Instance Selection using Genetic Algorithms for an Intelligent Ensemble Trading System

Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose a new approach to instance selection that uses genetic algorithms (GAs) to define a set of target labels that can identify the buying and selling signals and then select instances according to three performance measures of the trading system (i.e., the winning ratio, the payoff ratio, and the profit factor). An intelligent ensemble trading system with instance selection using GAs is then developed for investors in the stock market. An empirical study of the proposed model is conducted using 35 companies from the Dow Jones Industrial Average, the New York Stock Exchange, and the Nasdaq Stock Market from January, 2006 to December, 2016

Using Neural Networks to Forecast Volatility for an Asset Allocation Strategy Based on the Target Volatility

The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing a much more abundant source of data for comparison. In order to explore the impact of the proposed model, the results of this approach will be compared to different volatility forecast methodologies, such as the volatility index, the historical volatility, the exponentially weighted moving average (EWMA), and the generalized autoregressive conditional heteroskedasticity (GARCH) model. Trading measures are used to evaluate the performance of the models for forecasting volatility. An empirical study of the proposed model is conducted using the Korea Composite Stock Price Index 200 (KOSPI 200) and certificate of deposit interest rates from January, 2006 to February, 2016

A Case Study for Technical and Vocational Education and Training Professional Development Training in Untact Era: Focusing on Official Development Assistance Project in Botswana

The prolonged COVID-19 pandemic has disrupted the TVET professional development training, which was planned to be carried out in a face-to-face manner. This study aims to examine TVET professional development training based on the ADDIE model and provide implications for ODA and TVET professional development training in the post-COVID era. In this case study, a TVET training program for Botswana was developed based on the ADDIE model in an untact environment. Accordingly, the performance activities for each phase of analysis, design, development, implementation, and evaluation were analyzed, and based on this, implications for ODA and TVET were provided. This study identified the applicability of non-face-to-face education in the ODA project. Also, this study explored how to develop systematic and dimensional programs based on the ADDIE model in a non-face-to-face manner. In addition, sustainability could be secured through cooperative partnerships between donor and beneficiary countries from the perspective of SDGs Goal

Arbitrary-Scale Downscaling of Tidal Current Data Using Implicit Continuous Representation

Numerical models have long been used to understand geoscientific phenomena, including tidal currents, crucial for renewable energy production and coastal engineering. However, their computational cost hinders generating data of varying resolutions. As an alternative, deep learning-based downscaling methods have gained traction due to their faster inference speeds. But most of them are limited to only inference fixed scale and overlook important characteristics of target geoscientific data. In this paper, we propose a novel downscaling framework for tidal current data, addressing its unique characteristics, which are dissimilar to images: heterogeneity and local dependency. Moreover, our framework can generate any arbitrary-scale output utilizing a continuous representation model. Our proposed framework demonstrates significantly improved flow velocity predictions by 93.21% (MSE) and 63.85% (MAE) compared to the Baseline model while achieving a remarkable 33.2% reduction in FLOPs

Optimal Schedules in Multitask Motor Learning

Although scheduling multiple tasks in motor learning to maximize long-term retention of performance is of great practical importance in sports training and motor rehabilitation after brain injury, it is unclear how to do so. We propose here a novel theoretical approach that uses optimal control theory and computational models of motor adaptation to determine schedules that maximize long-term retention predictively. Using Pontryagin’s maximum principle, we derived a control law that determines the trial-by-trial task choice that maximizes overall delayed retention for all tasks, as predicted by the state-space model. Simulations of a single session of adaptation with two tasks show that when task interference is high, there exists a threshold in relative task difficulty below which the alternating schedule is optimal. Only for large differences in task difficulties do optimal schedules assign more trials to the harder task. However, over the parameter range tested, alternating schedules yield long-term retention performance that is only slightly inferior to performance given by the true optimal schedules. Our results thus predict that in a large number of learning situations wherein tasks interfere, intermixing tasks with an equal number of trials is an effective strategy in enhancing long-term retention

Ethnicity Toward Multiculturalism: Socio-Spatial Relations of the Korean Community in Honolulu, 1903-1940.

This research deals with the dynamic ethnic socio-spatial relations and the transformation of ethnic identities in the early-twentieth-century Honolulu, mainly focusing on the Korean community. Against the widely spread notion that the ethnic relations of Honolulu in those days were little associated with the racist ideology which was prevalent in the contemporary mainland cities, this research shows that white-supremacy ideology had exerted strong influence on the minority groups in Honolulu all the way through their immigration and settling-down process. Although Honolulu included a balanced population among several ethnic groups and thus had no ethnic division of majority and minority in numerical sense, it witnessed an unequal power distribution along ethnic lines and an application of mainland-style racialization or ethnicization to its social structure. Clear occupational stratification and residential segregation by ethnic groups in the early-twentieth-century Honolulu were nearly equal to situation in the mainland cities. On the basis of socio-spatial segregation, the dichotomized identity, Local versus Haole, evolved. Non-white minorities not only had to compete with each other for limited urban resources or employment opportunities, but also they had to negotiate a collective strategy to cope with an unfair social structure controlled by white supremacy. The coalescence of several ethnic groups into a Local identity was fostered by spatial propinquity of their residential neighborhood. Mixed concentration of non-white ethnic groups in a particular place contributed to the formation of a new pan-ethnic identity. The Korean community in Honolulu, most of whose members had been firstly imported to Hawai\u27ian sugar plantations within the context of colonial capitalism, went through the change of identity in adjusting to the ethnically divided social structure. When the community was incorporated into the Hawai\u27ian version of multi-ethnic identification process, Local versus Haole, its members\u27 identity as Koreans was also transformed into the identity as Korean-Americans, within the larger construct of Local identity. The transformed identity was a product of on-going inter-ethnic negotiation process embedded in the non-white multi-ethnic neighborhood

Analysis of Recognition and Educational Needs on Competency of Secondary School Informatics Teachers

Recently, efforts have been made to enhance educational competency based on Computational Thinking (CT) in relation to informatics education at home and abroad. Although many informatics teachers training programs are steadily operated in order to cultivate the education capacity in response to the changing demands of the times, this is done without acceptance of an opinion process of informatics teachers’ educational needs. Therefore, this study analyzed recognition and educational needs based on the competency model developed for the informatics teachers in secondary school. Educational needs were analyzed using Borich’s needs formula. The results of the study are as follows: First, the average of Required Competency Level (RCL) of each competency-unit was higher than average of Present Competency Level (PCL) and there were statistically significant differences between RCL and PCL. Secondly, the educational need of each competency-unit was highest in ‘Informatics Teaching and Learning Strategy Establishment and Operation’. Thirdly, the educational needs of Knowledge domain showed the highest educational needs in the competencies of the ‘Informatics Teaching and Learning Strategy Establishment and Operation’ obtaining more than 7 points. Finally, the educational needs of Skill domain took the 1st to 3rd priorities and indicated higher in the competencies of the same competency-unit as Knowledge domain