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The purpose of this paper is to explore the extent to which macroeconomic variables affect the Islamic stock market behavior in Malaysia in the post 1997 financial crisis period.

The paper employs the latest estimation technique of autoregressive distributed lag (ARDL) model approach to cointegration.

The results suggest that real effective exchange rate, money supply M3, treasury bill rate (TBR) and federal fund rate (FFR) seem to be suitable targets for the government to focus on, in order to stabilize the Islamic stock market and to encourage more capital flows into the market. As for the interest rates and stock returns relationship, the paper finds that when interest rates rise either domestically (TBR) or internationally (FFR), the Muslim investors will buy more Shari'ah compliant stocks; thereby escalating the Islamic stock prices.

The results of this study are limited to the post 1997 financial crisis period until the beginning of the year 2006 for a small open economy, Malaysia.

The paper reveals that both changes in the local monetary policy variables and in the US monetary policy as measured by the changes in the FFR have a significant direct impact on the Islamic stock market behavior in Malaysia.

The paper adopts the latest time series econometrics technique to test for cointegration, ARDL. And it is among the earliest attempts to investigate the long‐run effects of the macroeconomic variables changes either domestically or internationally on the Islamic stock market.

The purpose of this paper is to identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.

This study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.

The empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.

This study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.

The findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

This paper quantifies the effects of recent Federal Reserve interest rate changes, specifically recent hikes and cuts in the federal funds rate since 2015, on Midwest farmland values.

The authors apply three autoregressive distributed lag (ARDL) models to a panel data of state-level farmland values from 1963 to 2018 to estimate the dynamic effects of interest rate changes on the US farmland market. We focus on the I-states, Lakes states and Great Plains states. The models in the study capture both short-term and long-term impacts of policy changes on land values.

The authors find that changes in the federal funds rate have long-lasting impacts on farmland values, as it takes at least a decade for the full effects of an interest rate change to be capitalized in farmland values. The results show that the three recent federal funds rate cuts in 2019 were not sufficient to offset the downward pressures from the 2015–2018 interest rate hikes, but the 2020 cut is. The combined effect of the Federal Reserve's recent interest rate moves on farmland values will be positive for some time starting in 2022.

This paper provides the first empirical quantification of the immediate and long-run impacts of recent Federal Reserve interest rate moves on farmland values. The authors demonstrate the long-lasting repercussions of Federal Reserve's policy choices in the farmland market.

This paper aims to explain real exchange rate fluctuations by means of a model including both standard fundamentals and two alternative measures of inflation expectations for five inflation targeting countries (the UK, Canada, Australia, New Zealand and Sweden) over the period January 1993–July 2019.

Both a benchmark linear autoregressive distributed lag (ARDL) model and a nonlinear autoregressive distributed lag (NARDL) specification are considered.

The results suggest that the nonlinear framework is more appropriate to capture the behaviour of real exchange rates given the presence of asymmetries both in the long and short run. In particular, the speed of adjustment towards the purchasing power parity (PPP) implied long-run equilibrium is three times faster in a nonlinear framework, which provides much stronger evidence in support of PPP. Moreover, inflation expectations play an important role, with survey-based ones having a more sizable effect than market-based ones.

The focus on linearities and the estimation of a NARDL model, which is shown to outperform the linear ARDL model both within sample and out of sample, is an important contribution to the existing literature which has rarely applied this type of framework; the choice of an appropriate econometric method also makes the policy implications of the analysis more reliable; in particular, monetary authorities should aim to achieve a high degree of credibility to manage them and thus currency fluctuations effectively; the inflation targeting framework might be especially appropriate for this purpose.

The main purpose of this study is to investigate the determinants of foreign exchange reserve accumulation in a foreign exchange constrained economy, namely Ethiopia, over the period of 1981 up to 2017.

In this study, autoregressive distributed lag (ARDL) model is used. Besides, standard unit-root tests such as augmented Dickey Fuller (ADF) and Phillips–Perron (PP) tests are employed to check for the stationarity of the series.

According to the results of unit-root tests, our variables are found to be a mixture of I(0) and I(1), and none of our series is I(2). The results of our ARDL model indicates, in the short run, foreign exchange reserve accumulation of Ethiopia is negatively and significantly affected by inflation rate and exchange rate. But, in the long run, inflation rate affects foreign exchange reserve positively and significantly. Additionally, in the long run, external debt affects foreign exchange reserve positively. Similar to its effect in the short run, exchange rate also affects foreign exchange reserve negatively in the long run.

This paper has its originality as it contributes in reasoning out the factors determining, both in the short-run and long-run, foreign exchange deficiency in any developing country with foreign exchange deficiency, taking Ethiopian economy as a case study, and fills the scarce literature on the determinants of foreign exchange reserve accumulation in a developing country.

While Blockchain can serve us, Bitcoin threatens our survival. If Bitcoin is assumed to be a country, it will rank 38th globally for energy consumption. With 90.2 metric million tonnes of carbon dioxide, Bitcoin mining and trading has emerged as an environmental threat. The current study investigates how the trading-specific variables, the prices of Crypto Index and Ethereum, affect bitcoin-based energy consumption. Also, the role of mining-specific variables is analyzed.

The study uses monthly data from various sources collected from December 2018 to January 2023. The authors used the Autoregressive Distributed Lag (ARDL) Model to determine the short- and long-term relationships between variables. This study uses the Theory of Green Marketing and the Theory of Cross Elasticity of Demand as a theoretical lens.

The findings show that escalating crypto market index and Ethereum prices with a one-month lag increases bitcoin-specific electricity consumption and carbon emissions. Green investors may shift to cryptocurrencies based on consensus other than of Proof-of-Work. Ethereum behaves like a substitute for Bitcoin, reflected by the long-term positive relationship between Bitcoin's energy consumption and Ethereum prices.

The study analyses how the crypto market index and Ethereum price affect bitcoin-based energy use. The relationships identified are substantiated by the literature to provide suggestions to green investors and policymakers to mitigate the harmful impact of Bitcoin's colossal energy consumption on the natural environment.

This paper aims to contribute to the banking and housing market literature by proposing an alternative measure of rate of return for Islamic banks that is based on the rental rate of the property. This alternative Islamic mortgage pricing mechanism could be adopted by Islamic banks as a replacement for mortgage rates if it is found to be independent from any form of interest rates as required by Islamic law.

By investigating the short run and long run dynamics between rental price index (RPI) and the proposed Islamic Rental Rate (RR-I) and, three selected macroeconomic indicators in the UK via autoregressive distributed lag model, the authors examine the link between RPI, RR-I and the real economy.

The findings provide evidence that while RPI in the UK is significantly related to three leading macroeconomic variables, namely, gross domestic product (GDP), real effective exchange rate and interest rates measures, while RR-I is only impacted by changes in GDP. More importantly, the authors show that there is no short or long run dynamics between the rental rate and any form of interest rates.

This paper did not attempt to investigate the impact of the physical attributes of the rental property to formalize the model describing the relationship between RPI and RR-I. Also, other macroeconomic factors like household income growth, risk, house value growth rate and taxation could be included in future models.

As Rental Rate is not linked to the macroeconomic determinants, it is therefore more stable, resilient and sustainable and, at the same time, making the financing less risky for both parties, as they are less susceptible to economic vulnerabilities.

Some calculations incorporating the proposed RR-I can also be extended to the pricing of products based on other contracts such as Tawarruq, Bai Bithaman Ajil or even Murabahah for a fairer and just pricing to both the banks and customers.

The results suggest that Islamic banks should consider incorporating the proposed rental rate (RR-I) when pricing their home financing products, as this will lead to less dependence on interest rates for benchmarking. In addition, using the proposed rental rate (RR-I) reduces the exposure to the subjective evaluation by property valuators and speculative macroeconomic elements.

In a first of its kind, this paper tries to explore the long-run relationship between investors' sentiment and selected industries' returns over the period January 2010 to December 2021.

The paper uses 23 market and macroeconomic proxies to measure investor sentiment. Principal component analysis has been used to create sentiment sub-indices that represent investor sentiment. The autoregressive distributed lag (ARDL) model and other sophisticated econometric techniques such as the unit root test, the cumulative sum (CUSUM) stability test, regression, etc. have been used to achieve the objectives of the study.

The authors find that there is a significant relationship between sentiment sub-indices and industries' returns over the period of study. Market and economic variables, market ratios, advance-decline ratio, high-low index, price-to-book value ratio and liquidity in the economy are some of the significant sub-indices explaining industries' returns.

The study has relevant implications for retail investors, policy-makers and other decision-makers in the Indian stock market. Results are helpful for the investor in improving their decision-making and identifying those sentiment sub-indices and the variables therein that are relevant in explaining the return of a particular industry.

The study contributes to the existing literature by exploring the relationship between sentiment and industries' returns in the Indian stock market and by identifying relevant sentiment sub-indices. Also, the study supports the investors' irrationality, which arises due to a plethora of behavioral biases as enshrined in classical finance.

The purpose of this paper is to use a text-based sentiment indicator to explain variations in direct property market liquidity in the USA.

By means of an artificial neural network, market sentiment is extracted from 66,070 US real estate market news articles from the S&P Global Market Intelligence database. For training of the network, a distant supervision approach utilizing 17,822 labeled investment ideas from the crowd-sourced investment advisory platform Seeking Alpha is applied.

According to the results of autoregressive distributed lag models including contemporary and lagged sentiment as independent variables, the derived textual sentiment indicator is not only significantly linked to the depth and resilience dimensions of market liquidity (proxied by Amihud’s (2002) price impact measure), but also to the breadth dimension (proxied by transaction volume).

These results suggest an intertemporal effect of sentiment on liquidity for the direct property market. Market participants should account for this effect in terms of their investment decisions, and also when assessing and pricing liquidity risk.

This paper not only extends the literature on text-based sentiment indicators in real estate, but is also the first to apply artificial intelligence for sentiment extraction from news articles in a market liquidity setting.

This paper aims to examine the existence of the ripple effect from Amsterdam to the housing markets of other regions in The Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam.

The paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda–Yamamoto Granger Causality approach to study the lead-lag effects. It uses the autoregressive distributed lags (ARDL)-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks.

The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland.

The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation.

This paper focuses on The Netherlands for which the ripple effect has not yet been researched to the authors’ knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, the analysis controls for common house price fundamentals which most previous papers ignored.