scholarly journals Generalization of affine feedback stock trading results to include stop-loss orders

Automatica ◽  
2022 ◽  
Vol 136 ◽  
pp. 110051
Author(s):  
Chung-Han Hsieh
Keyword(s):  
2019 ◽  
Vol 7 (02) ◽  
pp. 51
Author(s):  
Adri Wihananto

Trading frequency can be said as the implementation from trader of commerce. This case based on positive or negative trader reaction given by trader information.  Stock trading in BEI always fluctuate with price of volume value and frequency particularly. Frequency itself shows the company  involved or not. In trading frequency, if the indicator frequency it self shown the higher point, it means better. In spite of the most important thing is how the fluctuation or value conversion itself. On the frequencies we also could see which stocks is interested by the investor. When trading frequency high, it  may be create sense of interest from investors.The aim of this research, in order to know how far the effect of trading frequency (X) with stock value (Y) using cover stock value. The information used is begin 2008 with sample from twelve property and real estate companies. According to the research can be conclude from twelve companies in Indonesia Stock Exchange in 2008, 75 % of trading frequency samples doesn’t have signification degree between trading frequency and stock value. This case can be explained count on smaller than t tableEvaluation of this research is the trading measuring frequency at property sector and real estate not influence to stock priceKeywords : Trading Frequency, Stock Price 


2018 ◽  
Vol 31 (4) ◽  
pp. 477-519
Author(s):  
Daeil Kang ◽  
◽  
Jong-Ho Park ◽  
Kyong Shik Eom

2012 ◽  
Author(s):  
Haiqiang Chen ◽  
Paul Moon Sub Choi ◽  
Yongmiao Hong

2020 ◽  
Author(s):  
Bochuan Dai ◽  
Ben R. Marshall ◽  
Nhut H. Nguyen ◽  
Nuttawat Visaltanachoti

2017 ◽  
Vol 93 (3) ◽  
pp. 25-57 ◽  
Author(s):  
Eli Bartov ◽  
Lucile Faurel ◽  
Partha S. Mohanram

ABSTRACT Prior research has examined how companies exploit Twitter in communicating with investors, and whether Twitter activity predicts the stock market as a whole. We test whether opinions of individuals tweeted just prior to a firm's earnings announcement predict its earnings and announcement returns. Using a broad sample from 2009 to 2012, we find that the aggregate opinion from individual tweets successfully predicts a firm's forthcoming quarterly earnings and announcement returns. These results hold for tweets that convey original information, as well as tweets that disseminate existing information, and are stronger for tweets providing information directly related to firm fundamentals and stock trading. Importantly, our results hold even after controlling for concurrent information or opinion from traditional media sources, and are stronger for firms in weaker information environments. Our findings highlight the importance of considering the aggregate opinion from individual tweets when assessing a stock's future prospects and value.


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