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The purpose of this paper is to examine the structure and dynamics of Singapore's Central Area office market. A long-run equilibrium relationship is tested and a short-run adjustment error correction model are estimated, incorporating appropriate serial error correction. The long-run equation is estimated for office rent, with office employment and available stock.

With the vector error correction model (VECM), the lagged rent, available stock, office employment, vacancy and occupied stock (OS) can impact the rental adjustment process. Equilibrium rent on the whole reacts positively to lagged rents, available stock, office employment, OS and negatively to vacancy rates (VC). Past levels of positive change in VC and rental growth can have negative effects on current OS.

While good economic conditions signaled by increases in rents increase the supply of new stock (available space), higher rents and VC dampen the long-term occupied space (space absorption) in accordance with economic theory. Available stock can be forecasted by past rent and absorption levels owing to the developer's profit-driven nature.

An understanding of the interaction between the macroeconomic variables and the Central Area office market is useful to domestic and foreign investors and developers, who then can better evaluate their decision making in commercial real estate investment and development projects.

It is implicit that the Singapore Central Area office market requires at least a year before any rental increase can potentially dampen the space demanded. Firms are attracted to locate there owing to agglomeration economies and they are willing to pay premium office rents in conjunction with office space intensification in the Central Area. Newly built space is positively affected by past rents. Urban Redevelopment Authority and private real estate developers should be wary of excess office sector vacancies by avoiding over supply, even though an increase in the supply of office space in the Central Area can have a positive impact on office rent in the longer term. Most of the office space development would tend to meet the demand in the long run. Rental stickiness is exemplified as rental changes are affected by lagged rent.

Policy makers are better enabled to stabilize the office sectors of the real estate market if so required.

The paper adopts the VECM and validated by empirical evidence, to investigate the long-run equilibrium relationship and short-term corrections underlying the dynamics of the Singapore Central office market. Delay in the restoration of equilibrium in real estate markets is attributed to factors like lease terms and supply lags.

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.

The purpose of this paper is to investigate the direction of causality between the monthly stock returns and the monthly net foreign investor flows, and the existence of feedback trading by foreign investors for the “blue chip” stocks of the Istanbul Stock Exchange (ISE), an emerging stock market.

Monthly net foreign investor flows and stock returns of “blue chip” stocks for the period January 1997‐June 2010 are used. Particularly, taking into account the structural changes in the foreign investor trading activity, the study focuses on the two sub‐periods – before and after the start of European Union (EU) accession negotiations of Turkey. The study uses the bivariate VAR Granger causality approach, impulse response analysis and uses regressions with the vector autoregressive structure and contemporaneous structure (without lag).

Unidirectional Granger causality running from monthly stock returns to monthly net foreign investor flows is detected for the pre‐EU accession negotiations period accompanied by a negative feedback trading effect, but a causality relationship and feedback trading could not be established for the post‐EU accession negotiations period. The relationship is a contemporaneous one rather than a lagged relationship for the latter period.

The studies on the impact of foreign investor flows utilize daily and/or monthly data depending on availability. For the ISE, only monthly full data is available. Further research on the effects of foreign investors in the ISE such as the base broadening and price pressure effects should be undertaken.

The findings are particularly useful for financial managers, portfolio managers, traders, domestic and foreign investors dealing with the ISE.

The article contributes to the limited empirical evidence on the price effects of foreign investor flows in the stock exchanges located in Eastern Europe, Middle East and surrounding countries. The study also considers the significant increase in the amount of foreign investor flows after the start of EU accession negotiations of Turkey and detects a change in the price effect. Unlike the strong empirical evidence of positive feedback trading in the finance literature, negative feedback trading is detected in the ISE, but only for the period before the start of EU accession negotiations. Using monthly data, any feedback trading could not be detected for the period after.

Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance.

The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market.

The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data.

The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

This study investigates the capital structure determinants of the Middle East tourism sector by examining intellectual capital (IC) efficiency and institutional governance along with firm-specific and macroeconomic variables. This research also identifies the determinants of capital structure for tourism companies in the Gulf Cooperation Council (GCC) and non-GCC countries.

Data were collected from 45 listed tourism companies of nine Middle Eastern countries over five years from 2014 to 2018. The data were analysed using ordinary least squares (OLS) regression and checked for robustness using the generalised methods of moment (GMM) estimation.

Overall, the results indicate that tourism companies rely more on short-term debt (STD) than long-term debt (LTD), thus decreasing liquidity and increasing financial risk. Furthermore, tourism companies in non-GCC countries have higher IC efficiency compared to those in GCC countries. The aggregate institutional index is much higher for GCC countries compared to non-GCC countries. The OLS estimations suggest IC efficiency and institutional governance index provide inconclusive evidence as a determinant of capital structure proxy. High capital employed efficiency (CEE) is associated with more leverage for tourism firms. Theoretically, the results support pecking order and trade-off theories due to the relationships between firm-specific indicators and debt.

This study closes the gap in the capital structure debate by providing valuable insights into IC efficiency and institutional governance. These two factors serve as capital structure determinants in the Middle East and the GCC and non-GCC regions.

This study examines the relationship between Australia's stock market and the five largest international markets for the period 1991 through 2001. Preliminary findings, using correlation statistics, indicated potential benefits to international diversification for the Australian investor. Further analysis, conducted in the VAR framework using the Johansen cointegration method, found that the Australian market has short and long run linkages with the United States, while tests with other markets found little evidence of interdependence. Moreover, only the US market was found to Granger‐cause the Australian market.

The purpose of this paper is to analyse the effects of a declining birth rate and an increasing old age‐population ratio on Ireland's economic output.

This paper utilises data on the birth rate, old‐age population ratio, economic output and labour effort of the Irish economy to estimate a vector‐autoregressive model. The results of this model are then analysed to test for the presence of Granger causality among these variables. In doing so it is possible to assess whether there are statistically significant causal relationships existing among these factors. Subsequently, impulse response functions are derived from this model in order to assess the magnitude of the causal relationships.

The results suggest that declining fertility rates and increases in the old‐age dependency ratio have a significant impact on labour effort and economic output. Labour effort is also found to explain variation in the fertility rate and economic output. Economic output is found to effect labour effort and the fertility rate.

The results derived in this paper raise interesting policy implications. It is evident that Ireland's declining birth rate and increasing old‐age population ratio are creating a demographic situation which will have implications for future economic growth. Policies need to be put in place to mitigate the negative effects these factors will have on Irish growth.

This paper adopts modern econometric techniques to assess the causal relationships which exist between the demographic and economic factors considered. These have not previously been applied to the Irish situation. In doing this, this paper provides an important insight into the changing dynamics of the Irish economy.

Observes that the timely and sufficient provision of industrial space helps to facilitate industrial activities that contribute to economic growth. An understanding of the interactions between the economy and the real estate market is useful to local policy decision makers and developers in ensuring the ready availability of industrial space without incurring unnecessary excess vacancy. This article hopes to build upon existing research by applying cointegration analysis and error‐correction modelling to examine the supply dynamics of Singapore’s industrial property market. The presence of long‐run equilibrium relationships between industrial property supply, economic and real estate variables is tested using cointegration analysis. The persistence profile examines the effect of system‐wide shocks to the cointegration relations to determine whether they are true cointegrating vectors.