Investment management in uncertainty

Terceño, Antonio; Andrés, Jorge; Barberà, M. G.; Lorenzana, Tomás

doi:10.1007/978-1-4613-0285-8_7

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…In addition, FLR is capable of handling noncrisp or fuzzy data. Several studies have proved the above statements and this is a fact in the related literatures (Kaufmann, 1986;Buckley, 1987;Li Calzi, 1990;Gil, 1999;Kutcha, 2000;Terceno et al, 2001 and for actuarial pricing, refer to Lemaire, 1990;Ostaszewski, 1993;Cummins and Derrig, 1997;Derrig and Ostaszewski, 1997;or Worzala et al, 1995;Terceno et al, 1996;Lenk et al, 1997;McGreal et al, 1998;Din et al, 2001;Pagourtzi et al, 2003). The features of the flexible neuro-fuzzy approach are also compared with previous approaches to show its advantages as shown in Table 4.…”

Section: Resultsmentioning

confidence: 80%

“…In addition, FLR is capable of handling noncrisp or fuzzy data. Several studies have proved the above statements and this is a fact in the related literatures (Kaufmann, ; Buckley, ; Li Calzi, ; Gil, ; Kutcha, ; Terceno et al ., and for actuarial pricing, refer to Lemaire, ; Ostaszewski, ; Cummins and Derrig, ; Derrig and Ostaszewski, ; or Worzala et al ., ; Terceno et al ., ; Lenk et al ., ; McGreal et al ., ; Din et al ., ; Pagourtzi et al ., ).…”

Section: Resultsmentioning

confidence: 99%

“…Ozelkan and Duckstein (2000) used a similar model with exerting slight changes, and more recently, Hojati et al (2005) considered two other models for estimating fuzzy coefficients, which are known as HBS1 and HBS2. For finding further applications of fuzzy sets theory in financial pricing, the interested reader could see Kaufmann (1986), Buckley (1987), Li Calzi (1990), Gil (1999), Kutcha (2000), Terceno et al (2001) and for actuarial pricing, refer to Lemaire (1990), Ostaszewski (1993), Cummins and Derrig (1997), Derrig and Ostaszewski (1997) or Terceno et al (1996).…”

Section: Introductionmentioning

confidence: 99%

“…For finding further applications of fuzzy sets theory in financial pricing, the interested reader could see Kaufmann (), Buckley (), Li Calzi (), Gil (), Kutcha (), Terceno et al . () and for actuarial pricing, refer to Lemaire (), Ostaszewski (), Cummins and Derrig (), Derrig and Ostaszewski () or Terceno et al . ().…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Flexible Neuro‐Fuzzy Approach for Improvement of Seasonal Housing Price Estimation in Uncertain and Non‐Linear Environments

Azadeh

Sheikhalishahi

Boostani

2014

South African J Economics

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Changes in housing price affect both individuals and government since they have substantial influence on the socio-economic conditions. Valuations of housing are necessary in order to assess the benefits and liabilities in housing sector. This study presents a flexible meta-modelling approach for improvement of housing price estimation in ambiguous and complex environments. It is composed of artificial neural network (ANN) and fuzzy linear regression (FLR). Seven FLR models are considered to cover latest approaches and viewpoints. Also, ANN is applied to data sets. The preferred FLR model is selected via mean absolute percentage of error (MAPE) for further considerations, and then the preferred FLR model and the best structure of ANN are applied to the data set. Finally, the preferred model is selected based on MAPE. The intelligent approach of this study is applied for estimation and forecasting housing price in Iran. The housing price in Iran mainly is based on eight economic indices including currency, oil income, general index, house service pricing index, rate of informal market, gross domestic production in basic price, added value of oil group and construction materials price. FLR is identified as the preferred model with lowest MAPE for housing price forecasting in Iran. This shows that the housing market of Iran is associated with severe environmental fuzziness and ambiguity. This is the first study that introduces a flexible neuro-fuzzy approach for improved estimation and forecasting of housing price in noisy, complex and uncertain environments.

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Section: Resultsmentioning

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Flexible Neuro‐Fuzzy Approach for Improvement of Seasonal Housing Price Estimation in Uncertain and Non‐Linear Environments

Azadeh

Sheikhalishahi

Boostani

2014

South African J Economics

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“…They consider it is much more realistic the use of discount rates based on fuzzy numbers (FN), since only data available is the one facilitated by experts. For applications of the theory of the fuzzy sets in finance, the following can be considered: Kaufmann [8], Li Calzi [9], Gil [10], Ostaszewski [11], Terceño et al [12,13], De Andrés and Terceño [14][15][16] and Terceño and Brotons [17].…”

Section: Introductionmentioning

confidence: 99%