dc.contributor.advisor | Laha, A. K. | |
dc.contributor.author | Verma, Vibhor | |
dc.contributor.author | Advani, Manish Suresh | |
dc.contributor.author | Garg, Akhil | |
dc.date.accessioned | 2019-08-19T22:45:51Z | |
dc.date.available | 2019-08-19T22:45:51Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | http://hdl.handle.net/11718/22362 | |
dc.description.abstract | Machine Learning and Natural Language Processing have become an integral part of
the Financial Markets. These algorithms are being developed every day to utilize the data
available with us and provide a prediction regarding the market’s behavior in future. It has
become integral for portfolio managers to use these advanced technologies to manage their
portfolio to mitigate risk as well as generate superior returns for the investors.
This project revolves around the concept of the Efficient Market Hypothesis and uses
Sentiment Analysis of world news to predict market movements. Our idea is to develop a
model that uses Natural Language Processing techniques and Neural Networks to forecast
price movement in the Index. We plan to obtain a functional relationship between top 20
headlines published during the day & predict the price movements of a composite stock index
and thus use the data to make predictions regarding future market movements.
We propose to use this model to first verify the efficient market hypothesis across US
markets. Further, if the data suggests that the financial markets doesn’t follow efficient
market hypothesis, we would also like to explore the possibility of creating a trading strategy
around this model and verify the feasibility of this strategy on the basis of risk associated with
strategy such as variance of the return or maximum drawdown of the returns. Through these
measures we wish to quantify whether this trading strategy could actually result in generating
alpha for the investor. | en_US |
dc.publisher | Indian Institute of Management Ahmedabad | en_US |
dc.relation.ispartofseries | SP_2519 | en_US |
dc.subject | Financial Markets | en_US |
dc.subject | Natural Language Processing | en_US |
dc.subject | Bag of Word Model | en_US |
dc.subject | Data Hashing | en_US |
dc.subject | Sentiment polarity | en_US |
dc.title | Forecasting stock price movement in index using world news | en_US |
dc.type | Student Project | en_US |