Using Historical Data for Stock Prediction of a Tech Company

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Prediction of a Tech Company</title> <meta name="description" content="Using Historical Data for Stock Prediction of a Tech Company"> <meta name="keywords" content="Finance, machine learning, opening price, stock market."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Using Historical Data for Stock Prediction of a Tech Company"> <meta name="citation_author" content="Sofia Stoica"> <meta name="citation_publication_date" content="2024/01/03"> <meta name="citation_journal_title" content="International Journal of Computer and Information Engineering"> <meta name="citation_volume" content="18"> <meta name="citation_issue" content="1"> <meta name="citation_firstpage" content="16"> <meta name="citation_lastpage" content="20"> <meta name="citation_pdf_url" content="https://publications.waset.org/10013442/pdf"> <link href="https://cdn.waset.org/favicon.ico" 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To this end, we use a dataset consisting of the stock prices over the past five years of 10 major tech companies: Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We implemented and tested three models – a linear regressor model, a k-nearest neighbor model (KNN), and a sequential neural network – and two algorithms – Multiplicative Weight Update and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement. </p> <iframe src="https://publications.waset.org/10013442.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Finance" title="Finance">Finance</a>, <a href="https://publications.waset.org/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/search?q=opening%20price" title=" opening price"> opening price</a>, <a href="https://publications.waset.org/search?q=stock%20market." title=" stock market."> stock market.</a> </p> <a href="https://publications.waset.org/10013442/using-historical-data-for-stock-prediction-of-a-tech-company" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013442/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013442/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013442/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013442/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013442/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013442/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013442/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013442/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013442/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013442/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">670</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] “Algorithmic Trading Market – Growth, Trends, Covid-19 Impact, and Forecasts (2023 – 2028)”. https://www.mordorintelligence.com/industry-reports/algorithmic-trading-market#:~:text=According%20to%20Wall%20Street%20data,largest%20and%20most%20liquid%20globally. <br>[2] Lydia Saad and Jeffrey M. 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Finance. https://finance.yahoo.com/. <br>[4] Jeremy Salvucci. “What are Opening & Closing Prices in The Stock Market”. https://www.thestreet.com/dictionary/o/opening-and-closing-prices. <br>[5] Cory Mitchell, reviewed by Samantha Silberstein, and fact checked by Skylar Clarine. “How to Use Stock Volume to Improve Your Trading”. https://www.investopedia.com/articles/technical/02/010702.asp#:~:text=Volume%20measures%20the%20number%20of,gathering%20strength%20to%20the%20downside. <br>[6] James Chen, reviewed by Gordon Scott, and fact checked by Pete Rathburn. “Stock Dividend: What it is And How it Works, With Example”. https://www.investopedia.com/terms/s/stockdividend.asp. <br>[7] Adam Hayes, review by Gordon Scott, and fact checked by Suzanne KvilHaug. “What a Stock Split is And How it Works, With an Example”. https://www.investopedia.com/terms/s/stocksplit.asp. <br>[8] Michael Galarnyk. “Understanding Train Test Split”. https://builtin.com/data-science/train-test-split. <br>[9] Scikit-learn home page. https://scikit-learn.org/stable/. <br>[10] Keras about page. https://keras.io/about/. <br>[11] Recast. “What is Linear Regression?”. https://getrecast.com/linear-regression/. <br>[12] Khalid Alkhatib, Hassan Najadat, Ismail Hmeidi, Mohammed K. Ali Shatnawi. “Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm” in the International Journal of Business, Humanities and Technology vol.3, no.3, March 2013, pp. 33 & 34. https://www.ijbhtnet.com/journals/Vol_3_No_3_March_2013/4.pdf. <br>[13] Adil Moghar, Mhamed Hamiche. “Stock Market Prediction Using LSTM Recurrent Neural Network” in the Procedia Computer Science vol 170, 2020, pp. 1169. https://www.sciencedirect.com/science/article/pii/S1877050920304865# <br>[14] Santhoopa Jayawardhana. “Sequence Models & Recurrent Neural Networks (RNNs)”. https://towardsdatascience.com/sequence-models-and-recurrent-neural-networks-rnns-62cadeb4f1e1. <br>[15] Darshan M. “How do Kernel Regularizes Work With Neural Networks”. https://analyticsindiamag.com/kernel-regularizers-with-neural-networks/. <br>[16] AWS. “What is Overfitting?”. https://aws.amazon.com/what-is/overfitting/. <br>[17] Dr. Robi Polikar. “Ensemble Learning”. http://www.scholarpedia.org/article/Ensemble_learning#:~:text=Ensemble%20learning%20is%20the%20process,%2C%20function%20approximation%2C%20etc.). <br>[18] Sanjeev Arora, Elad Hazan, Satyen Kale. “The Multiplicative Weights Update Method: A Meta Algorithm and Applications”, pp. 3. https://www.cs.princeton.edu/~arora/pubs/MWsurvey.pdf. <br>[19] Akash Desarda. “Understanding AdaBoost”. https://towardsdatascience.com/understanding-adaboost-2f94f22d5bfe. <br>[20] Sklearn documentation of Voting Regressor. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingRegressor.html. <br>[21] Jason Brownlee. “14 Different Types of Learning in Machine Learning”. https://machinelearningmastery.com/types-of-learning-in-machine-learning/. <br>[22] Sanjam Singh, Amandeep Kaur. “Twitter Sentiment Analysis For Stock Prediction”, published by the Proceedings of the Advancement in Electronics & Communication Engineering (AECE), July, 2022, pp. 674. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4157658. <br>[23] Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh. “Natural Language Processing: State of The Art, Current Trends and Challenges”, pp. 1 https://www.researchgate.net/publication/319164243_Natural_Language_Processing_State_of_The_Art_Current_Trends_and_Challenges. <br>[24] Shashank Gupta. “Sentiment Analysis: Concept, Analysis And Applications”. https://towardsdatascience.com/sentiment-analysis-concept-analysis-and-applications-6c94d6f58c17. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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Using Historical Data for Stock Prediction of a Tech Company