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A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks

Author

Listed:
  • Shubham Ekapure
  • Nuruddin Jiruwala
  • Sohan Patnaik
  • Indranil SenGupta

Abstract

In this paper, we implement a combination of technical analysis and machine/deep learning-based analysis to build a trend classification model. The goal of the paper is to apprehend short-term market movement, and incorporate it to improve the underlying stochastic model. Also, the analysis presented in this paper can be implemented in a \emph{model-independent} fashion. We execute a data-science-driven technique that makes short-term forecasts dependent on the price trends of current stock market data. Based on the analysis, three different labels are generated for a data set: $+1$ (buy signal), $0$ (hold signal), or $-1$ (sell signal). We propose a detailed analysis of four major stocks- Amazon, Apple, Google, and Microsoft. We implement various technical indicators to label the data set according to the trend and train various models for trend estimation. Statistical analysis of the outputs and classification results are obtained.

Suggested Citation

  • Shubham Ekapure & Nuruddin Jiruwala & Sohan Patnaik & Indranil SenGupta, 2021. "A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks," Papers 2107.14695, arXiv.org.
    • Handle: RePEc:arx:papers:2107.14695
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    References listed on IDEAS

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    1. Michael Roberts & Indranil SenGupta, 2020. "Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection," Applied Mathematical Finance, Taylor & Francis Journals, vol. 27(5), pages 374-395, September.
    2. Elisa Nicolato & Emmanouil Venardos, 2003. "Option Pricing in Stochastic Volatility Models of the Ornstein‐Uhlenbeck type," Mathematical Finance, Wiley Blackwell, vol. 13(4), pages 445-466, October.
    3. Baresa, Suzana & Bogdan , Sinisa & Ivanovic, Zoran, 2013. "Strategy Of Stock Valuation By Fundamental Analysis," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 4(1), pages 45-51.
    4. Semere Habtemicael & Indranil SenGupta, 2016. "Pricing variance and volatility swaps for Barndorff-Nielsen and Shephard process driven financial markets," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 1-35, December.
    5. Indranil SenGupta & William Nganje & Erik Hanson, 2021. "Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 39-55, March.
    6. Abarbanell, JS & Bushee, BJ, 1997. "Fundamental analysis, future earnings, and stock prices," Journal of Accounting Research, Wiley Blackwell, vol. 35(1), pages 1-24.
    7. Semere Habtemicael & Indranil Sengupta, 2016. "Pricing Covariance Swaps For Barndorff–Nielsen And Shephard Process Driven Financial Markets," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(03), pages 1-32, September.
    8. Michael Roberts & Indranil SenGupta, 2020. "Sequential hypothesis testing in machine learning, and crude oil price jump size detection," Papers 2004.08889, arXiv.org, revised Dec 2020.
    9. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
    10. Julia Jiang & Weidong Tian, 2019. "Semi-nonparametric approximation and index options," Annals of Finance, Springer, vol. 15(4), pages 563-600, December.
    11. Michael Roberts & Indranil SenGupta, 2020. "Infinitesimal generators for two-dimensional Lévy process-driven hypothesis testing," Annals of Finance, Springer, vol. 16(1), pages 121-139, March.
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    Cited by:

    1. Treena Basu & Olaf Menzer & Joshua Ward & Indranil SenGupta, 2022. "A Novel Implementation of Siamese Type Neural Networks in Predicting Rare Fluctuations in Financial Time Series," Risks, MDPI, vol. 10(2), pages 1-16, February.

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