Investor Sentiment and Trading Volume Reactions to Earnings Announcements

2013 ◽  
Author(s):  
Karim Jamal ◽  
Jason Lee ◽  
Haibin Wu
Keyword(s):  
Trading Volume ◽  
Earnings Announcements ◽  
Investor Sentiment

scholarly journals Investor Sentiment in an Artificial Limit Order Market

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Lijian Wei ◽  
Lei Shi
Keyword(s):  
Regime Switching ◽  
Trading Volume ◽  
Bayesian Learning ◽  
Investor Sentiment ◽  
Agent Based Modeling ◽  
Order Book ◽  
Limit Order ◽  
Agent Based ◽  
Bid Ask Spread ◽  
Limit Order Market

This paper examines the under/overreaction effect driven by sentiment belief in an artificial limit order market when agents are risk averse and arrive in the market with different time horizons. We employ agent-based modeling to build up an artificial stock market with order book and model a type of sentiment belief display over/underreaction by following a Bayesian learning scheme with a Markov regime switching between conservative bias and representative bias. Simulations show that when compared with classic noise belief without learning, sentiment belief gives rise to short-term intraday return predictability. In particular, under/overreaction trading strategies are profitable under sentiment beliefs, but not under noise belief. Moreover, we find that sentiment belief leads to significantly lower volatility, lower bid-ask spread, and larger order book depth near the best quotes but lower trading volume when compared with noise belief.

scholarly journals Investor Sentiments and Trading Volume’s Asymmetric Response: a Non-linear ARDL Approach Tested in PSX

2019 ◽  
Vol 5 (1) ◽  
pp. 47-56
Author(s):  
Irum Saba ◽  
Maria Shams Khakwani ◽  
Rehana Kouser ◽  
Abdul Wahab
Keyword(s):  
Trading Volume ◽  
Stock Exchange ◽  
Investor Sentiment ◽  
Market Liquidity ◽  
Short Term ◽  
Asymmetric Response ◽  
Ardl Approach ◽  
Investor Sentiments ◽  
Non Linear ◽  
Dynamic Linkages

The research paper entitled “Investor sentiments and trading volume’s asymmetric response: A non linear ARDL approach tested in PSX” is an attempt to investigate the dynamic linkages between trading volume and investor sentiments for Pakistan Stock Exchange (PSX) 100 index. Two sentiments indicators have been used to enlighten the linkages. These indicators are overconfidence and net optimism and pessimism. Trading volume has been used as a proxy for the measurement of market liquidity. Non-Linear Asymmetric Autoregressive Distributed Lag (NARDL) as well as Dynamic Conditional Correlation (DCC) GARCH have been used to explain the dynamic linkages between trading volume and investor sentiments. Empirical findings suggested an asymmetric long-term market liquidity reaction to investor sentiment as well as upcoming three-year correlation have been forecasted between the trading volume and investor sentiments. In the short term, stock market liquidity reacts rapidly and asymmetrically to changes in overconfidence sentiment while the net optimism and pessimism sentiment have insignificant short-term impact on trading volume.

Sign in / Sign up

Export Citation Format

Share Document