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Articles 1 - 10 of 10
Full-Text Articles in Finance and Financial Management
Examining Sustainable Overseas Investment Information-Sharing Model For Automobile Enterprises: A Multi-Modal Weight Network Approach, Yuan Cheng, Xiaofang Chen, Changbo Lin, Sheqing Ma, Jie Feng
Examining Sustainable Overseas Investment Information-Sharing Model For Automobile Enterprises: A Multi-Modal Weight Network Approach, Yuan Cheng, Xiaofang Chen, Changbo Lin, Sheqing Ma, Jie Feng
Research Collection School Of Accountancy
In an era of globalization, automotive companies are increasingly looking to make overseas investments to expand their production capacity and explore foreign markets. However, the outcomes of such investments are often influenced by a myriad of factors, including policy changes, social dynamics, and market conditions. To address the need for a comprehensive overseas investment information-sharing model, this research proposes an innovative approach based on a multi-modal weight network. This model aims to provide users with a global perspective on overseas investment opportunities, encompassing policy insights, and market dynamics. It integrates data from various sources, offering multi-dimensional information on investment regions, …
Evolve Path Tracer: Early Detection Of Malicious Addresses In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu
Evolve Path Tracer: Early Detection Of Malicious Addresses In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu
Research Collection School Of Computing and Information Systems
With the boom of cryptocurrency and its concomitant financial risk concerns, detecting fraudulent behaviors and associated malicious addresses has been drawing significant research effort. Most existing studies, however, rely on the full history features or full-fledged address transaction networks, both of which are unavailable in the problem of early malicious address detection and therefore failing them for the task. To detect fraudulent behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically, in addition to the general address features, we propose …
Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Research Collection School Of Computing and Information Systems
Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed …
Understanding Sentiment Through Context, Richard M.Crowley, M.H. Franco Wong
Understanding Sentiment Through Context, Richard M.Crowley, M.H. Franco Wong
Research Collection School Of Accountancy
We examine whether empirical results using text-based sentiment of U.S. annual reports depend on the underlying context, within documents, from which sentiment is measured. We construct a clause-level measure of context, showing that sentiment is driven by many different contexts and that positive and negative sentiment are driven by different contexts. We then construct context-level sentiment measures and examine whether sentiment works as expected at the context-level across four prediction problems. Our results demonstrate that document-level sentiment exhibits significant noise in prediction and suggest that document-level aggregation of sentiment leads to missed empirical nuances. The contexts driving sentiment results vary …
European Floating Strike Lookback Options: Alpha Prediction And Generation Using Unsupervised Learning, Tristan Lim, Aldy Gunawan, Chin Sin Ong
European Floating Strike Lookback Options: Alpha Prediction And Generation Using Unsupervised Learning, Tristan Lim, Aldy Gunawan, Chin Sin Ong
Research Collection School Of Computing and Information Systems
This research utilized the intrinsic quality of European floating strike lookback call options, alongside selected return and volatility parameters, in a K-means clustering environment, to recommend an alpha generative trading strategy. The result is an elegant easy-to-use alpha strategy based on the option mechanisms which identifies investment assets with high degree of significance. In an upward trending market, the research had identified European floating strike lookback call option as an evaluative criterion and investable asset, which would both allow investors to predict and profit from alpha opportunities. The findings will be useful for (i) buy-side investors seeking alpha generation and/or …
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Research Collection School Of Computing and Information Systems
The rise of Initial Coin Offerings (ICO) in recent times and their potential for investment opportunities have investors spending a lot of time researching ICOs or having to follow the crowd. This paper aims to explore four broad factors of ICOs: identity, credibility, investor sentiment, and price movement to develop a framework that is useful in determining ICO quality. Research is shown using data sources including public forums, chat groups, web sites, white papers as well as smart contract details. Finally, a system, based on the framework, is proposed that can be used to detect and regulate ICO activities and …
Moving Average Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Doyen Sahoo, Zhi-Yong Liu
Moving Average Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Doyen Sahoo, Zhi-Yong Liu
Research Collection School Of Computing and Information Systems
On-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock prices are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and …
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 …
Robust Median Reversion Strategy For On-Line Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Steven Hoi, Shuigeng Zhou
Robust Median Reversion Strategy For On-Line Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Steven Hoi, Shuigeng Zhou
Research Collection School Of Computing and Information Systems
On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies. Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. To overcome the limitation, we propose to exploit the reversion phenomenon by robust L1-median estimator, and design a novel on-line portfolio selection strategy named "Robust Median Reversion" (RMR), which makes optimal …
Would Position Limits Have Made Any Difference To The 'Flash Crash' On May 6, 2010, Wing Bernard Lee, Shih-Fen Cheng, Annie Koh
Would Position Limits Have Made Any Difference To The 'Flash Crash' On May 6, 2010, Wing Bernard Lee, Shih-Fen Cheng, Annie Koh
Research Collection School Of Computing and Information Systems
On May 6, 2010, the US equity markets experienced a brief but highly unusual drop in prices across a number of stocks and indices. The Dow Jones Industrial Average (DJIA) fell by approximately 9% in a matter of minutes, and several stocks were traded down sharply before recovering a short time later. Earlier research by Lee, Cheng and Koh (2010) identified the conditions under which a “flash crash” can be triggered by systematic traders running highly similar trading strategies, especially when they are “crowding out” other liquidity providers in the market. The authors contend that the events of May 6, …