A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study shows foreign institutional investment increases liquidity commonality in large Australian stocks.
Study shows corporate governance improves stock liquidity with noise traders' participation.
The study finds a liquidity premium in stock returns, but only after correcting for microstructure noise.
Study finds Indian mutual funds adjust cash holdings based on inflows, impacting stock purchases.
Develops a new model to better estimate cryptocurrency and stock volatility.
This paper analyzes the relationship between public disclosure, private information and stock liquidity in Tunisian context using a sample of 41 listed firms in the Tunis Stock Exchange in 2007. First, we find no evidence that there is a relation between public and private information. Second, Tunisian investors do not…
This study examines investor sentiment's impact on stock market liquidity and volatility using deep learning and TVP-VAR models.
Paper tackles liquidating stocks using reinforcement learning.
Predicting the intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, relevant studies on their predictability remain limited. This paper proposes a data-driven approach to predict intraday stock jumps using the in…
MiFID II impacts European stock liquidity and price formation.
We derive a "semi-analytic" solution for a stock loan in which the lender forces liquidation when the loan-to-collateral ratio drops beneath a certain threshold. We use this to study the sensitivity of the contract to model parameters.
We investigate the correlation properties of transaction data from the New York Stock Exchange. The trading activity f(t) of each stock displays a crossover from weaker to stronger correlations at time scales 60-390 minutes. In both regimes, the Hurst exponent H depends logarithmically on the liquidity of the stock, me…
Solves optimal liquidation problem for stock price following geometric Brownian motion.
Using recent advances in the econometrics literature, we disentangle from high frequency observations on the transaction prices of a large sample of NYSE stocks a fundamental component and a microstructure noise component. We then relate these statistical measurements of market microstructure noise to observable charac…
Study liquidity impact on spread option pricing.
Project forecasts liquidity withdrawal using machine learning models.
This paper analyzes stock market data to predict share prices using regression models.
Market liquidity plays a vital role in the field of market micro-structure, because it is the vigor of the financial market. This paper uses a variable called convexity to measure the potential liquidity provided by order-book. Based on the high-frequency data of each stock included in the SSE (Shanghai Stock Exchange)…
Optimal stock trading strategy with market orders and limit orders in a risky market.
We propose a framework to study the optimal liquidation strategy in a limit order book for large-tick stocks, with spread equal to one tick. All order book events (market orders, limit orders and cancellations) occur according to independent Poisson processes, with parameters depending on price move directions. Our goa…
New measures detect HFT activity, revealing its impact on stock prices.
PEARL uses AI to replicate private equity performance with liquid assets.
Quantum calculus models stock liquidity issues.
Study on market entry timing in stock liquidation with trading constraints.
Model predicts Chinese stock market liquidity and customer order behavior.
We present a simulation-and-regression method for solving dynamic portfolio allocation problems in the presence of general transaction costs, liquidity costs and market impacts. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and t…
Study finds a phase transition in flash crashes involving large and liquid stocks.
LSTM model predicts stock prices with high accuracy in stable sectors but struggles with volatile ones.
Study calculates liquidity costs for delta hedging of European options.
Model identifies order splitting and liquidity replenishment as necessary for the square-root law of market impact.
The study examines stock splits and their effects on companies, managers, and shareholders.
A new DRL model optimizes hedging with market impact for low-liquidity stocks.
A universal LSTM model outperforms asset-specific models in forecasting stock volatilities.
Quantum computer helps optimize stock portfolios.
New framework detects crypto wash trading using liquidity measures.
Predicts stock volatility using Twitter data and random forests.
The study uses equity order flow to forecast stock returns and resolves the liquidity premium puzzle.
Bid-ask spread is taken as an important measure of the financial market liquidity. In this article, we study the dynamics of the spread return and the spread volatility of four liquid stocks in the Chinese stock market, including the memory effect and the multifractal nature. By investigating the autocorrelation functi…
The paper discusses various practical consequences of treating economics and finance as an inherently dynamic and chaotic system. On the theoretical side this looks at the general applicability of the market-making pricing approach to economics in general. The paper also discuses the consequences of the endogenous crea…
Price gap, defined as the logarithmic price difference between the first two occupied price levels on the same side of a limit order book (LOB), is a key determinant of market depth, which is one of the dimensions of liquidity. However, the properties of price gaps have not been thoroughly studied due to the less avail…
We characterize the collective phenomena of a liquid market. By interpreting the behavior of a no-arbitrage N asset market in terms of a particle system scenario, (thermo)dynamical-like properties can be extracted from the asset kinetics. In this scheme the mechanisms of the particle interaction can be widely investiga…
We study the effect of liquidity freezes on an economic agent optimizing her utility of consumption in a perturbed Black-Scholes-Merton model. The single risky asset follows a geometric Brownian motion but is subject to liquidity shocks, during which no trading is possible and stock dynamics are modified. The liquidity…
The composition of natural liquidity has been changing over time. An analysis of intraday volumes for the S&P500 constituent stocks illustrates that (i) volume surprises, i.e., deviations from their respective forecasts, are correlated across stocks, and (ii) this correlation increases during the last few hours of the …
We develop a behavioral model for liquidity and volatility based on empirical regularities in trading order flow in the London Stock Exchange. This can be viewed as a very simple agent based model in which all components of the model are validated against real data. Our empirical studies of order flow uncover several i…
Study uses machine learning to predict high-frequency trading liquidity.
This paper develops a model of liquidity provision in financial markets by adapting the Madhavan, Richardson, and Roomans (1997) price formation model to realistic order books with quote discretization and liquidity rebates. We postulate that liquidity providers observe a fundamental price which is continuous, efficien…
Research proposes a model to estimate transaction costs and assess asset liquidity risk.
Liquidation is the process of selling a large number of shares of one stock sequentially within a given time frame, taking into consideration the costs arising from market impact and a trader's risk aversion. The main challenge in optimizing liquidation is to find an appropriate modeling system that can incorporate the…