Book ChapterDOI
Prediction of Stock Indices, Gold Index, and Real Estate Index Using Deep Neural Networks
Sahil Jain,Pratyush Mandal,Birendra Singh,Pradnya Kulkarni,Mateen Sayed +4 more
- pp 333-339
Reads0
Chats0
TLDR
In this article, the authors proposed a software system which can overcome human biases and provide an insight into the various schemes and opportunities in the market, which can pick up data from various sources and merge together their interdependencies to provide a set of visualizations of its previous history and plot its expected future growths.Abstract:
In this age, there is ample investment opportunities present in the market Choosing one out of it for putting in the resources so as to maximize the returns becomes a very tedious and volatile task as there are several factors affecting its performance Here, there is need to deploy a software system which can overcome human biases and provide an insight into the various schemes and opportunities The system will pick up data from various sources and merge together their interdependencies to provide a set of visualizations of its previous history and plot its expected future growths It shall consider historical data and news factors The classes of investment broadly considered for this project are Stocks, Gold and Real Estate The data obtained is to be trained using methods such as support vector machine, deep neural networks like CNN and LSTM and compared for their performance and accuracy and error values This aids the human in understanding the rate of investment as well as associated risks considering numerous variables present in the market which otherwise is ignoredread more
Citations
More filters
Machine Learning for Real Estate Time Series Prediction
TL;DR: In this article , the authors investigate the predictive performance on price time series of REITs (real estate investment trusts), stocks and bonds, of five different machine learning (ML) algorithms.
References
More filters
Proceedings ArticleDOI
Stock price prediction using LSTM, RNN and CNN-sliding window model
TL;DR: This work uses three different deep learning architectures for the price prediction of NSE listed companies and compares their performance and applies a sliding window approach for predicting future values on a short term basis.
Proceedings ArticleDOI
Predicting the Effects of News Sentiments on the Stock Market
TL;DR: The main contributions include the development of a sentiment analysis dictionary for the financial sector, theDevelopment of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market.
Proceedings ArticleDOI
Stock Market Prediction based on Social Sentiments using Machine Learning
TL;DR: Using sentiment analysis on the tweets collected using the Twitter API and also the closing values of various stocks, this work seeks to build a system that forecasts the stock price movement of various companies.
Book ChapterDOI
Gold Price Forecasting and Related Influence Factors Analysis Based on Random Forest
TL;DR: The findings show that the random forest is a powerful method to predict the trends of fluctuations of the gold price and validate that, by using the random Forest algorithm, there were only two factors must be considered to ensure the performance of the prediction, which were DJIA and S&P500.
Proceedings ArticleDOI
Detecting, quantifying and accessing impact of news events on Indian stock indices
TL;DR: This paper proposes the use of PESTEL factors to categorize market-impacting information, and presents a paragraph-vector based information classification mechanism that outperforms state of the art linear SVM on data from different stock indices.
Related Papers (5)
Predicting stock returns of Tehran exchange using LSTM neural network and feature engineering technique
Sina Dami,Mohammad Esterabi +1 more