Return on Investment: Explanation and a Case Study.

Nurses are uniquely positioned to significantly impact organizational and system improvement through improving quality and reducing costs. Using an evidenced based tool to identify costs and the financial benefit involved in any quality improvement project is invaluable in developing and evaluating proposals and allocation of resources to support the organization's financial health and viability. The return on investment analysis is an essential accounting tool that will provide nurse leaders with critical information quantifying costs and benefits of both financial and nonfinancial metrics to identify the feasibility, efficacy, risk or efficiency of a proposed project.

Heteroscedasticity effects as component to future stock market predictions using RNN-based models.

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

Northern Nevada Public Health: Utilizing the Public Health Workforce Calculator and Workforce Capacity Self-assessment Tools to Develop a Framework for Workforce Investment.

CONTEXT: Health departments nationally are critically understaffed and lack infrastructure support. By examining current staffing and allocations through a Foundational Public Health Services (FPHS) lens at the Northern Nevada Public Health (NNPH), there is an opportunity to make a strong case for greater investment if current dedicated full-time equivalents are inadequate and to guide which investments in public health workforce are prioritized. OBJECTIVE: To assess the use of the Public Health Workforce Calculator (calculator) and other tools to identify and prioritize FPHS workforce needs in a field application. DESIGN: Field application of the calculator in conjunction with the use of FPHS workforce capacity self-assessment tools. SETTING: NNPH. PARTICIPANTS: NNPH and Public Health Foundation (PHF). INTERVENTION: From June 2022 through April 2023, PHF collaborated with NNPH, serving Washoe County, to provide expertise and assistance as NNPH undertook an assessment of its workforce needs based upon the FPHS model. MAIN OUTCOME MEASURES: Comparison of the calculator output with FPHS workforce capacity self-assessment tools. RESULTS: The calculator and the FPHS capacity self-assessment process yielded complementary FPHS workforce capacity gap data. The use of a structured and transparent process, coupled with additional tools that included prioritizing needs, provided a viable and sustainable process for public health workforce investment planning. NNPH successfully utilized the results to bolster a supplemental funding request and a state public health appropriation. CONCLUSIONS: The use of the calculator and an FPHS workforce capacity self-assessment in a facilitated and structured process such as that used by NNPH to identify staffing priorities may hold promise as an approach that could be used to support decision-making and justification for infrastructure resources when funding for public health increases in the future.

The Shortfalls of Mental Health Compartment Models: A Call to Improve Mental Health Investment Cases in Developing Countries.

OBJECTIVES: There are irregularities in investment cases generated by the Mental Health Compartment Model. We discuss these irregularities and highlight the costing techniques that may be introduced to improve mental health investment cases. METHODS: This analysis uses data from the World Bank, the World Health Organization Mental Health Compartment Model, the United Nations Development Program, the Kenya Ministry of Health, and Statistics from the Kenyan National Commission of Human Rights. RESULTS: We demonstrate that the Mental Health Compartment Model produces irrelevant outcomes that are not helpful for clinical settings. The model inflated the productivity gains generated from mental health investment. In some cases, the model underestimated the economic costs of mental health. Such limitation renders the investment cases poor in providing valuable intervention points from the perspectives of both the users and the providers. CONCLUSIONS: There is a need for further calibration and validation of the investment case outcomes. The current estimated results cannot be used to guide service provision, research, and mental health programming comprehensively.

Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks.

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China's stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors' leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China's A-share market.

The impact of COVID-19 on cryptocurrency markets: A network analysis based on mutual information.

The purpose of our study is to figure out the transitions of the cryptocurrency market due to the outbreak of COVID-19 through network analysis, and we studied the complexity of the market from different perspectives. To construct a cryptocurrency network, we first apply a mutual information method to the daily log return values of 102 digital currencies from January 1, 2019, to December 31, 2020, and also apply a correlation coefficient method for comparison. Based on these two methods, we construct networks by applying the minimum spanning tree and the planar maximally filtered graph. Furthermore, we study the statistical and topological properties of these networks. Numerical results demonstrate that the degree distribution follows the power-law and the graphs after the COVID-19 outbreak have noticeable differences in network measurements compared to before. Moreover, the results of graphs constructed by each method are different in topological and statistical properties and the network's behavior. In particular, during the post-COVID-19 period, it can be seen that Ethereum and Qtum are the most influential cryptocurrencies in both methods. Our results provide insight and expectations for investors in terms of sharing information about cryptocurrencies amid the uncertainty posed by the COVID-19 pandemic.

Linkages between share pledging, stock price risk and profitability: Evidence from the P.R. China.

Share pledging has become popular as a method of loan collateral among Chinese shareholders. Our research used a sample of Chinese listed firms between 2008-2018 and produced two main findings. Firstly, we found a negative association between stock price risk and firm profitability. Our second finding was that the interaction effect of share pledging and stock price risk is greater on firm profitability than the effect of stock price risk itself. We examined the role of share pledging by modeling pooled OLS and fixed effects using share pledging behavior, controlling shareholders' share pledging and the share pledging ratio to reinforce the robustness of our results. Furthermore, we investigated the Davis Double Play effect of share pledging to analyze how share pledging affects stock price risk. We found that higher EPS and investor expectations cannot mitigate the positive impact of share pledging on stock price risk. That is, the reduction of EPS and the deterioration of investor expectations caused by share pledging risk will not further aggravate the stock price risk, as shareholders may have taken some managerial actions to affect the transmission mechanism.