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This work presents an extensive case study on modelling the DAX (Deutscher Aktienindex) index and United States Oil Fund (USO) exchange‐traded fund (Etf) time series with the financial agent‐based system learning financial agent‐based simulator (L‐FABS) that exploits simulated annealing as a learning method. The USO Etf time series is highly correlated with oil price behaviour, and the DAX index is based on the weighted and accumulated behaviour of the share prices of some of the largest companies traded on the Frankfurt Stock Exchange. These two time series are driven by completely different economic factors and thus provide two diverse empirical settings to evaluate the effectiveness of our methodology. Our experimentation shows that a relatively simple computational representation of real financial markets is effective in capturing the overall behaviour of the time series with varying approximation levels while the prediction target is moved into the future. The reported experimental investigation of L‐FABS shows that it is robust notwithstanding the learning method used and the data sets exploited. L‐FABS indeed produced a relatively low approximation error in several settings even when evaluated with respect to other modelling approaches, for example, 0.88% and 1.61% errors on average for 1 day ahead experiments in, respectively, DAX index and USO Etf. [ABSTRACT FROM AUTHOR]

The Pittsburgh Sleep Quality Index (PSQI) is a widely used measurement tool for assessing sleep disturbance and is broadly used in clinical and healthy populations. Yet, validation of the PSQI has rarely been carried out for a relatively large non-clinical sample in the United States. The aim for the current study is to examine the structure validity of the PSQI. Data from 2189 individuals (mean age 35.88) were analyzed by exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA). Furthermore, measurement invariance across age groups was examined using multigroup CFA. We found that a three-factor model (i.e., sleep efficiency, sleep latency, and sleep quality) fitted better than the commonly used single-factor structure. Specifically, for the three-factor model, the comparative fit index was 0.99, the Tucker–Lewis fit index was 0.99, and the root-mean-square error of approximation was 0.04. Those fit indices further indicated a good fit between the model and the observed data. Measurement invariance results suggested that the factor structure of PSQI is not comparable for different age groups, which has implications for future research studies. Our findings validated the factor structure of the PSQI on non-clinical populations and recommended the use of three separate factors to assess sleep quality. In addition, the findings demonstrated that different models should be used to assess sleep disturbance across age groups. However, further research is required to determine the appropriate cut-off points for different age groups. [ABSTRACT FROM AUTHOR]