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.
The paper analyzes trade execution strategies for large traders in a stochastic market environment.
Paper develops models for better HFT and algorithmic trading.
Optimal trading strategy with unobservable pricing errors for co-integrated assets.
Trading strategies are limited by position limits, leading to a finite number of unique strategies.
A deep Q-learning strategy optimizes portfolio trading efficiency.
We devise a USDCHF trading strategy using the dynamics of gold as a filter. Our strategy involves modelling both USDCHF and gold using a coupled hidden Markov model (CHMM). The observations will be indicators, RSI and CCI, which will be used as triggers for our trading signals. Upon decoding the model in each iteration…
Paper optimizes energy trading on DA markets using RL.
The study uses State Switching Markov Autoregressive models to identify and predict market regimes.
Proposes a new VIX futures trading strategy based on term structure modeling.
New framework for pricing derivatives in Hermite markets with reduced arbitrage opportunities.
Reinforcement learning improves trading performance on stock exchanges.
Strict local martingales may admit arbitrage opportunities with respect to the class of simple trading strategies. (Since there is no possibility of using doubling strategies in this framework, the losses are not assumed to be bounded from below.) We show that for a class of non-negative strict local martingales, the s…
We study optimal trade execution strategies in financial markets with discrete order flow. The agent has a finite liquidation horizon and must minimize price impact given a random number of incoming trade counterparties. Assuming that the order flow is given by a Poisson process, we give a full analysis of the prop…
Develops optimal liquidation strategies with stochastic price impact.
Study analyzes stock order transitions during US-China trade war using Markov chains.
Optimal futures trading strategy in a changing market model.
Study optimal liquidation strategies under partial information in high-frequency trading.
The paper proposes a new order slicing strategy to reduce market impact in large-volume trading.
In this research, we develop a trading strategy for the discrete-time optimal liquidation problem of large order trading with different market microstructures in an illiquid market. In this framework, the flow of orders can be viewed as a point process with stochastic intensity. We model the price impact as a linear fu…
Optimizes trading strategy considering alpha decay and transaction costs.
Modeling trading volume curves using hierarchical Poisson processes.
This paper studies the optimal VIX futures trading problems under a regime-switching model. We consider the VIX as mean reversion dynamics with dependence on the regime that switches among a finite number of states. For the trading strategies, we analyze the timings and sequences of the investor's market participation,…
In this work, we study a dynamic portfolio optimization problem related to pairs trading, which is an investment strategy that matches a long position in one security with a short position in another security with similar characteristics. The relationship between pairs, called a spread, is modeled by a Gaussian mean-re…
Hidden Markov model predicts profitable statistical arbitrage in Shanghai crude oil futures.
Paper finds optimal selling rule for pairs trading with stock constraints.
Market manipulation is a strategy used by traders to alter the price of financial securities. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools h…
The paper analyzes trading strategies using exponential moving averages.
QTNet uses deep reinforcement learning to automate trading strategies.
We analyze a tractable model of a limit order book on short time scales, where the dynamics are driven by stochastic fluctuations between supply and demand. We establish the existence of a limiting distribution for the highest bid, and for the lowest ask, where the limiting distributions are confined between two thresh…
This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to non-differentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex s…
In this work, we consider the optimal portfolio selection problem under hard constraints on trading volume amounts when the dynamics of the risky asset returns are governed by a discrete-time approximation of the Markov-modulated geometric Brownian motion. The states of Markov chain are interpreted as the states of an …
Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders we fit hidden Markov models to the time series of the sign of the tick by tick inventory var…
The paper uses PCA and HMM to forecast stock returns outperforming buy-and-hold.
A new system improves smart beta portfolio performance by reducing drawdowns and enhancing risk-adjusted returns.
In this paper we solve the discrete time mean-variance hedging problem when asset returns follow a multivariate autoregressive hidden Markov model. Time dependent volatility and serial dependence are well established properties of financial time series and our model covers both. To illustrate the relevance of our propo…
Investigates JM for reducing downside risk in market regimes.
Robo-advisors use MPC to create dynamic investment strategies.
This work models market regimes using CTMSTOU and simulates trading policies.
Technical trading rules and linear regressive models are often used by practitioners to find trends in financial data. However, these models are unsuited to find non-linearly separable patterns. We propose a decision tree forecasting model that has the flexibility to capture arbitrary patterns. To illustrate, we constr…
Develops RL for optimal market-making in non-Markov processes.
Constant Proportion Portfolio Insurance (CPPI) is an investment strategy designed to give participation in the performance of a risky asset while protecting the invested capital. This protection is however not perfect and the gap risk must be quantified. CPPI strategies are path-dependent and may have American exercise…
Hierarchical hidden Markov models predict market trends in financial time series.
AlphaCFG discovers alpha factors using grammar-guided search.
We propose a microstructural modeling framework for studying optimal market making policies in a FIFO (first in first out) limit order book (LOB). In this context, the limit orders, market orders, and cancel orders arrivals in the LOB are modeled as Cox point processes with intensities that only depend on the state of …
Improved DRQN-ARBR model for better stock trading performance.
We study a an optimal high frequency trading problem within a market microstructure model designed to be a good compromise between accuracy and tractability. The stock price is driven by a Markov Renewal Process (MRP), while market orders arrive in the limit order book via a point process correlated with the stock pric…
New trading strategy beats traditional grid in crypto markets.
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…