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A sentiment analysis-based machine learning approach for financial market prediction via news disclosures
Abstract
Stock market prediction plays an important role in financial decision-making for investors. Many of them rely on news disclosures to make their decisions in buying or selling stocks. However, accurate modelling of stock market trends via news disclosures is a challenging task, considering the complexity and ambiguity of natural languages used. Unlike previous work along this line of research, which typically applies bag-of-words to extract tens of thousands of features to build a prediction model, we propose a sentiment analysis-based approach for financial market prediction using news disclosures. Specifically, sentiment analysis is carried out in the pre-processing phase to extract sentiment-related features from financial news. Historical stock market data from the perspective of time series analysis is also included as an input feature. With the extracted features, we use a support vector machine (SVM) to build the prediction model, with its parameters optimised through particle swarm optimisation (PSO). Experimental results show that our proposed SVM and PSO-based model is able to obtain better results than a deep learning model in terms of time and accuracy. The results presented here are to date the best in the literature based on the financial news dataset tested. This excellent performance is attributed to the sentiment analysis done during the pre-processing stage, as it reduces the feature dimensions significantly.
... Nassirtoussi et al. [40] discovered that the sentiments extracted from financial news significantly impact gains or losses in financial markets. Chiong et al. [41] applied a sentiment analysis-based machine learning approach for financial market prediction and obtained good prediction performance. Similarly, Lutz et al. [42] proposed a novel machine learning approach to predict the sentencelevel polarity labels of financial news, and their model showed potential to assist investors in their decision-making. ...
... These RNNs include the conventional RNN (Sim-pleRNN), gated recurrent unit (GRU), and long short-term memory (LSTM), and have been selected considering their promising performance in processing sequential data [45]. Ensemble learning [46], [47] is then applied to aggregate the results of the three RNN models, with their weights optimised by the particle swarm optimisation (PSO) algorithm [41], to form an integrated weighted average output. This ensemble technique is an effective way of improving the prediction performance. ...
... (1) Sentiment analysis: Sentiment analysis is a useful tool for extracting sentiments and opinions from the context [59], and prior studies have demonstrated its effectiveness for extracting sentiment features from financial news [41]. We applied a Python package T extBlob (http://textblob.readthedocs.io/en/dev/) ...
Financial news disclosures provide valuable information for traders and investors while making stock market investment decisions. Essential but challenging, the stock market prediction problem has attracted significant attention from both researchers and practitioners. Conventional machine learning models often fail to interpret the content of financial news due to the complexity and ambiguity of natural language used in the news. Inspired by the success of recurrent neural networks (RNNs) in sequential data processing, we propose an ensemble RNN approach (long short-term memory, gated recurrent unit, and SimpleRNN) to predict stock market movements. To avoid extracting tens of thousands of features using traditional natural language processing methods, we apply sentiment analysis and the sliding window method to extract only the most representative features. Our experimental results confirm the effectiveness of these two methods for feature extraction and show that the proposed ensemble approach is able to outperform other models under comparison.
... The effects of sentiments on stock market volatility have received recent attention in the literature [27][28][29][30][31][32]. One core source of information for sentiment analysis is the news articles [27,28] and the other commonly used data source is the social media [33][34][35][36]. ...
... One core source of information for sentiment analysis is the news articles [27,28] and the other commonly used data source is the social media [33][34][35][36]. Using a Support Vector Machine (SVM) and Particle Swarm Optimisation (PSO), Chiong et al. [31] proposed a stock market predictive model based on sentiments analysis. The study recorded a positive association between stock volume and public sentiment. ...
... However, opinions and arguments about the depth of alertness and understanding drawn from a highly-quantitative approach (as typically employed in information fusion frameworks) will likely have to be balanced with the intuitions that can be gained from more socialhypothetical and subjective approaches in future research. [24,9,23,54,43,48,31,35,65,13,1,46,49,47,42,6,58,29,36,14,45,38,64,34,21,41,3,32,20,19,59,44,25,4,26,50,63,2,61,30,27,57,10,55,39,12,7,62,51,16,11,0,18,5,53,28,60,15,8,37,33,52] 0.8875 2 56 ...
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.
... Several studies have added sentiment information to their prediction models, with improved predictive power as a result (Li et al., 2020). For example, Chiong et al. (2018) developed a sentiment analysis method based on financial news disclosure, extracting sentiment-related features as input for the stock price prediction model. Compared with the prediction models without the inclusion of sentiment-related features, their proposed SVM and particle swarm optimization (PSO)-based model with sentiment feature extraction performed well in terms of accuracy and time. ...
... While individual features provide valuable information about the stock market, combining different aspects of these features through feature fusion is crucial to extract the intrinsic characteristics of the stock market. Feature fusion is performed in a manner that can be viewed as feature-level abstraction or object refinement of the processed data (Chiong et al., 2018). The fused features can be applied to all levels of models for subsequent stock market analysis. ...
Due to external factors such as political influences, specific events and sentiment information, stock prices exhibit randomness, high volatility and non-linear characteristics, making accurate predictions of future stock prices based solely on historical stock price data difficult. Consequently, data fusion methods have been increasingly applied to stock price prediction to extract comprehensive stock-related information by integrating multi-source heterogeneous stock data and fusing multiple decision results. Although data fusion plays a crucial role in stock price prediction, its application in this field lacks comprehensive and systematic summaries. Therefore, this paper explores the theoretical models used in each level of data fusion (data-level, feature-level and decision-level fusion) to review the development of stock price prediction from a data fusion perspective and provide an overall view. The research indicates that data fusion methods have been widely and effectively used in the field of stock price prediction. Additionally, future directions are proposed. For better performance of data fusion in the field of stock price prediction, future work can broaden the scope of stock-related data types used and explore new algorithms such as natural language processing (NLP) and generative adversarial networks (GAN) for text information processing.
... Therefore, given the simplicity, low cost, and high performance of the shallow ML approaches, we recommend using them whenever they are sufficient for the application at hand. However, more advanced algorithms can be used for more complex applications, especially in domains with large and high-dimensional (Chiong et al. 2018), Execution time (Joseph, Pramod, and Nair 2018), training time, and testing time (Zhang and Zheng 2017). ...
... Wantiez 2017;Hammad and Al-Awadi 2016;Han et al. 2020;He et al. 2018;Hu et al. 2013;Van Huynh et al. 2019;Imran et al. 2020;Indulkar and Patil 2021;Ismail et al. 2018;Jain and Kaushal 2018;Jain, Pamula, and Srivastava 2021;Jang et al. 2014;Janiesch, Zschech, and Heinrich 2021;Javaid et al. 2021;Jordan and Mitchell 2015;Joseph, Pramod, and Nair 2018;Kannadaguli and Bhat 2018; Karim and Das 2018; Kastrati et al. 2021; Kaur and Saini 2014; Khan et al. 2020; Kitchenham 2004; Kukolja et al. 2014; Lalata et al. 2019; Lee and Sdn Bhd 2011; Lee, Teng, and Hsiao 2012; Lin and He 2009; López and Cuadrado-Gallego 2019; Ly et al. 2018; Machado, Ribeiro, and e Sá 2019; Majeed, Mujtaba, and Beg 2020; Malheiro et al. 2013; Mankar et al. 2018; Medhat, Hassan, and Korashy 2014; Mehrabian 1996; Mite-Baidal et al. 2018; Muhammad and Shamim Hossain 2021; Muhammad et al. 2020)), microblogs (e.g. (Bilgin and Şentürk 2017; Elbagir and Yang 2018;Qian, Niu, and Shi 2018;Zhang, Lu, and Song 2017;Ismail et al. 2018;Anjaria and Reddy Guddeti 2014)), multimedia (e.g.(Appel et al. 2016;Chen et al. 2019;Tripathy, Agrawal, and Rath 2016;Van Huynh et al. 2019;Ly et al. 2018)), learning (e.g.(Lalata et al. 2019;Alm, Roth, and Sproat 2005;Ashwin et al. 2016;Pong-Inwong and Kaewmak 2017;Sultana et al. 2018)), finance (e.g.(Chiong et al. 2018;Mankar et al. 2018)), technology (e.g.(Joseph, Pramod, and Nair 2018;Muhammad and Shamim Hossain 2021;Studiawan, Sohel, and Payne 2020;Khan et al. 2020;Alkalbani et al. 2016)), and more. The cluster bar chart inFigure 7shows the domains and depicts the distribution of the ...
Emotion detection and Sentiment analysis techniques are used to understand polarity or emotions expressed by people in many cases, especially during interactive systems use. Recognizing users’ emotions is an important topic for human–computer interaction. Computers that recognize emotions would provide more natural interactions. Also, emotion detection helps design human-centred systems that provide adaptable behaviour change interventions based on users’ emotions. The growing capability of machine learning to analyze big data and extract emotions therein has led to a surge in research in this domain. With this increased attention, it becomes essential to investigate this research area and provide a comprehensive review of the current state. In this paper, we conduct a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: 1) increasing interest in this domain, 2) supervised machine learning (namely, SVM and Naïve Bayes) are the most popular algorithms, 3) Text datasets in the English language are the most common data source, and 4) most research use Accuracy to evaluate performance. Based on the findings, we suggest future directions and recommendations for developing human-centred systems.
... Therefore, given the simplicity, low cost, and high performance of the shallow ML approaches, we recommend using them whenever they are sufficient for the application at hand. However, more advanced algorithms can be used for more complex applications, especially in domains with large and high-dimensional (Chiong et al. 2018), Execution time (Joseph, Pramod, and Nair 2018), training time, and testing time (Zhang and Zheng 2017). ...
... Wantiez 2017;Hammad and Al-Awadi 2016;Han et al. 2020;He et al. 2018;Hu et al. 2013;Van Huynh et al. 2019;Imran et al. 2020;Indulkar and Patil 2021;Ismail et al. 2018;Jain and Kaushal 2018;Jain, Pamula, and Srivastava 2021;Jang et al. 2014;Janiesch, Zschech, and Heinrich 2021;Javaid et al. 2021;Jordan and Mitchell 2015;Joseph, Pramod, and Nair 2018;Kannadaguli and Bhat 2018; Karim and Das 2018; Kastrati et al. 2021; Kaur and Saini 2014; Khan et al. 2020; Kitchenham 2004; Kukolja et al. 2014; Lalata et al. 2019; Lee and Sdn Bhd 2011; Lee, Teng, and Hsiao 2012; Lin and He 2009; López and Cuadrado-Gallego 2019; Ly et al. 2018; Machado, Ribeiro, and e Sá 2019; Majeed, Mujtaba, and Beg 2020; Malheiro et al. 2013; Mankar et al. 2018; Medhat, Hassan, and Korashy 2014; Mehrabian 1996; Mite-Baidal et al. 2018; Muhammad and Shamim Hossain 2021; Muhammad et al. 2020)), microblogs (e.g. (Bilgin and Şentürk 2017; Elbagir and Yang 2018;Qian, Niu, and Shi 2018;Zhang, Lu, and Song 2017;Ismail et al. 2018;Anjaria and Reddy Guddeti 2014)), multimedia (e.g.(Appel et al. 2016;Chen et al. 2019;Tripathy, Agrawal, and Rath 2016;Van Huynh et al. 2019;Ly et al. 2018)), learning (e.g.(Lalata et al. 2019;Alm, Roth, and Sproat 2005;Ashwin et al. 2016;Pong-Inwong and Kaewmak 2017;Sultana et al. 2018)), finance (e.g.(Chiong et al. 2018;Mankar et al. 2018)), technology (e.g.(Joseph, Pramod, and Nair 2018;Muhammad and Shamim Hossain 2021;Studiawan, Sohel, and Payne 2020;Khan et al. 2020;Alkalbani et al. 2016)), and more. The cluster bar chart inFigure 7shows the domains and depicts the distribution of the ...
... Using traditional news, an analysis of the correlation between sentiment and stock price of 2 companies over a 10 year period using machine learning algorithms showed some positive results [27] but the authors noted improvements can be made. Specifically using news from financial based news organisations a high correlation between sentiment and stock prices volatility has been discovered [28,29]. ...
... Since correlation does not indicate causation so we can only speculate as to the reason. We didn't look at price volatility and a longer study that showed financial based news items had a strong correlation with stock price volatility [28,29]. In turbulent times, when volatility is high it has been observed that public mood does drive changes in stock prices [22]. ...
Twitter has been responsible for some major stock market news in the recent past, from rogue CEOs damaging their company to very active world leaders asking for brand boycotts, but despite its impact Twitter has still not been as impactful on markets as traditional news sources. In this paper we examine whether daily news sentiment of several companies and Twitter sentiment from their CEOs have an impact on their market performance and whether traditional news sources and Twitter activity of heads of government impact the benchmark indexes of major world economies over a period spanning the outbreak of the SAR-COV-2 pandemic. Our results indicate that there is very limited correlation between Twitter sentiment and price movements and that this does not change much when returns are taken relative to the market or when the market is calm or turbulent. There is almost no correlation under any circumstances between non-financial news sources and price movements, however there is some correlation between financial news sentiment and stock price movements. We also find this correlation gets stronger when returns are taken relative to the market. There are fewer companies correlated in both turbulent and calm economic times. There is no clear pattern to the direction and strength of the correlation, with some being strongly negatively correlated and others being strongly positively correlated, but in general the size of the correlation tends to indicate that price movement is driving sentiment, except in the turbulent economic times of the SARS-COV-2 pandemic in 2020.
... Hájek (2013) found that sentiment analysis improved the accuracy of neural networks and support vector machines in predicting future financial distress. Chiong (2018) found that sentiment analysis and support vector machines predicted stock market trends better than deep learning. Khan (2020) found that sentiment analysis and political situation features improved the accuracy of machine learning algorithms in predicting Pakistan's stock market by up to 20% over 7 days. ...
This research offers a pioneering investigation into the potential implications of late annual report announcements in the context of BIST100 companies. Annual reports serve as a pivotal source of comprehensive information, enabling stakeholders to evaluate a company's financial health, growth prospects, operational strategies, and potential risks. Recognizing this, our study harnesses artificial intelligence (AI) to scrutinize the timeliness of annual report announcements and their inherent sentiment. Using AI, we constructed a predictive model based on the announcement dates of annual reports between 2009 and 2021. The model estimated the release dates for 2022, facilitating the identification of companies that released their reports later than expected. These delayed reports were then subjected to an in-depth text mining, sentiment analysis, and schema analysis. Our text mining process utilized the robust Hidden Dirichlet Allocation (LDA) Subject Salient Terms (TST) method, known for its efficacy in revealing concealed topics in large text volumes. Our findings were striking; around 87% of statements in the delayed annual reports reflected a negative sentiment, while only 13% displayed a positive tone. Thus, late annual report announcements tend to have a generally pessimistic outlook, indicating they might indeed be bearers of adverse news. This research offers a unique perspective on the relationship between the timeliness of financial reports and the contained sentiment, ultimately contributing to the enhanced transparency and informed decision-making in financial markets. The study underscores the necessity for companies to maintain timely communication and suggests potential areas of interest for future investigations.
... The authors presented a news-based, sentiment-analysis-based approach to financial market forecasting. They created a predictive model using SVM and Particle Swarm Optimisation (PSO) to improve its parameters [31]. This paper comes up with query-based search value separation for negative and positive tweet classification using k-nearest neighbors (KNN) and Thresholding method [32]. ...
Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.
... ML-based methods can be broadly categorized into supervised and unsupervised learning. When doing FSA, supervised learning approaches require labeled data and include techniques such as Support Vector Machines (SVM) [144], Naive Bayes [145], KNN (K-Nearest Neighbor) [146], Random Forests [147] and Multi-layer perceptrons (MLPs) [148]. Unsupervised learning, in contrast, does not require labeled data and typically involves clustering techniques to discern sentiment [149]. ...
Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast amounts of data, and generating human-preferred contents. In this survey, we explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We provide a discussion of the progress and advantages of LLMs in financial contexts, analyzing their advanced technologies as well as prospective capabilities in contextual understanding, transfer learning flexibility, complex emotion detection, etc. We then highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications. For each application area, we delve into specific methodologies, such as textual analysis, knowledge-based analysis, forecasting, data augmentation, planning, decision support, and simulations. Furthermore, a comprehensive collection of datasets, model assets, and useful codes associated with mainstream applications are presented as resources for the researchers and practitioners. Finally, we outline the challenges and opportunities for future research, particularly emphasizing a number of distinctive aspects in this field. We hope our work can help facilitate the adoption and further development of LLMs in the financial sector.
... According to the above definition, sentiment analysis is a method for classifying text polarity into three levels of a document, sentence, or aspect (Coletta et al. 2014). In examining a document or sentence, it is assumed that only one sentiment is expressed (Chiong et al. 2018). The terminologies used in sentiment analysis can sometimes be confusing, as they are often used interchangeably. ...
Ensemble learning is a powerful technique for combining multiple classifiers to achieve improved performance. However, the challenge of applying ensemble learning to dynamic and diverse data, such as text in sentiment analysis, has limited its effectiveness. In this paper, we propose a novel reinforcement learning-based method for integrating base learners in sentiment analysis. Our method modifies the influence of base learners on the ensemble output based on the problem space, without requiring prior knowledge of the input domain. This approach effectively manages the dynamic behavior of data to achieve greater efficiency and accuracy. Unlike similar methods, our approach eliminates the need for basic knowledge about the input domain. Our experimental results demonstrate the robust performance of the proposed method compared to traditional methods of base learner integration. The significant improvement in various evaluation criteria highlights the effectiveness of our method in handling diverse data behavior. Overall, our work contributes a novel reinforcement learning-based approach to improve the effectiveness of ensemble learning in sentiment analysis.
... The authors presented a news-based, sentiment-analysis-based approach to financial market forecasting. They created a predictive model using SVM and Particle Swarm Optimisation (PSO) to improve its parameters [31]. This paper comes up with query-based search value separation for negative and positive tweet classification using k-nearest neighbors (KNN) and Thresholding method [32]. ...
Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.
... In a different study, [35] proposed a model that combined Particle Swarm Optimization (PSO) for feature selection with the Support Vector Machine (SVM) algorithm for classi cation. They applied this model to a nancial news dataset and found that it outperformed a deep learning approach in terms of accuracy and processing time. ...
In contemporary times, as financial content proliferates across the internet and social networks, accurately predicting future trends has become an everyday necessity for providing optimal investment strategies. Sentiment Analysis (SA), a prominent subject in artificial intelligence, is pivotal in revealing people's emotions and opinions on specific matters. This paper aims to leverage text-mining algorithms to categorize a text-based financial dataset through sentiment analysis. Furthermore, a novel hybrid feature selection model is introduced to enhance the accuracy and performance when studying economic text. Initially, a widely recognized financial text dataset (FiQA) was chosen. After applying preprocessing techniques encompassing data cleansing and feature extraction, the feature pool is reduced by utilizing ANOVA, RFI, and CHI2 algorithms. Subsequently, the features are refined using the Particle Swarm Optimization (PSO) approach. In the subsequent stages, the text is classified by the Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), K-Nearest Neighbour (KNN), Naïve Bayes, and Support Vector Machine (SVM) algorithms, all of which yield notable performance outcomes. The results show that the ANOVA-PSO hybrid model for LSTM classification achieves an accuracy rate of 75%, superior to other Feature selection models.
... When considering the deployment of classical ML models in FSA scenarios, a multitude of studies can be found in recent literature. For instance, in [49], the authors employ a Support Vector Machine (SVM) approach optimized through particle swarm optimization (PSO) for SA stock market prediction. Similarly, [50] employs Multivariate Linear Regression in conjunction with SA techniques to address the same stock market prediction problem. ...
Sentiment Analysis, also known as Opinion Mining, gained prominence in the early 2000s alongside the emergence of internet forums, blogs, and social media platforms. Researchers and businesses recognized the imperative to automate the extraction of valuable insights from the vast pool of textual data generated online. Its utility in the business domain is undeniable, offering actionable insights into customer opinions and attitudes, empowering data-driven decisions that enhance products, services, and customer satisfaction. The expansion of Sentiment Analysis into the financial sector came as a direct consequence, prompting the adaptation of powerful Natural Language Processing models to these contexts. In this study, we rigorously test numerous classical Machine Learning classification algorithms and ensembles against five contemporary Deep Learning Pre-Trained models, like BERT, RoBERTa, and three variants of FinBERT. However, its aim extends beyond evaluating the performance of modern methods, especially those designed for financial tasks, to a comparison of them with classical ones. We also explore how different text representation and data augmentation techniques impact classification outcomes when classical methods are employed. The study yields a wealth of intriguing results, which are thoroughly discussed.
... Traditional machine learning techniques have already been used to extract and classify news from trusted sources [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] . Despite the work done, there are limitations in these methods and there are still open questions, that are addressed in this paper: ...
In a first study, this paper argues and demonstrates that spiking neural networks (SNN) can be successfully used for predictive and explainable modelling of multimodal streaming data. The paper proposes a new method, where both time series and on-line news are integrated as numerical streaming data in the same time domain and then used to train incrementally a SNN model. The connectivity and the spiking activity of the SNN are then analyzed through clustering and dynamic graph extraction to reveal on-line interaction between all input variables in regard to the predicted one. The paper answers the main research question of how to understand the dynamic interaction of time series and on-line news through their integrative modelling. It offers a new method to evaluate the efficiency of using on-line news on the predictive modelling of time series. Results on financial stock time series and online news are presented. In contrast to traditional machine learning techniques, the method reveals the dynamic interaction between stock variables and news and their dynamic impact on model accuracy when compared to models that do not use news information. Along with the used financial data, the method is applicable to a wide range of other multimodal time series and news data, such as economic, medical, environmental and social. The proposed method, being based on SNN, promotes the use of massively parallel and low energy neuromorphic hardware for multivariate on-line data modelling.
... They explored the impact of local and global events by utilizing deep neural networks. The authors of [17] proposed a support vector machine (SVM) based sentiment analysis for the prediction of stock market trends. The study utilized TextBlob to extract the polarity score of each message. ...
COVID-19 affected the world’s economy severely and increased the inflation rate in both developed and developing countries. COVID-19 also affected the financial markets and crypto markets significantly, however, some crypto markets flourished and touched their peak during the pandemic era. This study performs an analysis of the impact of COVID-19 on public opinion and sentiments regarding the financial markets and crypto markets. It conducts sentiment analysis on tweets related to financial markets and crypto markets posted during COVID-19 peak days. Using sentiment analysis, it investigates the people’s sentiments regarding investment in these markets during COVID-19. In addition, damage analysis in terms of market value is also carried out along with the worse time for financial and crypto markets. For analysis, the data is extracted from Twitter using the SNSscraper library. This study proposes a hybrid model called CNN-LSTM (convolutional neural network-long short-term memory model) for sentiment classification. CNN-LSTM outperforms with 0.89, and 0.92 F1 Scores for crypto and financial markets, respectively. Moreover, topic extraction from the tweets is also performed along with the sentiments related to each topic.
... There is a vast literature on the attempts of using ML models to predict future trends of financial asset prices. Chiong et al. [14] proposed a sentiment analysis-based support vector machine for financial market prediction. Henrique et al. [15] presented a cluster based classifcation model for fnancial crisis prediction. ...
Based on the characteristics of the Chinese futures market, this paper builds a supervised learning model to predict the trend of futures prices and then designs a trading strategy based on the prediction results. The Precision, Recall and F1-score of the classification problem show that our model can meet the accuracy requirements for the classification of futures price movements in terms of test data. The backtest results show that our trading system has an upward trending return curve with low capital retracement.
... For sentiment analysis, SVM, which uses a hyperplane to partition data into multiple classes, is commonly used [91]. For stock market prediction, Chiong et al. [16] introduced a hybrid technique based on SVM and PSO. Xia et al. [83] created the PSDEE (Polarity Shift Detection Elimination and Ensemble) model for document-level sentiment analysis and to deal with the polarity shift problem. ...
Sentiment analysis is a type of contextual text mining that determines how people feel about emotional issues that are frequently discussed on social media. The sentiments of emotive data are analyzed using a variety of sentiment analysis approaches, including lexicon-based, machine learning-based, and hybrid methods. Unsupervised approaches, particularly clustering methods are preferred over other methods since they can be applied directly to unlabeled datasets. Therefore, a clustering method based on an improved exponential cuckoo search has been proposed in this study for sentiment analysis. The proposed clustering method finds the optimal cluster centers from emotive datasets, which are then utilized to determine the sentiment polarity of emotive contents. The proposed improved exponential cuckoo search is first tested on standard and CEC-2013 benchmark functions before being utilized to determine the best cluster centroids from sentimental datasets. To assess the efficiency of the proposed method, it has been compared with K-means, cuckoo search, grey wolf optimizer, grey wolf optimizer with simulated annealing, hybrid step size-based cuckoo search, and spiral cuckoo search on nine sentimental datasets. The Experimental results and statistical analysis have proven the efficacy of the proposed method.
... Analyzing financial texts everyday and extracting insights from them is a heavy task, therefore, researchers have been trying to leverage machine learning methods to derive insights. Research in this area has been mostly done on sentiment analysis of social media data to find out whether people are talking about the market in a positive, negative or neutral way [2,4,6,7,9,14,22,42]. These approaches miss an important point. ...
Graph neural networks (GNNs) have been utilized for various natural language processing (NLP) tasks lately. The ability to encode corpus-wide features in graph representation made GNN models popular in various tasks such as document classification. One major shortcoming of such models is that they mainly work on homogeneous graphs, while representing text datasets as graphs requires several node types which leads to a heterogeneous schema. In this paper, we propose a transductive hybrid approach composed of an unsupervised node representation learning model followed by a node classification/edge prediction model. The proposed model is capable of processing heterogeneous graphs to produce unified node embeddings which are then utilized for node classification or link prediction as the downstream task. The proposed model is developed to classify stock market technical analysis reports, which to our knowledge is the first work in this domain. Experiments, which are carried away using a constructed dataset, demonstrate the ability of the model in embedding extraction and the downstream tasks.
... The work suggested a concept-based ontology-based automated classification system with minimal concepts. These ideas, which are essentially the low-level concepts of an ontology and the instances of the ontology obtained via ontology reasoning, represent the text [34]. Background information produced from ontologies is widely used to enrich papers with semantics from the biomedical domain [35]. ...
For changing semantics, ontological and information presentation, as well as computational linguistics for Asian social networks are one of the most essential platforms for offering enhanced and real-time data mapping, as well as huge data access across diverse big data sources on the web architecture, information extraction mining, statistical modeling and data modeling, and database control, etc. The concept of opinion or sentiment analysis is often used to predict or classify the textual data, sentiment, affect, subjectivity, and other emotional states in online text. Recognizing the message's positive and negative thoughts or opinions by examining the author's goals will aid in a better understanding of the text's content in terms of the stock market. By extracting useful user information from many web sources, an intelligent Ontology and knowledge Asian Social Network solution enhances the business decision support process. On either hand, ontology is concerned primarily with problem-solving knowledge discovery. The utilization of Internet-based modernizations welcomed a significant effect on the Indian stock exchange. News related to the stock market in the most recent decade plays a vital role for the brokers or users. This article focusses on predicting stock market news sentiments based on their polarity and textual information using the concept of ontological knowledge-based Convolution Neural Network (CNN) as a machine learning approach. Optimal features are essential for the sentiment classification model to predict the stock's textual reviews' exact sentiment. Therefore, the swarm-based Artificial Bee Colony (ABC) algorithm is utilized with the Lexicon feature extraction approach using a novel fitness function. The main motivation for combining ABC and CNN is to accelerate model training, which is why the suggested approach is effective in predicting emotions from stock news.
... The algorithms showed good performance using unigrams with Sentiwordnet linguistic resource. Chiong et al. 39,40 carried out sentiment analysis in the preprocessing phase to extract sentiment-related features from financial news. Sentiment analysis and the sliding window method were used in this case to reduce feature dimensions. ...
Social media has been embraced by different people as a convenient and official medium of communication. People write or share messages and attach images and videos on Twitter, Facebook and other social media platforms. It therefore generates a lot of data that is rich in sentiments. Sentiment analysis has been used to determine the opinions of clients, for instance, relating to a particular product or company. Lexicon and machine learning approaches are the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is, however, distorted by noise, the curse of dimensionality, the data domains and the size of data used for training and testing. This article aims at developing a model for sentiment analysis of social media data in which dimensionality reduction and natural language processing with part of speech tagging are incorporated. The model is tested using Naïve Bayes, support vector machine, and K‐nearest neighbor algorithms, and its performance compared with that of two other sentiment analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.
... The role of news disclosure can be considered effective in this context as it can help in setting out parameters based on which the decision can be made. The use of the "Support Vector Machine or SVM" based prediction is identified to be an effective approach in the machine learning process [3]. (Source: [4]) SVM (Support Vector Machine) is an approach of machine learning that analyses and tests large datasets and helps to make optimised decisions [5]. ...
... Sentiment analysis based on traditional machine learning only needs to identify the emotional polarity of the text, which can be regarded as a supervised classification problem. Chiong et al. [8] extracted sentiment-related features from financial news and proposed Support Vector Machines (SVM) based on the particle swarm optimization algorithm for financial market prediction. Bourezk et al. [9] used the Naive Bayes algorithm to capture the public's investment sentiment. ...
With the rapid development of the Internet, and its enormous impact on all aspects of life, traditional financial companies increasingly focus on the user’s online reviews, aiming to promote competitiveness and quality of service in the products of this enterprise. Due to the difficulty of extracting comment text compared with structured data itself, coupled with the fact that it is too colloquial, the traditional model insufficiently semantically represents sentences, resulting in unsatisfactory extraction results. Therefore, this paper selects RoBERTa, a pre-trained language model that has exhibited an excellent performance in recent years, and proposes a joint model of financial product opinion and entities extraction based on RoBERTa multi-layer fusion for the two tasks of opinion and entities extraction. The experimental results show that the performance of the proposed joint model on the financial reviews dataset is significantly better than that of the single model.
... Feature extraction and spam tweets elimination are conducted on the data sets to improve prediction standards and effectiveness. The authors [4] presented sentiment analysis-based methods for forecasting the stock exchange market using news reports. In those papers, they try to predict data in single or multiple models, but they do not clearly show, which multiple models are the best models for an individual parameter of a specific stock market. ...
... Moreover, machine learning methods are also employed to solve sentiment analysis problems (Pandey et al. 2019a). Chiong et al. (2018) presented a hybrid approach using SVM and particle swarm optimization (PSO) for stock market prediction. To determine human attitudes, Tripathy et al. (2016) used four supervised methods: NB, maximum entropy (ME), stochastic gradient descent (SDE), and SVM. ...
Sentiment analysis is a type of contextual text mining that assesses how users feel about emotive topics that are frequently discussed on social media. To analyze the sentiments of the textual data, a number of sentiment analysis methods such as lexicon-based, machine learning-based, and hybrid methods have been proposed. Among all methods, unsupervised methods, especially clustering methods are generally preferred, as they can directly be applied over the unlabelled datasets. Therefore, in this paper, a roulette wheel-based cuckoo search clustering method has been proposed for sentiment analysis. The proposed clustering method finds the optimal cluster centroids from the contents of sentimental datasets which are further used for determining the sentiment polarity of a document. The efficiency of the proposed roulette wheel cuckoo search clustering method has been evaluated on nine sentimental datasets including Twitter and Spam review datasets and compared with K-means, cuckoo search, grey wolf optimizer, grey wolf optimizer with simulated annealing, hybrid step size based cuckoo search, and spiral cuckoo search. The experimental analysis shows that the proposed methods attain the best mean accuracy, mean precision, and mean recall over 80% of the datasets. To statistically validate the efficacy of the proposed approach, box plots and paired t-test are also carried out. From the statistical analysis and experimental findings, the efficacy of the proposed method can be observed. The proposed clustering approach has theoretical implications for further studies to examine the sentimental data. Furthermore, the proposed method has significant practical implications for establishing a system that can generate conclusive comments on any societal issue.
... The extracted features are fed as input to a support vector machine (SVM) to predict upward and downward trends in market behaviour. The parameters of the SVM are tuned using the particle swarm optimization technique, indicating that it has high accuracy and low complexity compared to DL models [165]. [60] implement the LSTM classifier on the large data of S&P 500 (ranging from 1992 to 2015) and compare its performance with the state of the art classifiers: random forest (RF), deep neural network and logistic repressor. ...
In this synopsis, the first solution introduces a hybrid deep learning model, which tackles the class imbalance problem and curse of dimensionality and low detection rate of existing models. The proposed model integrates benefits of both GoogLeNet and gated recurrent unit. The one dimensional EC data is fed into GRU to remember periodic patterns. Whereas, GoogLeNet model is leveraged to extract latent features from the two dimensional weekly stacked EC data. Furthermore , the time least square generative adversarial network is proposed to solve the class imbalance problem. The second solution presents a framework, which is employed to solve the curse of dimensionality issue. In literature, the existing studies are mostly concerned with tuning the hyperparameters of ML/ DL methods for efficient detection of NTL. Some of them focus on the selection of prominent features from data to improve the performance of electricity theft detection. However, the curse of dimensionality affects the generalization ability of ML/ DL classifiers and leads to computational, storage and overfitting problems. Therefore, to deal with above-mentioned issues, this study proposes a system based on metaheuristic techniques (artificial bee colony and genetic algorithm) and denoising autoencoder for electricity theft detection using big data in electric power systems. The third solution introduces a hybrid deep learning model for prediction of upwards and downwards trends in financial market data. The financial market exhibits complex and volatile behavior that is difficult to predict using conventional machine learning (ML) and statistical methods, as well as shallow neural networks. Its behavior depends on many factors such as political upheavals , investor sentiment, interest rates, government policies, natural disasters, etc. However, it is possible to predict upward and downward trends in financial market behavior using complex DL models. In this synopsis, we have proposed three solutions to solve different issues in smart grids and financial market. The validations of proposed solutions will be done in thesis work using real-world datasets.
... The extracted features are fed as input to a support vector machine (SVM) to predict upward and downward trends in market behaviour. The parameters of the SVM are tuned using the particle swarm optimization technique, indicating that it has high accuracy and low complexity compared to DL models [165]. [60] implement the LSTM classifier on the large data of S&P 500 (ranging from 1992 to 2015) and compare its performance with the state of the art classifiers: random forest (RF), deep neural network and logistic repressor. ...
Data science is an emerging field, which has applications in multiple disciplines; like healthcare, advanced image recognition, airline route planning, augmented reality, targeted advertising, etc. In this thesis, we have exploited its applications in smart grids and financial markets with three major contributions. In the first two contributions, machine learning (ML) and deep learning (DL) models are utilized to detect anomalies in electricity consumption (EC) data, while in third contribution, upwards and downwards trends in the financial markets are predicted to give benefits to the potential investors.
Non-technical losses (NTLs) are one of the major causes of revenue losses for electric utilities. In the literature, various ML and DL approaches are employed to detect NTLs. The first solution introduces a hybrid DL model, which tackles the class imbalance problem and curse of dimensionality and low detection rate of existing models. The proposed model integrates benefits of both GoogLeNet and gated recurrent unit (GRU). The one dimensional EC data is fed into GRU to remember periodic patterns. Whereas, GoogLeNet model is leveraged to extract latent features from the two dimensional weekly stacked EC data. Furthermore, the time least square generative adversarial network (TLSGAN) is proposed to solve the class imbalance problem. The TLSGAN uses unsupervised and supervised loss functions to generate fake theft samples, which have high resemblance with real world theft samples. The standard generative adversarial network only updates the weights of those points that are available at the wrong side of the decision boundary. Whereas, TLSGAN even modifies the weights of those points that are available at the correct side of decision boundary, which prevent the model from vanishing gradient problem. Moreover, dropout and batch normalization layers are utilized to enhance model’s convergence speed and generalization ability. The proposed model is compared with different state-of-the-art classifiers including multilayer perceptron (MLP), support vector machine, naive bayes, logistic regression, MLP-long short term memory network
and wide and deep convolutional neural network. The second solution presents a framework, which is employed to solve the curse of dimensionality issue. In literature, the existing studies are mostly concerned with tuning the hyperparameters of ML/ DL methods for efficient detection of NTL, i.e., electricity theft detection. Some of them focus on the selection of prominent features from data to improve the performance of electricity theft detection. However, the curse of dimensionality affects the generalization
ability of ML/ DL classifiers and leads to computational, storage and overfitting problems.
Therefore, to deal with above-mentioned issues, this study proposes a system based on metaheuristic techniques (artificial bee colony and genetic algorithm) and denoising autoencoder for electricity theft detecton using big data in electric power systems. The former (metaheuristics) are used to select prominent features. While the latter are utilized to extract high variance features from electricity consumption data. First, new features are synthesized from statistical and electrical parameters from the user’s consumption history. Then, the synthesized features are used as input to metaheuristic techniques to find a subset of optimal features. Finally, the optimal features are fed as input to the denoising autoencoder to extract features with high variance. The ability of both techniques to select and extract features is measured using a support vector machine. The proposed system reduces the overfitting, storage and computational overhead of ML classifiers. Moreover, we perform several experiments to verify the effectiveness of our proposed system and results reveal that the proposed system has higher performance
our counterparts. The third solution introduces a hybrid DL model for prediction of upwards and downwards trends in financial market data. The financial market exhibits complex and volatile behavior that is difficult to predict using conventional ML and statistical methods, as well as shallow neural networks. Its behavior depends on many factors such as political upheavals, investor sentiment, interest rates, government policies, natural disasters, etc. However, it is possible to predict upward and downward trends in financial market behavior using complex DL models. This paper therefore addresses the following limitations that adversely affect the performance of existing ML and DL models, i.e., the curse of dimensionality, the low accuracy of the standalone
models, and the inability to learn complex patterns from high-frequency time series data. The denoising autoencoder is used to reduce the high dimensionality of the data, overcoming the problem of overfitting and reducing the training time of the ML and DL models. Moreover, a hybrid DL model HRG is proposed based on a ResNet module and gated recurrent units. The former is used to extract latent or abstract patterns that are not visible to the human eye, while the latter retrieves temporal patterns from the financial market dataset. Thus, HRG integrates the advantages of both models. It is evaluated on real-world financial market datasets obtained from IBM, APPL, BA and WMT . Also, various performance indicators such as f1-score, accuracy, precision, recall, receiver operating characteristic-area under the curve (ROC-AUC) are used to check the performance of the proposed and benchmark models. The RG 2 achieves 0.95, 0.90, 0.82 and 0.80 ROC-AUC values on APPL, IBM, BA and WMT datasets respectively, which are higher than the ROC-AUC values of all implemented ML and DL models.
... Considering the factor related to high, open, close, low, and volume. Chiong et al. [13] suggest an SA-based algorithm for financial market predictions with news exposes. Precisely, the SA method is performed in the pre-processing stage for extracting sentiments correlated features from financial news. ...
Stock market direction prediction becomes an essential task in the business sector. The inherent volatile behavior of stock markets worldwide makes the prediction process difficult. The improvement in the prediction accuracy of the stock market direction prediction helps to avoid the risks involved in the investment process. In this aspect, this study designs a swallow swarm optimization (SSO) with a fuzzy support vector machine (FSVM) model for stock market direction prediction. The proposed SSO-FSVM model encompasses preprocessing, feature extraction, FSVM, and SSO based parameter tuning. The usage of the SSO algorithm to fine-tune the parameters involved in the FSVM model helps to significantly improve the overall predictive performance. To validate the improved performance of the SSO-FSVM model, a wide range of experiments were carried out using two benchmark datasets. The experimental outcomes reported the betterment of the SSO-FSVM model over the recent approaches in terms of several evaluation metrics.
... A number of comparison studies have been conducted to investigate the efficiency of PSO and GA [17][18][19][20][21][22][23]. Also, particle swarm optimization provides an important way in fine-tuning the parameters of finance models and deserved popularity in this field [24][25][26][27][28][29]. Taguchi's experimental design method has been used to define the user-defined parameters in a comparison study of six algorithms, including the PSO algorithm [30,31]. ...
One of the advantages of stochastic differential equations (SDE) is that they can follow a variety of different trends so that they can establish complex dynamic systems in the economic and financial fields. Although some estimation methods have been proposed to identify the unknown parameters in virtue of the results in the SDE model to speed up the process, these solutions only focus on using explicit approach to solve SDEs, and therefore they are not reliable to deal with data source merged being large and varied. Thus, this study makes progress in creating a new implicit way to fill in the gaps of accurately calibrating the unknown parameters in the SDE model. Essentially, the primary goal of the article is to generate rigid SDE simulation. Meanwhile, the particle swarm optimization method serves a purpose to search and simultaneously obtain the optimal estimation of the model unknown parameters in the complicated experiment of parameter space in an effective way. Finally, in an interest rate term structure model, it is verified that the method effectively deals with parameter estimation in the SDE model.
Stock price prediction is a pivotal task in fiscal requests, abetting investors make educated judgments. This research project proposes an ensemble model that combines sentiment analysis with the sliding window system for enhanced forecast accuracy. The paper involves extracting textual features similar as polarity and subjectivity through sentiment analysis and deriving historical data features using the sliding window method. These features are also integrated into a single dataset for model input. Three Recurrent Neural Network (RNN) models, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Neural Network (SimpleRNN), are trained using this dataset, and their performance criteria, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-Squared (R 2 ), are estimated. Also, ensemble models are constructed using Particle Swarm Optimization (PSO) and the Differential Evolution (DE) system for integrating the labors of different models. The finding advances the understanding on stock price forecasting techniques and offer insights into opting optimal models for real-world applications.
Stock prediction is not new; people have tried to predict stock price or good quality stocks for ages. Machine learning has opened a new direction for the problem of stock prediction. The critical factor deciding the success or failure of the machine learning model depends on the quality of features computed. Moreover, the concept of ensemble learning can significantly enhance the quality of prediction. The proposed model gathers data price time series data and news articles from open access domain and do necessary pre-processing. The model also calculates technical and sentiment features and uses them for training the ensemble model. Performance is compared to definite matrices and with other similar research.
Policies, legislation, surveillance, monitoring, direction, and enforcement, are heavily influenced by public opinion or emotion. Due to the increase in electronic data generation, it has been forced to do an automatic analysis of this opinion or feelings termed as opinion analysis. To process massive volumes of data, deep learning is now trending. Word embeddings serve an essential role of feature representatives in deep understanding. The present paper offers a novel deep learning architecture that represents hybrid embedding that deals with polysemy, semantic, and syntactic problems in a language representation. The effectiveness of a deep learning model is extremely sensitive to using hyperparameters. Here, the proposed a novel Hybrid-GFX–Attention–BiGRU–CNN model with a hyperband language model. Hyperband search is used to find optimal values for the model's hyperparameters. To justify classification results, statistical and graphical approaches have been used. We analyzed the model's efficacy using the MR and Hate speech data sets. The model’s performance is quite promising compared with existing state-of-the-art architectures.
The financial sector has witnessed considerable interest in the fields of stock prediction and reliable stock information analysis. Traditional deterministic algorithms and AI models have been extensively explored, leveraging large historical datasets. Volatility and market sentiment play crucial roles in the development of accurate stock prediction models. We hypothesize that traditional approaches, such as n-moving averages, may not capture the dynamics of stock swings, while online information influences investor sentiment, making them essential factors for prediction. To address these challenges, we propose an automated pipeline consisting of two modules: an N-Perception period power strategy for identifying potential stocks and a sentiment analysis module using NLP techniques to capture market sentiment. By incorporating these methodologies, we aim to enhance stock prediction accuracy and provide valuable insights for investors.KeywordsStock forecastingSentiment analysisAutomated pipelineN-perception strategyNLPMarket strategiesPredictive modelsNBOS-OPTMarket sentiment polarityN-observation period optimizer
Since the advent of the worldwide COVID‐19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision‐makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state‐of‐the‐art technologies has been focused on during the COVID‐19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID‐19 pandemic from a cross‐country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1‐score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID‐19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like.
Electronic word of mouth is one of the keys elements for marketing decision making. e-WOM has been focus of marketing research as technology and social media become larger part of consumers' lives. This study set out to examine e-wom concept with sentiment analysis methodology in service industry context. The structure of study is twofold including theoretical backgound of related concepts and application section. Theoretical background section contains electronic word of mouth, new consumer and sentiment analysis concepts, and included selected studies for sentiment analysis. The application section which this study has focus on includes a three-stage plan for sentiment analysis practices. Each stage has three different scenarios. One algorithm and one real-life application for each stage are included. Nine scenarios for different service organizations imply that sentiment analysis supported with other methodologies can contribute to understanding of electronic word of mouth.
Recently, sentiment analysis of social media has become a hot topic because of the huge amount of information that is provided in these networks. Twitter is a popular social media application offers businesses and government the opportunities to share and acquire information. This article proposes a technique that aims at measuring customers' satisfaction with cloud service providers, based on their tweets. Existing techniques focused on classifying sentimental text as either positive or negative, while the proposed technique classifies the tweets into five categories to provide better information. A hybrid approach of dictionary-based and Fuzzy Inference Process (FIP) is developed for this purpose. This direction was selected for its advantages and flexibility in addressing complex problems, using terms that reflect on human behaviors and experiences. The proposed hybrid-based technique used fuzzy systems in order to accurately identify the sentiment of the input text while addressing the challenges that are facing sentiment analysis using various fuzzy parameters.
Multimodality has shown to be helpful in several natural language processing tasks. Thus, adding multiple modalities to the traditional sentiment analysis has also proven to be useful. However, multimodality in a low resource setting for sentiment analysis is yet to be explored for several resource-constrained languages. Assamese is a low-resource language spoken mainly in the state of Assam in India. This paper presents an Assamese multimodal dataset comprising of 16,000 articles from the news domain as a benchmark resource. Secondly, we present a multi-stage multimodal sentiment analysis framework that concurrently exploits textual and visual cues to determine the sentiment. The proposed architecture encodes the news content collaboratively. The text branch encodes semantic content information by considering the semantic information of the news. At the same time, the visual branch encodes the visual appearance information from the news image. Then, an intermediate fusion-based multimodal framework is proposed to exploit the internal correlation between textual and visual features for joint sentiment classification. Finally, a decision-level fusion mechanism is employed on the three models to integrate cross-modal information effectively for final sentiment prediction. Experimental results conducted on the Assamese dataset built in-house demonstrate that the contextual integration of multimodal features delivers better performance (89.3%) than the best unimodal features (85.6%).
Number of employees are increases with growing in companies. Firms basically make salary increases for their employees in order not to lose their talents and moreover to increase them. Although there is not much problem in how to in-crease the salary in small organizations, this process should be carried out carefully in terms of many parameters in large organizations and should not result in negativities that may disrupt employee motivation. For companies with a large number of employees, creating a model in which the market conditions are determined correctly and all economic parameters are taken into account reveals the need for a process that needs to be worked on for months. In this context, a machine learning-based salary increase prediction system was designed with the study. Specific attributes were determined and a specific scale was developed for performance score for this study.
The prediction of stock price has always been a main challenge. The time series of stock price tends to exhibit very strong nonlinear characteristics. In recent years, with the rapid development of deep learning, the ability to automatically extract nonlinear features has significantly attracted scholars’ attention. However, the majority of the relevant studies have concentrated on prediction of the changes of stock market based on the data of the specific stock (e.g., transaction data, financial data, etc.), while those studies ignored the interaction between stocks of different industries, especially the interaction between the stocks of upstream enterprises and downstream enterprises in the industrial chain. This paper aims to propose a combination of transfer learning of industrial chain information and deep learning models, including multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), for stock market prediction. These models are used to predict the trend of the 379 stock market indices by industry in China, and the DM test was employed for validation of the prediction results. It can be concluded that RNNs are not necessarily such an optimal choice for the prediction when dealing with specific time series data, and it could be justified by using the local interpretable model-agnostic explanations (LIME) algorithm. Hence, the MLP was selected to effectively improve the accuracy of the prediction of the stock market indices based on the transfer learning of industrial chain information. The investment strategy is constructed according to the prediction results, and the yield of maturity exceeds that of the buy-and-hold strategy.
Although the terms of machine learning and deep learning have been widely used in the financial press and media, lack of agreement in the scientific and professional community about a holistic view of best practices, use cases, and trends still exists. Considering the need for filling this gap, the main aim of this study is to investigate and map the literature at the intersection of machine and deep learning as a subset, and finance and investment. This research proposes the use of bibliometric analysis of the literature that highlights the most important articles for this area of research. Specifically, this technique is applied to the literature about machine learning applications in investment and finance, resulting in a bibliographical review of the significant studies about the topic. The author evaluates papers indexed in the Scopus database. This study opens avenues for further research by concentrating on the importance of artificial intelligence and, specifically, machine learning in investment research and practice. Additionally, this review contributes by showing scholars and investment professionals in the areas in which machine learning can add value to investment research.
In this research, we survey prior findings of how machine learning is used for financial markets. Even though much research related to artificial intelligence has been published, our focus is on recent studies to financial markets with potential applications to blockchain-based cryptocurrencies. In summary, while various machine learning techniques have different focus their focus converges to obtain profit not only to predict future prices. Our summary shows the benefits of using such modeling to account for various financial market phenomena. We conclude that artificial intelligence technique, mainly machine learnings, for financial markets cannot acquire all the aspects of the given data as general artificial intelligence. Accordingly, it is natural to opt for a desirable approach depending on the structure and hierarchy of the dataset. Additionally, the choice of approach relies on the researcher’s view toward the market.
Online news blogs and websites are becoming influential to any society as they accumulate the world in one place. Aside from that, online news blogs and websites have efficient strategies in grabbing readers’ attention by the headlines, that being so to recognize the sentiment orientation or polarity of the news headlines for avoiding misinterpretation against any fact. In this study, we have examined 3383 news headlines created by five different global newspapers. In the interest of distinguishing the sentiment polarity (or sentiment orientation) of news headlines, we have trained our model by seven machine learning and two deep learning algorithms. Finally, their performance was compared. Among them, Bernoulli naïve Bayes and Convolutional Neural Network (CNN) achieved higher accuracy than other machine learning and deep learning algorithms, respectively. Such a study will help the audience in determining their impression against or for any leader or governance; and will provide assistance to recognize the most indifferent newspaper or news blogs.
Sentiment analysis is the process of determining the attitude or the emotional state of a text automatically. Many algorithms are proposed for this task including ensemble methods, which have the potential to decrease error rates of the individual base learners considerably. In many machine learning tasks and especially in sentiment analysis, extracting informative features is as important as developing sophisticated classifiers. In this study, a stacked ensemble method is proposed for sentiment analysis, which systematically combines six feature extraction methods and three classifiers. The proposed method obtains cross-validation accuracies of 89.6%, 90.7% and 67.2% on large movie, Turkish movie and SemEval-2017 datasets, respectively, outperforming the other classifiers. The accuracy improvements are shown to be statistically significant at the 99% confidence level by performing a Z-test.
Purpose
Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.
Design/methodology/approach
Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.
Findings
The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.
Originality/value
This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.
Text is not unadulterated fact. A text can make you laugh or cry but can it also make you short sell your stocks in company A and buy up options in company B? Research in the domain of finance strongly suggests that it can. Studies have shown that both the informational and affective aspects of news text affect the markets in profound ways, im- pacting on volumes of trades, stock prices, volatility and even future firm earnings. This paper aims to explore a computable metric of positive or negative polarity in financial news text which is consistent with human judgments and can be used in a quantita- tive analysis of news sentiment impact on fi- nancial markets. Results from a preliminary evaluation are presented and discussed.
Company disclosures greatly aid in the process of financial decision-making; therefore, they are consulted by financial investors and automated traders before exercising ownership in stocks. While humans are usually able to correctly interpret the content, the same is rarely true of computerized decision support systems, which struggle with the complexity and ambiguity of natural language. A possible remedy is represented by deep learning, which overcomes several shortcomings of traditional methods of text mining. For instance, recurrent neural networks, such as long short-term memories, employ hierarchical structures, together with a large number of hidden layers, to automatically extract features from ordered sequences of words and capture highly non-linear relationships such as context-dependent meanings. However, deep learning has only recently started to receive traction, possibly because its performance is largely untested. Hence, this paper studies the use of deep neural networks for financial decision support. We additionally experiment with transfer learning, in which we pre-train the network on a different corpus with a length of 139.1 million words. Our results reveal a higher directional accuracy as compared to traditional machine learning when predicting stock price movements in response to financial disclosures. Our work thereby helps to highlight the business value of deep learning and provides recommendations to practitioners and executives.
Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue that statistical metrics used to guide parameter tuning of forecasting models may not be meaningful, given the fact that the ultimate goal of forecasting is to facilitate investment decisions with expected profits in the future. The authors therefore introduce the Sharpe ratio into the process of model building and take it as the profit metric to guide parameter tuning rather than using the commonly adopted statistical metrics. The authors consider three widely used trading strategies, which include a na¨ıve strategy, a filter strategy and a dual moving average strategy, as investment scenarios. To verify the effectiveness of the proposed profit guided approach, the authors carry out simulation experiments using three global mainstream stock market indices. The results show that profit guided forecasting models are competitive, and in many cases produce significantly better performances than statistical error guided models. This implies that profit guided stock index forecasting is a worthwhile alternative over traditional stock index forecasting practices.
This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies. Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization.
In this chapter we consider bounds on the rate of uniform convergence. We consider upper bounds (there exist lower bounds as well (Vapnik and Chervonenkis, 1974); however, they are not as important for controlling the learning processes as the upper bounds).
In the history of research of the learning problem one can extract four periods that can be characterized by four bright events: (i) Constructing the first learning machines, (ii) constructing the fundamentals of the theory, (iii) constructing neural networks, (iv) constructing the alternatives to neural networks.
We examine whether stock price effects can be automatically predicted analyzing unstructured textual information in financial news. Accordingly, we enhance existing text mining methods to evaluate the information content of financial news as an instrument for investment decisions. The main contribution of this paper is the usage of more expressive features to represent text and the employment of market feedback as part of our word selection process. In our study, we show that a robust Feature Selection allows lifting classification accuracies significantly above previous approaches when combined with complex feature types. That is because our approach allows selecting semantically relevant features and thus, reduces the problem of over-fitting when applying a machine learning approach. The methodology can be transferred to any other application area providing textual information and corresponding effect data.
This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies. Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization.
