Visualizing, Comparing and Forecasting Stock Market Prediction

Stock market forecasting is becoming increasingly popular among academics. It is also an important topic in finance. Stock market forecasting and analysis assist investors in making educated judgements. To estimate the stock price, many prediction approaches such as technical analysis, fundamental analysis, time series analysis, and statistical analysis are all utilized, but none of these are proven to be efficient prediction methods. Stock prediction is considered to be incredibly difficult on account of the volatile nature of the stock market. The stock market works in cycles, what comes down goes up and vice versa. It is, therefore, considered unpredictable and uncontrollable. However, Machine learning is very effective in the field of stock market prediction as it contains a huge pool of data to be used. In the course of this paper, we aim to compare different Machine Learning algorithms used to predict stock prices and attempt to forecast stock prices using various prediction tools.

[1]  Devender Kumar Sharma,et al.  Integration of genetic algorithm with artificial neural network for stock market forecasting , 2021, International Journal of System Assurance Engineering and Management.

[2]  Rajeeva Shreedhara Bhat,et al.  Stock Market Prediction using Machine Learning , 2020, International Journal of Communication and Media Science.

[3]  Mithileysh Sathiyanarayanan,et al.  Visual Auxiliary Solutions to Analyse Social Media Data for Improving Marketing & Business , 2019, 2019 International Conference on contemporary Computing and Informatics (IC3I).

[4]  Mithileysh Sathiyanarayanan,et al.  Visual Analysis of Predictive Policing to Improve Crime Investigation , 2019, 2019 International Conference on contemporary Computing and Informatics (IC3I).

[5]  Farhan Aadil,et al.  A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation Between Stock Markets , 2018, Sustainability.

[6]  Fred Pyrczak,et al.  Coefficient of Determination , 2018, Making Sense of Statistics.

[7]  Mustansar Ali Ghazanfar,et al.  Using Machine Learning Classifiers to Predict Stock Exchange Index , 2017 .

[8]  Mithileysh Sathiyanarayanan,et al.  Social network visualization: Does partial edges affect user comprehension? , 2017, 2017 9th International Conference on Communication Systems and Networks (COMSNETS).

[9]  Mithileysh Sathiyanarayanan,et al.  Linear-time diagram: A set visualisation technique for personal visualisation to understand social interactions over time , 2016, 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I).

[10]  Mithileysh Sathiyanarayanan,et al.  Determining and Visualising E-mail Subsets to Support E-discovery , 2016 .

[11]  Mithileysh Sathiyanarayanan,et al.  Spherule diagrams with graph for social network visualization , 2016, 2016 8th International Conference on Communication Systems and Networks (COMSNETS).

[12]  Mithileysh Sathiyanarayanan,et al.  Design and evaluation of euler diagram and treemap for social network visualisation , 2015, 2015 7th International Conference on Communication Systems and Networks (COMSNETS).

[13]  Omar S. Soliman,et al.  A Machine Learning Model for Stock Market Prediction , 2014, ArXiv.

[14]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[15]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[16]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[17]  Dileep G. Menon,et al.  Predicting Market Prices Using Deep Learning Techniques , 2018 .

[18]  Zhifang He,et al.  Stock Price Prediction based on SSA and SVM , 2014, ITQM.

[19]  Zahid Iqbal,et al.  Efficient Machine Learning Techniques for Stock Market Prediction , 2013 .

[20]  Johannes Fürnkranz,et al.  Mean Absolute Error , 2010, Encyclopedia of Machine Learning and Data Mining.