Weather and Time Series Determinants of Liquidity in a Limit Order Market

2009 ◽  
Author(s):  
Juhani T. Linnainmaa ◽  
Ioanid Rosu
Keyword(s):  
Time Series ◽  
Limit Order ◽  
Limit Order Market

scholarly journals Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data

2020 ◽  
Vol 136 ◽  
pp. 183-189 ◽  
Author(s):  
Nikolaos Passalis ◽  
Anastasios Tefas ◽  
Juho Kanniainen ◽  
Moncef Gabbouj ◽  
Alexandros Iosifidis
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Limit Order Book ◽  
Time Series Forecasting ◽  
Order Book ◽  
Limit Order ◽  
Bag Of Features ◽  
Financial Time

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.

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