<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Predicting the stock market remains a challenging task due to the numerous influencing factors such as investor sentiment, firm performance, economic factors and social media sentiments. However, the profitability and economic advantage associated with accurate prediction of stock price draw the interest of academicians, economic, and financial analyst into researching in this field. Despite the improvement in stock prediction accuracy, the literature argues that prediction accuracy can be further improved beyond its current measure by looking for newer information sources particularly on the Internet. Using web news, financial tweets posted on Twitter, Google trends and forum discussions, the current study examines the association between public sentiments and the predictability of future stock price movement using Artificial Neural Network (ANN). We experimented the proposed predictive framework with stock data obtained from the Ghana Stock Exchange (GSE) between January 2010 and September 2019, and predicted the future stock value for a time window of 1 day, 7 days, 30 days, 60 days, and 90 days. We observed an accuracy of (49.4–52.95 %) based on Google trends, (55.5–60.05 %) based on Twitter, (41.52–41.77 %) based on forum post, (50.43–55.81 %) based on web news and (70.66–77.12 %) based on a combined dataset. Thus, we recorded an increase in prediction accuracy as several stock-related data sources were combined as input to our prediction model. We also established a high level of direct association between stock market behaviour and social networking sites. Therefore, based on the study outcome, we advised that stock market investors could utilise the information from web financial news, tweet, forum discussion, and Google trends to effectively perceive the future stock price movement and design effective portfolio/investment plans.</p></abstract>

Predicting the stock market remains a challenging task due to the numerous influencing factors such as investor sentiment, firm performance, economic factors and social media sentiments. However, the profitability and economic advantage associated with accurate prediction of stock price draw the interest of academicians, economic, and financial analyst into researching in this field. Despite the improvement in stock prediction accuracy, the literature argues that prediction accuracy can be further improved beyond its current measure by looking for newer information sources particularly on the Internet. Using web news, financial tweets posted on Twitter, Google trends and forum discussions, the current study examines the association between public sentiments and the predictability of future stock price movement using Artificial Neural Network (ANN). We experimented the proposed predictive framework with stock data obtained from the Ghana Stock Exchange (GSE) between January 2010 and September 2019, and predicted the future stock value for a time window of 1 day, 7 days, 30 days, 60 days, and 90 days. We observed an accuracy of (49.4–52.95 %) based on Google trends, (55.5–60.05 %) based on Twitter, (41.52–41.77 %) based on forum post, (50.43–55.81 %) based on web news and (70.66–77.12 %) based on a combined dataset. Thus, we recorded an increase in prediction accuracy as several stock-related data sources were combined as input to our prediction model. We also established a high level of direct association between stock market behaviour and social networking sites. Therefore, based on the study outcome, we advised that stock market investors could utilise the information from web financial news, tweet, forum discussion, and Google trends to effectively perceive the future stock price movement and design effective portfolio/investment plans.

Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana

Department of Computer Science and Informatics

University of Energy and Natural Resources

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Predicting the stock market remains a challenging task due to the numerous influencing factors such as investor sentiment, firm performance, economic factors...

Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana

Data publikacji: 05 cze 2020

Zakres stron: 33 - 42

DOI: https://doi.org/10.2478/acss-2020-0004

Słowa kluczowe
Artificial Neural Network, financial text mining, Ghana Stock Exchange, natural language processing, sentimental analysis, stock market prediction

© 2020 Isaac Kofi Nti et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana

Data publikacji: 05 cze 2020

Zakres stron: 33 - 42

DOI: https://doi.org/10.2478/acss-2020-0004

Słowa kluczoweArtificial Neural Network, financial text mining, Ghana Stock Exchange, natural language processing, sentimental analysis, stock market prediction

© 2020 Isaac Kofi Nti et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Słowa kluczowe
Artificial Neural Network, financial text mining, Ghana Stock Exchange, natural language processing, sentimental analysis, stock market prediction