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Reseach Article

Comparative Analysis of Hedge Funds in Financial Markets using Machine Learning Models

by Saurabh Aggarwal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 3
Year of Publication: 2017
Authors: Saurabh Aggarwal
10.5120/ijca2017913489

Saurabh Aggarwal . Comparative Analysis of Hedge Funds in Financial Markets using Machine Learning Models. International Journal of Computer Applications. 163, 3 ( Apr 2017), 25-29. DOI=10.5120/ijca2017913489

@article{ 10.5120/ijca2017913489,
author = { Saurabh Aggarwal },
title = { Comparative Analysis of Hedge Funds in Financial Markets using Machine Learning Models },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 3 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number3/27377-2017913489/ },
doi = { 10.5120/ijca2017913489 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:09:10.857473+05:30
%A Saurabh Aggarwal
%T Comparative Analysis of Hedge Funds in Financial Markets using Machine Learning Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 3
%P 25-29
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of this paper is to layout machine learning models for comparative analysis of hedge funds in financial markets for investments. It has been difficult to compare the hedge fund’s performance due to myriad of classification techniques adopted by hedge fund managers such as strategy model, asset class, liquidity score, hence producing disparity in numbers. The machine learning and deep learning neural network models have been effective in exploiting the exogenous and complex data interactions in financial domain datasets and producing useful insights. The author in this paper discusses models such as – semi supervised learning, decision tree learning and hybrid time series classification along with experimental results to juxtapose the hedge funds and hence producing useful results for investments. The analysis shows that the discussed models in the paper can be used for comparative analysis of funds and the hybrid time series classification model is more effective rather than using the semi-supervised and decision tree models individually.

References
  1. Wikipedia, What is a hedge Fund, https://en.wikipedia.org/wiki/Hedge_fund
  2. Hedge Fund Classification using K-means Clustering method, Nandita Das, 2003, 9th International Conference on Computing in Economics and Finance.
  3. Pattern extraction for Time Series Classification, Pierre Geurts, University of Liege, Department of Electrical and Computer Science.
  4. Deep Learning in Finance, NG Polson, JH Witte, JB Heaton, Feb 2016, University of Chicago
  5. A Comparison of Machine Learning Classifiers Applied to Financial Datasets , Pablo D. Robles-Granda and Ivan V. Belik, Proceedings of the World congress on Engineering and Computer science, 2010
  6. Machine Learning for Financial Market Prediction, Tristan Fletcher, PhD Theses, University College London, Computer Science
  7. Wikipedia What is a Semi-supervised learning https://en.wikipedia.org/wiki/Semi-supervised_learning
  8. Introduction to Semi-supervised learning, MIT Press release, Semi supervised learning introduction.
  9. Wikipedia, What is decision tree learning, https://en.wikipedia.org/wiki/Decision_tree_learning
  10. White paper: A method for comparing Hedge Funds Uri Kartoun, Washington DC, USA
  11. Machine Learning: Decision Trees, CS540, Jerry Zhu University of Wisconsin-Madison
  12. Deep Learning Architecture for Univariate Time Series Forecasting, Dmitry Vengertsev CS229 Technical report 2014.
  13. Multivariate Time Series Classification with Temporal Abstractions Iyad Batal, Lucia Sacchi, Riccardo BellazziMilos Hauskrecht, Proceedings of the Twenty-Second International FLAIRS Conference (2009), Department of Computer Science, University Of Pittsburg
  14. Wikipedia, what is Pearson Product-Moment Correlation Coefficienthttps://en.wikipedia.org/wiki/Pearson_correlation_coefficient.
Index Terms

Computer Science
Information Sciences

Keywords

Deep Learning Machine Learning Artificial Intelligence Hedge funds Financial Markets Decision Trees Time Series Classification