Stock Price Movement Prediction Using Technical Analysis and Sentiment Analysis

2020 ◽  
Author(s):  
Tommy Wijaya Sagala ◽  
Mei Silviana Saputri ◽  
Rahmad Mahendra ◽  
Indra Budi
Keyword(s):  
Sentiment Analysis ◽  
Stock Price ◽  
Technical Analysis ◽  
Price Movement ◽  
Movement Prediction

Dynamic Business Network Analysis for Correlated Stock Price Movement Prediction

2015 ◽  
Vol 30 (2) ◽  
pp. 26-33 ◽  
Author(s):  
Wenping Zhang ◽  
Chunping Li ◽  
Yunming Ye ◽  
Wenjie Li ◽  
Eric W.T. Ngai
Keyword(s):  
Network Analysis ◽  
Stock Price ◽  
Business Network ◽  
Price Movement ◽  
Movement Prediction

scholarly journals Stock price movement prediction using representative prototypes of financial reports

2011 ◽  
Vol 2 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Ming-Chih Lin ◽  
Anthony J. T. Lee ◽  
Rung-Tai Kao ◽  
Kuo-Tay Chen
Keyword(s):  
Stock Price ◽  
Price Movement ◽  
Financial Reports ◽  
Movement Prediction

Sentiment Analysis of Twitter Data to Examine the Movement of Exchange Rate and Sensex

2020 ◽  
Vol 17 (8) ◽  
pp. 3323-3327
Author(s):  
N. Chethan ◽  
R. Sangeetha
Keyword(s):  
Exchange Rate ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Text Data ◽  
Price Movement ◽  
Word Cloud ◽  
Twitter Data ◽  
R Programming ◽  
Sentiment Score ◽  
Key Events

In this paper tweets available on social media about USD/INR exchange rate, BSE Sensex, NSE Nifty have been collected and Sentiment Analysis using R programming has been performed. A sentiment score has been obtained for each of the sentences and also word cloud plot have been obtained. In this paper twitter feeds are collected using the keywords: USD/INR, #USD/INR, #BSE, #Sensex, #NSE. For the purpose of obtaining the tweets, R programming is used. In this study to obtain the word cloud plot, the sentiment has been classified across 8 categories viz Anticipation, anger, trust, surprise, sadness, joy, fear and disgust. On a day to day basis, Sentiment Analysis gives the overall sentiment on a given day stating if the sentiment for a given day is either Positive or Negative or whether it is Neutral. It also breaks down the tweets into various categories which help in identifying the moods of the investors not only by the sentiment but also by the number of tweets. Further, the word cloud plot offers a simple and effective way of capturing the key events or news which was discussed on Twitter. Sentiment analysis can be used effectively by investors to make a prediction of what direction the stock price movements will happen based on the sentiment prevailing in the market. This study also shows how R programming can be used to perform sentiment analysis on the stock price movement based on twitter feeds. Word cloud can be used to visualize text data in which the size of each word cloud denotes its significance.

scholarly journals Targeted Sentiments and Hierarchical Events based Learning Model for Stock Market Forecasting from Multi-Source Data

2020 ◽  
Vol 9 (4) ◽  
pp. 1763-1770
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Market Price ◽  
Movement Patterns ◽  
Event Extraction ◽  
Spotted Hyena ◽  
Price Movement ◽  
Training Time ◽  
Source Data

Stock market price movement forecast from multi-source data has gained massive interest in recent years. Studies were focussed on extracting the events and sentiments from different source data and employ them in learning the stock price movement patterns. This approach provided accurate and highly reliable forecasting as it involves multiple stock price indicators. However, some aspects of sentiment analysis and event extraction increase the training time and computation complexity in big data stock analysis. To overcome these issues, the hierarchical event extraction and the target dependent sentiment analysis are performed in this paper to improve the learning rate stock price movement patterns. In this paper, the events are hierarchically extracted from news articles using Deep Restricted Boltzmann Machine (DRBM). The target based sentiments from the tweets are detected using Improved Extreme Learning machine (IELM) whose parameters are optimally selected using Spotted Hyena Optimizer (SHO). The stock indicators obtained from these two processes are used in the learning process performed using Tolerant Flexible Multi-Agent Deep Reinforcement Learning (TFMA-DRL) model for analysing the stock patterns and forecasting the future stock trends. The forecasting results obtained by using the TFMA-DRL model by combining the stock indicators of targeted sentiments and hierarchical events are trustworthy and reliable. Evaluations are performed using three datasets collected for 12 months period from three sources of Twitter, Market News and Stock exchange. Results highlighted that the proposed stock forecasting model achieved 90% accuracy with minimum training time.

scholarly journals Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction

2020 ◽  
Author(s):  
Wei Li ◽  
Ruihan Bao ◽  
Keiko Harimoto ◽  
Deli Chen ◽  
Jingjing Xu ◽  
...  
Keyword(s):  
Correlation Matrix ◽  
Stock Price ◽  
Daily Basis ◽  
Market Impact ◽  
Convolutional Network ◽  
Price Movement ◽  
Extensive Experiment ◽  
Exact Time ◽  
Financial News ◽  
Movement Prediction

Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News. Another challenge is that the stocks in the market are not independent, which is omitted by previous works. To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Extensive experiment results show that our model outperforms the baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.

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