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Full-Text Articles in Physical Sciences and Mathematics
Stock Trend Prediction Using Candlestick Charting And Ensemble Machine Learning Techniques With A Novelty Feature Engineering Scheme, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu
Stock Trend Prediction Using Candlestick Charting And Ensemble Machine Learning Techniques With A Novelty Feature Engineering Scheme, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu
Information Technology & Decision Sciences Faculty Publications
Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment …
Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin
Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin
Research Collection School Of Computing and Information Systems
The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied to this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced …
Online Portfolio Selection: A Survey, Bin Li, Steven C. H. Hoi
Online Portfolio Selection: A Survey, Bin Li, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation …