Improving risk-adjusted performance in high-frequency trading

254 Pages·2017·4.27 MB·English

by  Vincent Vella

Improving risk-adjusted performance in
high-frequency trading: The role of
fuzzy logic systems
Vincent Vella
A thesis submitted for the degree of
Doctor of Philosophy
Centre for Computational Finance and Economic Agents (CCFEA)
University of Essex
CCFEA January 2017

Abstract
Inrecentyears,algorithmicandhigh-frequencytradinghavebeenthesubjectofincreasing
riskconcerns. Ageneraltheme that weadoptinthisthesis isthattradingpractitionersare
predominantlyinterestedinrisk-adjustedperformance. Likewise,regulatorsaredemanding
stricterriskcontrols.
First,wescrutiniseconventionalAImodeldesignapproacheswiththeaimtoincreasethe
risk-adjustedtradingperformanceoftheproposedfuzzylogicmodels. Weshowthatapplying
risk-returnobjectivefunctionsandaccountingfortransactioncostsimproveout-of-sample
results. Our experiments identify that neuro-fuzzy models exhibit superior performance
stability across multiple risk regimes when compared to popular neural network models
identified in AI literature. Moreover, we propose an innovative ensemble model approach
which combines multiple risk-adjusted objective functions and dynamically adapts risk-
toleranceaccordingtotime-varyingrisk.
Next,weextendourfindingstothemoneymanagementaspectsoftradingalgorithms. We
introduceaneffectivefuzzylogicapproachwhichdynamicallydiscriminatesacrossdifferent
regionsinthetrendandvolatilityspace. Themodelprioritiseshigherperformingregionsat
anintradaylevelandadaptscapitalallocationpolicieswiththeobjectivetomaximiseglobal
risk-adjustedperformance.
Finally,weexploretradingimprovementsthatcanbeattainedbyadvancingourtype-1
fuzzylogicideastohigher orderfuzzysystemsinviewofthe increasednoise(uncertainty)
that is inherent inhigh-frequency data. Wepropose an innovativeapproach to design type-2
iv
modelswith minimal increaseindesign andcomputationalcomplexity. As afurtherstep, we
identifya relationshipbetween theincreased tradingperformance benefitsof theproposed
type-2modelandhigherlevelsoftradingfrequencies.
Inconclusion,thisthesissetsaframeworkforpractitioners,researchersandregulatorsin
thedesignoffuzzylogicsystemsforbettermanagementofriskinthefieldofalgorithmic
andhigh-frequencytrading.
Iwouldliketodedicatethisthesistomylovingparents...

Acknowledgements
My most sincere thanks must go to my supervisor, Dr. Wing Lon Ng. His deep insight,
admirable dedication, disciplined and structured approach, andkeen eye fordetail, were not
justkeyattributesthatassistedmeinthecompletionofthisthesis, buthavealsoinspiredme
withinvaluablelifelessons. ForthisIamforeverinhisdebt.
IwouldalsoliketothankProf. EdwardTsangforhisassistanceanddirection,especially
towardstheendofmyjourney. Iwillremaingratefulforhisvaluablefeedback.
My appreciation also goes towards Prof. Hani Hagras for his helpful pointers and
discussions.
Lastbutnotleast,thisthesiswouldnothavebeenpossiblewithoutthepersonwhowas
alwaysbehindme-mywifeMaryAnn. Herunconditionalsupportwasalwaysasourceof
encouragementthatkeptmegoingduringtheresearchjourney.

Related Publications
Vella, V. and Ng, W. L. (2014a). Enhancing risk-adjusted performance of stock market
intradaytradingwithNeuro-Fuzzysystems. Neurocomputing,141:170–187.
Vella,V.andNg,W.L.(2014b). Enhancingintradaytradingperformanceofneuralnetwork
usingdynamicvolatilityclusteringfuzzyfilter. InProceedingsofthe2014IEEEConference
onComputationalIntelligenceforFinancialEngineering&Economics(CIFEr),London,
pages465–472. IEEE.
Vella, V. and Ng, W. L. (2015). A Dynamic Fuzzy Money Management Approach for
Controllingthe Intraday Risk-adjustedPerformanceof AITrading Algorithms. Intelligent
SystemsinAccounting,FinanceandManagement,22(2):153-178.
Vella, V. and Ng, W. L. (2016). Improving Risk-adjusted Performance in High Frequency
TradingUsingIntervalType-2FuzzyLogic. ExpertSystemswithApplications,55:70-86.