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This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

This study aims to determine how changes in macroeconomic indicators and the housing prices index (HPI) are related. These factors can cause short-term and long-term changes in the housing market in Spain.

The study used cointegrating regression, fully modified ordinary least squares and dynamic ordinary least squares methodologies. The models are trained using quarterly time series data for these parameters from 2010 to 2022. A comprehensive examination is conducted to explore the relationship between macroeconomic issues and fluctuations in the HPI.

The results indicate statistically significant short-run effects (p < 0.05) of economic growth, inflation, Spanish stock indices, foreign trade and the interest rate on HPI. The inflation variables, Spain’s stock indices, interest rate and monetary rate, have statistically significant long-run effects (p < 0.05) on HPI. The exchange rate, unemployment and money supply have no substantial impact on HPI in Spain.

The study’s findings significantly contribute to increased information concerning the level of investing activity in the Spanish housing sector. After conducting an in-depth study of both the long-run and short-run connections with HPI, the study proved to be highly effective in formulating appropriate policies.

The purpose of this paper is to investigate the performance of the arbitrage pricing theory (APT) in the Istanbul Stock Exchange (ISE) on a monthly basis, for the period January 2001 to September 2005.

This study examines six pre‐specified macroeconomic variables which are: the term structure of interest rate, unanticipated inflation, risk premium, exchange rate and money supply. All these are the same as those used by Chen, Roll and Roll for the US market. In this study, the authors develop one more variable namely unemployment rate, which has a relation with the stock return.

Using the OLS technique, the authors observed that there are some differences among the market portfolios. Before starting to comment on the result of OLS, the serial correlation problem was discussed by using Durbin‐Watson statistics. In this study, the critical values were ranged from between 1.33 and 1.81 (T=57, K=6). Our test results confirmed that in ten out of the 13 there were no serial correlations. Our results show that there are big differences among market portfolios against macroeconomic variables through the variation of R². In the remaining portfolios; there was no evidence to suggest.

In this paper, the authors face a problem that was no corporate bond in Turkey's market.

This analysis appears to be the first empirical test of APT using the CAPM formula for finding the risk premium point for ISE.

A series of environmental pollution issues and economic improvement go hand in hand. Since financial listed companies contribute significantly to the national economic development, China has been paying increasing attention to the development of the financial industry. The purpose of this paper is to explore the relationship among the development level of the financial industry, over-investment of the listed companies and environmental pollution through a macro-level and micro-level mechanism.

In this study, we adopt the 2011–2017 panel data of listed companies in the manufacturing industry to study the impacts of the financial industry on environmental pollution. Meanwhile, the paper uses a mediator model, and over-investment is introduced to the econometric model as a mediator to explore whether the development of the financial industry can affect the environmental pollution through over-investment.

A U-shaped relationship between financial industry development and environmental pollution was observed through a macro-perspective; additionally, over-investment of the listed companies significantly increased environmental pollution, along with a significant mediator effect of over-investment. A significant positive correlation was observed between financial industry development and environmental pollution in the East region of China, while the correlation was negative in the Central and West regions. The mitigation effect of financial industry development on environmental pollution was more significant in the low-end manufacturing industry, compared with basic and high-end manufacturing industries.

Strengthening the incentives and supervision toward company managers, reducing over-investment behaviors, encouraging suitable financial industry development to reduce financial risks, improving environmental conservation laws and regulations, and implementing stringent penalty mechanisms for environmental conservation are necessary.