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Articles 1 - 3 of 3
Full-Text Articles in Business
The Impact Of Nasd Rule 2711 And Nyse Rule 472 On Analyst Behavior: The Strategic Timing Of Recommendations Issued On Weekends, Yi Dong, Nan Hu
The Impact Of Nasd Rule 2711 And Nyse Rule 472 On Analyst Behavior: The Strategic Timing Of Recommendations Issued On Weekends, Yi Dong, Nan Hu
Research Collection School Of Computing and Information Systems
Amendments to NASD Rule 2711 and NYSE Rule 472, enacted in May 2002, mandate that sell-side analysts disclose the distribution of their security recommendations by buy, hold and sell category. This regulation enhances the transparency of analysts' information and mitigates the long-recognized optimistic bias in their recommendations. However, we find that analysts are more likely to issue sell recommendations or downgrade revisions on weekends when investors have limited attention after these rule changes. This pattern is more pronounced for prestigious analysts, who are more likely to influence stock prices. Market reaction tests reveal an incomplete immediate response and a greater …
Robust Median Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Hoi, Steven C. H., Shuigeng Zhou
Robust Median Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Hoi, Steven C. H., 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 portfolios based …
Olps: A Toolbox For On-Line Portfolio Selection, Bin Li, Doyen Sahoo, Hoi, Steven C. H.
Olps: A Toolbox For On-Line Portfolio Selection, Bin Li, Doyen Sahoo, Hoi, Steven C. H.
Research Collection School Of Computing and Information Systems
On-line portfolio selection is a practical financial engineering problem, which aims to sequentially allocate capital among a set of assets in order to maximize long-term return. In recent years, a variety of machine learning algorithms have been proposed to address this challenging problem, but no comprehensive open-source toolbox has been released for various reasons. This article presents the first open-source toolbox for "On-Line Portfolio Selection" (OLPS), which implements a collection of classical and state-of-the-art strategies powered by machine learning algorithms. We hope that OLPS can facilitate the development of new learning methods and enable the performance benchmarking and comparisons of …