SearcharxivSearch

arXiv · 1112.0342

Semiclosed Pricing Mechanism

Abstract

This paper aims at designing the different important components of a semi-closed simulated stock market (pricing mechanism, stock allocation and news generation). The purpose is to understand the interactions of the different aspects within a 'semi-closed' system. The complexity and nature of the system led to the process of modifying the pricing mechanism which is viewed from a different angle to the classical Brownian Motion and the Random Walk model. However, it incorporates the essence of these two fundamental theories and then investigates the matrix of investors' behaviours in relation to news feedbacks. This paper also explores the realm of randomly generated news to the responses of participants to determine rational and irrational behaviours. This is carried out through uncompressing the time within the experiment and looking at concordant and disconcordant behaviour. The focus is on how the modified pricing equation adapts to the conditions and uniqueness surrounding a semi-closed stock market. Thus, this paper looks at how a simple market system where the main determinants of share prices are news, demand and supply along with some filtering of the external forces can affect the behaviours of investors in terms of their portfolio composition. The return distributions can then be stipulated as arising from rational or irrational trajectories and subsequently be simulated and matched via the proposed modified Brownian Motion model to empirical return distributions in specific time periods and markets.

Explore related subjects

Keep this discovery

BibTeXRIS

Dr. Gurjeet Dhesi, Mohammad Abdul Washad Emambocus, Muhammad Bilal Shakeel. 2012-07-11. Semiclosed Pricing Mechanism. https://arxiv.org/abs/1112.0342

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Deep Learning of Robust Market Making under Regime-Switching Order Flow

Classical market-making strategies based on stochastic control, such as the Avellaneda-Stoikov and the Gu\'{e}ant-Lehalle-Fernandez-Tapia (GLFT) extension, provide closed-form quoting rules, but rest on assumptions that break down at realistic microstructure timescales. One of them is that order flow is stationary, while empirical evidence points to the existence of regimes, possibly associated with algorithmic execution of metaorders. In this case, existing methods provide negative PnL. In this paper, we develop a deep reinforcement-learning market maker (RLMM) - a Rainbow-style distributional DQN (C51) which is calibrated and tested in a zero-intelligence limit order book. We find that, in the stationary setting, RLMM outperforms GLFT across the entire observed risk-return frontier. The RLMM is more robust to flow asymmetry than GLFT, but, like any stationarily trained strategy, it still suffers large drawdowns from inventory saturation under persistent directional imbalance. Augmenting the state of RLMM with two auxiliary signals - a Bayesian online change-point filter over the directional flow bias and a queue-adjusted quote-exposure imbalance -restores profitability. A final scenario-bandit step that reweights low-return regime scenarios further improves performance under random-persistence and correlated-direction stress.

q-fin.TR

dexamine: A Python package for Uniswap event data on Ethereum

Decentralized exchanges record trading and liquidity provision on public blockchains, but empirical analysis requires interpreting these records and linking them to execution metadata. dexamine is a Python package that parses Uniswap v2 and v3 events on Ethereum. It converts transaction receipt logs into observations of trades and liquidity changes, with token quantities, pool state, transaction order, and gas information. The package separates data retrieval, contract metadata, protocol interpretation, and output construction. The repository provides recorded Ethereum responses and an offline reproducible example, and version 1 has been used to construct data for an empirical study of price discovery in decentralized markets.

q-fin.TR

The Double-Edged Sword of Short-Selling Bans

We develop a theoretical model that endogenizes the regulator's decision to impose short-selling bans to prevent large stock price declines. Empirically, we test the model's predictions using the cross-sectional variation in short-selling restrictions implemented across European countries in 2020. Consistent with our model, we find that bans had a detrimental effect on liquidity and failed to support the average price levels, but were effective in limiting large price drawdowns. Finally, we show that the effectiveness of the bans depends on the share of informed stockholders, a central variable in our framework, thus informing the design of more effective regulatory responses.

q-fin.TR