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Found 21 papers, 12 papers with code
Epistemic Limits of Empirical Finance: Causal Reductionism and Self-Reference
The clarion call for causal reduction in the study of capital markets is intensifying.
Anomalous diffusion and price impact in the fluid-limit of an order book
Concretely, we demonstrate the price impact for flash limit-orders and market orders and show how the numerical method generate kinks in the price impact.
Variations on the Reinforcement Learning performance of Blackjack
Blackjack or "21" is a popular card-based game of chance and skill.
Many learning agents interacting with an agent-based market model
Further, we examine whether the inclusion of optimal execution agents that can learn is able to produce dynamics with the same complexity as empirical data.
A simple learning agent interacting with an agent-based market model
We consider the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event driven agent-based financial market model.
Simulation and estimation of an agent-based market-model with a matching engine
This is possible when agent-based model interactions occur asynchronously via order matching using a matching engine in event time to replace sequential calendar time market clearing.
Geometric insights into robust portfolio construction
We investigate and extend the results of Golts and Jones (2009) that an $\alpha$-weight angle resulting from unconstrained quadratic portfolio optimisations has an upper bound dependent on the condition number of the covariance matrix.
The efficient frontiers of mean-variance portfolio rules under distribution misspecification
Mean-variance portfolio decisions that combine prediction and optimisation have been shown to have poor empirical performance.
Simulation and estimation of a point-process market-model with a matching engine
Here we consider a 10-variate Hawkes process with simple rules to simulate common order types which are submitted to a matching engine.
Calibrating an adaptive Farmer-Joshi agent-based model for financial markets
We replicate the contested calibration of the Farmer and Joshi agent based model of financial markets using a genetic algorithm and a Nelder-Mead with threshold accepting algorithm following Fabretti.
The Epps effect under alternative sampling schemes
Concretely, we find that the Epps effect is present under all three definitions of time and that correlations emerge faster under trade time compared to calendar time, whereas correlations emerge linearly under volume time.
Learning low-frequency temporal patterns for quantitative trading
The framework is proved on daily sampled closing time-series data from JSE equity markets.
Using the Epps effect to detect discrete processes
The Epps effect is key phenomenology relating to high frequency correlation dynamics in financial markets.
A Framework for Online Investment Algorithms
This article provides a workflow that can in-turn be embedded into a process level learning framework.
Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms
We implement and test kernel averaging Non-Uniform Fast Fourier Transform (NUFFT) methods to enhance the performance of correlation and covariance estimation on asynchronously sampled event-data using the Malliavin-Mancino Fourier estimator.
Revisiting the Epps effect using volume time averaging: An exercise in R
We revisit and demonstrate the Epps effect using two well-known non-parametric covariance estimators; the Malliavin and Mancino (MM), and Hayashi and Yoshida (HY) estimators.
Systematic Asset Allocation using Flexible Views for South African Markets
The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and derives a forward looking distribution that is consistent with this view while remaining close as possible to the prior distribution.
Learning the dynamics of technical trading strategies
A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. (2012) on both daily sampled and intraday time-scales.
Fast Super-Paramagnetic Clustering
We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm.
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
We implement a master-slave parallel genetic algorithm (PGA) with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions.
