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The purpose of this paper is to utilize the proposed function transformation to make the original data series meet the properties of smooth ratio being lessen and stepwise ratio deviation being reduced, so that to improve the accuracy of grey forecasting model.

According to the characteristics of anti-cotangent functional graph variation, the theory of functional transformation and grey system modeling, the authors proposed a grey model based on the transformation of Aarc cot x+B function.

The calculated result of practical example shows that the proposed method is both valid on improving fitting effectiveness and forecasting accuracy.

The proposed method in this paper can effectively improve the accuracy of forecasting of high-growth original data series (derivative of data series is not only greater than 1 but also increasing).

The paper succeeds in providing an effective function transformation to make the smooth ratio and stepwise ratio deviation reduced significantly.

Ratios were constructed using bidding data for highway construction projects in Texas to study whether there are useful patterns in project bids that are indicators of the project completion cost. The use of the ratios to improve predictions of completed project cost was studied.

Ratios were calculated relating the second lowest bid, mean bid, and maximum bid to the low bid for the highway construction projects. Regression and neural network models were developed to predict the completed cost of the highway projects using bidding data. Models including the bidding ratios, low bid, second lowest bid, mean bid and maximum bid were developed. Natural log transformations were applied to the data to improve model performance.

Analysis of the bidding ratios indicates some relationship between high values of the bidding ratios and final project costs that deviate significantly from the low bid amount. Addition of the ratios to neural network and regression models to predict the completed project cost were not found to enhance the predictions. The best performing regression model used only the low bid as input. The best performing neural network model used the low bid and second lowest bid as inputs.

The nature of bid ratios that can describe the pattern of bids submitted for a project and the relationship of the ratios to project outcomes were studied. The ratio values may be useful indicators of project outcome that can be used by construction managers.

The real estate sector in India has assumed growing importance with the liberalisation of the economy. Developments in the real estate sector are being influenced by the developments in the retail, hospitality and entertainment (e.g. hotels, resorts and cinema theatres) segment, economic services (e.g. hospitals, schools) and information technology-enabled services (such as call centres), and vice versa. This paper aims to study the determinants of capital structure by taking into account 125 major Bombay Stock Exchange (BSE) listed real estate companies selected on the basis of their market capitalisation.

To discover what determines capital structure, nine firm level explanatory variables (profitability-EBIT margin, return on assets, earnings volatility, non-debt tax shield, tangibility, size, growth, age debt service ratio and tax shield) were selected and regressed against the appropriate capital structure measures, namely, total debt to total assets, long-term debts to total assets, short-term debts to total assets, total liabilities to total liabilities plus equity, total debt to capital used and total debt to total liabilities plus equity. A sample of 125 real estate companies was taken and secondary data were collected. Consequently, multivariate regression analysis was made based on financial statement data of the selected companies over the study period of 2009-2015.

The major findings of the study indicated that profitability, size, age, debt service capacity growth and tax shield variables are the significant firm-level determinants.

The present study is carried out by taking data of only 25 companies listed on the BSE and time period covered from 2009 from 2015. Time period and sample size may be limitations of the current study.

The present study is an empirical analysis of the determinants of leverage of real estate sector in India with most recent available data. Different regression equations have been formed to develop the models using firm-specific determinants and different measures of leverage or capital structure. Data were regressed using SPSS application software, and the resulting (or obtained) regression outputs are analysed. This study will help the Indian real estate companies to the know the impact of different variables while raising short-term and long-term loans.

The current study will benefit all stakeholders of society who are fascinated to be acquainted with the financing of real estate companies and the factors affecting long-term and short-term financing of this sector. Specifically, public engrossed in different modes of investment and financial institution will be the prime gainers.

The present study has been completed using authentic data from the annual reports and database. This study uses explanatory variables and different measures of leverage which were limited in use in previous studies. Moreover, this research is a comprehensive study that deals with developing different regression models by using diverse measures of leverage.

Auditors are required to perform analytical procedures during the planning and concluding phases of the audit. Such procedures typically use data aggregated at a high level. The authors investigate whether artificial neural networks, a more sophisticated technique for analytical review than typically used by auditors, may be effective when using high level data.

Data from companies operating in the dairy industry were used to train an artificial neural network. Data with and without material seeded errors were used to test alternative techniques.

Results suggest that the artificial neural network approach was not significantly more effective (taking into account both Type I and II errors) than traditional ratio and regression analysis, and none of the three approaches provided more overall effectiveness than a purely random procedure. However, the artificial neural network approach did yield considerably fewer Type II errors than the other methods, which suggests artificial neural networks could be a candidate to improve the performance of analytical procedures in circumstances where Type II error rates are the primary concern of the auditor.

The authors extend the work of Coakley and Brown (1983) by investigating the application of artificial neural networks as an analytical procedure using aggregated data. Furthermore, the authors examine multiple companies from one industry and supplement financial information with both exogenous industry and macro-economic data.

Variations in price/earnings (P/E) ratios are explained in a rational expectations framework by a number of fundamental factors, such as differences in growth expectations and risk. The purpose of this paper is to use a regression model and data from four sample periods (1996, 2000, 2001, and 2008) to separate the earnings/price (E/P) ratio into two parts – the portion of E/P that is related to fundamental determinants and a residual portion that cannot be explained by fundamentals. The authors use the residual portion as an indicator of over or undervaluation; a large negative residual is consistent with overvaluation while a large positive residual implies undervaluation. The authors find that stocks with larger negative residuals are associated with lower subsequent returns and reward-to-risk ratio, while stocks with larger positive residuals are associated with higher subsequent returns and reward-to-risk ratio. This pattern persists for both one and two-year holding periods.

This study uses a regression methodology to decompose E/P into two parts – the portion of E/P than is related to fundamental determinants and a residual portion that cannot be explained by fundamentals. Focussing on the second portion allows us to isolate a potential indicator of stock over or undervaluation. Using a sample of stocks from four time periods (1996, 2000, 2001, and 2008, the authors calculate the residuals from a regression model of the fundamental determinants of cross-sectional variation in E/P. These residuals are then ranked and used to divide the stock sample into deciles, with the first decile containing the stocks with the highest negative residuals (indicating overvaluation) and the tenth decile containing stocks with the highest positive residuals (indicating undervaluation). Total returns for subsequent one and two-year holding periods are then calculated for each decile portfolio.

The authors find that high positive residual stocks substantially outperform high negative residual stocks. This is true even after risk adjustments to the portfolio returns. The residual E/P appears to accurately predict relative stock performance with a relatively high degree of accuracy.

The findings of this paper provide some important implications for practitioners and investors, particularly for the stock selection, fund allocations, and portfolio strategies. Practitioners can still rely on a valuation measure such as E/P as a useful tool for making successful investment decisions and enhance portfolio performance. Investors can earn abnormal returns by allocating more weights on stocks with high E/P multiples. Portfolios of high E/P multiples or undervalued stocks are found to enjoy higher risk-adjusted returns after controlling for the fundamental factors. The most beneficial performance holding period return will be for a relatively short period of time ranging from one to two years. Relying on the E/P valuation ratios for a long-term investment may add little value.

Practitioners and academics have long relied on the P/E ratio as an indicator of relative overvaluation. An increase in the absolute value of P/E, however, does not always indicate overvaluation. Instead, a high P/E ratio can simply reflect changes in the fundamental factors that affect P/E. The authors find that stocks with larger negative residuals are associated with lower subsequent returns and coefficients of variation, while stocks with larger positive residuals are associated with higher subsequent returns and coefficients of variation. This pattern persists for both one and two-year holding periods.

The P/E ratio is widely used, particularly by practitioners, as a measure of relative stock valuation. The ratio has been used in both cross-sectional and time series comparisons as a metric for determining whether stocks are under or overvalued. An increase in the absolute value of P/E, however, does not always indicate overvaluation. Instead, a high P/E ratio can simply reflect changes in the fundamental factors that affect P/E. If interest rates are relatively low, for example, the time series P/E should be correspondingly higher. Similarly, if the risk of a stock is low, that stock’s P/E ratio should be higher than the P/E ratios of less risky stocks. The authors examine the cross-sectional behavior of the P/E (the authors actually use the E/P ratio for reasons explained below) after controlling for factors that are likely to fundamentally affect this ratio. These factors include the dividend payout ratio, risk measures, growth measures, and factors such as size and book to market that have been identified by Fama and French (1993) and others as important in explaining the cross-sectional variation in common stock returns. To control for changes in these primary determinants of E/P, the authors use a simple regression model. The residuals from this model represent the unexplained cross-sectional variation in E/P. The authors argue that this unexplained variation is a more reliable indicator than the raw E/P ratio of the relative under or overvaluation of stocks.

The purpose of this paper is to predict the population of Anhui province. The authors analyze the trend of the main demographic indicators.

On the basis of the main methods of statistics, this paper studies the tendency of the population of Anhui province. It mainly analyzes the sex structure and the age structure of the current population. Based on the GM(1,1) model, this paper forecasts the total population, the population sex structure, and the population age structure of Anhui province in the next ten years.

The results show that the total population was controlled well, but there have been many problems of the population structure, such as the aging population, high sex ratio, heavy social dependency burden, and the declining labor force.

This paper forecasts the main indexes of the population of Anhui province and provides policy recommendations for the government and the relevant departments.

This paper utilizes data analysis method and the grey forecasting model to study the tendency of the population problems in Anhui province.

The purpose of this paper is to explore capital gains, income, and total returns in various property markets in Europe. In a comparative study the nature of returns for different commercial and residential properties is investigated. Hereby, total returns, income returns, and capital growth are distinguished. The paper further presents an analysis of the risk‐return relationship of the different markets and investigates the interactions between property markets, other local financial markets, and macroeconomic variables.

Focusing on the risk‐return relationship of the different asset classes and countries, the Sharpe ratio is used as a risk‐adjusted performance measure to investigate the European markets. Using a simple linear regression model, a comparison of the European commercial property markets with respect to their returns and risk are provided. Finally, a capital asset pricing model (CAPM) and factor models based on arbitrage pricing theory (APT) are used in an effort to further explain the spreads and risk premiums for individual property markets.

The large differences between the markets regarding spreads, risk premiums, and risk‐return relationships are found. Overall, the Dutch market can be regarded as giving the highest compensation for the risk taken by the investors in the last decade, while the German market performed the worst and was the only market with negative capital growth rates for the considered period. Applying the CAPM, It has also been found total returns in commercial property markets are not significantly related to the performance of stock market indices. On the other hand, factor models using macroeconomic variables are able to explain a higher fraction of property total return spreads over the risk‐free rate in the considered countries. But depending on the country, different macroeconomic variables were estimated to be significant such that there is no single factor model available that could be applied to all European markets. Overall, these findings indicate that classic financial models drawing on existing datasets are unable to satisfactorily explain the performance of property as an asset class. On the other hand, the fact that property office markets yield relatively high returns that exhibit rather low correlations with stock market returns, makes them a very suitable candidate for portfolio diversification.

The paper investigates the risk‐return relationship in various European property markets. The large differences between the markets observed also partly explain the diversity of literature results on this relationship across single countries by, e.g. Goetzmann, Englund, or Bourassa et al. By using classic financial models like the CAPM or APT a contribution to the literature is made by explaining the factors that actually determine property returns over the risk free rate in different countries.

The purpose of this paper is to develop a criterion for selection of critical sub‐processes when all the sub‐processes cannot be taken up simultaneously for improvement. There exist various methods but the practitioners get utterly confused because of the existence of these multiple options. In this paper, the goal is to assist practitioners in the selection of the critical sub‐processes.

The authors discuss various statistical methods such as correlation and regression, simulation, basic statistics such as average, standard deviation, coefficient of variation % (C.V.%), etc. for the selection and identification of the critical sub‐processes. The strengths and weaknesses of these methods have been compared through empirical analysis based on real‐life case examples.

The stepwise regression and simulation have been found to yield identical results. However, from the perspective of application, stepwise regression has been found to be a preferred option.

The roadmap thus evolved for the selection of the critical sub‐processes will be of great value to the practitioner, as it will help them understand the ground reality in an unambiguous manner, resulting in a superior strategy for process improvement.

To identify predictors of corporate financial distress, using the discriminant and logit models, in an emerging market over a period of economic turbulence and to reveal the comparative predictive and classification accuracies of the models in this different environmental setting.

The research relies on a sample of 27 failed and 27 non‐failed manufacturing firms listed in the Istanbul Stock Exchange over the 1996‐2003 period, which includes a period of high economic growth (1996‐1999) followed by an economic crisis period (2000‐2002). The two well‐known methods, discriminant analysis and logit, are compared on the basis of a better overall fit and a higher percentage of correct classification under changing economic conditions. Furthermore, this research attempts to reveal the changes, if any, in the bankruptcy predictors, from those found in the earlier studies that rested on the data from the developed markets.

The logistic regression model is found to have higher classification power and predictive accuracy, over the four years prior to bankruptcy, than the discriminant model. In this research, the discriminant and logit models identify the same number of significant predictors out of the total variables analyzed, and six of these are common in both. EBITDA/total assets is the most important predictor of financial distress in both models. The logit model identifies operating profit margin and the proportion of trade credit within total claims ratios as the second and third most important predictors, respectively.

This paper reveals the accuracy with which the discriminant and logit models work in an emerging market over a period when firms face high uncertainty and turbulence. This study may be extended to other emerging markets to eliminate the limitation of the small sample size in this study and to further validate the use of these models in the developing countries. This can serve to make the methods important decision tools for managers and investors in these volatile markets.