Hi̇sse Senedi̇ Geti̇ri̇leri̇nde Doğrusal Olmayan Di̇nami̇kler: Türki̇ye'den Kanitlar

Sülkü, Seher Nur; Ürkmez, Emre

doi:10.18092/ulikidince.349846

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…The main research focus of Sülkü and Ürkmez (2018) was examining nonlinear dynamics of Borsa Istanbul index daily returns with a BDS nonlinearity test. They found that daily returns can be forecasted in short-term periods but not predictable in long run.…”

Section: Introductionmentioning

confidence: 99%

Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul

Aliyev

2019

IJFS

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Market efficiency has been analyzed through many studies using different linear methods. However, studies on financial econometrics reveal that financial time series exhibit nonlinear patterns because of various reasons. This paper examines market efficiency at Borsa Istanbul using a smooth transition autoregressive (STAR) type nonlinear model. I develop nonlinear ARCH and STAR models, a linear AR model and random walk model for 10 years' weekly data and then out-of-sample forecast next 12 weeks' return. Comparing forecast performance powers, I find that the STAR model outperforms random walk, that is Borsa Istanbul returns are predictable at the given period. The results show that the shareholders may earn abnormal return and identify the direction of the return change for the next week with at least 66% accuracy. Contrary to the linear level studies, these findings show that the Borsa Istanbul is not weak form efficient at nonlinear level within the studied period.

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Section: Introductionmentioning

confidence: 99%

Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul

Aliyev

2019

IJFS

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Türki̇ye’deki̇ Bankalarin Hi̇sse Senedi̇ Geti̇ri̇leri̇nde Fraktal Pi̇yasa Hi̇potezi̇ni̇n Testi̇

Karakaya

Atukalp

2022

Hacettepe Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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Bankalarda volatilite yapısının modellenmesiyle, bankaların yanında ekonominin genelini ilgilendiren risk ve belirsizliklerin karakteristik yapısı ortaya konulmaktadır. Bu çalışmada, Türkiye’deki bankaların hisse senedi getirilerindeki volatilitenin tahmin edilmesi amaçlanmıştır. Çalışmanın inceleme dönemi 5 Ocak 2010 - 31 Aralık 2020’dir. Ding, Granger ve Engle (1993) tarafından önerilen doğrusal olmayan asimetrik koşullu volatilite analiz yöntemiyle (APGARCH) bankaların hisse senetlerinin getiri volatilitesi tahmin edilmiştir. Çalışmada öncelikle getirilerin durağanlığı, ARCH etkisi, asimetri yapısı ve doğrusallık özellikleri test edilmiştir. Ardından, APGARCH modeliyle, bankaların getiri volatilitesindeki şokun yüksek kalıcılığa sahip olduğu, asimetri etkisinin bulunduğu ve uzun dönem hafıza özelliğinin olduğu ortaya konmuştur. Bulgular, Türkiye’deki bankaların hisse senedi getiri volatilitesinde Etkin Piyasalar Hipotezi’nin yerine Fraktal Piyasa Hipotezi’nin varlığını destekleyici niteliktedir. Buna göre hisse senedi fiyatlarında bağımlılık tespit edilmiştir. Dolayısıyla, yatırımcıların teknik analiz varsayımlarını dikkate aldıkları söylenebilir.

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BİST 100 getiri zaman serisinin kaotik analizi ve ANFIS ile kısa dönemli öngörülebilirliği

Molla

Çağıl

Uyaroğlu

2021

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Application of chaos analysis to BIST 100 return series  Long-term memory effect and linearity test of BIST 100 returns  Short-term return forecast of the BIST 100 index Figure A. Process steps applied to BIST 100 returns Purpose: The realization of short-term forecasts of BIST 100 returns provides company managers with the advantage of keeping company values high, while providing investors with opportunities to reduce transaction costs and provide advantages. In this paper, it is aimed to investigate whether the BIST 100 return series has a chaotic structure and to predict successfully in the short term. Theory and Methods: To analyze the chaos of the BIST 100 return series, reconstruction of phase space, correlation dimension and Lyapunov exponential methods were used. However, linearity of this series was examined by BDS (Brock, Dechert and Scheinkman) test. In the next step, the Hurst exponential coefficient, which shows whether this series has a long-term memory effect, was determined by the transformed width method. Then, Artificial Neural Networks (ANN) and Adaptive Network Based Fuzzy Inference System (ANFIS) methods were used in the estimation of the BIST 100 index. BIST 100 returns were obtained by applying logarithmic transformations to the outputs of the model that was successful in index estimation. Results: As a result of the chaos analysis, it was found that the BIST 100 return series had a chaotic structure. Then, it was observed that this series was not linear with BDS test. However, Hurst exponential coefficient was examined and it was concluded that this series had long term memory effect. Then, as a result of estimating this series with the most successful model, the mean absolute percentage error percentage (MAPE) value was found between 10%-20%. This shows that the BIST 100 return estimate is consistent. Conclusion: The ANFIS method shows a successful result in the short-term estimation of BIST 100 returns with chaotic properties.

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Hi̇sse Senedi̇ Geti̇ri̇leri̇nde Doğrusal Olmayan Di̇nami̇kler: Türki̇ye'den Kanitlar

Cited by 9 publications

References 24 publications

Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul

Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul

Türki̇ye’deki̇ Bankalarin Hi̇sse Senedi̇ Geti̇ri̇leri̇nde Fraktal Pi̇yasa Hi̇potezi̇ni̇n Testi̇

BİST 100 getiri zaman serisinin kaotik analizi ve ANFIS ile kısa dönemli öngörülebilirliği

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