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In this study, the relative accuracy of four well known methods for forecasting are compared The methods are applied to the time series of earnings per share for a random sample of United States corporations over a lengthy period of time. All the time series exhibit both period‐to‐period movements and seasonal fluctuation. The four models are, (1) Holt‐Winters multiplicative exponential smoothing model, (2) univariate Box‐Jenkins model, (3) linear autoregression of data seasonally adjusted by the Census II–XII method, and (4) linear autoregression of the data seasonally adjusted by the X11‐ARIMA method. The study of financial data of this type is important because (1) these data exhibit time series properties of trend, seasonality, and cycle, (2) earnings per share forecasts are important for purposes of financial planning and investment; and (3) previous studies of this nature were not as exhaustive in terms of the statistical analysis of the results

Studies of capital market efficiency are important because they infer that there are predictable properties of the time series of prices of traded securities on organised markets. We examine the weak form of the efficient markets hypothesis to indicate its usefulness in terms of the results of this study. Furthermore, this study of individual securities prices of traded securities on organised markets corroborate previous findings of studies of stock market indexes both in the United States and for foreign stock exchanges that daily patterns are present in the times series of securities prices. You will note also, that the models identified reflect the closing prices on one day less the closing price on the previous day. In this way, we study returns and not average or closing prices.

Earnings forecasts provide useful numerical information concerning the expectations of a firm's future prospects and indicate management's ability to anticipate a firms changing internal structure and external environment. The accuracy of these earnings forecasts that has been given so much attention is due to the S.E.C.'s position on financial forecasts and the issuance of the Statement of Position by the AICPA. These statements are important since they, in part, have motivated researchers to the importance of forecasting financial information. Consequently, if the disclosure of earnings forecasts in financial reports is permissable, the improvement of financial forecasts should be one of the primary concerns of the AICPA, the SEC, and numerous other interested groups.

Earnings forecasts provide useful numerical information concerning the expectations of a firm's future prospects and indicate management's ability to anticipate a firm's changing internal structure and external environment. The reasons for studying the accuracy of earnings forecasts is due to the Securities and Exchange Commission's position on financial forecasts and the issuance of a Statement of Position by the AICPA. These statements are important since they, in part, have motivated researchers to the importance of forecasting financial information. Consequently, if the disclosure of earnings forecasts in financial reports is permissable, the improvement of financial forecasts should be one of the primary concerns of the AICPA, the SEC, and numerous other interested groups.

The purpose of this paper is to indicate the existence of certain time series characteristics in daily stock returns of four small Asian (Pacific basin) financial markets. It aims to study efficient capital markets (efficient markets hypothesis (EMH)) as results may infer that there are predictable properties of the time series of prices of traded securities on organized markets in Singapore, Malaysia, Korea and Indonesia.

The paper analyses daily variations in financial market data obtained from the Sandra Ann Morsilli Pacific‐basin Capital Markets Research Center (PACAP).

The weak form efficiency test example examines the wide range of trading rules available to common investors. Some theorists try to convince everyone that the weak form of EMH is acceptable due to the weight of academic opinion. The paper finds that for short‐term (daily) changes, the markets of four of the smaller Pacific‐basin stock markets have predictable properties, which leads to the conclusion that the weak‐form EMH does not hold for these markets.

The study is limited to those firms and exchanges studied and the time period covered.

There have been all too few studies of these small financial markets up to now and there is no other study utilizing these data on the Pacific basin (Asia). The results are unique and original.

This paper seeks to study capital market efficiency, because results may infer that there are predictable properties of the time series of prices of traded securities on organized markets in Hong Kong, the third largest exchange in the Pacific‐Basin of Asia.

The weak form of the efficient markets hypothesis is examined to indicate its usefulness in terms of the results of this study. Do the data indicate that the times series of closing prices is a random walk or are their predictable properties?

It will be noted from the results that the model identifies predictive short‐term properties that exist in the data of returns of Hong Kong Exchanges for the period studied.

Conclusions are limited to those firms studied and the time period covered.

For the securities exchanges in Hong Kong, evidence indicates that the weak form of the efficient markets hypothesis does not characterize the trading market.

In this study the martingale hypothesis concerning the stock index futures market is analyzed. The purpose is to understand how this notion concerning the behavior of the index futures affects the forecasting process. In addition, the forecasting of both daily and weekly stock index futures is examined. For daily forecasting, we find that the martingale method outperforms stepwise autoregressive and exponential smoothing methods However, for weekly forecasts, the stepwise autoregressive method is best.

The purpose of this paper is to suggest better methods for monitoring the diagnostic and treatment services for providers of public health and the management of public health services. In particular, the authors examine the construction and use of industrial quality control methods as applied to the public providers, in both the prevention and cure for infectious diseases and the quality of public health care providers in such applications including water quality standards, sewage many others. The authors suggest implementing modern multivariate applications of quality control techniques and/or better methods for univariate quality control common in industrial applications in the public health sector to both control and continuously improve public health services. These methods entitled total quality management (TQM) form the foundation to improve these public services.

The study is designed to indicate the great need for TQM analysis to utilize methods of statistical quality control. All this is done to improve public health services through implementation of quality control and improvement methods as part of the TQM program. Examples of its use indicate that multivariate methods may be the best but other methods are suggested as well.

Multivariate methods provide the best solutions when quality and reliability tests show indications that the variables observed are inter-correlated and correlated over time. Simpler methods are available when the above factors are not present.

Multivariate methods will provide for better interpretation of results, better decisions and smaller risks of both Type I and Type II errors. Smaller risks lead to better decision making and may reduce costs.

Analysts will improve such things as the control of water quality and all aspects of public health when data are collected through experimentation and/or periodic quality management techniques.

Public health will be better monitored and the quality of life will improve for all especially in places where public development is undertaking rapid changes.

The manuscript is original because it uses well known and scientific methods of analyzing data in area where data collection is utilized to improve public health.

This chapter introduces the Sustainable Development Goals (SDGs) and considers how criminological research, policy and practice can advance this global agenda. It critically accounts for the complex geopolitical, institutional and ideological landscapes that gave rise to this agenda and the challenges this poses for implementing the SDGs today. The chapter also raises important questions about the viability and consequentiality of global efforts to govern the nexus between crime, justice and sustainable development on account of the gravest threat to humanity, climate change. We conclude that all of these issues highlight the need for scholars and practitioners with expertise on crime and justice to approach this agenda from a critical standpoint. At the same time, we acknowledge that the SDGs remain the best global framework that we have for promoting safer and more equitable societies.

State and local public pension plans cover a significant number of workers and represent a major component of the nation's retirement system. This study examined the size‐administrative cost relationship of public pension plans to ascertain whether cost savings can be realized by increasing pension plan size. The results indicated that while the consolidation of smaller plans will generate administrative cost savings, the consolidation of larger plans will generate savings only up to an optimal membership size at which point cost savings will end. In addition, optimal size was found to differ for active and beneficiary members indicating that membership composition needs to be considered when assessing the potential for cost savings.