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Found 18 papers, 2 papers with code
Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model
Optimum portfolios are designed on the selected seven sectors.
Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model
Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector.
Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks
Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain.
Machine Learning: Algorithms, Models, and Applications
The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed.
Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model
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.
Stock Portfolio Optimization Using a Deep Learning LSTM Model
The predicted and the actual returns of each portfolio are found to be high, indicating the high precision of the LSTM model.
Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Market
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.
Design and Analysis of Robust Deep Learning Models for Stock Price Prediction
Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve.
Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH
Volatility clustering is an important characteristic that has a significant effect on the behavior of stock markets.
Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model
Even more challenging is to build a system for constructing an optimum portfolio of stocks based on the forecasted future stock prices.
Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models
Designing robust frameworks for precise prediction of future prices of stocks has always been considered a very challenging research problem.
Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models
Prediction of stock price and stock price movement patterns has always been a critical area of research.
Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models
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.
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models.
Machine Learning Applications in Misuse and Anomaly Detection
Machine learning and data mining algorithms play important roles in designing intrusion detection systems.
A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models
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.
Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries
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.
A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing
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.
