Forecasting banking sectors in Indian stock markets using machine intelligence

Arjun, R; Suprabha, K. R.

doi:10.3233/his-190266

Cited by 4 publications

(3 citation statements)

References 63 publications

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“…Additionally, investor sentiment and group dynamics have been the primary factor of stock market outcomes that require more empirical Indian studies [46], [47]. The usage of hybrid models being rarely experimented gives mixed results, so they hold potential in the scenario of emerging markets [48]- [50] that consider the security aspects as well [51].…”

Section: Discussionmentioning

confidence: 99%

A Bibliometric Review of Stock Market Prediction: Perspective of Emerging Markets

Somanathan

Rama

2020

Applied Computer Systems

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The objective of the paper is to identify predictive models in stock market prediction focusing on a scenario of the emerging markets. An exploratory analysis and conceptual modelling based on the extant literature during 1933 to 2020 have been used in the study. The databases of Web of Science, Scopus, and JSTOR ensure the reliability of the literature. Bibliometrics and scientometric techniques have been applied to the retrieved articles to create a conceptual framework by mapping interlinks and limitations in past studies. Focus of research is hybrid models that integrate big data, social media, and real-time streaming data. Key finding is that actual phenomena affecting stock market sectors are diverse and, hence, limited in generalization. The future research must focus on models empirically validated within the emerging markets. Such an approach will offer an insight to analysts and researchers, policymakers or regulators.

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Section: Discussionmentioning

confidence: 99%

A Bibliometric Review of Stock Market Prediction: Perspective of Emerging Markets

Somanathan

Rama

2020

Applied Computer Systems

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“…Econometrics-based statistical analysis relies mainly on historical trading data, corporate financial data, and macro data to identify and describe patterns of change in stock data over time and predict future stock trends [30,32,33]. Several machine learning algorithms were used to detect patterns in the large amount of financial information, including support vector machines (SVM), artificial neural networks (ANN), Parsimonious Bayes, and Random Forest [24,34].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Daradkeh

2022

Electronics

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Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

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“…(1) Daily exercise It refers to the capricious daily rise and fall of stock prices, and we cannot predict how much it will change. This change is caused by the influence of news quality and some other technical factors, and usually lasts from a few hours to a few days [9][10]. Since this is a short-term volatility, it is only relevant to some trailing and exiting stock investors [11][12].…”

Section: Predictability Of the Securities Marketmentioning

confidence: 99%

Prediction Method and Optimization of Stock Trend Based on Neural Network

2021

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The stock market is characterized by the coexistence of high risks. In order to seek benefits and avoid risks, people do not hesitate to explore its laws and seek the best forecasting methods and methods. The purpose of this paper is to study the stock trend prediction method and optimization based on neural network. The predictability of the securities market is expounded. On the basis of the prediction model based on the historical stock transaction data, a stock prediction model integrating news and investor sentiment is constructed. The advantage of linear and complex time series forecasting problems, the stock training data is used as the input data of the improved CLSTM and BiLSTM deep neural network, and the stock trend forecasting model is trained. Continuously optimize and improve the model, select appropriate parameters to obtain a good stock trend prediction model. By continuously revising the LSTM level, the accuracy of the stock trend prediction model is significantly improved.

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Forecasting banking sectors in Indian stock markets using machine intelligence

Cited by 4 publications

References 63 publications

A Bibliometric Review of Stock Market Prediction: Perspective of Emerging Markets

A Bibliometric Review of Stock Market Prediction: Perspective of Emerging Markets

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Prediction Method and Optimization of Stock Trend Based on Neural Network

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