Prediction Models for Indian Stock Market

Nayak, Aparna; Pai, M. M. Manohara; Pai, Radhika M.

doi:10.1016/j.procs.2016.06.096

Cited by 120 publications

(44 citation statements)

References 7 publications

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“…MKL performs better on most tests and accuracy of news-based model is similar to naïve combination model. Aparna Nayak combined all available data about selected companies especially historical data, news and tweets [15]. First, continuous trend pattern is calculated from the last three days prices (1 if the last three days trend continue in the same direction else it's equal to 0) then volume variation is compared to the trend at the same day and volume variation pattern is established.…”

Section: Related Workmentioning

confidence: 99%

Stock Market Forecasting Model From Multi News Data Source Using a Two-Level Learning Algorithm

Hicham¹

2021

TURCOMAT

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Stock prediction retains the attention of a large part of the community. The emergence of new indicators mostly extracted from the web makes this domain of research challenging and in a continuous evolution. The present work tries to address the question of how to model financial news from multi-data source for the purpose of forecasting stock movement. We combined different news sources to enhance the accuracy of stock movement prediction. Data are collected from four financial news websites and proceeded individually by Support Vector Machine (SVM) algorithm then we aggregate outputs using an Artificial Neural Network (ANN) algorithm. Experiments were conducted and the results have shown that the designed two-level learning SVC&ANN algorithm has achieved better accuracy than simple news analysis models using a single information source.

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Section: Related Workmentioning

confidence: 99%

Stock Market Forecasting Model From Multi News Data Source Using a Two-Level Learning Algorithm

Hicham¹

2021

TURCOMAT

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“…To predict future value or behavior from those observations or patterns it will then iteratively learn from data, unlike typical computer programs. The purpose of machine learning is to program computers to use sample data as an experience or model and use the patterns of this data to predict the future based on that data (Nayak et al, 2016;Feng et al, 2019). Machine Learning not only deals with database problems but is also an application of artificial intelligence (AI).…”

Section: Machine Learningmentioning

confidence: 99%

Ultimate Prediction of Stock Market Price Movement

Nabi¹,

Saeed²,

Harron³

et al. 2019

Journal of Computer Science

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Investment in the stock market is currently very popular due to its economic gain. Numerous researchers' and academicians' work is focused on financial time series prediction due to its data availability and profitability. Therefore, this study presents the design and implementation of a novel binary classification framework to predict stock market trends. The framework is composed of data preprocessing, feature engineering, feature selection and classification algorithms. The model is built on multiple sector stock market companies' data collected from NASDAQ over a period of ten years. Various feature selection algorithms are applied in combination with several machine learning algorithms. Furthermore, as the new contribution, we have constructed two new features which have been found to be promising in terms of improving overall performance. Ultimately, a collaboration of feature selection and classification techniques is employed. The application of Principal Component Analysis (PCA) with Multilayer Perceptron and Support Vector Machine (SVM) to added featured datasets shows 100% accuracy on the majority of datasets. In summary, an intensive comparison is presented among the various feature selection and classification algorithms.

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“…Rupa et al [17] have made a review study of rainfall prediction using neuro fuzzy inference system and suggested that it is an alternative to traditional metrological approaches in rainfall prediction. Nayak et al [18] have made the prediction for Indian Stock Market and suggested that Decision Boosted Tree performed better than SVM and Logistic Regression. Bushara et al [19] have made a review on computational intelligence in weather forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Soft Computing Techniques for Weather Change Predictions in Delhi

Mantri*,

Jayasingh

2019

IJRTE

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 Abstract: Weather forecasting and warning is the application of science and technology to predict the state of the weather for a future time of a given location. The emergence of adverse effects of weather has endangered the life of general public in previous years. The unpredicted flood and super cyclone in many places have created havoc. The government and private agencies are working on its behaviours but still it is challenging and incomplete. But, the application of soft computing techniques in weather prediction has made a significant perfomance now a days. This research work presents the comparative study of soft computing techniques like MultiLayer Perceptron(MLP), Support Vector Machine(SVM) and J48 Decision Tree for forecasting the weather of Delhi with ten years data comprising of temperature, dew, humidity, air pressure, wind speed and visibility. This paper tries to describe the comparison among above models using four different error values like Relative Absolute Error(RAE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Root Relative Squared Error(R 2 ) with a proposed model by defining new algorithm. Further the performance can be enhanced if textmining will be applied in this proposed model.

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Prediction Models for Indian Stock Market

Cited by 120 publications

References 7 publications

Stock Market Forecasting Model From Multi News Data Source Using a Two-Level Learning Algorithm

Stock Market Forecasting Model From Multi News Data Source Using a Two-Level Learning Algorithm

Ultimate Prediction of Stock Market Price Movement

Soft Computing Techniques for Weather Change Predictions in Delhi

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