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Exploiting Data Science for Measuring the Performance of Technology stocks

Computers, Materials & Continua 2023年第9期76卷 2979-2995页

作者： Tahir Sher Abdul Rehman Dongsun Kim Imran Ihsan Department of Creative Technologies Air UniversityIslamabad44230Pakistan School of Computer Science and Engineering Kyungpook National UniversityDaegu41566Korea

The rise or fall of the stock markets directly affects investors’interest and ***,it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant *** our proposed study,six supervised machine learning(ML)strategies and deep learning(DL)models with long short-term memory(LSTM)of data science was deployed for thorough analysis and measurement of the performance of the technology *** discussion are Apple Inc.(AAPL),Microsoft Corporation(MSFT),Broadcom Inc.,Taiwan Semiconductor Manufacturing Company Limited(TSM),NVIDIA Corporation(NVDA),and Avigilon Corporation(AVGO).The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022(seventeen years)with 4280 *** already noted,multiple studies have been performed to resolve this problem using linear regression,support vectormachines,deep long short-termmemory(LSTM),and many other *** this research,the Hidden Markov Model(HMM)outperformed other employed machine learning ensembles,tree-based models,the ARIMA(Auto Regressive IntegratedMoving Average)model,and long short-term memory with a robust mean accuracy score of *** statistical analyses and measurements for machine learning ensemble algorithms,the Long Short-TermModel,and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series ***,the proposed research found the best model to be HMM,and LSTM was the second-best model that performed well in all aspects.A developedmodel will be highly recommended and helpful for early measurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments.

关键词： Machine learning data science smart environments stocks movement deep learning stock marketing

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