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.

OPTIMAL EXECUTION COST FOR LIQUIDATION THROUGH A LIMIT ORDER MARKET

2016 ◽  
Vol 19 (01) ◽  
pp. 1650004 ◽  
Author(s):  
ETIENNE CHEVALIER ◽  
VATHANA LY VATH ◽  
SIMONE SCOTTI ◽  
ALEXANDRE ROCH
Keyword(s):  
Optimal Solution ◽  
Jump Process ◽  
Arrival Process ◽  
Order Book ◽  
Continuous Control ◽  
Limit Order ◽  
Bid Ask Spread ◽  
Limit Orders ◽  
Market Orders ◽  
Limit Order Market

We study the problem of optimally liquidating a large portfolio position in a limit-order market. We allow for both limit and market orders and the optimal solution is a combination of both types of orders. Market orders deplete the order book, making future trades more expensive, whereas limit orders can be entered at more favorable prices but are not guaranteed to be filled. We model the bid-ask spread with resilience by a jump process, and the market-order arrival process as a controlled Poisson process. The objective is to minimize the execution cost of the strategy. We formulate the problem as a mixed stochastic continuous control and impulse problem for which the value function is shown to be the unique viscosity solution of the associated variational inequalities. We conclude with a calibration of the model on recent market data and a numerical implementation.

scholarly journals Trade informativeness and liquidity in Bitcoin markets

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255515
Author(s):  
J. Christopher Westland
Keyword(s):  
Trading Volume ◽  
Limit Order Book ◽  
Small Sample ◽  
Market Liquidity ◽  
Order Book ◽  
Information Asymmetries ◽  
Limit Order ◽  
Limit Orders ◽  
Market Orders ◽  
Book Markets

Liquid markets are driven by information asymmetries and the injection of new information in trades into market prices. Where market matching uses an electronic limit order book (LOB), limit orders traders may make suboptimal price and trade decisions based on new but incomplete information arriving with market orders. This paper measures the information asymmetries in Bitcoin trading limit order books on the Kraken platform, and compares these to prior studies on equities LOB markets. In limit order book markets, traders have the option of waiting to supply liquidity through limit orders, or immediately demanding liquidity through market orders or aggressively priced limit orders. In my multivariate analysis, I control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. The current research offers the first empirical study of Glosten (1994) to yield a positive, and credibly large transaction cost parameter. Trade and LOB datasets in this study were several orders of magnitude larger than any of the prior studies. Given the poor small sample properties of GMM, it is likely that this substantial increase in size of datasets is essential for validating the model. The research strongly supports Glosten’s seminal theoretical model of limit order book markets, showing that these are valid models of Bitcoin markets. This research empirically tested and confirmed trade informativeness as a prime driver of market liquidity in the Bitcoin market.

scholarly journals The Relation between Intraday Limit Order Book Depth and Spread

2021 ◽  
Vol 9 (4) ◽  
pp. 60
Author(s):  
Alexandre Aidov ◽  
Olesya Lobanova
Keyword(s):  
Generalized Method Of Moments ◽  
Limit Order Book ◽  
Order Book ◽  
Inverse Relation ◽  
Limit Order ◽  
Market Participants ◽  
Market Depth ◽  
Bid Ask Spread ◽  
Explanatory Factors ◽  
Trading Decisions

Prior studies that examine the relation between market depth and bid–ask spread are often limited to the first level of the limit order book. However, the full limit order book provides important information beyond the first level about the depth and spread, which affects the trading decisions of market participants. This paper examines the intraday behavior of depth and spread in the five-deep limit order book and the relation between depth and spread in a futures market setting. A dummy-variables regression framework is employed and is estimated using the generalized method of moments (GMM). Results indicate an inverse U-shaped pattern for depth and an increasing pattern for spread. After controlling for known explanatory factors, an inverse relation between the limit order book depth and spread is documented. The inverse relation holds for depth and spread at individual levels in the limit order book as well. Results indicate that market participants actively manage both the price (spread) and quantity (depth) dimensions of liquidity along the five-deep limit order book.

scholarly journals Order Book Dynamics with Liquidity Fluctuations: Limit Theorems and Large Deviations

2021 ◽  
Author(s):  
Helder Rojas ◽  
Anatoly Yambartsev ◽  
Artem Logachov
Keyword(s):  
Large Deviations ◽  
Stochastic Models ◽  
Limit Theorems ◽  
Law Of Large Numbers ◽  
Limit Order Book ◽  
Order Book ◽  
Limit Order ◽  
Bid Ask Spread ◽  
Large Numbers ◽  
The One

We propose a class of stochastic models for a dynamics of limit order book with different type of liquidities. Within this class of models we study the one where a spread decreases uniformly, belonging to the class of processes known as a population processes with uniform catastrophes. The law of large numbers (LLN), central limit theorem (CLT) and large deviations (LD) are proved for our model with uniform catastrophes. Our results allow us to satisfactorily explain the volatility and local trends in the prices, relevant empirical characteristics that are observed in this type of markets. Furthermore, it shows us how these local trends and volatility are determined by the typical values of the bid-ask spread. In addition, we use our model to show how large deviations occur in the spread and prices, such as those observed in flash crashes.

scholarly journals A MATHEMATICAL APPROACH TO ORDER BOOK MODELING

2013 ◽  
Vol 16 (05) ◽  
pp. 1350025 ◽  
Author(s):  
FRÉDÉRIC ABERGEL ◽  
AYMEN JEDIDI
Keyword(s):  
Exponential Convergence ◽  
Agent Based Modeling ◽  
Price Formation ◽  
Order Book ◽  
Continuous Time Markov Chain ◽  
Mathematical Study ◽  
Microscopic Description ◽  
Arrival Times ◽  
Agent Based ◽  
Functional Central Limit

Motivated by the desire to bridge the gap between the microscopic description of price formation (agent-based modeling) and the stochastic differential equations approach used classically to describe price evolution at macroscopic time scales, we present a mathematical study of the order book as a multidimensional continuous-time Markov chain and derive several mathematical results in the case of independent Poissonian arrival times. In particular, we show that the cancellation structure is an important factor ensuring the existence of a stationary distribution and the exponential convergence towards it. We also prove, by means of the functional central limit theorem (FCLT), that the rescaled-centered price process converges to a Brownian motion. We illustrate the analysis with numerical simulation and comparison against market data.

scholarly journals Spoofing the Limit Order Book: A Strategic Agent-Based Analysis

Games ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 46
Author(s):  
Xintong Wang ◽  
Christopher Hoang ◽  
Yevgeniy Vorobeychik ◽  
Michael P. Wellman
Keyword(s):  
Limit Order Book ◽  
Trading Strategies ◽  
Order Book ◽  
Market Manipulation ◽  
Trading Behavior ◽  
Limit Order ◽  
Agent Based ◽  
Total Surplus ◽  
Private Values ◽  
Belief Learning

We present an agent-based model of manipulating prices in financial markets through spoofing: submitting spurious orders to mislead traders who learn from the order book. Our model captures a complex market environment for a single security, whose common value is given by a dynamic fundamental time series. Agents trade through a limit-order book, based on their private values and noisy observations of the fundamental. We consider background agents following two types of trading strategies: the non-spoofable zero intelligence (ZI) that ignores the order book and the manipulable heuristic belief learning (HBL) that exploits the order book to predict price outcomes. We conduct empirical game-theoretic analysis upon simulated agent payoffs across parametrically different environments and measure the effect of spoofing on market performance in approximate strategic equilibria. We demonstrate that HBL traders can benefit price discovery and social welfare, but their existence in equilibrium renders a market vulnerable to manipulation: simple spoofing strategies can effectively mislead traders, distort prices and reduce total surplus. Based on this model, we propose to mitigate spoofing from two aspects: (1) mechanism design to disincentivize manipulation; and (2) trading strategy variations to improve the robustness of learning from market information. We evaluate the proposed approaches, taking into account potential strategic responses of agents, and characterize the conditions under which these approaches may deter manipulation and benefit market welfare. Our model provides a way to quantify the effect of spoofing on trading behavior and market efficiency, and thus it can help to evaluate the effectiveness of various market designs and trading strategies in mitigating an important form of market manipulation.

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