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

  • 1222: Intelligent Multimedia Data Analytics and Computing
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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

References

  1. Abe M, Nakagawa K Cross-sectional stock price prediction using deep learning for actual investment management. Proceed 2020 Asia Service Sci Software Eng Conf:9–15

  2. Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7)

  3. Bathla G (2020) Stock Price prediction using LSTM and SVR. Int Conf Parall, Distrib Grid Comput (PDGC)

  4. Borovkova S, Tsiamas I (2019) An ensemble of LSTM neural networks for high-frequency stock market classification. J Forecast 38(6):600–619

    Article  MathSciNet  Google Scholar 

  5. Cakra YE, Trisedya BD (2015) "Stock price prediction using linear regression based on sentiment analysis," in in 2015 international conference on advanced computer science and information systems (ICACSIS)

  6. Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst Appl 83:187–205

    Article  Google Scholar 

  7. Eapen J, Bein D, Verma A Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction. IEEE 9th Ann Comput Commun Workshop Conference (CCWC):0264–0270

  8. Feng F, He X, Wang X, Luo C, Liu Y, Chua TS (2019) Temporal relational ranking for stock prediction. ACM Trans Inform Syst (TOIS) 37(2):1–30

    Article  Google Scholar 

  9. Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654–669

    Article  MathSciNet  MATH  Google Scholar 

  10. Gers FA, Schraudolph NN, Schmidhuber J (2002) Learning precise timing with LSTM recurrent networks. J Mach Learn Res:115–143

  11. Gunduz H, Yaslan Y, Cataltepe Z (2017) Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations. Knowl-Based Syst 137:138–148

    Article  Google Scholar 

  12. Guresen E, Kayakutlu G, Daim TU (2011) Using artificial neural network models in stock market index prediction. Expert Syst Appl 38(8):10389–10397

    Article  Google Scholar 

  13. Hao Y, Gao Q (2020) Predicting the trend of stock market index using the hybrid neural network based on multiple time scale feature learning. Appl Sci 10(11):3961

    Article  Google Scholar 

  14. Hiransha M, Gopalakrishnan EA, Menon VK, Soman KP NSE stock market prediction using deep-learning models. Procedia Comput Sci 132:1351–1362

  15. Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6(2):107–116

    Article  MathSciNet  MATH  Google Scholar 

  16. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  17. Hoseinzade E, Haratizadeh S (2019) CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Syst Appl 129:273–285

    Article  Google Scholar 

  18. Hu G, Hu Y, Yang K, Yu Z, Sung F, Zhang Z, Xie F, Liu J, Robertson N, Hospedales T, Miemie Q Deep stock representation learning: From candlestick charts to investment decisions. IEEE Int Conf Acoustics, Speech Signal Process

  19. Huang JY, Liu JH (2020) Using social media mining technology to improve stock price forecast accuracy. J Forecast 39(1):104–116

    Article  MathSciNet  Google Scholar 

  20. Huang W, Nakamori Y, Wang SY (2005) Forecasting stock market movement direction with support vector machine. Comput Oper Res 32(10):2513–2522

    Article  MATH  Google Scholar 

  21. Idrees SM, Alam MA, Agarwal P (2019) A prediction approach for stock market volatility based on time series data. IEEE Access 7:17287–17298

    Article  Google Scholar 

  22. Jiang W (2021) Applications of deep learning in stock market prediction: recent progress. Expert Syst Appl:115537

  23. Khare K, Darekar O, Gupta P, Attar VZ Short term stock price prediction using deep learning. IEEE Int Conf Recent Trends Electron, Inf Comm Technol (RTEICT):482–486

  24. Kim HY, Won CH (2018) Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst Appl 103:25–37

    Article  Google Scholar 

  25. Kusuma RMI, Ho TT, Kao WC, Ou YY and Hua KL, "Using deep learning neural networks and candlestick chart representation to predict stock market," arXiv preprint arXiv:1903.12258, 2019.

  26. Liu G, Wang X (2018) A numerical-based attention method for stock market prediction with dual information. Ieee Access 7:7357–7367

    Article  Google Scholar 

  27. Long W, Lu Z, Cui L (2019) Deep learning-based feature engineering for stock price movement prediction. Knowl-Based Syst 164:163–173

    Article  Google Scholar 

  28. Lu W, Li J, Wang J, Qin L (2021) A CNN-BiLSTM-AM method for stock price prediction. Neural Comput & Applic 33(10):4741–4753

    Article  Google Scholar 

  29. Mehtab S and Sen J "Stock price prediction using convolutional neural networks on a multivariate timeseries," arXiv preprint arXiv:2001.09769, 2020.

  30. Nikou M, Mansourfar G, Bagherzadeh J (2019) Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intell Syst Account, Finance Management 26(4):164–174

    Article  Google Scholar 

  31. H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song and R. Ward, "Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 4, no. 694–707, p. 24, 2016.

  32. Palangi H, Ward R, Deng L (2016) Distributed compressive sensing: a deep learning approach. IEEE Trans Signal Process 64(17):4504–4518

    Article  MathSciNet  MATH  Google Scholar 

  33. Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock market index using fusion of machine learning techniques. Expert Syst Appl 42(4):2162–2172

    Article  Google Scholar 

  34. Rezaei H, Faaljou H, Mansourfar G (2021) Stock price prediction using deep learning and frequency decomposition. Expert Syst Appl 169:114332

    Article  Google Scholar 

  35. Sak H, Senior AW, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling

    Book  Google Scholar 

  36. Selvin S, Vinayakumar R, Gopalakrishnan EA, Menon VK, Soman KP (2017) Stock price prediction using LSTM, RNN and CNN-sliding window model. Int Conf advanc Comput, Commun Inform

  37. Shah D, Isah H, Zulkernine F (2019) Stock market analysis: A review and taxonomy of prediction techniques. Int J Financial Stud 7(2):26

    Article  Google Scholar 

  38. Sharaf M, Hemdan EED, El-Sayed A, El-Bahnasawy NA (2021) StockPred: a framework for stock Price prediction. Multimed Tools Appl 80(12):17923–17954

    Article  Google Scholar 

  39. Sharma A, Bhuriya D, Singh U (2017) Survey of stock market prediction using machine learning approach. Int Conf Electron, Commun Aerospace technol (ICECA) 2:506–509

    Article  Google Scholar 

  40. Shen S, Jiang H, Zhang T (2012) Stock market forecasting using machine learning algorithms. Depart Electr Eng, Stanford Univ, Stanford, CA:1–5

  41. Shui-Ling YU, Li Z (2017) Stock price prediction based on ARIMA-RNN combined model. DEStech Transactions on Social Science, Education and Human Sci

  42. Siami-Namini S, Tavakoli N, Namin AS (2018) A comparison of ARIMA and LSTM in forecasting time series. IEEE Int Conf Mach Learn Appl (ICMLA):1394–1401

  43. Singh R, Srivastava S (2017) Stock prediction using deep learning. Multimed Tools Appl 76(18):18569–18584

    Article  Google Scholar 

  44. Wu JMT, Li Z, Herencsar N, Vo B, Lin JCW (2021) A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. Multimedia Systems:1–20

  45. Zhong X, Enke D (2017) Forecasting daily stock market return using dimensionality reduction. Expert Syst Appl 67:126–139

    Article  Google Scholar 

  46. Zhou Z, Gao M, Liu Q, Xiao H (2020) Forecasting stock price movements with multiple data sources: evidence from stock market in China. Physica A: Stat Mech Appl 542:123389

    Article  Google Scholar 

  47. Zhu C, Yin J, Li Q (2014) A stock decision support system based on DBNs. J Comput Inform Syst 10(2):883–893

    Google Scholar 

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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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