Search results

1 – 10 of over 2000
Article
Publication date: 15 August 2024

Srivatsa Maddodi and Srinivasa Rao Kunte

This study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and…

Abstract

Purpose

This study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.

Design/methodology/approach

Hybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.

Findings

Our study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.

Originality/value

To the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Details

Managerial Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 19 September 2024

Jorge Iván Pérez Rave, Rafael Fernández Guerrero and Andres Salas Vallina

A methodological approach is required that complements studies based on surveys, providing a perspective with greater truthfulness and coverage. The study aims to develop a…

Abstract

Purpose

A methodological approach is required that complements studies based on surveys, providing a perspective with greater truthfulness and coverage. The study aims to develop a methodology to validate psychological/managerial constructs using data from Google Trends, taking as a case study a critical thinking (CT) scale in organizational domains previously supported by survey data.

Design/methodology/approach

The developed methodology consists of eight stages, in which the following is integrated: (1) Internet search interest data (19 Spanish-speaking countries); (2) deductive research processes (e.g. theoretical model, linguistic manifestations, fieldwork, data matrix, analysis statistical, reporting); (3) psychometric properties (e.g. construct validity, criterion validity, reliability) and (4) objective data to examine criterion validity (e.g. unemployment rate).

Findings

The application of the methodology produces evidence that supports the reliability (Cronbach’s alpha, Guttman’s λ4), construct validity (intra-correlations and correlations with reference variables: “entrepreneurship,” “critical thinking,” “soccer,” “beer,” “pornography”) and criterion validity (prediction of unemployment rate) of the CT scale.

Research limitations/implications

The methodology makes it possible to support or invalidate the quality of construct measurement scales by planning, capturing and processing data available on the internet.

Practical implications

This manuscript is useful for research in business management (and related areas), which is intensive in the use of psychological/managerial constructs.

Originality/value

The methodology uses a new type of evidence; it is noninvasive, usually more truthful than responses to surveys, and has greater coverage of people participating indirectly in the study.

Details

Baltic Journal of Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5265

Keywords

Article
Publication date: 10 January 2024

Nugroho Saputro, Putra Pamungkas, Irwan Trinugroho, Yoshia Christian Mahulette, Bruno Sergio Sergi and Goh Lim Thye

This paper investigated whether a bank’s popularity and depositors' fear of Google search volume could affect bank deposits and credit.

Abstract

Purpose

This paper investigated whether a bank’s popularity and depositors' fear of Google search volume could affect bank deposits and credit.

Design/methodology/approach

The authors used two different quarterly data from Google Trends and banking data from 2012 Q1 to 2020 Q1. Based on available data, Google Trends data start from 2012. The authors exclude data after 2020 Q1 because the Covid-19 pandemic arguably increased the volume of Internet users due to shifting behavior to online activities. They merged and cleaned the data by winsorizing at 5 and 95 percentiles to avoid any outlier problems, reaching 74 banks in the sample. They used panel data estimation of quarterly data following Levy-Yeyati et al. (2010) and Trinugroho et al. (2020).

Findings

The results show that a higher search volume of a bank’s name leads to higher deposits. A higher search volume of depositor fear reduces deposits and credit. The authors also found that banks with high risk and a high search volume of their name have a significantly lower volume of deposits.

Originality/value

To the best of the authors’ knowledge, not many papers in banking and finance have used Google Trends data to gauge related issues regarding depositors' behavior. The authors have filled a gap in the literature by investigating whether the popularity of Google search and depositors' fear could impact deposits and credit. This study also attempted to establish whether Google Trends data could be a reliable source of information to predict depositors' behavior by using a Zscore to measure bank risk.

Details

Managerial Finance, vol. 50 no. 6
Type: Research Article
ISSN: 0307-4358

Keywords

Open Access
Article
Publication date: 21 March 2024

Giovanni De Luca and Monica Rosciano

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty…

Abstract

Purpose

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

Design/methodology/approach

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

Findings

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

Originality/value

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

Details

Journal of Tourism Futures, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2055-5911

Keywords

Article
Publication date: 21 October 2023

Alex Rudniy, Olena Rudna and Arim Park

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed…

1250

Abstract

Purpose

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

Design/methodology/approach

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

Findings

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

Originality/value

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

Practical implications

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

Details

Journal of Fashion Marketing and Management: An International Journal, vol. 28 no. 3
Type: Research Article
ISSN: 1361-2026

Keywords

Article
Publication date: 16 July 2024

Sirine Ben Yaala and Jamel Eddine Henchiri

This study aims to predict stock market crises in the Middle East North Africa (MENA) regions by leveraging the nonlinear autoregressive neural network with exogenous inputs…

42

Abstract

Purpose

This study aims to predict stock market crises in the Middle East North Africa (MENA) regions by leveraging the nonlinear autoregressive neural network with exogenous inputs (NARX) model with two measures of investor sentiment: the ARMS indicator and Google Trends' search volume of positive and negative words.

Design/methodology/approach

Employing a novel approach, this study utilizes the NARX model with ten neurons in the hidden layer and the Levenberg–Marquardt training algorithm. It evaluates model performance through learning, validation and test errors, as well as correlation analysis between predicted and actual crises.

Findings

The NARX model, incorporating investor sentiment, has proven to be a reliable tool for forecasting crises, helping market participants understand data complexity and avoid crisis consequences. The divergence in how investors interpret market news, with some focusing solely on negative developments and others valuing positive outcomes, highlights the predictive nature of the optimistic and pessimistic sentiments captured by the model.

Research limitations/implications

This study advocates for integrating behavioral approaches into stock market crisis prediction, highlighting the significance of investor sentiment and deep learning. It advances crisis mechanism understanding and opens avenues in behavioral finance. Integration of these findings into finance and economics education could enhance students' risk understanding and mitigation strategies.

Practical implications

The adoption of NARX models, incorporating investor sentiment, empowers market participants to proactively manage crises, adjust strategies, enhance asset protection and make informed decisions. These models enable them to minimize losses, maximize returns and diversify portfolios effectively in response to market fluctuations. These insights also guide policymakers such as governments, regulatory institutions and financial organizations in formulating crisis prevention and mitigation policies, bolstering economic and financial stability.

Social implications

This research reduces economic uncertainty, safeguards individuals' savings and investments and promotes a stable financial climate.

Originality/value

This study is one of the first attempts to demonstrate the detection and prediction of stock market crises, specifically in the MENA stock market, using the NARX model. It offers a robust forecasting model using machine learning and investor sentiment, providing decision-making support for investment strategies and policy development aimed at enhancing financial and economic stability.

Details

Journal of Financial Regulation and Compliance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1358-1988

Keywords

Open Access
Article
Publication date: 23 January 2024

Stefano Cosma and Daniela Pennetta

This work aims to explore the effects of (equity and non-equity) strategic alliances between banks and FinTechs on FinTechs' online visibility.

Abstract

Purpose

This work aims to explore the effects of (equity and non-equity) strategic alliances between banks and FinTechs on FinTechs' online visibility.

Design/methodology/approach

For a sample of 124 Italian FinTechs, the authors measured online visibility through their website ranking (Google PageRank) and website traffic (Google Trends). Consistent to the historical depth of these measures, the authors separately investigated the effect of equity and non-equity (contractual) agreements on online visibility by means of ordinal logistic regressions and diff-in-diff analysis.

Findings

Strategic alliances with banks enhance FinTechs' online visibility. Although both equity and contractual agreements positively influence the popularity of FinTechs' website achieved through the activity of internal and external online content creators (websites ranking), only equity agreements are effective in attracting Internet users (website traffic).

Practical implications

When deciding to interact with banks, FinTechs' managers should consider that equity agreements may be a powerful strategic choice for enlarging the customer base and boosting visibility of FinTechs.

Social implications

Fostering strategic alliances between banks and FinTechs contributes to FinTechs' growth, generating virtuous mechanisms of innovation, financial inclusion and better allocative efficiency of the financial system.

Originality/value

This work expands marketing knowledge and literature regarding online visibility determinants, by investigating the benefits of strategic alliances and cooperation in the market, while providing an empirical strategy replicable by future marketing studies.

Details

International Journal of Bank Marketing, vol. 42 no. 5
Type: Research Article
ISSN: 0265-2323

Keywords

Open Access
Article
Publication date: 20 October 2023

Peterson K. Ozili

This paper aims to investigate the determinants of global interest in central bank digital currency (CBDC). It assessed whether global interest in sustainable development and…

1727

Abstract

Purpose

This paper aims to investigate the determinants of global interest in central bank digital currency (CBDC). It assessed whether global interest in sustainable development and cryptocurrency are determinants of global interest in CBDC.

Design/methodology/approach

Google Trends data were analyzed using two-stage least square regression estimation.

Findings

There is a significant positive relationship between global interest in sustainable development and global interest in CBDC. There is a significant positive relationship between global interest in cryptocurrency and global interest in the Nigeria eNaira CBDC. There is a significant negative relationship between global interest in CBDC and global interest in the eNaira CBDC. There is a significant positive relationship between global interest in CBDC and global interest in the China eCNY. There is a significant negative relationship between global interest in cryptocurrency and global interest in the Sand Dollar and DCash.

Originality/value

The literature has not empirically examined whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC. This study fills a gap in the literature by investigating whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC.

Article
Publication date: 9 February 2024

Nhung Thi Nguyen, An Tuan Nguyen and Dinh Trung Nguyen

This paper aims to examine the effects of investor sentiment on the development of the real estate corporate bond market in Vietnam.

Abstract

Purpose

This paper aims to examine the effects of investor sentiment on the development of the real estate corporate bond market in Vietnam.

Design/methodology/approach

The research uses an autoregressive distributed lag (ARDL) model with quarterly data. Additionally, the study employs Google Trends search data (GVSI) related to topics such as “Real Estate” and “Corporate Bond” to construct a sentiment index.

Findings

The empirical outcomes reveal that real estate market sentiment improves the growth of the real estate corporate bond market, while stock market sentiment reduces it. Also, there is evidence of a long-run negative effect of corporate bond market sentiment on the total value of real estate bond issuance. Further empirical research evidences the short-term effect of sentiment and economic factors on corporate bond development in the real estate industry.

Research limitations/implications

Due to difficulties in collecting data, this paper has the limited sample of 54 valid quarterly observations. Moreover, the sentiment index based on Google search volume data only reflects the interest level of investors, not their attitudes.

Practical implications

These results yield important implications for policymakers in respect of strengthening the corporate bond market platform and maintaining stability in macroeconomic and monetary policies in order to promote efficient and sustainable market development.

Social implications

The study offers some suggestions for regulators and governments to improve the real estate corporate bond market.

Originality/value

This is the first quantitative study to examine the effect of sentiment factors on real estate corporate bond development in Vietnam.

Details

Review of Behavioral Finance, vol. 16 no. 4
Type: Research Article
ISSN: 1940-5979

Keywords

Article
Publication date: 22 December 2023

Rujing Xin and Yi Jing Lim

This study employs bibliometric analysis to map the research landscape of social media trending topics during the COVID-19 pandemic. The authors aim to offer a comprehensive…

189

Abstract

Purpose

This study employs bibliometric analysis to map the research landscape of social media trending topics during the COVID-19 pandemic. The authors aim to offer a comprehensive review of the predominant research organisations and countries, key themes and favoured research methodologies pertinent to this subject.

Design/methodology/approach

The authors extracted data on social media trending topics from the Web of Science Core Collection database, spanning from 2009 to 2022. A total of 1,504 publications were subjected to bibliometric analysis, utilising the VOSviewer tool. The study analytical process encompassed co-occurrence, co-authorship, citation analysis, field mapping, bibliographic coupling and co-citation analysis.

Findings

Interest in social media research, particularly on trending topics during the COVID-19 pandemic, remains high despite signs of the pandemic stabilising globally. The study predominantly addresses misinformation and public health communication, with notable focus on interactions between governments and the public. Recent studies have concentrated on analysing Twitter user data through text mining, sentiment analysis and topic modelling. The authors also identify key leading organisations, countries and journals that are central to this research area.

Originality/value

Diverging from the narrow focus of previous literature reviews on social media, which are often confined to particular fields or sectors, this study offers a broad view of social media's role, emphasising trending topics. The authors demonstrate a significant link between social media trends and public events, such as the COVID-19 pandemic. The paper discusses research priorities that emerged during the pandemic and outlines potential methodologies for future studies, advocating for a greater emphasis on qualitative approaches.

Peer review

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2023-0194.

Details

Online Information Review, vol. 48 no. 4
Type: Research Article
ISSN: 1468-4527

Keywords

1 – 10 of over 2000