Authors :
Joy Kapadia; Kaniskaran Thirunavukkarasu; Kashish Gulecha; Krish Kotak; Krish Parmar
Volume/Issue :
Volume 6 - 2021, Issue 10 - October
Google Scholar :
http://bitly.ws/gu88
Scribd :
https://bit.ly/3Cjwb4F
Abstract :
Operations Research and Financial markets
share a relation like Tom and Jerry, both do well when
they are alone, but when they are together, things are
even better. In this paper, we have reviewed the
application of operations research in financial markets.
Historically, various Operations research techniques like
Game theory, network analysis, Markov chain, neural
networks have been implemented to solve the
irrationalities arising from the financial markets. Our
research paper attempted to outperform the local index
using the weights resulting from the linear programming
problem under certain constraints. In addition to this,
we have run a simple Monte Carlo simulation with the
geometric Brownian motion (GBM) model to forecastthe
future price movement of a multinational company.
Operations Research and Financial markets
share a relation like Tom and Jerry, both do well when
they are alone, but when they are together, things are
even better. In this paper, we have reviewed the
application of operations research in financial markets.
Historically, various Operations research techniques like
Game theory, network analysis, Markov chain, neural
networks have been implemented to solve the
irrationalities arising from the financial markets. Our
research paper attempted to outperform the local index
using the weights resulting from the linear programming
problem under certain constraints. In addition to this,
we have run a simple Monte Carlo simulation with the
geometric Brownian motion (GBM) model to forecastthe
future price movement of a multinational company.
