Ease Of Movement Research Articles

SELF SPONSORED TRAINING AND EMPLOYEES’ INTENTION TO LEAVE THE ORGANIZATION: THE MODERATING ROLE OF PERCEIVED ORGANIZATIONAL SUPPORT – CASE STUDY AT SMALL AND MEDIUM ENTERPRISES (SMEs) IN DONG THAP PROVINCE, VIET NAM

In the context of a highly competitive human resource market, investing in improving human capital is increasingly of interest to many people, because human capital is one of the most important capital sources of an organization, especially Individuals paying their own training costs is the current trend. Based on human capital theory (HCT), organizational equilibrium theory (TOE), and organizational support theory (POS), this study aims to develop a comprehensive model of self-sponsored training (SST), human capital (HC), Desirable of Movement (DM), Ease of Movement (EM), Intention to leave the organization (IL) and Perceived organizational support (POS). Using a survey questionnaire, this study collected data from 509 participants. The study hypotheses were evaluated using piecewise linear squared structural equation modeling (PLS-SEM). Research results show that SST has a positive impact on HC; HC positively affects DM and EM; DM, EM promote IL. POS plays an important moderating role in the relationship between DM and IL. From there, the study proposes some management implications to limit the intention to leave the organization of employees at small and medium-sized enterprises (SMEs) in Dong Thap province, Vietnam.

Intelligent technical analysis based equivolume charting for stock trading using neural networks

It has been long recognized that trading volume provides valuable information for understanding stock price movement. As such, equivolume charting was developed to consider how stocks appear to move in a volume frame of reference as opposed to a time frame of reference. Two technical indicators, namely the volume adjusted moving average (VAMA) and the ease of movement (EMV) indicator, are developed from equivolume charting. This paper explores the profitability of stock trading by using a neural network model developed to assist the trading decisions of the VAMA and EMV. The generalized regression neural network (GRNN) is chosen and utilized on past S&P 500 index data. For the VAMA, the GRNN is used to predict the future stock prices, as well as the future width size of the equivolume boxes typically utilized on an equivolume chart, for calculating the future value of the VAMA. For the EMV, the GRNN is also used to predict the future value of the EMV. The idea is to further exploit the equivolume potential by using a forecasting system to predict the future equivolume measurements, allowing investors to enter or exit trades earlier. The results show that the stock trading using the neural network with the VAMA and EMV outperforms the results of stock trading generated from the VAMA and EMV without neural network assistance, the simple moving averages (MA) in isolation, and the buy-and-hold trading strategy.