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

Komariah, Kokoy Siti; Machbub, Carmadi; Prihatmanto, Ary Setijadi; Sin, Bong-Kee

doi:10.9717/kmms.2016.19.7.1107

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…They show that the accuracy of the exchange rate forecasting model with the sentiment factors of public opinion is improved. Komariah et al (2016) perform a study on efficient market hypothesis to predict exchange rate trends using sentiment analysis of Twitter data. They take daily Twitter sentiments using the hybrid approach of the lexicon-based approach and the naive Bayes classifier to analyze the currency exchange rate movement of Indonesia Rupiah vs US dollar as a way of testing the Efficient Market Hypothesis.…”

Section: Literature Reviewmentioning

confidence: 99%

Untitled

2023

AJBE

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ATINER is an independent and non-profit Association with a Mission to become a forum where Academics and Researchers from all over the world can meet in Athens, exchange ideas on their research and discuss future developments in their disciplines, as well as engage with professionals from other fields. Athens was chosen because of its long history of academic gatherings, which go back thousands of years to Plato's Academy and Aristotle's Lyceum. Both these historic places are within walking distance from ATINER's downtown offices. Since antiquity, Athens was an open city. In the words of Pericles, Athens"…is open to the world, we never expel a foreigner from learning or seeing". ("Pericles' Funeral Oration", in Thucydides, The History of the Peloponnesian War). It is ATINER's mission to revive the glory of Ancient Athens by inviting the World Academic Community to the city, to learn from each other in an environment of freedom and respect for other people's opinions and beliefs. After all, the free expression of one's opinion formed the basis for the development of democracy, and Athens was its cradle. As it turned out, the Golden Age of Athens was in fact, the Golden Age of the Western Civilization. Education and (Re)searching for the 'truth' are the pillars of any free (democratic) society. This is the reason why Education and Research are the two core words in ATINER's name.

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Section: Literature Reviewmentioning

confidence: 99%

Untitled

2023

AJBE

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“…For example, few studies have explored the political discourse on Twitter in the US presidential election (Yaqub et al, 2020), while other studies have focused on information Price of Bitcoin for our analysis dissemination during a public health crisis (Ilyas et al, 2021;Lent et al, 2017). In addition, previous studies have explored the relationship between Twitter sentiment and volatility in national currencies (Ilyas et al, 2020;Komariah et al, 2015;Ozturk and Ciftci, 2014). Recent studies have investigated the correlation between tweet sentiment and changes in the financial markets (Aich et al, 2017;Cakra and Distiawan Trisedya, 2015;Gupta and Singal, 2017;Ilyas et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Bitcoin’s price spike in context of Elon Musk’s Twitter activity

Zaman

Yaqub

Saleem

2022

GKMC

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Purpose The purpose of this paper is to explore the effect of Elon Musk’s Twitter bio change on January 29, 2021 on the discourse around Bitcoin (BTC) on Twitter and to understand how these changes relate to the changes in Bitcoin price around that time. Design/methodology/approach This study implements sentiment analysis and text mining on Twitter data to explore changes in public sentiments toward Bitcoin after Elon Musk’s Twitter bio change. Furthermore, it uses Bitcoin price data obtained from the Binance exchange to understand its relation with Twitter discussion. Findings Elon Musk’s bio change on Twitter on January 29 increased the tweet volume mentioning Bitcoin. This increase in tweets had a strong positive correlation with Bitcoin price and preceded the rise in Bitcoin price. Although the bio change had an apparent effect on the tweet volume, there was no considerable effect on the tweet sentiments, indicating that tweet sentiment is a poor predictor of Bitcoin price. Originality/value This paper proposes an understanding of how social media influencers, like Elon Musk, affect the discourse around Bitcoin and can, in turn, have an impact on Bitcoin price.

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“…Many commercial companies and research institutions have developed stock predictors based on statistical models, and it is expected to obtain more regular information in the analysis of historical data or directly assist decision making; but the stock market is dynamic, non‐linear and complex, therefore it is still very difficult to predict stock trend effectively. Mandelbrot proposed that normal distribution cannot explain the yield sequence of stocks reasonably, and many abnormal properties in financial transactions cannot be explained based on the effective market hypothesis [1]. But the invalid market proposed by Benjamin Graham gives the possibility and practical significance to stock forecasting.…”

Section: Introductionmentioning

confidence: 99%

Research on stock trend prediction method based on optimized random forest

Yin

Zhang

2021

CAAI Trans on Intel Tech

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As a complex hot problem in the financial field, stock trend forecasting uses a large amount of data and many related indicators; hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis. Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem, and it has an auxiliary role in the prediction of stock trend. This study uses historical trading data of four listed companies in the USA stock market, and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend prediction. This study applies the exponential smoothing method to process the initial data, calculates the relevant technical indicators as the characteristics to be selected, and proposes the D-RF-RS method to optimize random forest. As the random forest is an ensemble learning model and is closely related to decision tree, D-RF-RS method uses a decision tree to screen the importance of features, and obtains the effective strong feature set of the model as input. Then, the parameter combination of the model is optimized through random parameter search. The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization, which is 0.18 higher than the average accuracy of light gradient boosting machine model. Combined with the performance of the ROC curve and Precision-Recall curve, the stability of the model is also guaranteed, which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

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

Cited by 4 publications

References 6 publications

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Analysis of Bitcoin’s price spike in context of Elon Musk’s Twitter activity

Research on stock trend prediction method based on optimized random forest

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