PREDICTING NEXT TRADING DAY CLOSING PRICE OF QATAR EXCHANGE INDEX USING TECHNICAL INDICATORS AND ARTIFICIAL NEURAL NETWORKS

Accurate prediction of stock market price is of great importance to many stakeholders. Artificial neural networks (ANNs) have shown robust capability in predicting stock price return, future stock price and the direction of stock market movement. The major aim of this study is to predict the next trading day closing price of the Qatar Exchange (QE) Index using historical data from 3 January 2010 to 31 December 2012. A multilayer perceptron ANN architecture was used as a prediction model with 10 market technical indicators as input variables. The experimental results indicate that ANNs are an effective modelling technique for predicting the QE Index with high accuracy, outperforming the well‐established autoregressive integrated moving average models. To the best of our knowledge, this is the first attempt to use ANNs to predict the QE Index, and its performance results are comparable to, and sometimes better than, many stock market predictions reported in the literature. The ANN model also revealed that the weighted and simple moving averages are the most important technical indicators in predicting the QE Index, and the accumulation/distribution oscillator is the least important such indicator. The analysis results also indicated that the ANNs are resilient to stock market volatility. Copyright © 2014 John Wiley & Sons, Ltd.     

Social externalities of women empowerment: Evidence from suffrage movements of late nineteenth and early twentieth century United States

Previous literature suggests that empowering women is associated with children's improved outcomes. However, little is known about its effects on children's later-life crime and incarceration. We argue that women empowerment through suffrage law changes during the late nineteenth and early twentieth centuries in the US generated incentives for women to invest in their children's human capital. The accumulated human capital then has the potential to reduce future incarceration of children. We use full-count censuses 1920–1930, implement a difference-in-difference framework, and empirically show that childhood exposure to suffrage laws is associated with considerable reductions in incarceration. The effects appear to be primarily driven by decreases in male and Black incarceration. The balancing tests rule out the concern that the effects are driven by demographic compositional changes or endogenous changes in other state-level characteristics. Furthermore, an event-study analysis rejects the concern that the effects are driven by preexisting trends in incarceration among exposed cohorts. The findings of this research note offer informative implications for overlooked externalities of women empowerment in a historical setting.     

Inverse data envelopment analysis without convexity: double frontiers

Decisions in Economics and Finance - In this research, inverse data envelopment analysis (IDEA) approaches are proposed to measure inputs changes for output perturbations made while the convexity...