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

@article{De2020ConstructionOC,
  title={Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network},
  author={Shankhyajyoti De and Arabin Kumar Dey and Deepak Gauda},
  journal={Annals of Data Science},
  year={2020},
  pages={1 - 14}
}
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… Expand

References

SHOWING 1-10 OF 29 REFERENCES
Investigation Into The Effectiveness Of Long Short Term Memory Networks For Stock Price Prediction
The effectiveness of long short term memory networks trained by backpropagation through time for stock price prediction is explored in this paper. A range of different architecture LSTM networks areExpand
A Robust Predictive Model for Stock Price Forecasting
Prediction of future movement of stock prices has been the subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannotExpand
Deep learning-based feature engineering for stock price movement prediction
TLDR
Experimental results show that the proposed novel end-to-end multi-filters neural network outperforms traditional machine learning models, statistical models, and single-structure networks in terms of the accuracy, profitability, and stability. Expand
Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques
TLDR
Experimental results show that the performance of all the prediction models improve when these technical parameters are represented as trend deterministic data, and random forest outperforms other three prediction models on overall performance. Expand
A LSTM-based method for stock returns prediction: A case study of China stock market
TLDR
The presented paper modeled and predicted China stock returns using LSTM and improved the accuracy of stock returns prediction from 14.3% to 27.2% compared with random prediction method. Expand
A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction
TLDR
This paper designs deep learning model using 715 novel input-features configured on the basis of technical analyses to predict patterns of stock price fluctuation more precisely, and designs and analyzed several models using different configuration of input- Features and target-vectors. Expand
Stock Price Forecast Based on LSTM Neural Network
TLDR
This paper finds that LSTM can be well used in stock price forecasting, and the criterion of the pros and cons of the model is the mean square error between predicted value and real value. Expand
Indian stock market prediction using artificial neural networks on tick data
TLDR
This article uses neural networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared. Expand
Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market
TLDR
A comparative study of fundamental and technical analysis based on different parameters of the stock market prediction techniques using time series analysis and machine learning algorithms such as the artificial neural network. Expand
Automatic Block-Length Selection for the Dependent Bootstrap
Abstract We review the different block bootstrap methods for time series, and present them in a unified framework. We then revisit a recent result of Lahiri [Lahiri, S. N. (1999b). TheoreticalExpand
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