Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support

Leigh, William; Purvis, Russell L.; Ragusa, James M.

doi:10.1016/s0167-9236(01)00121-x

Cited by 229 publications

(97 citation statements)

References 41 publications

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“…Moreover, client propensity to subscribe a bank product may evolve through time (e.g., changes in the economic environment). Hence, for achieving a robust predictive evaluation we adopt the more realistic fixedsize (of length W ) rolling windows evaluation scheme that performs several model updates and discards oldest data [18]. Under this scheme, a training window of W consecutive contacts is used to fit the model and then we perform predictions related with the next K contacts.…”

Section: Discussionmentioning

confidence: 99%

A data-driven approach to predict the success of bank telemarketing

Moro

Cortez

Rita

2014

Decision Support Systems

671

373

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We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DT), neural network (NN) and support vector machine.Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation phase, using the most recent data (after July 2012) and a rolling windows scheme. The NN presented the best results (AUC=0.8 and ALIFT=0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers.

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Section: Discussionmentioning

confidence: 99%

A data-driven approach to predict the success of bank telemarketing

Moro

Cortez

Rita

2014

Decision Support Systems

671

373

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“…As the volatility of the environments of organizations increases, existing models are unable to cope with this problem (Chang, Lai, and Lai 2006). Dealing with emerging risks requires a combination of established and new models, as well as more efficient, unified, and adaptive applications (Leigh, Purvis, and Ragusa 2002). Additionally, systems should incorporate expert knowledge (Barnett 1988), which is especially valuable due to its causal nature (Nadkarni and Shenoy 2004).…”

Section: 3mentioning

confidence: 99%

Improving Environmental Scanning Systems Using Bayesian Networks

Welter¹,

Mayer

Quick

2013

Bus Res

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“…Yao and Tan [34] present some evidence for the applicability of neural network (NN) models in predicting currency exchange rates, where time series data and technical analyses -like the moving average to discover the principles of currency exchange rate movement -are fed to a NN. Leigh et al [35] show the prospects for applicating the modern approach of hybrid methods to assess buying opportunities in the stock market by TA and NN. Lam [36] studies the applicability of NNs -especially the back propagation algorithm -to integrating fundamental and technical analyses for the forecasting of financial performance.…”

Section: The Structurementioning

confidence: 99%

Developing a Modular Portfolio Selection Model for Short-Term and Longterm Market Trends and Mass Psychology

Jasemi

Kimiagari

2011

The South African Journal of Industrial Engineering

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In an effort to model stock markets, many researchers have developed portfolio selection models to maximise investor satisfaction. However, this field still needs more accurate and comprehensive models. Development of these models is difficult because of unpredictable economic, social, and political variables that affect stock market behaviour. In this paper, a new model with three modules for portfolio optimisation is presented. The first module derives the efficient frontier through a new approach; the second presents an intelligent mechanism for emitting trading signals; while the third module integrates the outputs of the first two modules. Some important features of the model in comparison with others are: 1) consideration of investors' emotions -the psychology of the market -that arises from the three above-mentioned factors; 2) significant loosening of simplifying assumptions about markets and stocks; and 3) greater sensitivity to new data. OPSOMMINGIn 'n poging om aandelemarkte te modelleer het verskeie navorsers portefeulje-seleksiemodelle ontwikkel om beleggers se tevredenheid te maksimiseer. Desnieteenstaande word meer akkurate en omvattende modelle benodig. Die ontwikkeling van hierdie modelle word bemoeilik deur die onvoorspelbare ekonomiese, sosiale en politiese veranderlikes wat aandelemarkte se gedrag raak. In hierdie artikel word 'n nuwe model voorgehou wat bestaan uit drie modules vir portefeulje-optimisering. Die eerste module bepaal die doelmatigheidsgrens op 'n nuwe metode; die tweede hou 'n intelligente meganisme voor om transaksieseine te lewer terwyl die derde module die uitsette van die eerste twee modules integreer. Sommige van die belangrike eienskappe van die model wat dit van ander onderskei is: 1) konsiderasie van die beleggers se emosies -die sielkunde van die mark -wat ontstaan vanweë die genoemde faktore; 2) betekenisvolle verslapping van die vereenvoudigende aannames oor market en aandele; en 3) verhoogde sensitiwiteit tot nuwe data.

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Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support

Cited by 229 publications

References 41 publications

A data-driven approach to predict the success of bank telemarketing

A data-driven approach to predict the success of bank telemarketing

Improving Environmental Scanning Systems Using Bayesian Networks

Developing a Modular Portfolio Selection Model for Short-Term and Longterm Market Trends and Mass Psychology

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