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.
Optimizes stock execution costs using stochastic control theory.
This paper studies how social media posts, especially by executives, affect stock prices.
Optimizes large stock order execution with LSTM neural networks.
We extend the framework of trading strategies of Gatheral [2010] from single stocks to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cros…
HRT uses bi-level reinforcement learning to optimize stock selection and execution in multi-asset equity markets.
Stock trading based on Kelly's celebrated Expected Logarithmic Growth (ELG) criterion, a well-known prescription for optimal resource allocation, has received considerable attention in the literature. Using ELG as the performance metric, we compare the impact of trade execution delay on the relative performance of high…
The study examines how brokers' identity affects their trading strategies on the Toronto Stock Exchange.
We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We…
TT-DAC-PS: A deterministic actor-critic approach for optimal trade execution
Optimal trade execution is an important problem faced by essentially all traders. Much research into optimal execution uses stringent model assumptions and applies continuous time stochastic control to solve them. Here, we instead take a model free approach and develop a variation of Deep Q-Learning to estimate the opt…
An ability to postpone one's execution without penalty provides an important strategic advantage in high-frequency trading. To elucidate competition between traders one has to formulate to a quantitative theory of formation of the execution price from market expectations and quotes. This theory was provided in 2005 by …
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 …
Be it for taking advantage of stock undervaluation or in order to distribute part of their profits to shareholders, firms may buy back their own shares. One of the way they proceed is by including Accelerated Share Repurchases (ASR) as part of their repurchase programs. In this article, we study the pricing and optimal…
The paper analyzes optimal execution strategies for traders with inventory processes influenced by Brownian motion.
We consider the optimal trade execution strategies for a large portfolio of single stocks proposed by Almgren (2003). This framework accounts for a nonlinear impact of trades on average market prices. The results of Almgren (2003) are based on the assumption that no shares of assets per unit of time are trade at the be…
Study analyzes order transitions in high, medium, and low market cap stocks using Markov chains.
We analyse the optimal exercise of an executive stock option (ESO) written on a stock whose drift parameter falls to a lower value at a change point, an exponentially distributed random time independent of the Brownian motion driving the stock. Two agents, who do not trade the stock, have differing information on the c…
Study optimal stock purchases under fluctuating market resilience.
TradeR uses RL to execute trades in real markets, minimizing surprise and catastrophe.
We model the impact costs of a strategy that trades a basket of correlated instruments, by extending to the multivariate case the linear propagator model previously used for single instruments. Our specification allows us to calibrate a cost model that is free of arbitrage and price manipulation. We illustrate our resu…
We provide an empirical investigation aimed at uncovering the statistical properties of intricate stock trading networks based on the order flow data of a highly liquid stock (Shenzhen Development Bank) listed on Shenzhen Stock Exchange during the whole year of 2003. By reconstructing the limit order book, we can extra…
We present a new microscopic stochastic model for an ensemble of interacting investors that buy and sell stocks in discrete time steps via limit orders based on individual forecasts about the price of the stock. These orders determine the supply and demand fixing after each round (time step) the new price of the stock …
Study uses machine learning to optimize stock trading strategies.
Paper proposes a fair stock trading strategy using multi-agent reinforcement learning.
Simulates realistic execution and costs in limit order books.
Paper tackles liquidating stocks using reinforcement learning.
We study the average price impact of a single trade executed in the NYSE. After appropriate averaging and rescaling, the data for the 1000 most highly capitalized stocks collapse onto a single function, giving average price shift as a function of trade size. This function increases as a power that is the order of 1/2 f…
Stock exchanges are considered major players in financial sectors of many countries. Most Stockbrokers, who execute stock trade, use technical, fundamental or time series analysis in trying to predict stock prices, so as to advise clients. However, these strategies do not usually guarantee good returns because they gui…
NoxTrader predicts stock returns using LSTM for profitable trading.
The paper addresses optimal execution for multi-asset portfolios using Ornstein-Uhlenbeck dynamics.
Solves optimal liquidation problem for stock price following geometric Brownian motion.
We provide some theoretical extensions and a calibration protocol for our former dynamic optimal execution model. The Hawkes parameters and the propagator are estimated independently on financial data from stocks of the CAC40. Interestingly, the propagator exhibits a smoothly decaying form with one or two dominant time…
Reinforcement learning is explored as a candidate machine learning technique to enhance existing analytical solutions for optimal trade execution with elements from the market microstructure. Given a volume-to-trade, fixed time horizon and discrete trading periods, the aim is to adapt a given volume trajectory such tha…
DanSmp predicts stock movement using a hybrid-relational MKG and dual attention networks.
Deep learning models predict stock prices with high accuracy.
Deep learning models predict stock prices with high accuracy.
The paper uses data science to predict stock trends of Amazon, Apple, Google, and Microsoft.
This paper conducts an empirically study on the trade package composed of a sequence of consecutive purchases or sales of 23 stocks in Chinese stock market. We investigate the probability distributions of the execution time, the number of trades and the total trading volume of trade packages, and analyze the possible s…
FinRL-X unifies trading components for AI and rule-based strategies.
Deep learning models predict stock prices with high accuracy and speed.
Motivated by the literature on investment flows and optimal trading, we examine intraday predictability in the cross-section of stock returns. We find a striking pattern of return continuation at half-hour intervals that are exact multiples of a trading day, and this effect lasts for at least 40 trading days. Volume, o…
Study analyzes price change patterns across different market capitalizations using Markov chains.
Study uses DNM theory to detect early warning signals of market instability.
Market impact is reduced when orders are filled with concentrated counterparts.
Based on the order flow data of a stock and its warrant, the immediate price impacts of market orders are estimated by two competitive models, the power-law model (PL model) and the logarithmic model (LG model). We find that the PL model is overwhelmingly superior to the LG model, regarding the robustness of the estima…
Model predicts Chinese stock market liquidity and customer order behavior.
We introduce a model in which a regulator employs mechanism design to embed her human capital beta signal(s) in a firm's capital structure, in order to enhance the value of her post career change indexed executive stock option contract with the firm. We prove that the agency cost of this revolving door behavior increas…
Hybrid RL method optimizes trading by balancing continuous and discrete actions.