Forecasting variance of NiftyIT index with RNN and DNN Article Swipe
Chigurupati Karthik
,
Raghunandan
,
B. Ashwath Rao
,
N. V. Subba Reddy
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2161/1/012005
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2161/1/012005
A time series is an order of observations engaged serially in time. The prime objective of time series analysis is to build mathematical models that provide reasonable descriptions from training data. The goal of time series analysis is to forecast the forthcoming values of a series based on the history of the same series. Forecasting of stock markets is a thought-provoking problem because of the number of possible variables as well as volatile noise that may contribute to the prices of the stock. However, the capability to analyze stock market leanings could be vital to investors, traders and researchers, hence has been of continued interest. Plentiful arithmetical and machine learning practices have been discovered for stock analysis and forecasting/prediction. In this paper, we perform a comparative study on two very capable artificial neural network models i) Deep Neural Network (DNN) and ii) Long Short-Term Memory (LSTM) a type of recurrent neural network (RNN) in predicting the daily variance of NIFTYIT in BSE (Bombay Stock Exchange) and NSE (National Stock Exchange) markets. DNN was chosen due to its capability to handle complex data with substantial performance and better generalization without being saturated. LSTM model was decided, as it contains intermediary memory which can hold the historic patterns and occurrence of the next prediction depends on the values that preceded it. With both networks, measures were taken to reduce overfitting. Daily predictions of the NIFTYIT index were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized admirably to make daily predictions of the NiftyIT data. The LSTM-RNN outpaced the DNN in terms of forecasting and thus, grips more potential for making longer-term estimates.
Related Topics
Concepts
Overfitting
Computer science
Artificial neural network
Econometrics
Recurrent neural network
Generalizability theory
Time series
Stock exchange
Artificial intelligence
Machine learning
Stock market index
Index (typography)
Generalization
Stock (firearms)
Stock market
Statistics
Economics
Mathematics
Finance
Mechanical engineering
Horse
World Wide Web
Mathematical analysis
Paleontology
Engineering
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2161/1/012005
- OA Status
- diamond
- Cited By
- 4
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205125524
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4205125524Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2161/1/012005Digital Object Identifier
- Title
-
Forecasting variance of NiftyIT index with RNN and DNNWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Chigurupati Karthik, Raghunandan, B. Ashwath Rao, N. V. Subba ReddyList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2161/1/012005Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1742-6596/2161/1/012005Direct OA link when available
- Concepts
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Overfitting, Computer science, Artificial neural network, Econometrics, Recurrent neural network, Generalizability theory, Time series, Stock exchange, Artificial intelligence, Machine learning, Stock market index, Index (typography), Generalization, Stock (firearms), Stock market, Statistics, Economics, Mathematics, Finance, Mechanical engineering, Horse, World Wide Web, Mathematical analysis, Paleontology, Engineering, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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