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Many studies have analysed the impact of various variables on the ability of companies to raise capital. While most of these studies are sector-agnostic, literature on the effects of macroeconomic variables on sectors that established over the last 20 years like property technology and financial technology, is scarce. This study aims to identify macroeconomic factors that influence the ability of both sectors and is extended by real estate variables.

The impact of macroeconomic and real estate related factors is analysed using multiple linear regression and quantile regression. The sample covers 338 observations for PropTech and 595 for FinTech across 18 European countries and 5 deal types between 2000–2001 with each observation representing the capital invested per year for each deal type and country.

Besides confirming a significant impact of macroeconomic variables on the amount of capital invested, this study finds that additionally the real estate transaction volume positively impacts PropTech while the real estate yield-bond-gap negatively impacts FinTech.

For PropTech and FinTech companies and their investors it is critical to understand the dynamic with mac-ro variables and also the real estate industry. The direct connection identified in this paper is critical for a holistic understanding of the effects of measurable real estate variables on capital investments into both sectors.

The analysis fills the gap in the literature between variables affecting investment into firms and effects of the real estate industry on the investment activity into PropTech and FinTech.

This study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that have shaped this market development in Ukraine in recent years.

The study uses a comprehensive data set encompassing relevant macroeconomic indicators and historical apartment prices. Multifactor linear regression (MLR) and ridge regression (RR) models are constructed to identify the impact of multiple predictors on apartment prices. Additionally, the ARIMAX model integrates time series analysis and external factors to enhance modelling and forecasting accuracy.

The investigation reveals that MLR and RR yield accurate predictions by considering a range of influential variables. The hybrid ARIMAX model further enhances predictive performance by fusing external indicators with time series analysis. These findings underscore the effectiveness of a multidimensional approach in capturing the complexity of housing price dynamics.

This research contributes to the real estate modelling and forecasting literature by providing an analysis of multiple linear regression, RR and ARIMAX models within the specific context of property price prediction in the turbulent Ukrainian real estate market. This comprehensive analysis not only offers insights into the performance of these methodologies but also explores their adaptability and robustness in a market characterized by evolving dynamics, including the significant influence of external geopolitical factors.

This paper aims to estimate a statistical model of the country risk determination as represented by the country price earnings ratio (PE) to identify potentially mispriced countries. It uses the gross domestic product (GDP) growth rate and a dummy indicator for market-related events (i.e. financial crises), both approximating the business cycle. The model is used to compare a major Asian country’s (i.e. Japan) risk with Western countries’ risk.

The model used finance variables such as the systemic, non-diversifiable, risk and foreign direct investments to characterize any country risk. A random effects model with panel data estimated the effects of macroeconomic and financial variables on PE. The simultaneity problem was checked using two stage least squares and some lagged independent variables.

The results explained to investors the country risk contributing factors: PE was positively correlated with variables that may increase dividends and market risk premia similar to GDP growth rates and total risk and negatively correlated with variables that increase market risk, namely, nominal risk-free interest rates and financial crises. Japan’s PE seemed to exceed most of the Western countries considered here, implying lower risks, lower interest rates and higher growth in the major Asian country Japan.

This paper focuses on the effectiveness of country risk measures in predicting periods of intense instability, similar to financial crises. This study contributes a model to measure market risk premium, using PE (or inversely, the earnings yield) as a proxy variable. Investors can use this risk measure in picking less risky stocks to include in their portfolio, calling for liberalizing Asian countries’ financial markets to improve their stock market capitalization.

This paper aims to study the impact of economic freedom and some key macroeconomic variables on the foreign direct investment (FDI) inflow in Brazil.

An econometric model is developed that includes FDI inflow as the dependent variable and macroeconomic variables such as the output, current account balance, the real exchange rate, openness and economic freedom as explanatory variables. Annual time series data from 1995 to 2022 is used. Before carrying out the estimation, the time series properties of the data are diagnosed using unit root tests and cointegration tests. Since the data series were found to be stationary in the first difference form and the variables in the model were cointegrated, an error correction model is developed and estimated.

The findings demonstrate that the size of the market (gross domestic product), current account balance and the economic freedom index significantly influence FDI inflow to Brazil. Although the signs of openness and the real exchange rate align with theoretical expectations, they do not attain statistical significance.

To the best of the authors’ knowledge, this is the first formal study on the impact of economic freedom on the FDI inflow in Brazil. The finding of this study adds value to the understanding of FDI dynamics in Brazil, highlighting the critical role of economic freedom and market size in attracting foreign investment.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

This study investigates the effect of intellectual capital (IC) and its components on the technical efficiency of Indian commercial banks after controlling the influence of bank-specific and macroeconomic variables.

The study selects a sample of 37 listed Indian commercial banks from 2005 to 2019 and uses the two-step data envelopment analysis (DEA) approach. Banks' technical efficiency scores are first estimated, while the relationship between IC and technical efficiency is examined in the second stage using the panel data Tobit model.

This study's findings suggest a fluctuating trend in the technical efficiency of Indian banks. Notably, from 2015 onwards, a declining technical efficiency trend is observed for all banks. However, private-sector banks outperform public-sector banks in terms of technical efficiency. This study's regression analysis indicates a positive relationship between IC and banks' technical efficiency scores. Further, by decomposing IC into its components like human capital, structural capital and capital employed, the study's findings show that human capital and structural capital enhance banks' technical efficiency. Notably, capital employed reduces technical efficiency. Moreover, bank size, diversification, capitalization, net interest margin and the country's growth rate significantly drive Indian banks' efficiency. In contrast, their operating cost ratio and the country's inflation negatively influence the same.

This study makes a novel endeavor to examine the IC and bank's technical efficiency nexus in the Indian context, encompassing a period of landmark banking reforms.

This study aims to explore the potential impact of country-level governance in corporate financing structures.

A two-step system generalized method of moment was used due to the endogeneity issue. The whole sample comprises 3,761 firms in five economies – China, India, Pakistan, Singapore and South Korea – from 2007 to 2016.

The results indicate that the debt option for financing is not favorable under governments with an adequate governance arrangement. However, there is a direct and significant link between country governance and equity financing because in adequate governance arrangements, the possibilities of information asymmetry are minimal and businesses consider equity a more appropriate and safer financing instrument. In contrast, firms prefer to trade-credit financing in poor governance economies, which confirms an adverse link between trade credit and adequate governance.

The country’s governance should be considered a sensitive matter when deciding about corporate financing.

This arrangement of variables has not been previously analyzed in the literature, suggesting the study’s novelty.

This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value.

Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model.

Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results.

It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions.

While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security.

The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices.

This paper aims to evaluate the performance of the multiple linear regression (MLR) using a fixed-effects model (FE) and artificial neural network (ANN) models to predict the level of customer deposits on a sample of Tunisian commercial banks.

Training and testing datasets are developed to evaluate the level of customer deposits of 15 Tunisian commercial banks over the 2002–2021 period. This study uses two predictive modeling techniques: the MLR using a FE model and ANN. In addition, it uses the mean absolute error (MAE), R-squared and mean square error (MSE) as performance metrics.

The results prove that both methods have a high ability in predicting customer deposits of 15 Tunisian banks. However, the ANN method has a slightly higher performance compared to the MLR method by considering the MAE, R-squared and MSE.

The findings of this paper will be very significant for banks to use additional management support to forecast the level of their customers' deposits. It will be also beneficial for investors to have knowledge about the capacity of banks to attract deposits.

This paper contributes to the existing literature on the application of machine learning in the banking industry. To the author's knowledge, this is the first study that predicts the level of customer deposits using banking specific and macroeconomic variables.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.