A comparison of machine‐learning classifiers for selecting money managers

Ludwig, Robert; Piovoso, Michael J.

doi:10.1002/isaf.262

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…ANN applications have been widely used for forecasting in a variety of areas [9], [11], [13], [16], [21]. ANN was used for the solution of numerous financial problems [9].…”

Section: Related Workmentioning

confidence: 99%

Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting

Abdulkadir

Yong

Marimuthu

et al. 2014

Mining Intelligence and Knowledge Exploration

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Abstract. Financial data is characterized as non-linear, chaotic in nature and volatile thus making the process of forecasting cumbersome. Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. The proposed hybrid model is compared with commonly used Elman-NARX and static forecasting model employed by financial analysts. Experimental results on Bursa Malaysia KLCI data show that the proposed hybrid model outperforms the other two commonly used models.

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“…ANN applications have been widely used for forecasting in a variety of areas [9], [11], [13], [16], [21]. ANN was used for the solution of numerous financial problems [9].…”

Section: Related Workmentioning

confidence: 99%

Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting

Abdulkadir

Yong

Marimuthu

et al. 2014

Mining Intelligence and Knowledge Exploration

View full text Add to dashboard Cite

show abstract

“…-Decision Trees (DTs): Decision-tree learning is a symbolic induction method that produces syntactically simple, easily interpreted rules [11]. In DTs, the knowledge extracted from the given data is organized in a recursive hierarchical structure represented with the help of nodes and branches [12].…”

Section: -Machine Learning Approachesmentioning

confidence: 99%

Browser Extension based Hybrid Anti-Phishing Framework using Feature Selection

Maurya¹,

Saini²,

Jain³

2019

IJACSA

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Phishing is one of the socially engineered cybersecurity attacks where the attacker impersonates a genuine and legitimate website source and sends emails with the intention of stealing sensitive personal information. The phishing websites' URLs are usually spread through emails by luring the users to click on them or by embedding the link to fake website replicating any genuine e-commerce website inside the invoice or other documents. The phishing problem is very wide and no single solution exists to mitigate all the vulnerabilities properly. Thus, multiple techniques are often combined and implemented to mitigate specific attacks. The primary objective of this paper is to propose an efficient and effective anti-phishing solution that can be implemented at the client-side in the form of a browser extension and should be capable to handle real-time scenarios and zero-day attacks. The proposed approach works efficiently for any phishing link carrier mode as the execution on clicking on any link or manually entering URL in the browser doesn't proceed unless the proposed framework approves that the website associated with that URL is genuine. Also, the proposed framework is capable to handle DNS cache poisoning attacks even if the system's DNS cache is somehow infected. This paper first presents a comprehensive review that broadly discusses the phishing life cycle and available anti-phishing countermeasures. The proposed framework considers the pros and cons of existing methodologies and presents a robust solution by combining the best features to ensure that a fast and accurate response is achieved. The effectiveness of the approach is tested in a real-time dataset consisting of live phishing and legitimate website URLs and the framework is found to be 98.1% accurate in identifying websites correctly in very less time.

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“…Such studies prove that the nonlinear model presents more consistent results for stock exchange market. For this reason, ANN applications have been widely used in a variety of areas in financial markets [8], [9]. Reference [9] confirmed that ANN was used for the solution of numerous financial problems.…”

Section: Introductionmentioning

confidence: 98%

Analysis of Artificial Neural Network for Financial Time Series Forecasting

Tarsauliya¹,

Kant²,

Kala³

et al. 2010

IJCA

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Financial forecasting has been challenging problem due to its high non-linearity and high volatility. An Artificial Neural Network (ANN) can model flexible linear or non-linear relations-hip among variables. ANN can be configured to produce desired set of output based on set of given input. In this paper we attempt at analyzing the usefulness of artificial neural network for forecasting financial data series with use of different algorithms such as backpropagation, radial basis function etc. With their ability of adapting non-linear and chaotic patterns, ANN is the current technique being used which offers the ability of predicting financial data more accurately. "A x-y-1 network topology is adopted because of x input variables in which variable y was determined by the number of hidden neurons during network selection with single output." Both x and y were changed.

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A comparison of machine‐learning classifiers for selecting money managers

Cited by 10 publications

References 14 publications

Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting

Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting

Browser Extension based Hybrid Anti-Phishing Framework using Feature Selection

Analysis of Artificial Neural Network for Financial Time Series Forecasting

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