Forecasting stock returns using first half an hour order imbalance

2020 ◽  
Author(s):  
Xiaojun Chu ◽  
Jianying Qiu
Keyword(s):  
Stock Returns ◽  
Order Imbalance

scholarly journals Order imbalance and stock returns: Evidence from China

2007 ◽  
Vol 47 (5) ◽  
pp. 637-650 ◽  
Author(s):  
Catherine Shenoy ◽  
Ying Jenny Zhang
Keyword(s):  
Stock Returns ◽  
Order Imbalance

Order imbalance beta and stock returns

2020 ◽  
Vol 52 (56) ◽  
pp. 6100-6113 ◽  
Author(s):  
Chunpeng Yang ◽  
Xiaoyi Hu
Keyword(s):  
Stock Returns ◽  
Order Imbalance

Order imbalance and individual stock returns: Theory and evidence

2004 ◽  
Vol 72 (3) ◽  
pp. 485-518 ◽  
Author(s):  
Tarun Chordia ◽  
Avanidhar Subrahmanyam
Keyword(s):  
Stock Returns ◽  
Order Imbalance

Big data analytics, order imbalance and the predictability of stock returns

2021 ◽  
pp. 100717
Author(s):  
Erdinc Akyildirim ◽  
Ahmet Sensoy ◽  
Guzhan Gulay ◽  
Shaen Corbet ◽  
Hajar Novin Salari
Keyword(s):  
Big Data ◽  
Stock Returns ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Order Imbalance

Relationship Between Market Orders and Stock Returns: Evidence from Taiwan

2014 ◽  
Vol 17 (02) ◽  
pp. 1450013 ◽  
Author(s):  
Chiao Yi Chang ◽  
Andy Chien ◽  
Ya-Ting Hsu
Keyword(s):  
Regression Model ◽  
Stock Returns ◽  
Reference Point ◽  
Trading Volume ◽  
Positive Relationship ◽  
Stock Index ◽  
Panel Regression ◽  
Empirical Results ◽  
Order Imbalance ◽  
The Relationship

Unlike previous studies that adopted price as the reference point in this paper we employ the adjusted order imbalance that relates to volume as a reference. We examine the relationship between a firm's characteristics and stock returns. Adjusted order imbalance, including trading direction of stock index and trading volume of individual stock and stock index, is freely and easily obtained by investors in Taiwan. Employing the panel regression model, this paper found prior adjusted order imbalance has a significantly positive relationship with individual stock returns. Additionally, empirical results show that adjusted order imbalance enhances the impacts of the value and size variables.

Freedom Indices and Capital Asset Pricing Model: Some Malaysian Evidence

2020 ◽  
Vol 1 (1) ◽  
pp. 1-21
Author(s):  
Ying Tay Lee ◽  
Devinaga Rasiah ◽  
Ming Ming Lai
Keyword(s):  
Human Rights ◽  
Asset Pricing ◽  
Stock Returns ◽  
Firm Size ◽  
Explanatory Power ◽  
Least Square ◽  
Press Freedom ◽  
Capital Asset Pricing ◽  
Capital Asset ◽  
Cross Sectional

Human rights and fundamental freedoms such as economic, political, and press freedoms vary widely from country to country. It creates opportunity and risk in investment decisions. Thus, this study is carried out to examine if the explanatory power of the model for capital asset pricing could be improved when these human rights movement indices are included in the model. The sample for this study comprises of 495 stocks listed in Bursa Malaysia, covering the sampling period from 2003 to 2013. The model applied in this study employed the pooled ordinary least square regression estimation. In addition, the robustness of the model is tested by using firm size as a controlled variable. The findings show that market beta as well as the economic and press freedom indices could explain the cross-sectional stock returns of the Malaysian stock market. By controlling the firm size, it adds marginally to the explanation of the extended CAP model which incorporated economic, political, and press freedom indices.

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