Top

Annals of Data Science

Published in:

22-07-2020

Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network

Authors: Shankhajyoti De, Arabin Kumar Dey, Deepak Kumar Gouda

Published in: Annals of Data Science | Issue 2/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In this paper, we show an innovative way to construct bootstrap confidence interval of a signal estimated based on a univariate LSTM model. We take three different types of bootstrap methods for dependent set up. We prescribe some useful suggestions to select the optimal block length while performing the bootstrapping of the sample. We also propose a benchmark to compare the confidence interval measured through different bootstrap strategies. We illustrate the experimental results through some stock price data set.

previous article Demand Prediction in the Automobile Industry Independent of Big Data

next article Predictive Inference and Parameter Estimation from the Half-Normal Distribution for the Left Censored Data

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Syst Appl 42(1):259–268CrossRef

Sen J, Chaudhuri T (2017) A robust predictive model for stock price forecasting. In: Proceedings of the 5th international conference on business analytics and intelligence (ICBAI 2017), Indian Institute of Management, Bangalore, India

Zhang K, Zhong G, Dong J, Wang S, Wang Y (2019) Stock market prediction based on generative adversarial network. Procedia Comput Sci 147:400–406CrossRef

Song Y, Lee JW, Lee J (2019) A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction. Appl Intell 49(3):897–911CrossRef

Saini A, Sharma A (2019) Predicting the unpredictable: an application of machine learning algorithms in Indian stock market. Ann Data Sci. https://doi.org/10.1007/s40745-019-00230-7CrossRef

Tang H, Dong P, Shi Y (2019) A new approach of integrating piecewise linear representation and weighted support vector machine for forecasting stock turning points. Appl Soft Comput 78:685–696CrossRef

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

Khoa NLD, Sakakibara K, Nishikawa I (2006) Stock price forecasting using back propagation neural networks with time and profit based adjusted weight factors. In: 2006 SICE-ICASE international joint conference. IEEE, pp 5484–5488

Jiang Q, Tang C, Chen C, Wang X, Huang Q (2018) Stock price forecast based on LSTM neural network. In: International conference on management science and engineering management. Springer, pp 393–408

10.

Selvamuthu D, Kumar V, Mishra A (2019) Indian stock market prediction using artificial neural networks on tick data. Financ Innov 5(1):16CrossRef

11.

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

12.

Shi Y, Tang Yr, Long W (2019) Sentiment contagion analysis of interacting investors: evidence from China’s stock forum. Physica A 523:246–259CrossRef

13.

Shi Y, Tang Y, Cui Lx, Wen L (2018) A text mining based study of investor sentiment and its influence on stock returns. Econ Comput Econ Cybern Stud Res 52(1):183–199

14.

Chen K, Zhou Y, Dai F (2015) A LSTM-based method for stock returns prediction: a case study of China stock market. In: 2015 IEEE international conference on big data (big data). IEEE, pp 2823–2824

15.

Jia H (2016) Investigation into the effectiveness of long short term memory networks for stock price prediction. arXiv preprint arXiv:1603.07893

16.

Kreiss JP, Paparoditis E (2011) Bootstrap methods for dependent data: a review. J Korean Stat Soc 40(4):357–378CrossRef

17.

Radovanov B, Marcikić A (2014) A comparison of four different block bootstrap methods. Croat Oper Res Rev 5(2):189–202CrossRef

18.

Hall P, Horowitz JL, Jing BY (1995) On blocking rules for the bootstrap with dependent data. Biometrika 82(3):561–574CrossRef

19.

Nordman DJ, Lahiri SN et al (2014) Convergence rates of empirical block length selectors for block bootstrap. Bernoulli 20(2):958–978CrossRef

20.

Politis DN, White H (2004) Automatic block-length selection for the dependent bootstrap. Econom Rev 23(1):53–70CrossRef

21.

Lahiri SN (1999) Theoretical comparisons of block bootstrap methods. Ann Stat 27(1):386–404CrossRef

22.

Demirel OF, Willemain TR (2002) Generation of simulation input scenarios using bootstrap methods. J Oper Res Soc 53(1):69–78CrossRef

Title: Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network
Authors: Shankhajyoti De
Arabin Kumar Dey
Deepak Kumar Gouda
Publication date: 22-07-2020
Publisher: Springer Berlin Heidelberg
Published in: Annals of Data Science / Issue 2/2022
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-020-00307-8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 2/2022

Bayesian Survival Analysis of Type I General Exponential Distributions

A Comprehensive Survey of Loss Functions in Machine Learning

Marshall–Olkin Alpha Power Inverse Exponential Distribution: Properties and Applications

A Bayesian Meta-Analysis on the Association Between Beta-Carotene and Bone Mineral Density

Demand Prediction in the Automobile Industry Independent of Big Data

Odd Chen-G Family of Distributions