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Expert Systems With Applications 54 (2016) 193–207

Contents lists available at ScienceDirect

Expert Systems With Applications

journal homepage: www.elsevier.com/locate/eswa

Evaluating machine learning classiﬁcation for ﬁnancial trading:

An empirical approach

Eduardo A. Gerlein

a , c , ∗

, Martin McGinnity

a , b

, Ammar Belatreche

, Sonya Coleman

Intelligent Systems Research Centre, University of Ulster, Londonderry, UK

School of Science and Technology, Nottingham Trent University, Nottingham, UK

Electronics Department, Pontiﬁcia Universidad Javeriana, Bogotá, Colombia

a r t i c l e i n f o

Keywords:

Trading

Financial forecasting

Computer intelligence

Data mining

Machine learning

FOREX markets

a b s t r a c t

Technical and quantitative analysis in ﬁnancial trading use mathematical and statistical tools to help in-

vestors decide on the optimum moment to initiate and close orders. While these traditional approaches

have served their purpose to some extent, new techniques arising from the ﬁeld of computational intel-

ligence such as machine learning and data mining have emerged to analyse ﬁnancial information. While

the main ﬁnancial engineering research has focused on complex computational models such as Neu-

ral Networks and Support Vector Machines, there are also simpler models that have demonstrated their

usefulness in applications other than ﬁnancial trading, and are worth considering to determine their ad-

vantages and inherent limitations when used as trading analysis tools. This paper analyses the role of

simple machine learning models to achieve proﬁtable trading through a series of trading simulations in

the FOREX market. It assesses the performance of the models and how particular setups of the models

produce systematic and consistent predictions for proﬁtable trading. Due to the inherent complexities of

ﬁnancial time series the role of attribute selection, periodic retraining and training set size are discussed

in order to obtain a combination of those parameters not only capable of generating positive cumulative

returns for each one of the machine learning models but also to demonstrate how simple algorithms

traditionally precluded from ﬁnancial forecasting for trading applications presents similar performances

as their more complex counterparts. The paper discusses how a combination of attributes in addition to

technical indicators that has been used as inputs of the machine learning-based predictors such as price

related features, seasonality features and lagged values used in classical time series analysis are used to

enhance the classiﬁcation capabilities that impacts directly into the ﬁnal proﬁtability.

1. Introduction

Data mining is the process of ﬁnding hidden patterns within

data using automatic or semi-automatic methods ( Witten, Frank,

& Hall, 2011 ). In particular, machine learning (ML) techniques

have shown impressive performance in solving real life classi-

ﬁcation problems in many different areas such as communica-

tions ( Di, 2007 ), internet traﬃc analysis ( Nguyen & Armitage,

2008 ), medical imaging ( Wernick, Yang, Brankov, Yourganov, &

Strother, 2010 ), astronomy ( Freed & Lee, 2013 ), document anal-

ysis ( Khan, Baharudin, Khan, & E-Malik, 2009 ), biology ( Zamani

& Kremer, 2011 ) and time series analysis ( Qi & Zhang, 2008 ).

∗

Corresponding author at: Electronics Department, Pontiﬁcia Universidad Javeri-

ana, Bogotá, Colombia. Tel. +5713208320 ext 5549 .

E-mail addresses: gerlein-e@email.ulster.ac.uk (E.A. Gerlein),

tm.mcginnity@ulster.ac.uk (M. McGinnity), a.belatreche@ulster.ac.uk (A. Belatreche),

sa.coleman@ulster.ac.uk (S. Coleman).

Although complex models such as Neural Networks (NN) and

Support Vector Machine (SVM) techniques are studied within

the ML ﬁeld, several other approaches also exist, characterized

by a greater degree of simplicity when compared with NN and

SVM. Despite this apparent simplicity, some of the ML tech-

niques may be well-suited for quantitative analysis within the

ﬁnancial industry, as their capabilities for ﬁnding hidden patterns

in large amounts of data may help in ﬁnancial forecasting for trad-

ing.

Financial trading of securities using technical and quantitative

analysis has been traditionally modelled by statistical techniques

for time series analysis such as the ARMA ( Box et al., 1994 ) and

ARIMA models, and the more sophisticated ARCH technique ( Engle,

1982 ). In contrast to these statistical approaches, complex mod-

els coming from the ML ﬁeld have emerged attempting to pre-

dict future movements of securities’ prices ( Yoo, Kim, & Jan, 2007 ).

The extensive literature has shown how some ML techniques spe-

cializing in classiﬁcation and regression tasks have demonstrated

http://dx.doi.org/10.1016/j.eswa.2016.01.018

194 E.A. Gerlein et al. / Expert Systems With Applications 54 (2016) 193–207

that they are well-suited for quantitative analysis within the ﬁ-

nancial industry, as their capabilities of ﬁnding hidden patterns

in large amounts of ﬁnancial data may help in derivatives pric-

ing, risk management and ﬁnancial forecasting. One of the most

published projects that uses such techniques in ﬁnancial applica-

tions is Standard & Poor’s Neural Fair Value 25 portfolio ( Smicklas,

2008 ), which selects on a weekly basis 25 stocks using an artiﬁcial

NN, from a total of 30 0 0 stocks yield relative to that of the S&P

500 index, attempting to outperform the market by calculating a

stock’s weekly fair value based on fundamental analysis. Particu-

larly for securities trading, the utility of complex models such as

NN, SVM and hybrid models ( Cai, Hu, & Lin, 2012 ) have been ex-

tensively studied and have led to promising results. Nevertheless,

information regarding the incorporation of such methods into trad-

ing ﬂoor operations tends to remain hidden to the public, for com-

mercial proprietary reasons ( Yamazaki & Ozasa, n.d.; Duhigg, 2006;

Patterson, 2016) .

In terms of ﬁnancial trading, analysts in the industry (usually

referred to as “quants”) have developed technical indicators that

are used to identify the most suitable moments to open and close

trades, and are possibly the most popular tools currently used

in technical trading. Published research that aims to incorporate

Computational Intelligence (CI) in ﬁnancial prediction shows how

those technical indicators have been used as inputs to ML models

to ﬁnd the hidden patterns or relationships among them, in order

to predict future prices, trends or a percentage of conﬁdence in

those predictions. With the possible exception of long term aver-

ages, those technical indicators are constructed using information of

prices over short periods in the past, no more than 20–30 trading

periods in order to incorporate the historical behaviour in a sin-

gle value. The selected trading period is part of the trading strat-

egy and might vary from long frames of 1 day to small frames of

1 min ute, or even smaller time windows as in the case of high fre-

quency trading. The construction of such indicators can be seen as

a process used in large scale time series data sets called dimension

reduction ( Wang et al., 2005 ) that attempts to transform the series

to another domain seeking a version of the data set that might be

much simpler to analyse. In contrast to time series analysis where

the data set is seen as a whole entity, ML classiﬁcation tasks con-

struct independent instances that are representative examples of

the concept to be learned.

Financial predictions that incorporate ML approaches construct

the training, test and off-sample data sets as a collection of in-

stances using popular technical indicators as reported in a number

of papers. Hence, an instance is created usually using the value of

the current price and the instantaneous values of the mentioned

indicators, generating a static picture of the situation of the market

for the exact time that the instance is constructed. In this scenario,

each instance , i.e. prices and their correspondent technical indica-

tors used as attributes, becomes itself an independent example of

the problem, which avoids the time dependence in the series, ap-

proaching the problem as simple classiﬁcation task rather than a

time series analysis in the strict meaning of the word. The hypoth-

esis in this case is that once a ML model is trained, it may be able

to classify individual instances using the technical indicators as at-

tributes, due to the fact that those unseen instances represent in

turn the invariant circumstances of the market at certain points

in time, and that the result of the classiﬁcation task can be inter-

preted as a trend forecast. The main implication of this hypothesis

is that the ﬁnancial forecasting can beneﬁt from the use of simpler

ML techniques rather than using complex time series analysis ap-

proaches, simplifying the use of computational resources while at

the same time avoiding indexing and ordering issues in the data

sets.

This paper addresses the question of the usefulness of low-

complex ML classiﬁers in ﬁnancial trading, and in particular

will demonstrate if such low-complexity binary classiﬁcation

approaches are able to generate consistent proﬁtable trading over

an extensive period of time. The paper’s main contribution resides

in the fact that simple machine learning models that tradition-

ally have been precluded from ﬁnancial applications, as opposed

to the more complex NN and SVM, can be used to generate prof-

itable transactions on the long term with the correct combina-

tion of periodic retraining, training set size and attribute selection.

The work is motivated mainly by the results reported in ( Barbosa,

2011 ), which claims outstanding ﬁnancial results using simple clas-

siﬁers. In this case, a simple model is characterized by the low

computational requirements for both the training and the classi-

fying process due to the inherent simplicity of the learned model

(instance-based classiﬁers, decision trees and rule-based learners).

While the main objective of ML classiﬁcation is to maximise ac-

curacy, this might not be the best metric to evaluate the perfor-

mance of such systems when used in the context of ﬁnancial trad-

ing. The most important metric when assessing trading strategies

is undoubtedly proﬁtability, reﬂected in this paper as cumulative

return over a speciﬁc trading period. In this paper, it is developed

an empirical comparison between average accuracy and cumula-

tive return as the main metrics of performance of a set of six ma-

chine learning models (OneR, C4.5, JRip, Logistic Model Tree, KStar

and Naïve Bayes). The models produce a binary classiﬁcation used

later to predict price movement (up or down in the next trading

period) for the USDJPY currency pair using six hour time frames

over a trading time-frame of six years. The six hour time frame

was selected to be able to validate and compare with the results

reported in ( Barbosa & Belo, 2008b ), although the same approach

used in the experiments can be applied to different time frames. A

set of experiments was conducted where the results of modifying

three variables were studied: training set size, period of retraining

and number of attributes for the training and test sets. The results

show relative low accuracy, only a few points over 50%, but at the

same time, very promising results in terms of proﬁtability. Later,

further experiments are conducted on simulated trades over the

same period of time using EURGPB and EURUSD currency pairs,

and similar results are reported.

The remainder of the paper is organised as follows: Section 2

discusses related work using ML in ﬁnancial forecasting applica-

tions. Section 3 presents the general experimental setup, describ-

ing the data sets, and the attribute selection to feed the models

and brieﬂy describes the different ML algorithms used in the ex-

periments as well as their particular parameter set up in order to

present comprehensive information for future experiment replica-

tion. Section 4 discusses the results detailing each one of the sim-

ulated trading scenarios. Finally, Section 5 concludes the paper and

explores future work.

2. Machine learning in ﬁnancial forecasting

Within the ﬁnancial trading chain two main areas can be iden-

tiﬁed, where the use of ML techniques have reported particularly

successful implementations: derivatives pricing, risk management

and ﬁnancial forecasting. Financial forecasting is possibly the most

important application within ML for data mining in Capital Mar-

kets. ML techniques for forecasting include expert or rule-based

systems, decision trees, NNs and genetic computing. Applications

within the trading cycle such as Algorithmic Trading Engines

and

Algorithmic trading engines in the buy side, are essentially semi-automatic

computer aided systems that help retail investors to take the best ﬁnancial de-

cisions in terms of high returns at lowest possible risks, and by means of pro-

gramming speciﬁc rules the system are capable of transmitting pre- and post-trade

data about quotes and trades to other market participants ( Hendershott, 2003 Chan,

2008 ). The literature also reports the use of algorithmic trading engines in the sell

E.A. Gerlein et al. / Expert Systems With Applications 54 (2016) 193–207 195

Order Matching Engines

( Hendershott, 2003 ) have the potential to

incorporate different levels of CI, and in particular ML techniques.

The majority of existing ML-based methods for trading use techni-

cal indicators as part of the training attributes extracted from the

ﬁnancial series instead of using the raw prices as a training vector.

Maggini, Giles, and Horne (1997 ) had pointed out that there is an

inherent diﬃculty in generating statistically reliable technical indi-

cators , due to the fact that the rules inferred to produce accurate

predictions are changing continually in ﬁnancial time series, and

that it is even possible to evidence the presence of a high number

of contradictory instances in the training sets due to the fact that

market data exhibit statistical characteristics found in other types

of time series. This situation is reﬂected in the large volume of pa-

pers ( Chen & Shih, 2006; Eng, Li, Wang, & Lee, 2008; Kim, 2003;

Lee, Park, O, Lee, & Hong, 2007; Li & Kuo, 2008; Tenti, 1996 ) that

have reported accuracies under 60% with ML models which have

shown impressive performance in areas other than ﬁnancial pre-

diction. According to Sewell and Yan (2008 ), for certain markets

such as futures and FOREX, it may be necessary to generate pre-

dictions with an accuracy marginally higher than the one obtained

by a random classiﬁer to obtain proﬁts due to two main factors:

low costs and leverage.

Artiﬁcial NNs are probably the most common method utilized

in ﬁnancial forecasting. Early works such as that of Tenti ( Tenti,

1996 ) compared the performance of three recurrent neural net-

works based on their returns in the simulated forecasts on cur-

rency futures. The inputs to the networks include technical indica-

tors (average directional movement index, trend movement index

and the rate of change). Tenti also takes into account trading costs,

and reports positive returns in the trading simulation, demonstrat-

ing that NN techniques can indeed be used as forecasting tools. Lu

and Wu (2009 ) show another example of stock market forecasting

with artiﬁcial neural networks. The paper compared a NN model’s

performance against the ARIMA model, predicting the direction of

future values of the S&P 500 Index. The experiments showed that

the NN-based system outperformed the ARIMA model only in sta-

ble market conditions, since the system only exhibits a modest 23%

level of accuracy against the ARIMA’s 42% in more volatile scenar-

ios. Kamruzzaman and Sarker (2003 ) compared the performance

of the ARIMA model with several NN models when forecasting ex-

change rates of currency pairs in the FOREX market. The NNs were

trained with back-propagation, scaled conjugate gradient and back-

propagation with Bayesian regularization, using exchange rates in

the previous period and moving averages as inputs. The accuracy

in the prediction as well as the normalized mean square error and

the mean absolute error were used to measure global performance,

showing that all NN models outperformed the ARIMA model with

an accuracy of 80%. Taking into account that only the best results

obtained were reported and that, in general ML techniques do not

present high levels of accuracy with unseen data that differ sig-

niﬁcantly from the training sets, these impressive results must be

considered with care. ( McDonald, Coleman, McGinnity, Li, & Bela-

treche, 2014 ), investigate the effectiveness of a number of machine

learning algorithms and their combinations at generating one-step

ahead forecasts of a number of ﬁnancial time series. The authors

found that hybrid models, consisting of a linear statistical model

and a non-linear machine learning algorithm, are effective at fore-

side, brokers and investment banks, to manage the vast amount of daily orders

from the clients usually arrived before the market opening hours, deciding how

and when to execute those orders taking into account existing regulations and at

the same time, minimizing the effect on prices [53].

An order matching engine is a trading system that facilitates the exchange of

ﬁnancial instruments between multiple parties by means of a transaction algorithm

that translates orders into trades pairing buyers with sellers in terms of transaction

prices and quantities ( Hendershott, 2003 ).

casting future values of the series, particularly in terms of the fu-

ture direction of the series.

While artiﬁcial NNs are considered the most popular technique

in ﬁnancial forecasting, other reports also show promising results

with different data mining models. Using SVM, Kim (2003 ) at-

tempted to forecast daily price directions of the KOSPI stock in-

dex. The model used technical analysis indicators (momentum,

Williams %R and commodity channel index) as inputs and the best

accuracy obtained after training several models with different pa-

rameters was 57.83%. The work also presented a comparison with

the back propagation NN (with 54.76% of accuracy) and nearest-

neighbour model (51.98% of accuracy). This middle-range level of

accuracy is expected due to the high volatility of ﬁnancial time se-

ries, but to achieve it several models needed to be trained. This

study concludes that no single model is perfectly suited in all mar-

ket conditions, and even more importantly, the models must be

retrained frequently to maintain the forecasts accurate. In another

study, Tay and Cao (2001 ) also compared SVM with back propa-

gation NN to forecast prices of ﬁve types of futures contracts. On

average, the SVM approach obtained better accuracy than the back

propagation NN, but also in middle-range levels: 47.7% by SVM

against 45.0% obtained by back propagation NN. SVM also out-

performed a back propagation NN in the work presented by Chen

and Shih (2006 ), where these techniques were used to predict the

value of six Asian indices, obtaining 57.2% level of accuracy with

SVM and 56.7% with NN models.

Apart from NN and SVM, in the study presented in ( Maggini et

al., 1997 ) the authors proposed a heuristic method to select dif-

ferent inputs for a non-linear machine learning algorithm, discard-

ing the option of time series prediction and limiting the problem

to classiﬁcation to determine the class of price variation, although

there is no speciﬁcation in the paper if the problem is restricted to

a binary (up/down) or multi-class classiﬁcation (up/down/stable)

problem. The selected method is the K-nearest neighbours with a

sliding window dataset used to retrain the model at every time

step. The metric selected to evaluate the performance was mean

square error. The paper concludes that it is impossible to predict

price variation with enough accuracy, discouraging the use of this

approach and attributing the poor results to the weakness of the

model and a poor selection of inputs that might affect the price

movement. Nevertheless, the authors seem to be focused on ac-

curacy and do not provide any ﬁnancial results in the trading pe-

riod used in the simulation. J. Li, Tsang, and Park (1999 ) predicted

expected return in the Dow Jones Industrial Average using Finan-

cial Genetic Programming, and compared it with random decisions

and C4.5 decision tree classiﬁcation. They used some simple tech-

nical indicators such as short and long term moving averages and

long and short term price ﬁlters. The authors did not focus only

on accuracy of predictions but also on annualized returns and pos-

itive returns of a simulated set of investments following the pre-

dictions. They reported over 60% in positive returns and over 40%

in annualized returns over a trading period of four years for the

Genetic Programming model and over 40% for the C4.5 decision

tree. In both cases, the results represent an outstanding ﬁnancial

return even without taking into account trading costs, which sug-

gest that technical indicators might generate proﬁtable rule-based

models to predict complex ﬁnancial time series.

Barbosa and Belo presented several reports using single agents

to execute algorithmic trading in the FOREX Market ( Barbosa &

Belo, 2008b ), a micro-society managing a hedge fund ( Barbosa &

Belo, 2010 ) and a multi-agent system for multiple markets trad-

ing ( Barbosa & Belo, 2008a ), focusing on proﬁtability and maxi-

mum drawdown as performance metrics. The proposed architec-

ture is divided into three modules in charge of (a) predicting the

immediate next trend by means of an ensemble of binary classi-

ﬁers, (b) a risk management module to decide how much to invest

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