Improving the profitability of Technical Analysis through intelligent algorithms

Pelusi, Danilo; Tivegna, Massimo; Ippoliti, Pierluigi

doi:10.1080/09720510.2013.821336

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Suitable combinations of intelligent techniques have been proposed by Pelusi for different applications. [17], [18], [19], [20], [21], [22], [23], [24], [25].…”

Section: Literature Surveymentioning

confidence: 99%

Control of Drum Boiler Dynamics via an Optimized Fuzzy Controller

Pelusi

Mascella

Tallini

et al. 2020

International Journal of Simulation: Systems, Science &Amp; Technology

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“…Suitable combinations of intelligent techniques have been proposed by Pelusi for different applications. [17], [18], [19], [20], [21], [22], [23], [24], [25].…”

Section: Literature Surveymentioning

confidence: 99%

Control of Drum Boiler Dynamics via an Optimized Fuzzy Controller

Pelusi

Mascella

Tallini

et al. 2020

International Journal of Simulation: Systems, Science &Amp; Technology

Self Cite

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“…Among the recent advances in optimization algorithms [3], evolutionary algorithms (EAs) have proven successful in overcoming difficulties with traditional optimization techniques either in their standard version, or hybridized [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization

et al. 2017

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Portfolio management based on mean-variance portfolio optimization is subject to different sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancy between target and present portfolios, caused by trading strategies, may expose investors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user's preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance-covariance matrix.

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“…1. Thus, the data has trend and seasonality characteristics which are commonly encountered in time series analysis [11,12]. In this section, we will firstly present a FLUBE to define the uncertainty in the forecast.…”

Section: The Australian Monthly Electricity Consumption Data Setmentioning

confidence: 99%

“…[14] The PICP is measured by counting the number of target values covered by the constructed PIs. The PICP shows in which probability target values will be covered by the lower and upper bounds and thus is defined as: (6) where is the number of samples and is [11]:…”

Section: Performance Indexmentioning

confidence: 99%

An Approach to Represent Time Series Forecasting via Fuzzy Numbers

Şahin

Kumbasar

Yeşil

et al. 2014

2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation

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Abstract-This paper introduces a new approach for estimating the uncertainty in the forecast through the construction of Triangular Fuzzy Numbers (TFNs). The interval of the proposed TFN presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE). Here, instead of the representing the forecast with a crisp value with a Prediction Interval (PI), the level of uncertainty associated with the point forecasts will be quantified by defining TFNs (linguistic terms) within the uncertainty interval provided by the FLUBE. This will give the opportunity to handle the forecast as linguistic terms which will increase the interpretability. Moreover, the proposed approach will provide valuable information about the accuracy of the forecast by providing a relative membership degree. The demonstrated results indicate that the proposed FLUBE based TFN representation is an efficient and useful approach to represent the uncertainty and the quality of the forecast.

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Improving the profitability of Technical Analysis through intelligent algorithms

Cited by 10 publications

References 7 publications

Control of Drum Boiler Dynamics via an Optimized Fuzzy Controller

Control of Drum Boiler Dynamics via an Optimized Fuzzy Controller

Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization

An Approach to Represent Time Series Forecasting via Fuzzy Numbers

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