Finance Analytics in Business

The objective of this chapter is to analyse the performance of the UiPath (PATH) company on the New York Stock Exchange, in the context of the war between Russia and Ukraine, and to predict the closing price of the PATH stock using autoregressive integrated moving average with (ARIMAX) and without (ARIMA) exogenous variable methods and autoregressive neural networks (NNAR, NNARX).

UiPath has gained a significant reputation in the IT market and has become a point of interest in recent years. However, the current context is marked by an event of international impact, the war between Russia and Ukraine. In this context, this analysis will consider performance from two perspectives: forecasts of the closing price and forecasts of the closing price with an exogenous variable, namely the war between Russia and Ukraine.

In the analysis that follows, we will address a forecast of the stock closing price using ARIMA, ARIMAX, NNAR and NNARX, as well as analysis of changing points and structural breaks of the series.

The changing points in the mean and variance but also the breaks in the structure justify the course of the closing price. From the information extracted in the analysis, it can be concluded that market sentiment is currently pessimistic due to the downward trend in the price. Both the public and the shareholders are disappointed with the performance of PATH stock and are waiting for the next change point that will change the trend of the series.

The study is called for to eliminate the noise between the significant macro variables from the perspective of the cause-and-effect approach to indicate why and how the return of solar projects is being affected by these.

The study aims to investigate the spread between unit selling electricity prices of a monthly production of 250 KW solar project installed in Türkiye and USD/TRY.

A relational framework is designed by drawing on the variables determined as crude oil prices, United States (US) 2-year yield, Dollar Index (DXY), USD/TRY, the annual inflation rate of Türkiye, and unit selling electricity prices. Then, a multivariate approach is performed through Matlab to analyse the correlational relationships and structure the curve estimation models.

The observations show that the gradually rising spread between unit selling electricity price and USD/TRY signals the reduction in return-on-investment rate of solar energy projects because of the particular causes of the European energy crisis by the reason of Russia and Ukraine war and escalating risks in DXY and US treasury yields as a result of federal fund rate hikes against inflationary pressures. Solar energy investments are delicate instruments to global oil shocks and higher DXY in controlling Inflation and currency volatility; therefore, resilient policies should solicit the demand because of environmental and economic reasons to reduce the external dependency of Türkiye.

By examining the impact of the day of the week during the COVID-19 pandemic and the subsequent economic recession, it is possible to provide insights into market behaviour during volatile times that can be furnished to investors and policymakers for informed decisions.

This study investigates the day-of-the-week effect on the Colombo Stock Exchange (CSE), with particular emphasis on the variations in this effect during the COVID-19 pandemic and the subsequent economic crisis.

The study applies the Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model, allowing for the evaluation of asymmetric responses to positive and negative shocks. The data span from January 2006 to December 2022 and are segmented into different periods: the entire sample, war and post-war periods, the COVID-19 pandemic and the economic crisis period, each reflecting distinct market conditions.

The study uncovers a significant day-of-the-week effect on the CSE. Mondays and Tuesdays typically show a negative effect, while Thursdays and Fridays display a positive impact. However, this pattern shifts notably during the COVID-19 pandemic, with all weekdays exhibiting significant positive impact, and varies further across different waves of the pandemic. The economic crisis period also shows unique weekday effects, particularly before and after an important political event.

In particular, it is worth mentoring new and more efficient solutions that can meet the increasingly specific needs of each company, especially in food management. A business intelligence (BI) solution can help your food company better understand and manage business processes more effectively. Management information is essential for all levels of an organisation to make quick and correct decisions. However, what exactly is BI, and what can it mean for a food company?

The PRISMA stands for (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and content analysis strategy used the SLR (systematic literature review) methodology to examine 151 papers published in peer-reviewed academic journals and industry reports between 2016 and 2023.

The findings show that artificial intelligence and digitalisation are linked to the UN 2030 Agenda. BI management ranks first (66%), followed by crop and land mapping systems (40%), agricultural machinery monitoring tools (39%) and decision support systems (31%). The road to digital transformation remains extended, with the main impediments being more compatibility between enterprise systems and a shortage of expertise.

The section relating to methodological perspective adopts the PRISMA methodology for systematic review. Interoperability is easily managed by assigning qualified teams to projects. The added value of a consulting firm with extensive project management experience in the food industry is closely related to the results achieved.

BI: What exactly is it, and why a data-driven culture is essential in the food and beverage industry?

Banks play a vital role in the economy. Investigating their competitive environment is crucial to ensuring economic stability and development. The FinTech disruption has risks and opportunities for incumbent banks, and it can be valuable to investigate its effects on banking performance. Therefore, the aim of this study is to assess whether investment in FinTech is associated with better performance of Indian banks during 2012–2018.

To do this, a sample of Indian banks was investigated between 2012 and 2018 using k-means and hierarchical cluster analysis, ANOVA, and pairwise comparison tests.

Results of the analysis strongly suggest that investment in FinTech is associated with better banking performance. Higher FinTech investments, represented by mobile transaction volume, are associated with higher efficiency scores and accounting-based performance. In particular, banks that invest in FinTech and have relatively low non-performing loans have a 7.7% higher Return on Employment (ROE) than banks with exceptionally low FinTech use and no significant investment in smart branches.

Therefore, it can be recommended that Indian banks adopt a forward-looking strategic approach when making investment decisions regarding new technologies. Failing to adapt to the FinTech disruption may result in poor value creation prospects in the long run.

To the best of the authors' knowledge, this is the first study that analyses. We are not aware of any similar study on whether investment in FinTech is associated with better performance of the Indian banks during 2012–2018.

In the last 10 years, the global financial services industry has significantly benefited from fintech. As the Indian entrepreneurial ecosystem continues to change, more fintech-use case-driven firms are created, and more investors are supporting these enterprises. India is acknowledged as a powerful fintech centre internationally.

The goal of the current research is to comprehend the revolutionary landscape of the Indian financial system.

Methodology: The research methodology entails a thorough review of several research papers and government reports better to understand fintech's role in the Indian financial system. This requires examining the trends, regulations and technical breakthroughs driving the fintech ecosystem to present a comprehensive picture of its influence.

The present chapter indicates that the fintech industry is flourishing in India. Over the following years, technological improvements will fuel the market's continuous expansion and change how financial products and services are produced, distributed and used.

The great recession of 2008–2009 busted the market bubble and highlighted the loopholes in the banking sector related to excessive leverage and inadequate capital. It has led to the increased rigidity of financial regulations, forcing banks to focus more on compliance rather than moving towards innovation. All these factors together led to the emergence of new players in the financial market in the name of financial technology (Fintech) companies. With the help of Fintech, banking operations are now being revolutionised and transformed into techno-friendly systems. They, hence, can promise to act as a game changer for the banking sector as a whole.

This chapter aims to understand different perspectives of Fintech and how it helps the banking sector to improve its operations. This chapter will also offer insight into various types of Fintech instruments used by the banking sector, collaboration between banks and Fintech, and the benefits of its application to the banking sector.

This chapter attempts to lay out a literature review on Fintech. It examines the implications of applying Fintech in the banking sector to revolutionise its traditional banking operations and achieve its pre-established targets. Different techniques banks use to match up with Fintech and adapt it easily in its organisational structure.

This chapter presents a list of challenges linked to the application of financial technology in the banking industry. The chapter will also address the difficulties of using Fintech and ways to deal with them.

The present study apotheosises on the relationship between blockchain and fintech and its impact on the financial services sector. It then gauges into the various aspects that have been included in the published literature by the authors all across the world.

Post-pandemic, technology has led to tremendous opportunities in the financial sector, and the customers have started assessing financial services in online mode, thus enabling companies to innovate their business strategies. The present chapter aims to explore the areas where blockchain is benefitting the financial sector.

The objectives of this study were achieved through systematic review of literature performed with the help of bibliographic analysis. Further, a PRISMA model was developed, and the networks were derived with the help of Scopus analyser software and VOSviewer Version 1.6.15.

It is observed that since the introduction of the term and development of the first blockchain, a lot of work has been performed in the said domain, but it is still at a nascent stage. It is thus discovered that the string specific research documents got published in Scopus database from 2016 onwards, and a considerable amount is being performed by authors from China, the United States and India.

The present chapter exhibits the various dimensions in which a lot has to be yet explored and needs to devise innovative and full proof strategies which will enable to overcome the challenges, thus strengthening the position and working of the companies in Industry 5.0.

In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative banking channels, services and solutions disruptions. Thus, this chapter intends to determine AI's place in contemporary banking and stock market trading.

Stock market forecasting is hampered by the inherently noisy environments and significant volatility surrounding market trends. There needs to be more research on the mantle of AI in revolutionising banking and stock market trading. Attempting to bridge this gap, the present research study looks at the function of AI in banking and stock market trading.

The researchers have synthesised the literature pool. They undertook a systematic review and meta-synthesis method by identifying the major themes and a systematic literature review aided in the critical analysis, synthesis and mapping of the body of existing material.

The study's conclusions demonstrated the efficacy of AI, which has played a robust role in banking and finance by reducing risk and operational costs, enabling better customer experience, improving regulatory complaints and fraud detection and improving credit and loan decisions. AI has revolutionised stock market trading by forecasting future prices or trends in financial assets, optimising financial portfolios and analysing news or social media comments on the assets or firms.

AI's debut in banking and finance has brought sea changes in banking and stock market trading. AI in the banking industry and capital market can provide timely and apt information to its customers and customise the products as per their requirements.

Research and development (R&D) is a vital strategy for firms to sustain their competitive locus and profitability in the global marketplace. Therefore, the existing research is engrossed in the correlation between firm performance (FP) and R&D intensity (RDI) meta-analysis. It also examined the ‘Type of Firm’ as a moderator in this relationship.

This study is motivated by its potential to address existing knowledge gaps, guide decision-making, influence policy and contribute to advancing theoretical and practical insights in the domain of business, economics and innovation.

This study is based on the secondary data. The researcher uses ‘Meta- Essentials 1.5’ for meta-analysis covering the studies of developed and emerging economies from 1985 to 2022.

The outcome conveys a small effect of magnitude between RDI and FP. It also indicates the positively significant linkage between them, directing that investing in R&D projects leads to improvement in the performance of companies. It also points out that private firms engaging in R&D activities have a negative while public firms have a positive correlation with their performance.

Understanding this linkage is imperative as it aids managers in making strategic decisions, the government in funding research-related schemes and investors in choosing R&D projects for investment.

Fintech provides the necessary ecosystem for businesses to accept payments for goods and services in the most seamless manner. It can also be said that innovation in Fintech is one of the growth drivers for businesses in today's globalised market.

Fintech is revamping the entrepreneurship business by bridging the gap between the market and real-time access to investment. It provides entrepreneurs with numerous advantages like easy access to resources, reduced expenses and better customer experience. Hence, this research has focused on evaluating the impact of Fintech business on entrepreneurship business in the global market.

A mixed method of data collection has been used to conduct the research in which primary data have been collected using an online survey and secondary data have been collected from online articles and peer-reviewed journals. An online survey of 51 business managers recruited from the social networking platform LinkedIn has been done to collect primary data. Secondary data have been collected from the online database Google Scholar which has been published in the last five years.

The findings of the study have highlighted the various impacts that Fintech has had on entrepreneurship business in the global market and the reason why it is such an important factor for growth.

This study developed a new analytical model to quantify the influence of business intelligence (BI) adoption on bank performance. An in-depth review of academic literature revealed a significant research gap exists in investigating BI's performance impacts, especially in the under-studied Indian banking context. Additionally, customer relationship management (CRM) was incorporated as a moderating variable given banks' large customer databases.

A survey was administered to 413 employees across leading Indian banks to collect empirical data for evaluating the conceptual model. Relationships between variables were analysed using partial least squares structural equation modelling (PLS-SEM). This technique is well-suited for theory building with smaller sample sizes and non-normal data.

Statistical analysis supported the hypothesised positive effect of BI adoption on bank performance dimensions including growth, internal processes, customer satisfaction, and finances. Furthermore, while CRM did not significantly moderate this relationship, its inclusion represents an incremental contribution to the limited academic literature on BI in Indian banking.

The model provides a quantitative basis for strategies leveraging BI's performance benefits across the variables studied. Moreover, the literature review revealed an important knowledge gap and established a testable framework advancing BI theory in the Indian banking context. Significant future research potential exists through model replication, expansion, and empirical verification.

This research thoroughly reviewed existing academic literature to develop a novel testable model absent in prior studies. It provides a robust conceptual foundation and rationale for ongoing scholarly investigation of BI's deployment and organisational impacts.

The popularity of cryptocurrency and blockchain technology has been increasing in recent years. Thus, the study uses bibliometric analysis to examine the development of research on cryptocurrency and blockchain trends.

The very few researchers analyse the bibliometric trends in blockchain and cryptocurrency research to classify the articles according to research methodology and journal quality. Further, a complete study of citations or co-citations based on co-occurrence analysis needs to be added to the bibliometric research. Therefore, it is required to study this topic in detail.

The VOSviewer software and Scopus analysis are used to conduct a bibliometric study on the biographies of articles published on cryptocurrency and blockchain trends. A total of 1,186 papers from the Scopus database are retrieved to analyse the trends in this field of research.

The study examines the total citations, papers with the most citations, authors and journals, prominent institutions and country contributions. In addition to listing the top 10 most significant articles with their years of publication and total citations, this study provides insight into the top 10 prominent journals of cryptocurrency and blockchain trends. Additionally, during the past 15 years, the United States and the United Kingdom have received the most citations and publications on cryptocurrencies and blockchain trends. This study also identifies and critically investigates the top 10 journals in the specialised field with the highest Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR) and citation scores.

The purpose of this research is to examine the connections between liquidity risk, credit risk, and bank profitability in India.

In order to examine the interlinkage between liquidity risk, credit risk, and profitability of banks in India, the researcher has gathered data from all commercial banks in India from 2004–2005 to 2020–2021. The data sources included in this study encompass the International Country Risk Guide, World Development Indicators and Reserve Bank of India (RBI). Seemingly Unrelated Regression (SUR) has been utilised for the study.

Findings of this research identified that liquidity risk is inversely proportional to credit risk. Return on assets (ROA) and return on equity (ROE) are both impacted negatively by liquidity risk. ROA is impacted positively by credit risk, while ROE is impacted negatively by it. The profitability of banks is harmed by the interaction between liquidity risk and credit risk. It also shows that law and order, are beneficial to bank earnings and risk management. The capital risk-adjusted ratio has a negative relationship with bank profitability, indicating the need for better capital allocation.

The originality of this work lies in its unique contributions, It emphasises explicitly the Indian context, thereby providing insights tailored to this particular setting. It employs the SUR methodology, a statistical approach allowing for a more comprehensive data analysis. Additionally, it identifies and explores interaction effects, which can shed light on the complex relationships between variables.

COVID-19 has been the subject of a number of inquiries recently. All country's capital market practices have been affected by the COVID-19 outbreak. Economic woes, along with the stock market crash, have hit emerging markets and developing economies in a variety of directions.

This study is an attempt to focus on the Indian economy to provide the gist of the situation and recovery mode of an economy with the help of growth indicators of the economy.

This study is based on secondary data. The researchers applied some econometric tools, viz, unit root test Augmented Dickey-Fuller (ADF), Panel Granger Causality, and Panel ARDL Bound Test were applied to examine the relationship of economic indicators and stock market benchmark in two periods: March 2020–June 2021 (during period) and July 2021 to March 2022 (post period).

The findings of this study explored the different causal relationships for the selected variables in both periods. The study discussed the reasons for ARDL (Auto Regressive Distributed Lag) bound for all selected factors. The study revealed the story of crude oil prices and Gold as trusted investment avenues during the crises.

As we know, the capital market's backlash is reflected in movements in stock prices and stock exchange volume, which are concerned with the economic effects of the pandemic and urged the segment to react. Investors can use the information in the event to make investment decisions.

The present era, in its pursuits for economic development, has equated development with affluence. The balance between economic development and using natural resources for the purpose needs to be solved. The previous civilisations became extinct less because of foreign invasions and more due to neglecting the ecological environment. In the same way, this civilisation is also digging its own grave.

After reviewing the available literature, it is proposed to study in the context of the Punjab state of India. The pattern of receipts and expenditures of funds utilised for ecological upgradation emphasises evaluating the performance of the funds utilised for ecological improvement. Furthermore, most of the study has concentrated on the experiences of developed economies. In contrast, there have been minimal studies explicitly addressing the circumstances of emerging countries.

The study is confined to Punjab and is based on secondary data. The Punjab government collected the annual data on expenditures and receipts from the last 10 years. The nature of the receipts and expenditures for the entire 11 sectors is determined through descriptive statistics. Moreover, the regression model and compound annual growth rate with the help of semi log model have been used to examine the extent of government funds. A line chart shows the pattern of government funding.

The government can implement changes or create new environmental protection policies based on the results. As a whole, the research contributes to better environmental protection policy. The study concludes that a thorough examination of money flow in and out is essential.