Predicting Stock Price Using Sentimental Analysis Through Twitter Data

2020 ◽  
Author(s):  
Niveditha N. Reddy ◽  
E. Naresh ◽  
Vijaya Kumar B.P.
Keyword(s):  
Stock Price ◽  
Twitter Data

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 Can Social Media Predict Soccer Clubs’ Stock Prices? The Case of Turkish Teams and Twitter

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110041
Author(s):  
Altuğ Tanaltay ◽  
Amirreza Safari Langroudi ◽  
Raha Akhavan-Tabatabaei ◽  
Nihat Kasap
Keyword(s):  
Social Media ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Twitter Data ◽  
Strong Indicator ◽  
Soccer Teams ◽  
Betting Odds

Finance literature in sports focuses on three main methods of stock price prediction in soccer: based on match results, pre-match expectations, or match importance. For pre-match expectations, betting odds is commonly used as the indicator of investors’ sentiments. We propose to include Twitter data as another indicator of this variable, and analyze the links among soccer match results, sentiments, and stock returns of the four major Turkish soccer teams. Our results show that social media can be a strong indicator of pre-match expectations and investors’ sentiments in stock price prediction.

ANALISIS PENGARUH INFLASI, KURS DAN IHSG TERHADAP RETURN REKSA DANA SAHAM PT NIKKO SECURITIES INDONESIA PERIODE 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Muhammad Rois Rois ◽  
Manarotul Fatati Fatati ◽  
Winda Ihda Magfiroh
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Equity Fund ◽  
Stock Price Index

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

Family Firms and Stock Price Crash Risk

2019 ◽  
Vol 10 (4) ◽  
pp. 77-86
Author(s):  
Hae-Young Ryu ◽  
Soo-Joon Chae
Keyword(s):  
Stock Price ◽  
Family Firms ◽  
Crash Risk

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

PENGARUH FREKUENSI PERDAGANGAN PADA SEKTOR PROPERTY DAN REAL ESTATE TERHADAP HARGA SAHAM

2019 ◽  
Vol 7 (02) ◽  
pp. 51
Author(s):  
Adri Wihananto
Keyword(s):  
Real Estate ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Trading ◽  
Trading Frequency ◽  
Important Thing ◽  
Know How ◽  
Stock Value ◽  
Measuring Frequency ◽  
Case Based

Trading frequency can be said as the implementation from trader of commerce. This case based on positive or negative trader reaction given by trader information.  Stock trading in BEI always fluctuate with price of volume value and frequency particularly. Frequency itself shows the company  involved or not. In trading frequency, if the indicator frequency it self shown the higher point, it means better. In spite of the most important thing is how the fluctuation or value conversion itself. On the frequencies we also could see which stocks is interested by the investor. When trading frequency high, it  may be create sense of interest from investors.The aim of this research, in order to know how far the effect of trading frequency (X) with stock value (Y) using cover stock value. The information used is begin 2008 with sample from twelve property and real estate companies. According to the research can be conclude from twelve companies in Indonesia Stock Exchange in 2008, 75 % of trading frequency samples doesn’t have signification degree between trading frequency and stock value. This case can be explained count on smaller than t tableEvaluation of this research is the trading measuring frequency at property sector and real estate not influence to stock priceKeywords : Trading Frequency, Stock Price 

Analisis Kinerja Keuangan Perusahaan dengan Menggunakan Pendekatan Economic Value Added (EVA) dan Market Value Added (MVA) (Studi Kasus pada PT Astra International, Tbk. Periode Tahun 20082012)

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdul Hamid
Keyword(s):  
Financial Performance ◽  
Financial Services ◽  
Stock Price ◽  
Economic Value ◽  
Composite Index ◽  
Value Added ◽  
Secondary Data ◽  
Study Approach ◽  
Market Value Added ◽  
The Interest Rate

This study is a qualitative study using a case study approach to the PT. Astra International, Tbk. The object of this research is PT. Astra International, Tbk. PT. Astra International, Tbk is a company engaged in six business sectors, namely: automotive,financial services, heavy equipment, mining and energy, agribusiness, information technology, infrastructure and logistics. Researchers chose PT. Astra International, Tbk as research objects due in the year 2012, PT. Astra International, Tbk managed to rank first in the list of 100 Best Companies to Go Public by the 2011 financial performance of Fortune magazines Indonesia. The data used in this research is secondary data, the financial statements. Astra International, Tbk 20082012. Other secondary data used is the interest rate of Bank Indonesia Certificates (SBI), the Jakarta Composite Index (JCI), and thecompanys stock price began the year 20082012. This study aims to determine the companys financial performance by the use of EVA and MVA approach, therefore the data analysis technique used is the EVA and MVA. Based on the value EVA of the year 2008 2012, PT. Astra International, Tbk has good financial performance that managed to meet the expectations of the company and the investors. Based on the value of MVA during the years 20082012, PT. Astra International, Tbk managed to create wealth and prosperity for companies and investors. It concluded that financial performance. AstraInternational, Tbk for five years was satisfactory.

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