no code implementations • 17 Mar 2014 • Dieter Hendricks, Diane Wilcox, Tim Gebbie
We implement a master-slave parallel genetic algorithm (PGA) with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions.
1 code implementation • 30 Mar 2020 • Andrew Paskaramoorthy, Terence van Zyl, Tim Gebbie
This article provides a workflow that can in-turn be embedded into a process level learning framework.
no code implementations • 12 Oct 2019 • Ann Sebastian, Tim Gebbie
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.
no code implementations • 5 Dec 2019 • Patrick Chang, Roger Bukuru, Tim Gebbie
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.
no code implementations • 20 Apr 2021 • Ivan Jericevich, Murray McKechnie, Tim Gebbie
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.
no code implementations • 19 Jun 2021 • Andrew Paskaramoorthy, Tim Gebbie, Terence van Zyl
Mean-variance portfolio decisions that combine prediction and optimisation have been shown to have poor empirical performance.
no code implementations • 13 Jul 2021 • Lara Dalmeyer, Tim Gebbie
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.
no code implementations • 13 Mar 2023 • Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
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.
no code implementations • 9 Oct 2023 • Derick Diana, Tim Gebbie
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.
no code implementations • 28 Nov 2023 • Daniel Polakow, Tim Gebbie, Emlyn Flint
The clarion call for causal reduction in the study of capital markets is intensifying.
1 code implementation • 9 Aug 2023 • Avish Buramdoyal, Tim Gebbie
Blackjack or "21" is a popular card-based game of chance and skill.
2 code implementations • 21 May 2020 • Patrick Chang, Etienne Pienaar, Tim Gebbie
The Epps effect is key phenomenology relating to high frequency correlation dynamics in financial markets.
1 code implementation • 23 Nov 2020 • Patrick Chang, Etienne Pienaar, Tim Gebbie
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.
2 code implementations • 5 Mar 2020 • Patrick Chang, Etienne Pienaar, Tim Gebbie
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.
1 code implementation • 12 Aug 2020 • Joel da Costa, Tim Gebbie
The framework is proved on daily sampled closing time-series data from JSE equity markets.
1 code implementation • 5 May 2021 • Ivan Jericevich, Patrick Chang, Tim Gebbie
Here we consider a 10-variate Hawkes process with simple rules to simulate common order types which are submitted to a matching engine.
1 code implementation • 22 Aug 2022 • Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
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.
2 code implementations • 17 Aug 2021 • Ivan Jericevich, Patrick Chang, Tim Gebbie
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.
1 code implementation • 6 Mar 2019 • Nicholas Murphy, Tim Gebbie
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.
2 code implementations • 5 Oct 2018 • Lionel Yelibi, Tim Gebbie
We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm.
5 code implementations • 2 Aug 2019 • Lionel Yelibi, Tim Gebbie
We consider the problem of fast time-series data clustering.