# Results and Discussion\n## Performance metrics (RMSE, MAE)\n## Comparison with other approaches\n# Conclusion and Future Work","[{\"question\":\"Why is stock market forecasting considered difficult and important?\",\"answer\":\"Stock markets show high uncertainty and volatility, making consistent prediction challenging. Accurate forecasting is valuable for investors’ timely decisions and potential reliability in finance.\"},{\"question\":\"What data and preprocessing steps are used for the LSTM forecasting model?\",\"answer\":\"The study collects extensive historical price data including open, close, high, low, and volume traded. Values are normalized and converted into a format suitable for LSTM time-series learning.\"},{\"question\":\"How is the LSTM model’s performance evaluated?\",\"answer\":\"Model accuracy is measured using error metrics such as root mean squared error (RMSE) and mean absolute error (MAE). Results are compared with traditional statistical methods and simpler machine learning models.\"}]","Simulation and Assessment of Stock Market Forecasting Using Machine Learning Methodology | PDF",1785732950,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"simulation-and-assessment-of-stock-market-forecasting-using-machine-learning-methodology","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/simulation-and-assessment-of-stock-market-forecasting-using-machine-learning-methodology/120943/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is stock market forecasting considered difficult and important?","Question",{"text":74,"@type":75},"Stock markets show high uncertainty and volatility, making consistent prediction challenging. Accurate forecasting is valuable for investors’ timely decisions and potential reliability in finance.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What data and preprocessing steps are used for the LSTM forecasting model?",{"text":79,"@type":75},"The study collects extensive historical price data including open, close, high, low, and volume traded. Values are normalized and converted into a format suitable for LSTM time-series learning.",{"name":81,"@type":72,"acceptedAnswer":82},"How is the LSTM model’s performance evaluated?",{"text":83,"@type":75},"Model accuracy is measured using error metrics such as root mean squared error (RMSE) and mean absolute error (MAE). 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