scholarly journals A Study on Efficient Market Hypothesis to Predict Exchange Rate Trends Using Sentiment Analysis of Twitter Data

2016 ◽  
Vol 19 (7) ◽  
pp. 1107-1115 ◽  
Author(s):  
Kokoy Siti Komariah ◽  
Carmadi Machbub ◽  
Ary S. Prihatmanto ◽  
Bong-Kee Sin
Keyword(s):  
Exchange Rate ◽  
Sentiment Analysis ◽  
Efficient Market Hypothesis ◽  
Efficient Market ◽  
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 HIDING IN PLAIN SIGHT: SENTIMENT ANALYSIS AND THE EFFICIENT MARKET HYPOTHESIS

2021 ◽  
Author(s):  
DAVID LITWIN
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Academic Research ◽  
Efficient Market Hypothesis ◽  
Press Releases ◽  
Major Research ◽  
Efficient Market ◽  
Machine Learning Model ◽  
Stock Type ◽  
Methodological Component

The stock market is a notoriously complex and unpredictable system, and because of this has always been an alluring subject for academic research seeking to make the unpredictable more predictable. This major research project is no different as it aims to quantify the predictive value of financial sentiment, determine which sentiments are most meaningful, when they are most meaningful, and if meaningful sentiment varies depending on type of stock. To pursue these goals, the project finds its theoretical footing in Eugene Fama’s Efficient Market Hypothesis and Daniel Kahneman’s Prospect Theory. However, the methodological component of this project enters into emerging territory as it employs sentiment analysis and machine learning, which have only recently been made possible by advances in technology and communications practices. Specifically, through the use of the Loughran-McDonald dictionary for financial sentiment, corporate press releases were analyzed and tested using a Random Forest machine learning model. The results from this project show that financial senitiment found in press releases does provide a slight predictive edge, however the sentiments responsible for that edge vary based on type of stock, type of fluctuation being predicted, and timeframe.

scholarly journals Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

2021 ◽  
Vol 67 (3) ◽  
pp. 3451-3461
Author(s):  
Samreen Naeem ◽  
Wali Khan Mashwani ◽  
Aqib Ali ◽  
M. Irfan Uddin ◽  
Marwan Mahmoud ◽  
...  
Keyword(s):  
Machine Learning ◽  
Exchange Rate ◽  
Sentiment Analysis ◽  
Exchange Rate Forecasting ◽  
Twitter Data

scholarly journals HIDING IN PLAIN SIGHT: SENTIMENT ANALYSIS AND THE EFFICIENT MARKET HYPOTHESIS

2021 ◽  
Author(s):  
DAVID LITWIN
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Academic Research ◽  
Efficient Market Hypothesis ◽  
Press Releases ◽  
Major Research ◽  
Efficient Market ◽  
Machine Learning Model ◽  
Stock Type ◽  
Methodological Component

The stock market is a notoriously complex and unpredictable system, and because of this has always been an alluring subject for academic research seeking to make the unpredictable more predictable. This major research project is no different as it aims to quantify the predictive value of financial sentiment, determine which sentiments are most meaningful, when they are most meaningful, and if meaningful sentiment varies depending on type of stock. To pursue these goals, the project finds its theoretical footing in Eugene Fama’s Efficient Market Hypothesis and Daniel Kahneman’s Prospect Theory. However, the methodological component of this project enters into emerging territory as it employs sentiment analysis and machine learning, which have only recently been made possible by advances in technology and communications practices. Specifically, through the use of the Loughran-McDonald dictionary for financial sentiment, corporate press releases were analyzed and tested using a Random Forest machine learning model. The results from this project show that financial senitiment found in press releases does provide a slight predictive edge, however the sentiments responsible for that edge vary based on type of stock, type of fluctuation being predicted, and timeframe.

Value Migration in the Context of Efficient Market Hypothesis

2009 ◽  
Vol 3 (1) ◽  
pp. 49-55
Author(s):  
Wiesław M. Grudzewski ◽  
Irena K. Hejduk ◽  
Dariusz Siudak
Keyword(s):  
Efficient Market Hypothesis ◽  
Efficient Market

An Empirical Study on Efficient Market Hypothesis with reference to FMCG Sector

GIS Business ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 109-126
Author(s):  
Nitin Tanted ◽  
Prashant Mistry
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Controversial Issues ◽  
Efficient Market ◽  
Price Movements ◽  
Run Test ◽  
Stock Price Movements

One of the highly controversial issues in the area of finance is “Efficient Market Hypothesis”. Efficient Market Hypothesis states that, “In an efficient market, all available price information is reflected in the stock prices and it is not possible to generate abnormal returns compared to other investors.” A lot of studies conducted previouslyto test the Efficient Market Hypothesis, confirmed the theory until recent years, when some academicians found it to be non-applicable in financial markets. According to them, it is possible to forecast the stock price movements using Technical Analysis. The results of various studies have been inconclusive and indefinite about the issue. This study attempted to test the efficiency of FMCG Sector stocks in India in its weak form. For the study, closing prices of top 10 stocks from Nifty FMCG index has been taken for the 5-year period ranging from 1st October 2014 to 30th September 2019. Wald-Wolfowitz Run test has been used to test the haphazard movements in the stock price movements. The results indicated that FMCG sector stocks does support the Efficient Market Hypothesis and exhibit efficiency in its weak form. Hence, it is not possible to accurately predict the price movements of these stocks.

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