The relative importance of textual indexes in predicting the future performance of banks: A connection weight approach

Iqbal, Javid; Saeed, Abubakr; Khan, Rao Aamir

doi:10.1016/j.bir.2022.10.004

Cited by 7 publications

(3 citation statements)

References 78 publications

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“…Machine learning is one of the most important domains of computational intelligence, focusing on the model that provides better-hidden insight into data. Researchers have used these machine-learning methods, including artificial Neural Networks (Iqbal et al, 2023), Support Vector Machines (Gong et al, 2019), Bayesian Networks (Li, 2010), and similar machine-learning techniques. The ANN is the most widely used machine learning method due to its accuracy and better prediction, including the area in commodity price forecasting (Hamid & Iqbal, 2004;Khashei & Bijari, 2010;Lineesh et al, 2010;Ramyar & Kianfar, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The ANN is the most widely used machine learning method due to its accuracy and better prediction, including the area in commodity price forecasting (Hamid & Iqbal, 2004;Khashei & Bijari, 2010;Lineesh et al, 2010;Ramyar & Kianfar, 2019). Most of the studies highlight the importance of neural network-based machine learning models giving better forecasting results as compared to the conventional econometric models (Gogas et al, 2018;Iqbal et al, 2023;Jardin, 2017;Ristolainen, 2018). The advantage of applying artificial neural networks is that they can accurately estimate nonlinear functions (Ali & Yang, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Nexus and Impact of News Sentiment on NYSE, Gold Prices, and WTI Oil Using The Neural Network Approach

Iqbal,

Ahmed,

Ramzan

2024

BJMT

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The study aims to determine the impact of news sentiment during COVID-19 and the nexus between the NYSE, Gold prices, and WTI oil prices. For this purpose, we use hourly data of news sentiment and commodity prices hourly data from February 10, 2020, hours 1000 to March 06, 2020 hours 1600 hours, with the help of a multilayer artificial neural network. Further, We use the connection weight approach to observe the relative importance of input variables that help predict output variables. The results suggest that all the indexes are interconnected, where the increase/decrease of any index impacts the other commodity index. Further, the news index remains the second highest predictor for gold price and WTI oil price indexes. In addition, the news index also seems to be an essential predictor of these commodities. Keywords: NYSE, WTI Oil, Gold, Covid-19, Neural Network, Connection Weight

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting the Nexus and Impact of News Sentiment on NYSE, Gold Prices, and WTI Oil Using The Neural Network Approach

Iqbal,

Ahmed,

Ramzan

2024

BJMT

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“…Iqbal and Riaz (2021) use management sentiment in certain parts of the annual report to predict financial performance. Similarly, using machine learning models, Iqbal et al (2022) suggest that sentiments constructed from annual reports are relatively more important than financial data. These studies on conventional banks witness that textual information in corporate documents contains an additional source of information that can help predict financial performance over and above financial data.…”

Section: Introductionmentioning

confidence: 99%

Predicting the future financial performance of Islamic banks: a sentiment analysis approach

Iqbal,

Sohail,

Malik

2023

IMEFM

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Purpose This study aims to predict the financial performance of Islamic banks with sentiments of management from the textual information in annual reports. Design/methodology/approach The study uses data from 33 Islamic banks in six Islamic countries from 2006 to 2020. The authors estimate the model using the system GMM because it helps dealing with endogeneity problem, which are inherent in panel data. Findings The findings of the study reveal that there is a strong relationship between the sentiment expressed by management in annual reports and the current (future) financial performance of Islamic banks. The higher the positive sentiments of management, the better financial performance. In addition, the study also suggests that negative sentiments using term frequency-inverse document frequency is linked to a decrease in banks’ financial performance. Research limitations/implications The study does not present the Islamic view on sentiment analysis in the context of Islamic scriptures due to the unavailability of a relevant dictionary. Practical implications The findings of the study suggest that developing accurate models with the help of textual information for performance prediction of Islamic banks help shareholders, regulators and policymakers avoid devastating events. Using textual information may also help reduce the information asymmetry between the management and shareholders, which may lead to more efficient bank supervision. The study can also help investors evaluate their prospective investments in the Islamic bank. Originality/value To the best of the authors’ knowledge, this study is the first of its kind that uses management sentiments for performance prediction of the Islamic banking sector. It may add a valuable contribution to the existing literature.

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Growth of Novel Cinnamon-Bentonite Loaded Chitosan Nanospikes for the Confiscation of Congo Red: Adsorption Studies and ANN Modeling

Zahir,

Mahmood,

Aslam

et al. 2023

J Polym Environ

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The relative importance of textual indexes in predicting the future performance of banks: A connection weight approach

Cited by 7 publications

References 78 publications

Forecasting the Nexus and Impact of News Sentiment on NYSE, Gold Prices, and WTI Oil Using The Neural Network Approach

Forecasting the Nexus and Impact of News Sentiment on NYSE, Gold Prices, and WTI Oil Using The Neural Network Approach

Predicting the future financial performance of Islamic banks: a sentiment analysis approach

Growth of Novel Cinnamon-Bentonite Loaded Chitosan Nanospikes for the Confiscation of Congo Red: Adsorption Studies and ANN Modeling

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