no code implementations • 2 Mar 2022 • Jaydip Sen, Sidra Mehtab, Abhishek Dutta, Saikat Mondal
Optimum portfolios are designed on the selected seven sectors.
no code implementations • 6 Feb 2022 • Jaydip Sen, Saikat Mondal, Sidra Mehtab
Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector.
no code implementations • 6 Feb 2022 • Jaydip Sen, Sidra Mehtab, Abhishek Dutta, Saikat Mondal
Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain.
no code implementations • 6 Jan 2022 • Jaydip Sen, Sidra Mehtab, Rajdeep Sen, Abhishek Dutta, Pooja Kherwa, Saheel Ahmed, Pranay Berry, Sahil Khurana, Sonali Singh, David W. W Cadotte, David W. Anderson, Kalum J. Ost, Racheal S. Akinbo, Oladunni A. Daramola, Bongs Lainjo
The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed.
no code implementations • 9 Nov 2021 • Jaydip Sen, Saikat Mondal, Sidra Mehtab
This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from the web over a specified time interval and predicting their future prices for a specified forecast horizon, and forecasts the future stock prices.
no code implementations • 8 Nov 2021 • Jaydip Sen, Abhishek Dutta, Sidra Mehtab
The predicted and the actual returns of each portfolio are found to be high, indicating the high precision of the LSTM model.
no code implementations • 23 Jul 2021 • Jaydip Sen, Sidra Mehtab
Three portfolios are built for each of the seven sectors chosen for this study, and the portfolios are analyzed on the training data based on several metrics such as annualized return and risk, weights assigned to the constituent stocks, the correlation heatmaps, and the principal components of the Eigen portfolios.
no code implementations • 17 Jun 2021 • Jaydip Sen, Sidra Mehtab
Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve.
no code implementations • 28 May 2021 • Jaydip Sen, Sidra Mehtab, Abhishek Dutta
Volatility clustering is an important characteristic that has a significant effect on the behavior of stock markets.
no code implementations • 6 Apr 2021 • Jaydip Sen, Abhishek Dutta, Sidra Mehtab
Even more challenging is to build a system for constructing an optimum portfolio of stocks based on the forecasted future stock prices.
no code implementations • 28 Mar 2021 • Jaydip Sen, Sidra Mehtab
Designing robust frameworks for precise prediction of future prices of stocks has always been considered a very challenging research problem.
no code implementations • 7 Nov 2020 • Sidra Mehtab, Jaydip Sen, Subhasis Dasgupta
Prediction of stock price and stock price movement patterns has always been a critical area of research.
no code implementations • 22 Oct 2020 • Sidra Mehtab, Jaydip Sen
In this approach, the open values of the NIFTY 50 index are predicted on a time horizon of one week, and once a week is over, the actual index values are included in the training set before the model is trained again, and the forecasts for the next week are made.
4 code implementations • 20 Sep 2020 • Sidra Mehtab, Jaydip Sen, Abhishek Dutta
In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models.
no code implementations • 10 Sep 2020 • Jaydip Sen, Sidra Mehtab
Machine learning and data mining algorithms play important roles in designing intrusion detection systems.
no code implementations • 17 Apr 2020 • Sidra Mehtab, Jaydip Sen
We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data.
no code implementations • 10 Jan 2020 • Sidra Mehtab, Jaydip Sen
Based on the NIFTY data during the said period, we build various predictive models using machine learning approaches, and then use those models to predict the Close value of NIFTY 50 for the year 2019, with a forecast horizon of one week.
1 code implementation • 9 Dec 2019 • Sidra Mehtab, Jaydip Sen
Based on the data of 2015 to 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week.