Predicting stock prices based on informed traders' activities using deep neural networks

被引：7
|
作者：
Na, Haejung [1 ]
Kim, Soonho [2 ]
机构：
[1] Calif State Univ Los Angeles, Coll Business & Econ, 5151 State Univ Dr, Los Angeles, CA 90032 USA
[2] Pukyong Natl Univ, Business Sch C25 605, Yongsorho 45, Busan 48513, South Korea
来源：
ECONOMICS LETTERS | 2021年 / 204卷
关键词：
Artificial neural network; Informed investors; Stock price prediction; Market failure; INVESTOR SENTIMENT; DAILY HAPPINESS; INFORMATION-CONTENT; MARKET; RETURNS; MODEL; RISK; NEWS; PREDICTABILITY; PATTERNS;
D O I：
10.1016/j.econlet.2021.109917
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
This study investigates the predictive power of informed traders' activities in stock price movements by employing neural networks. Specifically, we examine whether informed investors' trading activities can predict drastic changes in stock prices in the subsequent 5-day period. Our empirical results show that the probability of the model being correct can be as high as 74%. In addition, the simulated trading strategies based on our trained model lead to significantly positive risk-adjusted returns and show strong performance measures. Overall, we find that informed traders' activities contain informational content and may provide actual investors with information that is useful for stock price prediction. (C) 2021 Elsevier B.V. All rights reserved.
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页数：7
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