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Stocks of year 2020: prediction of high variations in stock prices using LSTM

  • 1222: Intelligent Multimedia Data Analytics and Computing
  • Published:
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Abstract

Stock Market movement is highly volatile, complex, and non-linear. Several researchers have proposed innovative approaches to predict stock price movement using traditional data analytics, machine learning, or deep learning. Data scientists have proved that if effective mathematical models are deployed, stock prices can be predicted with very high accuracy. Deep learning is the most popular technique used for stock price prediction due to its effective results in time-series based and non-linear patterns. In the Year 2020, stock prices variations are too high to be analyzed by traditional approaches. Very few research works have been carried out to predict high variations in stock prices during this time. The main motive of this research is to investigate whether deep learning can predict so high variations in stock prices in the Year 2020 and build proposed neural network model. In this paper, Long Short-Term Memory (LSTM) is used with adam optimizer and sigmoid activation function to train and test the model. Various stock indexes data are extracted using Yahoo Finance API. Window size of 60 days is used as stock prices are dependent on the previous day’s prices. Experiment analysis has proved that LSTM using our layers set up was able to predict stock prices with adequate accuracy. Mean Absolute Percentage Error (MAPE) values are better than traditional data analytics techniques. The values of MAPE score calculated using our proposed approach are 3.89, 1.21, 3.01, 1.19, 2.03, and 0.86 for NSE, BSE, NASDAQ, NYSE, Dow Jones, and Nikkei 225 respectively for duration Jan 2010 to March 2020.

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Notes

  1. https://candorinvesting.com/2020/06/20/is-stock-market-going-to-crash/

  2. https://en.wikipedia.org/wiki/2020_stock_market_crash

  3. https://in.finance.yahoo.com

Abbreviations

ANN:

Artificial Neural Network

AR:

AutoRegression

ARCH:

AutoRegressive Conditional Heteroscedasticity

ARIMA:

AutoRegressive Integrated Moving Average

ARMA:

AutoRegressive Moving Average

BSE:

Bombay Stock Exchange

CNN:

Convolutional Neural Network

GPU:

Graphics Processing Unit

LSTM:

Long Short-Term Memory

MAPE:

Mean Absolute Percentage Error

MLP:

Multilayer Perceptron

MPE:

Mean Percentage Error

MSE:

Mean Squared Error

NASDAQ:

National Association of Securities Dealers Automated Quotations

NIFTY:

National Stock Exchange Fifty

NSE:

National Stock Exchange of India

NYSE:

New York Stock Exchange

PCA:

Principal Component Analysis

RBFNN:

Radial Basis Function Neural Network

RBM:

Restricted Boltzmann Machine

RMSE:

Root Mean Squared Error

RNN:

Recurrent Neural Network

SVM:

Support Vector Machine

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Authors and Affiliations

  1. University of Petroleum and Energy Studies, Dehradun, India

    Gourav Bathla

  2. Thapar Institute of Engg. & Tech, Patiala, India

    Rinkle Rani

  3. Punjabi University, Patiala, India

    Himanshu Aggarwal

Authors
  1. Gourav Bathla
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  2. Rinkle Rani
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  3. Himanshu Aggarwal
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Correspondence to Gourav Bathla.

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Authors Gourav Bathla, Rinkle Rani and Himanshu Aggarwal declare that they have no conflict of interest.

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Bathla, G., Rani, R. & Aggarwal, H. Stocks of year 2020: prediction of high variations in stock prices using LSTM. Multimed Tools Appl 82, 9727–9743 (2023). https://doi.org/10.1007/s11042-022-12390-5

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