Technical analysis and sentiment embeddings for market trend prediction

Picasso, Andrea; Merello, Simone; Ma, Yukun; Oneto, Luca; Wang, Zhaoxia

doi:10.1016/j.eswa.2019.06.014

Cited by 228 publications

(138 citation statements)

References 25 publications

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“…Furthermore, this study can extend the framework by applying a deep learning-based model that automatically optimizes parameters according to learning data [40,41]. In addition, corporate opinion data can be applied to a variety of areas in managerial works [42]. In other words, the purpose of analysis can be expanded to a model applicable to various industries by applying it to marketing and other areas, such as the development of products and technologies, rather than from a financial investment perspective.…”

Section: Discussionmentioning

confidence: 99%

Developing an Risk Signal Detection System Based on Opinion Mining for Financial Decision Support

et al. 2019

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Companies have long sought to detect financial risks and prevent crises in their business activities. Investors also have a great need to identify risks and utilize them for investment. Thus, several studies have attempted to detect financial risk. However, these studies had limitations in that various data were not exploited and diverse perspectives of the firm were not reflected. This can lead to wrong choices for investment. Thus, the purpose of this study was to propose risk signal prediction models based on firm data and opinion mining, reflecting both the perspectives of firms and investors. Furthermore, we developed a process to obtain real time firm related data and convenience visualization. To develop this process, a credit event was defined as an event that led to a critical risk of the firm. In the next step, the firm risk score was calculated for a firm having a possible credit event. This score was calculated by combining the firm activity score and opinion mining score. The firm activity score was calculated based on a financial statement and disclosure data indicator, while the opinion mining score was calculated based on a sentiment analysis of news and social data. As a result, the total firm risk grade was derived, and the risk level was proposed. These processes were developed into a system and illustrated by real firm data. The results of this study demonstrate that it is possible to derive risk signals through integrated monitoring indicators and provide useful information to users. This study can help users make decisions. It also provides users an opportunity to identify new investment momentums.

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Section: Discussionmentioning

confidence: 99%

Developing an Risk Signal Detection System Based on Opinion Mining for Financial Decision Support

et al. 2019

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“…The approach presented in [27], the authors proposed to combine the technical and fundamental analysts approaches to market trend forecasting through the use of conventional machine learning techniques applied to time series prediction and sentiment analysis. In [28], experiments have been conducted on more than five years of real Hong Kong stock market data using four different sentiment dictionaries.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

Kilimci¹,

Duvar²

2020

IEEE Access

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To forecast the movement directions of stocks, exchange rates, and stock markets are significant and an active research area for investors, analysts, and researchers. In this paper, word embedding and deep learning-based direction prediction of Istanbul Stock Exchange (BIST 100) is proposed by analyzing nine banking stocks with high volume in BIST 100. Though English news articles have been employed for forecasting of market direction previously, to the best of our knowledge, Turkish news articles and user comments from social media and different platforms have not been utilized with the combination of deep learning techniques and word embedding methods to predict the direction of Turkish stocks and market. For this objective, long short-term memory networks, recurrent neural networks, convolutional neural networks as deep learning algorithms and Word2Vec, GloVe, and FastText as word embedding models are evaluated. To demonstrate the effectiveness of proposed model, four different sources of Turkish news are collected. The news articles about stocks from Public Disclosure Platform (KAP), text-based technical analysis of each stock from Bigpara, user comments from both Twitter and Mynet Finans platforms are gathered. Experiment results demonstrate that the combination of deep learning techniques and word embedding methods have a great potential to predict the direction of BIST 100. INDEX TERMS Deep learning techniques, financial sentiment analysis, stock market prediction, word embedding methods This work is licensed under a Creative Commons Attribution 4.

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“…Knowledge graph can enrich the structured representation of the news event and effectively retain feature vectors for the news event. The main feature extraction in the previous studies [28,29] is sentiment analysis, which neglected the event characteristics in the text. Furthermore, the existing literature [23,29] had proved the positive effect of technical indicators on stock market prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The main feature extraction in the previous studies [28,29] is sentiment analysis, which neglected the event characteristics in the text. Furthermore, the existing literature [23,29] had proved the positive effect of technical indicators on stock market prediction. In summary, our research highlights syntax analysis in financial news, which also incorporates with other features extraction (stock data, technical indicators, and bagof-words).…”

Section: Literature Reviewmentioning

confidence: 99%

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Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

et al. 2019

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An increasing number of the renowned company's investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.Hindawi Complexity Volume 2019, Article ID 9202457, 15 pages https://doi.

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Technical analysis and sentiment embeddings for market trend prediction

Cited by 228 publications

References 25 publications

Developing an Risk Signal Detection System Based on Opinion Mining for Financial Decision Support

Developing an Risk Signal Detection System Based on Opinion Mining for Financial Decision Support

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

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