Algorithmic Trading: Concepts, Perspectives, and Technical Notes by Zeyu Zheng

Author:Zeyu Zheng [Zheng, Zeyu]
Language: eng
Format: azw3
Publisher: UNKNOWN
Published: 2017-07-23T16:00:00+00:00

7. Key Points on News Trading

- Traders in the financial market have always been the first adopters of new technology to increase the speed of information flows and to trade faster than others. There has been increasing use of news information in algorithmic trading in order to react real-time to market events and to enable better decision making.

- There are varying degrees of market efficiency—weak, semi-strong, and strong—that reflect differences in how the market absorbs information.

- Price reaction to news is dependent on liquidity, expectation, and psychology. Traders care about four aspects of new information: relevance, intensity, duration, and persistence. Economic theories sometimes provide only limited guidance on how market prices should react to certain news.

Noise trading increases market liquidity and provides opportunities for -

information traders to profit. A trader’s course of action becomes a variable in the market itself, having the potential to change other market participants’ strategies and thus impact the strategy outcome.

- The qualitative narratives of textual news can be converted into quantified news sentiment classifications or sentiment scores to be acted on by trading algorithms. Filtering and processing may be needed before scoring. Linking the sentiment score to price change series, trading volume, and other market variables allows traders to develop systematic trading models.

- Message can scored by simple word count or through the more complex word vector regression. Results from several methods can be aggregated to enhance the robustness of sentiment scoring.

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