Financial Research Research Articles

Research methodology in finance

Purpose- Our research aims to explore the different philosophical and epistemological concepts and approaches in traditional finance and behavioral finance, based on the analysis of scientific articles. We aim to provide a comprehensive insight into the theoretical foundations that guide these two branches of finance, with particular emphasis on the epistemological and methodological nuances that distinguish them. Methodology- Our research is based on a careful analysis of the relevant scientific literature in traditional and behavioural finance. By giving specific importance to epistemological aspects, we try to discover the paradigms of thought that influence the understanding and interpretation of financial phenomena within these two fields. Similarly, our meticulous examination of this research aims to identify the preferred methodological approaches. Findings- The results of the analysis show that traditional and behavioural finance have different epistemological and methodological approaches. most traditional financiers follow a quantitative research approach based on numerical data and a purely statistical analysis. However, behaviorists have opted for methodological triangulation, which further explains the combination of qualitative and quantitative approaches to achieve stronger results and that one can modify or complement the other. Behaviorists recognize the importance of cognitive and emotional biases in the decision-making process of individuals, leading to more holistic models and approaches. With regard to epistemology, most research in Finance on the positivist paradigm, on objectivity and on the relationship of cause and effect. Conclusion- In conclusion, our study highlights the importance of developing research methodologies adapted to each discipline of management sciences, taking into account the specificities of each field. Moreover, the analysis of the scientific literature in traditional and behavioural finance shows that researchers must be aware of the different epistemological and methodological perspectives in order to conduct rigorous and valid research. Keywords: Epistemology, methodology, management sciences, traditional finance, behavioural finance. JEL Codes: B40, G10, G40

Demographic Dynamics in Investment Behavior: A Comprehensive Study on Gender, Occupation, and Financial Decision-Making Patterns

This study explores the intricate interplay between demographic factors, specifically gender and occupation, and various dimensions of investment behavior. A significant gender disparity in participation is acknowledged, emphasizing the need for equitable representation in financial research. The majority of participants perceive their investment knowledge as "Intermediate," underscoring a moderate level of confidence. The study investigates information sources, decision-making preferences, portfolio review frequencies, financial goals, and preferred investment types, unraveling nuanced patterns across genders and occupations. Findings reveal a shared reliance on "Financial News" and "Professional Advice" for investment decisions, with notable gender-based differences. Preferences for independent decision-making or financial advisor assistance exhibit balanced distributions. Weekly portfolio reviews emerge as a common practice, particularly in service occupations. "Wealth Accumulation" stands out as the dominant financial goal, though gender and occupation nuances surface. The most prevalent investment type is "Stock & Mutual Funds," with variations by gender and occupation. The study concludes by offering recommendations for tailoring communication strategies, enhancing educational initiatives, adopting client-centric approaches, and aligning products and services with diverse investor goals and preferences. By shedding light on these demographic dynamics, the study provides actionable insights for financial institutions, advisors, and policymakers seeking to better understand and serve the diverse needs of investors in an ever-evolving financial landscape.

The Scientology of Hypothesis Testing in Empirical Research: Emphasizing Economic Significance

Over the decades, scientists across various disciplines have cautioned against the practice of over-emphasizing statistical significance at the expense of economic or practical significance. It seems that empirical researchers in the 21st century have not heeded the caution because meeting statistical significance targets continue to take precedence over the wider discovery and publication of scientifically objective findings. Statistically insignificant results are rarely reported, in part due to publication bias towards statistically significant findings. The survey finds that although large sample sizes are widely used in empirical economics and finance research, none of the surveyed papers adopted alternative methods of hypothesis testing, such as Bayesian methods. All of them explicitly or implicitly used the classical Fisher’s hypothesis testing methods. This study finds that discussions on economic significance in nearly all papers (almost 97% of papers) only wrote one sentence or two regarding the magnitude of the effect of the regressors and a declaration that the findings were economically significant. We recommend that to enhance research credibility, other methods of hypothesis testing such as Bayesian methods, should be adopted. Journal article publishers should encourage the publication of statistically insignificant empirical findings. Economic or practical significance should be emphasized and comprehensively discussed.

Financial Market Stress and Commodity Returns: A Dynamic Approach

This paper examines the relationship between commodity index returns and the Office of Financial Research Financial Stress Index (OFR FSI). Utilizing the S&P GSCI and its five sub-indices (agriculture, livestock, energy, industrial metals, and precious metals), we find that the causal relationship between financial market stress and commodity index returns is conditional on the sample period examined and the methodology employed. We also note that stress in financial markets has a negative relationship with commodity index returns during low commodity return states; however, during high commodity return states, financial market stress exhibits a positive relationship with commodity index returns. Our findings highlight the importance of considering a time-varying framework for analyzing commodity return dynamics.

Transforming the Finance Industry in China with ChatGPT"

This paper reviews how ChatGPT is transforming the finance industry in China. We discuss the fundamental principles of NLP and ChatGPT, and explain the comparative advantages of ChatGPT over traditional NLP models. The paper highlights the applications of ChatGPT in China’s finance industry, such as the applications in customer service, financial forecast and analysis, financial risk management, and financial research. The paper also addresses the challenges and limitations associated with the application of ChatGPT, such as the issues related to real-time database update, fine tuning the model, data leakage and ethical problems. We propose a few potential solutions including establishing relevant laws and regulations, educating financial professionals, and exploring future applications. These recommendations are aimed at facilitating the responsible and efficient integration of ChatGPT within the Chinese finance sector.

A Study on the Problem of Starting and Depositing of Ming Dynasty Fields Based on the Perspective of HGIS

The origination, transportation, and retention of field levies are the core contents of the Ming Dynasty financial research, and in the past, the academic community's attention to the origination, transportation, and retention problems only stayed in the discussion of the amount of field levies and the institutional level. With the use of digital humanities and historical geographic information systems in historical research, the spatial information implied in the Ming dynasty field levies can also be presented in the study. The book "Wanli Accounting Record" compiled by the Ministry of Revenue during the Wanli period contains the collection and storage of field levies at all levels of provinces and counties in the Ming Dynasty, and the historical documents are converted into data and then the GIS software is utilized to present them in a hierarchical manner on a nationwide scale. The results of the hierarchical presentation are further analyzed by using spatial analysis software to analyze the spatial relationship between the field levies in each state and county in the Ming Dynasty.

Green fintech: Developing a research agenda

AbstractDigitalization and sustainability have been the core drivers of transformation of the financial industry in recent years. In this context, green fintech plays a major role, which, however, is still an unexplored field in business, information systems and finance research. This paper conducts a systematic literature analysis and develops a research agenda based on a framework, which is derived from clustering 74 academic research papers. The framework consists of the four clusters strategy, organization, technology, and potentials along nine dimensions. The research agenda reveals that green fintech is still a very premature field of research. The analysis shows that areas like customer‐ and government‐related services, insurance‐oriented approaches and SDGs which focus on life on land and life below water are still rare and that most of the approaches focus on blockchain technology, while other financial technologies like artificial intelligence are still underrepresented.

Pursuing responsible finance in a world of risking risks

Pursuing responsible finance in a world of risking risks Marc Brightman and James Christopher Mizes, from the University of Bologna, explore the importance of responsible finance in a world of rising risks. For over a decade, experts have been sounding the alarm about the new risks that crises like climate change, political unrest, and pandemics pose for global financial stability. Financial investors worldwide have joined a chorus of economists, analysts, and central bankers focused on harnessing financial markets’ power to prevent the worst effects of today’s planetary “polycrisis”. (1) And yet, these professionals have long run up against an enormous obstacle: how can the future effects of these crises be anticipated? And who exactly is responsible for preventing them? At the University of Bologna, our team of anthropologists at the Hau of Finance research project is pursuing answers to these pressing political questions by observing what we call “the moral turn” in global finance.

Research landscape of energy transition and green finance: A bibliometric analysis

This study utilizes bibliometric analysis to examine historical and present research patterns in the area of energy transition and green finance and to forecast potential future domains. Using the bibliometric method, 328 scholarly articles from the Web of Science database were evaluated. This paper identifies influential publications, maps the research landscape, and forecasts emerging tendencies through co-citation and co-word analyses. Co-citation analysis found three main clusters, while co-word analysis revealed four main clusters. Despite the growing significance of research on energy transition and green finance research, further in-depth investigation is necessary to offer a thorough depiction of the research domain. This research represents a pioneering endeavour in the utilization of bibliometric analysis to investigate the interrelationship between two items. It offers valuable insights into the rapidly expanding field of energy transition and green finance, effectively highlighting its contours and indicating potential future developments.

GAN-Enhanced Nonlinear Fusion Model for Stock Price Prediction

Stock price prediction is a significant field of finance research for both academics and practitioners. Numerous studies have proved that the stock movement can be fully reflect various internal features of stock price including non-stationary behavior, high persistence in the conditional variance. The fusion of time-series prediction model such as Auto-Regressive Integrated Moving Average (ARIMA) and neural network is an availability but difficult approach for stock price prediction. Although the orientation has been studied through some methods in different research, there are still difficulties with the poor capture ability of time-series features and insufficient effectiveness of integrating temporal feature and frequency domain information. In this paper, we propose a Generative Adversarial Network (GAN) framework with the Convolution Neural Networks (CNN) as the discriminator and a hybrid model as the generator for forecasting the stock price. The hybrid model includes Attention-based Convolution Neural Networks (ACNN), Long Short-Term Memory (LSTM), and ARIMA model. Moreover, this proposed framework uses the Generative Adversarial patten and Attention Mechanism to achieve effective analysis and feature extraction for stock price movement. The extensive experiments in different history periods of dataset demonstrate an improvement in forecasting of stock price using our model as compared to the baseline models.

Take the aTrain. Introducing an interface for the Accessible Transcription of Interviews

Research in behavioral and experimental finance becomes more multifaceted and the analysis of data from speech interactions more important. This raises the need for technical support for researchers using qualitative data generated from speech interactions. aTrain serves this need and is an open-source, offline transcription tool with a graphical interface for audio data in multiple languages. It requires no programming skills, runs on most computers, operates without internet, and ensures data is not uploaded to external servers. aTrain combines OpenAI’s Whisper transcription models with speaker recognition and provides output that integrates with MAXQDA and ATLAS.ti. Available on the Microsoft Store for easy installation, its source code is also accessible on GitHub. aTrain, designed for speed on local computers, transcribes audio files at 2-3 times the audio duration on mobile CPUs using the highest-accuracy Whisper transcription models. With an entry-level graphics card, this speed improves to 30% of the audio duration.

Predicting Financial Inclusion in Peru: Application of Machine Learning Algorithms

Financial inclusion is a fundamental and multidimensional matter that has acquired importance on the global agenda in recent years. In addition, it is still a source of great interest and concern for lawmakers, international organizations, scholars, and financial institutions worldwide. In that regard, this research focuses on Peru to assess the country’s financial inclusion condition, which continues to face significant hurdles in providing financial services to its whole population despite economic improvement. The aim of this article is twofold, based on recent data on demand for financial services and financial culture in the country: (1) to empirically test how machine learning methods, such as decision trees, random forests, artificial neural networks, XGBoost, and support vector machines, can be a valuable complement to standard models (i.e., generalized linear models like logistic regression) for assessing financial inclusion in Peru, and (2) to identify the most influential sociodemographic factors on financial inclusion assessment in the country. The results may catalyze the integration of machine learning techniques into the Peruvian financial system, garnering the interest of finance researchers and policymakers committed to augmenting financial access and utilization among Peruvian consumers.

Strategic Management Methods in Islamic Economics and Finance Research

This study tries to review the application of research using the Strategic Management method in the theme of Islamic economics and finance. It uses descriptive statistical analysis based on selected 72 article publications. The entire sample publications have published from 2007 to 2019. The data is then processed and analyzed into 4 clusters using the VOSviewer application program to find out the bibliometric map of research development in the field of strategic management indexed by Scopus. The results show that research using the Strategic Management method is still dominated by Islamic banking followed by Halal Industry, and Islamic microfinance. The rest are issues about waqf, BMT, Islamic Finance, Islamic Economic Development, Sharia Compliance. Indonesia, Malaysia and Iran are the 3 countries with the most study areas compared to other countries. The most widely used method is the SWOT Analysis, IFE EFE method, Delphi Method, QSPM Method

Executive compensation, risk and performance: evidence from the USA

Purpose Given the serious question raised by the subprime of the 2008 global financial crisis over the rising practices of excessive rewarding of executives in the USA and European firms, the executive pay-performance nexus has emerged as a popular topic of debate in the contemporary corporate finance research. Conducted mostly on the Anglo-Saxon contexts, research outcomes have been inconclusive and dichotomous. Considering this backdrop, this study aims to investigate the endogenous relationship between executive compensation and risk taking in the context of the USA. Design/methodology/approach Using a large sample of non-financial firms from 2010 to 2020 based on panel data and two-stage least square regression. In this study, the riskier corporate decision is measured as book leverage and ratio of R&D expense to total assets. Chief executive officers’ (CEO) experience and age are used as instrumental variables, and these are expected to influence compensation incentives and, hence, affect firm riskiness indirectly. Firm size, return on assets and CEO turnover are reported to affect compensation and corporate decisions, therefore, included as control variables. Given that higher executive compensation is related to riskier corporate decision in firms, this study incorporates total wealth (i.e. accumulated equity related compensation) as an additional proxy of compensation, and this selection is justifiable by the perfect contracting notion of the agency theory. Findings The results of this study show a significant positive and increasing nexus among compensation and riskier corporate decisions. Besides, the compensation level proxied through the percentage of each form of compensation in total compensation is very important as greater equity and greater salary diminishes risk taking. Practical implications The outcomes of this study have useful implications for firm stakeholders and policymakers. Originality/value The level of pay measured by the percentage of each type of compensation in total compensation is of utmost importance as it can increase or decrease risk taking in corporate decisions.

Research Trend on Social Banking

The implementation of corporate social responsibility is not only for industrial companies that produce negative impacts on the environment and society, but also other sectors such as: services, insurance, communications, bank and non-bank financial institutions. Banks and other financial service providers play an important role as they are not only recipients but also providers of socially responsible investments. Banks can benefit by applying various basic ideas related to CSR to their policies related to human resources and community involvement. This study aims to determine the development map and trends of Social Banking published by reputable journals in the field of Economics and finance. The data analyzed were more than 1744 publications of Scopus indexed research publications. The export data was then processed and analyzed using the R Biblioshiny application program to determine the bibliometric map of the development of the role of Social Banking. The results showed that the number of publications on the development of the role of economic and financial research continues to increase.

Growth and Collaboration in Sustainable Finance Literature: Bibliometric Analysis

Objective: Research in the field of sustainable finance aims to understand the development and trends of sustainable finance over time and the relationship of keywords related to sustainable finance and research developments with authors who are very influential in further research. This research helps identify projects or sectors that contribute positively to sustainability and identify environmental and social risks that may result from investment activities. Additionally, to encourage innovation and development of financial products that support sustainability goals. Theoritical framework: Sustainable finance promotes sustainable business practices, including transparency, prevention of human rights violations, diversity, and positive societal contributions. The greenwashing phenomenon occurs a lot nowadays, where companies or products claim to have a positive or sustainable environmental impact, but the reality is inconsistent with these claims. Enhancing supervision, transparency, and strict sanctions are crucial to address these issues. Efforts are necessary to increase understanding and education about sustainable finance so that more parties can take relevant actions. Methods: Bibliometric analysis, there are dozens of tools to collect and analyze data. In this research, the tool to measure sustainable finance trends is Scopus, one of the popular academic databases for bibliometric analysis. This tool ensures access to scholarly journals, conferences, and other academic literature. Scopus offers rich information on publications, citations, citation index, and other metrics for bibliometric analysis. VOS viewer is a visualization tool to visualize collaboration networks, keyword clustering, and citation patterns in bibliometric analysis. Result & Conclusion: English is the most widely used language, with 644 total publications or 96.55% of Russian, French, German, Italian, Spanish and Ukrainian. In 2020, the publication trends related to sustainable finance were the most researched at 77 publications. It is identified that in 2022 the emergence of climate risks and opportunities associated with climate change will continue to be the research focus. There is a yellow cluster signifying the novelty associated with sustainable finance, i.e., Nigeria, New Zealand, Greece, and Finland. The second cluster is marked in light green. In 2021, sustainable finance research will be carried out in Italy, Germany, Spain, China, Bahrain, Malaysia and Indonesia. Furthermore, the third cluster marked in solid green in 2020, the United Kingdom dominates research, and the last cluster in purple in 2019 includes Switzerland, Denmark, Brazil, Canada, the United States, and South Africa. Implications: Implications of this study is Sustainable finance entails managing risks and uncertainties associated with environmental and social factors. Measuring and managing these risks involve assumptions and predictions that may have uncertainties. Contribution / Originality: Originality in this research is understanding the development, trends of sustainable finance over time, and understanding the relationship of keywords related to sustainable finance, and the advancement of research with authors who are prominent in further study.

Learning and processing framework using Fuzzy Deep Neural Network for trading and portfolio rebalancing

Trading strategies are an interesting topic of financial research. Moving Average Convergence Divergence (MACD) indicator is susceptible to performing worse than expected in unstable financial markets. This paper first presents a data-driven Interpretable Fuzzy Deep Neural Network (IFDNN) that provides insight into neural network inferences using fuzzy logic. Fuzzy rules are induced from the inference process of Neural Networks. Next, a learning and processing framework is proposed using IFDNN to detect trend reversals by forecasting look-ahead prices. IFDNN not only learns the drifts and shifts in market patterns, but also provides traders an option to dive into the reasoning behind why Neural Networks predict certain values. Genetic Algorithms are used to optimise trading parameters of the proposed framework. The proposed framework can perform portfolio rebalancing. The effectiveness of the framework is evaluated on three financial market indexes. The whipsaw effects cause frequent entrances and exits from the market. In this paper, a custom percentage oscillator is implemented to avoid this issue. The performances of the proposed framework using f-MACD are compared with those of the vanilla MACD. Two types of Reinforcement Learning models, Advantage Actor Critic and Deep Deterministic Policy Gradient are incorporated into the proposed framework with results compared.

Measuring aggressive decision-making behavior: A confirmatory factor analysis

This research paper aims to develop and validate a questionnaire for measuring aggressive investor behavior (AIB). The design of the questionnaire holds significant importance, given the absence of validated questionnaires in the current body of literature. Currently, limited research is available on aggressive investor behavior. In order to integrate behavioral finance theory and market microstructure into the study of decision-making behavior, it is essential to develop and validate a questionnaire model for aggressive investor behavior. This research involved three stages. First, the compilation of a questionnaire. Second, test its validity and reliability. Finally, after conducting statistical analysis, a total of 40 investors participated in the pre-survey. A confirmatory factor analysis (CFA) was conducted with 324 investors as respondents. The questionnaire developed to assess aggressive investor behavior and its determinant factors has been proven valid and reliable. The questionnaire is composed in straightforward language, making it simple to understand. However, using this questionnaire in other areas may necessitate further adjustments and modifications. To the best of the authors' knowledge, no previous studies have developed a questionnaire to assess aggressive investor behavior from the perspective of the Indonesian stock market. The research findings offer valuable insights for scholars and financial institutions keenly interested in finance research. This study provides a guideline in the form of a valid questionnaire to facilitate research, especially concerning aggressive investor behavior. Additionally, this research contributes to the literature on financial behavior by providing a validated questionnaire for further exploration.

How do the determinants of investment decisions get prioritized? Peeking into the minds of investors

PurposeInvestment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.Design/methodology/approachThe current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.FindingsInformation availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.Research limitations/implicationsOnly active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.Practical implicationsThis research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.Originality/valueTo the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

Mapping the evolution of green finance through bibliometric analysis

This bibliometric study seeks to analyze the intellectual structure and development of green finance research over a nearly 30-year period. Using Scopus data, a comprehensive analysis of 1,487 English-language publications on green finance was conducted. The scope of the analysis spans the years from 1997 to 2024. The analysis investigates numerous facets of green finance scholarship, such as publication and citation trends, influential works, authorship networks, geographic concentrations, conceptual links, and developmental phases. Since 2015, the number of publications and citations has increased significantly, indicating a significant rise in academic and industry interest. China is the leader in terms of research output and influence, demonstrating its dominance in the field. However, it is essential to note that other Asia-Pacific countries, such as Japan and Malaysia, have also made significant contributions to the field. Public policies, government initiatives, and the participation of the private sector are crucial to accelerating green investments and promoting sustainability, as evidenced by numerous citations to scholarly works on the subject. The study investigates a number of prospective future research avenues, including green bonds, green credit policies, green investment, green financial regulation, and green financial technology applications. Despite its heavy reliance on English-language Scopus sources, this quantitative longitudinal mapping provides valuable insights into the emergence and evolution of green finance as a significant multidisciplinary research field. AcknowledgmentIt is essential to express gratitude to all those who contributed to the success of this study, particularly those at the Ho Chi Minh University of Banking, Vietnam.