Forecasting currency exchange rates is an important issue in finance. This topic has received much attention, particularly in econometrics and financial selection of variables that influence forecasts. In this paper, a new forecasting model is constructed: we adopt a Genetic Algorithm (GA) to provide the optimal variables weight and we select the optimal set of variables as the input layer neurons, and then we predict the exchange rates with the Back Propagation Network (BPN), called the GABPN model. Basically, we expect improved variable selection to provide better forecasting performance than a random method. As a result, our experiments showed that the GABPN obtained the best forecasting performance and was highly consistent with the actual data. Within the selected 27 variables, only 10 variables play critical factors in influencing forecasting performance; moreover, the GABPN method with proper variables even outperformed the case with full variables. In addition, the proposed model provides valuable information in financial analysis by providing the correct variables that most influence exchange rate trends.
This paper discuss the associations and model construction between Taiwan and Korea's exchange rate markets during the period from January 2000 to July 2008. The empirical results show that the mutual effects of the Taiwan and the Korea's exchange rate markets may construct in bivariate IGARCH (1, 1) model. The empirical result also shows that there exists the positive relations between Taiwan and Korea's exchange rate markets -namely two exchange rate market return's volatility are synchronized influenced, and the average estimation value of the DCC coefficient of two exchange rate markets equals to 0.4073. The Japan's exchange rate return's volatility will also affect the variation risk of the Taiwan's exchange rate market, but the Japan's exchange rate return's volatility will not affect the variation risk of the Korea's exchange rate market. Furthermore, Taiwan and Korea's exchange rate markets do not have the asymmetrical effect in the research period. Keyword Exchange rate market returns, DCC, bivariate IGARCH model, Student's t distribution, asymmetrical effect.
Aging is accompanied by changes in organ degeneration, and susceptibility to multiple diseases, leading to the frequent occurrence of adverse drug reactions resulting from polypharmacy (PP) and potentially inappropriate medications (PIM) in older patients. This study employs a retrospective cohort design and investigates the association of PP with PIM among older patients with high rates of medical utilization. Using records from a national pharmaceutical care database, an experimental group is formed from patients meeting these criteria, who are then offered home pharmaceutical care. Correspondingly, a control group is formed by identifying older patients with regular levels of use of medical services who had been dispensed medications at community pharmacies. Multivariate logistic regression is performed to assess the association between the rate of PIM and variables, including age, gender, and PP. The study finds that experimental PP participants had a higher rate of PIM prescription (odds ratio (OR) = 5.4) than non-PP control participants (all p < 0.001). In clinical practice, additional caution is required to avoid PIMs. Patients engaged in continuously using long-term medication should take precautions in daily life to alleviate related discomforts. Pharmacists should serve as a bridge between patients and physicians to enhance their health and improve their quality of life.
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