A5019635005- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
- Energy Load and Power Forecasting
- Knowledge Management and Sharing
- Technology Adoption and User Behaviour
- Organizational and Employee Performance
- Financial Markets and Investment Strategies
- Spam and Phishing Detection
- Groundwater and Isotope Geochemistry
- Imbalanced Data Classification Techniques
- Financial Distress and Bankruptcy Prediction
- Soil and Unsaturated Flow
- Groundwater flow and contamination studies
- Complex Systems and Time Series Analysis
- Cyberloafing and Workplace Behavior
University of Electronic Science and Technology of China
2020-2022
Christian Service University College
2017
Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in market. Investment market has a big risk associated with it. Minimizing prediction error reduces investment risk. Machine learning (ML) models typically perform better than statistical econometric models. Also, ensemble ML have been shown literature be able produce superior performance single In this work, we compare effectiveness tree-based (Random Forest...
The stock market is one of the key sectors a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting very challenging task has attracted serious interest from researchers many fields such as statistics, artificial intelligence, economics, finance. An accurate prediction reduces investment risk in market. Different approaches have been used predict performances Machine learning (ML) models are typically superior those statistical...
Forecasting stock market behavior has received tremendous attention from investors, and researchers for a very long time due to its potential profitability. Predicting is regarded as one of the extremely challenging applications series forecasting. While there divided opinion on efficiency markets, numerous empirical studies which are widely accepted have shown that predictable some extent. Statistical based methods machine learning models used forecast analyze market. Machine (ML) typically...
Mobile Money Fraud is advancing in developing countries. We propose a solution to this problem based on machine learning. Labeled data from financial transactions which include mobile money are, however, skewed towards the negative class. Machine learning models built with such datasets are unreliable as prediction algorithms will be biased investigate performance of different sampling and weighting techniques Adaptive Synthetic Sampling (ADASYN) Minority Oversampling Technique (SMOTE)....
BACKGROUND: Organizations develop knowledge management (KM) strategies with the intention to leverage across all functional areas. A system (KMS) is used facilitate KM processes such as creation, storage, and application of knowledge. However, mere adoption deployment KMS do not warrant its effective use knowledge-sharing efforts. OBJECTIVE: This study investigates facilitative role social capital in by considering three dimensions –cognitive (i.e., shared norms), relational trust),...
BACKGROUND: Knowledge is a source of competitive and strategic resource for many small- medium-sized enterprises (SMEs). Securing knowledge assets through secured management systems (KMS) critical concern among SMEs. Due to the socio-technical nature KMS, security quality KMS an influential factor in adoption by OBJECTIVE: This study examines effects task-technology fit on SMEs Ghana. It further investigates whether or not affects ease use METHODS: Using structured questionnaire, data were...