Tracking Multiple Social Media for Stock Market Event Prediction

Wang, Wei; Chakraborty, Prithwish; Self, Nathan; Chen, Feng; Ramakrishnan, Naren

doi:10.1007/978-3-319-62701-4_2

Cited by 27 publications

(25 citation statements)

References 22 publications

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“…Focusing on the food and beverage industry, the study finds that semi-strong form of efficient market hypothesis does not hold true in terms of marketing event of exhibition. Jin et al (2017) focused on financial market trends on significant returns of stock market. Using the Delta Naïve Bayes approach, the study finds that multisource predictions consistently outperform the single source predictions.…”

Section: Previous Studiesmentioning

confidence: 99%

Impact of Covid-19 on Islamic Stock Markets: An Investigation using Threshold Volatility and Event Study Models

Irfan

Kassim

Dhimmar

2021

IJIEF

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The Covid-19 is an unexpected event in the world history with substantial socio-economic impact on the global economy. The global financial market was also badly affected as reflected by the extreme volatility as well as weak performances in the stock markets all over the world. How do the Islamic stock markets in various parts of the world behave during the Covid-19 shock? The objective of this study is to identify the impact of the Covid-19 pandemic as declared by the World Health Organization on the Islamic stock markets. Using the threshold volatility and event study models, the study analyses the impact of the Covid-19 announcement on the Islamic stock indices in the Indian Stock Exchange (represented by the Bombay Stock Exchange - BSE Shariah Index) and Indonesian Stock Exchange (represented by the Jakarta Islamic Indices - JII). With the date of event identified as 11th March 2020, the event window consists of 60, 30, and 20 days. The results show that the BSE Shariah and JII have positive coefficients, with the BSE Shariah Index shows negative response to the announcement of Covid-19 as global pandemic. On the other hand, the JII reacted positively to the event. The study shows the reaction of a stock exchange is dependent on other economic factors unique to the country, resulting in the events impact of the Covid-19 to vary from one country to another.

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Section: Previous Studiesmentioning

confidence: 99%

Impact of Covid-19 on Islamic Stock Markets: An Investigation using Threshold Volatility and Event Study Models

Irfan

Kassim

Dhimmar

2021

IJIEF

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“…Previous studies [1,7,8,25,[43][44][45] have attempted to examine the joint impacted of different stock-related information sources for predicting stock price movement, a high percentage (63%) of these studies employed 2 data sources. In comparison, 37% used 3 data sources (see Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…The quest for improvement in prediction accuracy has led to the examination of additional data source lately. The following studies [9,[41][42][43] probed the effect of web search queries on stock market volatility and reported that web search queries could effectively predict stock price volatility. However, search queries are limited to territory where the user is searching from; hence its effects on stock price movement cannot be generalised.…”

Section: Studies Based On Qualitative Datasetmentioning

confidence: 99%

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A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

2021

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The stock market is very unstable and volatile due to several factors such as public sentiments, economic factors and more. Several Petabytes volumes of data are generated every second from different sources, which affect the stock market. A fair and efficient fusion of these data sources (factors) into intelligence is expected to offer better prediction accuracy on the stock market. However, integrating these factors from different data sources as one dataset for market analysis is seen as challenging because they come in a different format (numerical or text). In this study, we propose a novel multi-source information-fusion stock price prediction framework based on a hybrid deep neural network architecture (Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM)) named IKN-ConvLSTM. Precisely, we design a predictive framework to integrate stock-related information from six (6) heterogeneous sources. Secondly, we construct a base model using CNN, and random search algorithm as a feature selector to optimise our initial training parameters. Finally, a stacked LSTM network is fine-tuned by using the tuned parameter (features) from the base-model to enhance prediction accuracy. Our approach's emperical evaluation was carried out with stock data (January 3, 2017, to January 31, 2020) from the Ghana Stock Exchange (GSE). The results show a good prediction accuracy of 98.31%, specificity (0.9975), sensitivity (0.8939%) and F-score (0.9672) of the amalgamated dataset compared with the distinct dataset. Based on the study outcome, it can be concluded that efficient information fusion of different stock price indicators as a single data source for market prediction offer high prediction accuracy than individual data sources.

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“…OSINT includes data from sources such as newspapers, blogs, discussion groups, radio, social media websites, press conferences, journals, technical reports, etc. Online Social Media (OSM) is an OSINT source providing data that is ingested by AI tools working in various fields like finance [15] and cybersecurity [21]. Some of the most commonly used OSM are Twitter, Reddit 1 , etc.…”

Section: Introductionmentioning

confidence: 99%

Preventing Poisoning Attacks On AI Based Threat Intelligence Systems

Khurana

Mittal

Piplai

et al. 2019

2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)

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As AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we use an ensembled semi-supervised approach to determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc.

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Tracking Multiple Social Media for Stock Market Event Prediction

Cited by 27 publications

References 22 publications

Impact of Covid-19 on Islamic Stock Markets: An Investigation using Threshold Volatility and Event Study Models

Impact of Covid-19 on Islamic Stock Markets: An Investigation using Threshold Volatility and Event Study Models

A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

Preventing Poisoning Attacks On AI Based Threat Intelligence Systems

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