Stock market prediction and Portfolio selection models: a survey

Rather, Akhter Mohiuddin; Sastry, V. N.; Agarwal, Arun

doi:10.1007/s12597-016-0289-y

Cited by 54 publications

(25 citation statements)

References 129 publications

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“…Hence, it is compulsory to ensure that the provided parameters are appropriate and reliable. Several approaches have been introduced to overcome such problems in order to improve existing forecasting model accuracy, such as ANN (Hipp and Yates, 2011;Rather et al, 2017;Alwee et al, 2013;Yeh, 2013) and SVR (Wu et al, 2009;De Oliveira and Ludermir, 2014;Wu and Lu, 2012).…”

Section: Hybrid Techniquesmentioning

confidence: 99%

“…Most artificial intelligence models are sensitive to parameters. Such problems have been addressed by several researchers, especially in ANN (Yeh, 2013;Rather et al, 2017;Hipp and Yates, 2011) and SVR (Wu et al, 2007;Wu et al, 2009;De Oliveira and Ludermir, 2014). These researchers have applied a promising solution to such problems by integrating other artificial intelligence techniques into existing artificial intelligence models, developing a new hybrid model (Wu et al, 2009;De Oliveira and Ludermir, 2014;Hipp and Yates, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Techniques and External Factors used in Crime Forecasting in Violence and Property: A Review

Nabi

Saeed

Haron

2020

Journal of Computer Science

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Crime forecasting is beneficial in providing useful information to authorities in planning effective crime prevention measures. The two types of analysis used in crime forecasting are univariate and multivariate. Comparatively, multivariate analysis provides better forecasting accuracy because of its ability to discover crime patterns not previously seen. Crime is strongly influenced by several external factors, including economic, social and demographic. Hence, an analysis is needed to identify and select relevant factors that influence crime and can later be used to improve forecasting accuracy. Neighborhood Component Analysis (NCA) is a reliable form of analysis for identifying significant relationships between factors and crime data. Several model types have been introduced in crime forecasting, including statistical and artificial intelligence models. Recently, the artificial intelligence model has come into favour because of its ability to handle nonlinearity patterns in crime data well. Within the artificial intelligence model, Gradient Tree Boosting (GTB) shows good performance as it produces a robust and reliable forecast result. GTB uses least square function as a loss function for error fitting during training. Findings show that, in addition to using least square function, implementing other standard mathematical functions that fit to the crime data increases forecasting accuracy. In other cases, both NCA and GTB are sensitive to parameters input. Dragonfly Algorithm (DA) is a promising, nature inspired metaheuristic algorithm that is capable of solving such problems.

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Section: Hybrid Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques and External Factors used in Crime Forecasting in Violence and Property: A Review

Nabi

Saeed

Haron

2020

Journal of Computer Science

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“…In the last decade, the application of artificial intelligence techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM), fuzzy logic and genetic programming in crime forecasting has been extensively studied by researchers due to their capability to produce high forecasting performance accuracy. This is because artificial intelligence techniques possess some nonlinear functions which are able to detect nonlinear patterns in data (Rather et al, 2017). Hence, they are able to discover a new crime pattern that never occurred in the past (Alwee, 2014).…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Gradient Tree Boosting Using Dragonfly Algorithm in Crime Forecasting and Analysis

Khairuddin¹,

Ali²,

Alwee³

et al. 2019

Journal of Computer Science

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Crime forecasting and analysis are very important in predicting future crime patterns and beneficial to the authorities in planning effective crime prevention measures. One of the challenges found in crime analysis is the crime data itself as its form, representation and distribution are varied and unpredictable. To handle such data, most researchers have been focusing on applying various Artificial Intelligence (AI) techniques as an analytical tool. Among them, Gradient Tree Boosting (GTB) is a newly emerged AI technique for forecasting especially in crime analysis. GTB possesses a unique feature among other AI techniques which is its robustness towards any data representation and distribution. Subsequently, this study would like to adopt GTB in modelling crime rates based on 8 defined crime types. Similar to other AI techniques, GTB's overall performance is heavily influenced by its input parameter configuration. To assess such a challenge, this study would like to propose a hybrid DA-GTB crime forecasting model that is equipped with a metaheuristic optimization algorithm called Dragonfly Algorithm (DA) in optimizing GTB's three main parameters namely number of trees, size of individual trees and learning rate. From the experimental result obtained, the application of DA for parameter optimization yielded a positive impact in enhancing GTB forecasting performance as it produced the smallest error compared to nonoptimized GTB. This indicates that the proposed model is able to perform well using time series data with a limited and small sample size.

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“…There are other review studies on artificial intelligence and ANN based financial forecasting methods such as (Bahrammirzae, 2010), (Rather, Sastry, & Agarwal, 2017), (Zhang, Patuwo, & Hu, 1998), (Adya & Collopy, 1998), (Paliwal & Kumar, 2009), (Atsalakisa & Valavanisb, 2009). For example, (Bahrammirzaee, 2010) reviewed comparative studies where ANN, expert systems (ES) and hybrid systems were compared each other and also with traditional statistical methods.…”

Section: Introductionmentioning

confidence: 99%

“…For example, (Bahrammirzaee, 2010) reviewed comparative studies where ANN, expert systems (ES) and hybrid systems were compared each other and also with traditional statistical methods. (Rather et al, 2017) described a more general framework by separating studies based on single asset prediction models (which contains autoregressive moving average, singular and hybrid models) with portfolio selection models. (Paliwal & Kumar, 2009) reviewed comparative studies of multilayered feedforward neural networks and statistical techniques used for prediction and classification in the areas of accounting and finance, health and medicine, engineering and manufacturing, marketing, general applications.…”

Section: Introductionmentioning

confidence: 99%

Stock Market Prediction Performance of Neural Networks: A Literature Review

İcan¹,

Çeli̇k²

2017

IJEF

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In this paper, previous studies featuring an artificial neural networks based prediction model have been reviewed. The main purpose of this review is to examine studies which use directional prediction accuracy (also known as hit ratio) or profitability of the model as a benchmark since other forecast error measures -namely mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE) and mean squared error (MSE) -have been criticized for the argument that they are not able to actually show how useful the prediction model is, in terms of financial gains (i.e. for practical usage). In order to meet the publication selection criteria mentioned above, a large number of publications have been examined and 25 of papers satisfying the criteria are selected for comparison. Classification of the eligible papers are summarized in a table format for future studies.

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Stock market prediction and Portfolio selection models: a survey

Cited by 54 publications

References 129 publications

Artificial Intelligence Techniques and External Factors used in Crime Forecasting in Violence and Property: A Review

Artificial Intelligence Techniques and External Factors used in Crime Forecasting in Violence and Property: A Review

Parameter Optimization of Gradient Tree Boosting Using Dragonfly Algorithm in Crime Forecasting and Analysis

Stock Market Prediction Performance of Neural Networks: A Literature Review

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