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The purpose of the paper is to investigate the causal relationships and speed of adjustment of stock prices to changes in macroeconomic information (MEI) in Ghana from 1996 to 2018 using monthly data. The paper seeks to conduct the investigation at individual MEI level rather than the composite MEI.

Quantitative approach was used in this paper. Monthly data span of 1996–2018 was used. The delay and half-life technique was used to determine the speed with which the information resulting from the changes in the macroeconomic are evident in the stock price. Thereafter, Toda–Yamamoto Granger no-causality approach was used to examine the causal relationship amongst variables.

The paper revealed that although the market adjustment to MEI has improved, the speed is till slow. The exchange rate exhibited the slowest speed in respect of the market reaction while the market reaction to money supply was the fastest. Toda–Yamamoto Granger no-causality estimation also revealed a bi-directional causality between MEI (gross domestic product, interest rate and money supply) and stock price and uni-directional relationship flowing from MEI (the exchange rate and foreign direct investment) to stock price. The paper also found no causality between inflation and stock price.

The findings although revealed improved level of market efficiency in comparison with the earlier data, the speed of adjustment is still undesirable. Rigorous approach should be adopted for the implementation of major reforms such as alternative market so as to increase the number of share listing and to increase the scope of investors' participation to enhancing trading volume and marketability and ultimately speed up information diffusion.

The practical implication of the low level of information processing rate of Ghana Stock Exchange (averagely more than a month) is that astute investors and market analysts could employ MEI to outperform the market prior to their infusion onto the stock market.

This study is one of the few studies in the Ghanaian literature that has extended the investigation of the speed of adjustment beyond composite or aggregate macroeconomic level estimation to estimation at individual variable level. This contribution is very relevant since each macroeconomic variable has unique characteristics and require specific policy framework, it is important to consider the speed of adjustment from the perspective of each of the individual variables.

In estimating the inputs into the modern portfolio theory (MPT) portfolio optimisation problem, it is usual to use equal weighted historic data. Equal weighting of the data, however, does not take account of the current state of the market. Consequently this approach is unlikely to perform well in any subsequent period as the data is still reflecting market conditions that are no longer valid. The need for some return weighting scheme that gives greater weight to the most recent data would seem desirable. Therefore, this study uses returns data which are weighted to give greater weight to the most recent observations to see if such a weighting scheme can offer improved ex ante performance over that based on unweighted data.

Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared to time-based methods. CBM models greatly inform maintenance decisions. This research examines three CBM fault prognostics models: logical analysis of data (LAD), artificial neural networks (ANNs) and proportional hazard models (PHM). A methodology, which involves data pre-processing, formulating the models and analyzing model outputs, is developed to apply and compare these models. The methodology is applied on NASA’s Turbofan Engine Degradation data set and the structural health monitoring (SHM) data set from a Nova Scotia Bridge. Results are evaluated using three metrics: error, half-life error and a cost score. This paper concludes that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably, and its predictions show much larger variance than the predictions from the other three methods. Based on these conclusions, the purpose of this paper is to provide recommendations on the appropriate situations in which to apply these three prognostics models.

LAD, ANNs and PHM methods are adopted to perform prognostics and to calculate the mean residual life (MRL) of eqipment using NASA’s Turbofan Engine Degradation data set and the SHM data set from a Nova Scotia Bridge. Statistical testing was used to evaluate the statistical differences between the approaches based on these metrics. By considering the differences in these metrics between the models, it was possible to draw conclusions about how the models perform in specific cases.

Results were evaluated using three metrics: error, half-life error and a cost score. It was concluded that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably and its predictions show much larger variance than the predictions from the other three methods. Overall the models predict failure after it has already occurred (negative error) when the residual life is large and vice versa.

It was concluded that a good CBM prognostics model for practical implications can be determined based on three main considerations: accuracy, run time and data type. When accuracy is a main concern, as in the case where impacts of failure are large, LAD and feedforward neural network are preferred. The preference changes when run time is considered. If data can be easily collected and updating the model is performed often, the ANNs and LAD are preferred. On the other hand, if CM data are not easily obtainable and existing data are not representative of the population’s behavior, data type comes into play. In this case, PHM is preferred.

Previous research in the literature performed reviews of multiple independent studies on CBM techniques performed on different data sets. They concluded that it is typically harder to implement artificial intelligence models, because of difficulties in data procurement, but these approaches offer improved performance as compared to more traditional model-based and statistical approaches. In this research, the authors further investigate and compare the performance and results from two major artificial intelligence models, namely, ANNs and LAD, and one pioneer statistical model, PHM over the same two real life prognostics data sets. Such in-depth comparison and review of major CBM techniques was missing in current literature of CBM field.

Line's recently proposed technique for correcting the ‘apparent’ half‐life to allow for the rate of growth of the literature and Vickery's critical analysis of the proposal are both further analysed. Using the concept of utility and considering the sampling variances involved, the paper shows that Line's technique is both questionable and impractical, and that a further factor—the growth of the number of contributors—needs to be allowed for in Vickery's analysis. A collaborative empirical investigation is proposed.

Aging research has traditionally been an important research topic in the field of Library and Information Science(LIS). The study of aging enables us to grasp the extent of development and the status of aging in LIS. The purpose of this paper is to explore the current law of aging in LIS and to research the impact of interdisciplinary citations on the aging of the discipline.

By using citation analysis methods and fitting them using the Barnett aging model, the aging law of LIS is explored with the help of aging indicators such as citing half-life and Price Index. For interdisciplinary study, the authors explore the pattern of interdisciplinary citations distribution by distinguishing LIS and non-LIS citations by journal name.

The results show that LIS is currently aging slowly and has reached a relatively mature stage. It has a high reliance on archival literature. The interdisciplinary citations distribution is broadly consistent with the overall citation distribution, and interdisciplinary citations can increase the age of applicability of the literature.

Based on LIS journal citation data, the paper validates the rationality of Barnett model applied in the field of literature aging research using nonlinear regression analysis, which can effectively reflect the aging law of literature and enable scholars to predict its development trend more accurately. In addition, according to the current trend of interdisciplinary citation, this paper explores the impact of interdisciplinary citations on the aging of the literature and provides a new idea for future aging research.

The aim of this chapter is to explore whether price dynamics is homogenous across Emerging Europe. We employ dynamic panel estimation techniques (including the Pooled Mean Group estimator of Pesaran, Smith, and Shin) over the 2003–2013 period and use Germany, and respectively, the European Union (EU) as the references. Results highlight some heterogeneity across the Emerging Europe members in terms of price convergence speed. Findings are robust across different specifications.

Terminology and parameters for describing the relation between figures for the use of library literature and for its age are discussed. Unless a correction for growth is applied, half‐life fails a simple test of suitability, as do Brookes's ageing and utility factors. Results based on cumulated data have other important disadvantages. Relative ‘use‐per‐item’ figures are satisfactory. The need to relate terminology more closely to the facts they represent is stressed: item‐consultation decay rate, and 50% consultation probability age are suggested and defined. In the context of citation studies, item‐citation decay rate and 50% citation probability age are equivalent and avoid similar objections. In data from NRLSI, item‐consultation decay rates are only approximately exponential. Different rates are demonstrated for updating and basic searches, for academic and other types of readers, and for types of literature. For some historical searches negative decay rates were found in the NRLSI and BML.

The term ‘obsolescence’ occurs frequently in the literature of librarianship and information science. In numerous papers we are told how most published literature becomes obsolete within a measurable time, and that an item receives half the uses it will ever receive (‘half‐life’) in a few years. ‘Obsolescence’ is however very rarely defined, and its validity, interest, and practical value are often assumed rather than explained. Before reviewing studies on ‘obsolescence’, therefore, it is necessary to look at the concept and to identify the reasons why it should be of interest.

This paper investigates whether mean reversion holds for a panel of 16 OECD stock price indices for the period 1970 to 2011.

We employ seemingly unrelated regression (SUR)-based linear and non-linear unit root tests which are not only able to exploit the power of panel data analysis but also account for cross sectional dependencies as well as identify which panel members are stationary.

In contrast to a literature that offers mixed findings on stationarity, it was found that most of our sample is characterized as mean- or trend-reverting with approximated half-lives in the region of three to five years.

In contrast to other panel unit root tests of stock prices, the authors identify which individual panel members are stationary and non-stationary using a SURADF test. A further novelty of our approach is that we also develop a SUR-based panel KSS test that allows us to explore the possibility that stock prices exhibit non-linear stationarity.