Analysis of Price Volatility in BIST 100 Index With Time Series: Comparison of Fbprophet and LSTM Model

AKER, Yusuf

doi:10.31590/ejosat.1066722

Cited by 4 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their study found the ARIMA models to outperform the deep learning models with regard to predicting the considered price indexes. Aker (2022) likewise examined price volatility of the BIST-100 Index by comparing LSTM and Facebook Prophet (Fbprophet) methods and suggested the LSTM model to have outperformed the Fbprophet model based on the RMSE, mean absolute error (MAE), and mean squared error (MSE) evaluation metrics. Pakel and Özen (2021) also investigated a volatility analysis of the BIST-100 Index using GARCH models and identified two significant shocks in the BIST-100 (i.e., currency shock and COVID-19 pandemic shock) in 2018 and 2020, respectively.…”

Section: Empirical Applicationsmentioning

confidence: 99%

Improving Forecast Accuracy Using Combined Forecasts with Regard to Structural Breaks and ARCH Innovations

Aser¹,

Firuzan²

2022

ekoist

View full text Add to dashboard Cite

Accurate forecasts about the future are vital in time series analyses, but accurately modeling complex structures in the data is always challenging. Two major sources of complexity are autoregressive conditional heteroskedasticity (ARCH) effects on data as well as structural breaks in the data, as these affect the quality of data and hence reduce forecast accuracy. In this regard, combining forecast types has been a helpful strategy for improving forecast accuracy for more than 50 years since Bates and Granger's (1969) original paper. Hence, this paper aims to examine if the gains from combined forecasts are sustained regarding cases with structural breaks and ARCH innovations. Moreover, the study explores which forecast combination schemes are optimal for those cases by combining the exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), and artificial neural network (ANN) forecast models using simple and regression-based combination procedures. These methods are implemented in both simulated series and over empirical data from two popular Turkish stock exchanges (i.e., BIST-30 and BIST-100 Indexes). The study has found regressionbased forecast combination methods to significantly improve forecast accuracy regarding cases with structural breaks and conditional heteroscedasticity. Dynamically weighted combinations show greater accuracy improvement compared to their static counterparts when the data contain a trend. Simple combination schemes, including simple averages, just perform better than single methods for ETS and ARIMA, while they barely outperform ANN. In conclusion, ANN is found to be the best-performing individual forecasting method for all cases and designs.

show abstract

Section: Empirical Applicationsmentioning

confidence: 99%

Improving Forecast Accuracy Using Combined Forecasts with Regard to Structural Breaks and ARCH Innovations

Aser¹,

Firuzan²

2022

ekoist

View full text Add to dashboard Cite

show abstract

“…Since commodities prices variations impact the life of millions of people globally, several techniques had been developed to forecast prices uctuation of these items with the application of machine learning being one of the most widespread methods of forecasting contemporaneously (Gumus & Kiran, 2017;Guo, 2019). The application of machine learning techniques had been widely used to predict prices of electricity (Yang et al, 2022), stock values (Kamalov et al, 2021;Aker, 2022), and even COVID-19 spread (Cha q et al, 2020; Gaur, 2020). Despite the utilization of machine learning has the potential to bring a wide-range of improvements to the cultivation of aquatic organisms by monitoring and predicting water quality parameters and feeding of the organisms (Yang et al, 2020), studies using this approach to predict seafood prices are scarce.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning algorithms to forecast prices of Sardinella brasiliensis commercialized in a South American supply center

França

Oliveira²,

Andrade

2022

Preprint

View full text Add to dashboard Cite

Small pelagic fishes develop important role in human nutrition especially in emergent countries which are considered an affordable source of protein ensuring food security, and with its fishery being source of income for several populations around the world. Despite fish nutritional composition present several benefits for human health, prices are pointed as the main factor to choose seafood as components of diet, highlighting the relevance of the economic analysis of these items once disturbances in its prices might alter the feeding patterns of populations worldwide. This study aimed to analyze the Brazilian Sardine (Sardinella brasiliensis) prices dynamics in one of the main markets of northeastern Brazil, evaluate possible reasons for its peaks and use machine learning techniques to forecast its future prices. The dataset used was obtained in the Pernambuco Supply and Logistics Center (PSLC) website, which contains a historical series of sardine’s prices from 2013 to 2022. The dataset was divided in train and test sections, the train section modelled using the Fbprophet library and a long-short term memory neural network in order forecast the future prices, then the test dataset was used to evaluate the predictions based in the root mean square error, mean absolute error and mean absolute percentage error metrics. Both algorithms reached low error metrics in its forecasts, however LSTM predictions were significantly better presenting lower error metrics than Fbprophet, showing their usability in the economic context of marine sciences opening the door to further studies of the dynamics of food prices around the world.

show abstract

Türkiye'de Sofralık Zeytin Fiyatlarındaki Dalgalanmalar: ARIMA-GARCH Yaklaşımıyla Volatilite Araştırması

ŞENGÜL

2023

ANADOLU Ege Tarımsal Araştırma Enstitüsü Dergisi

View full text Add to dashboard Cite

Bu çalışmada, Ocak 2008-Aralık 2022 döneminde Türkiye'de sofralık zeytin fiyatlarının volatilitesini analiz etmek amacıyla ARIMA-GARCH modeli kullanılmıştır. Çalışma zeytin piyasasının volatilite dinamiklerini derinlemesine anlamayı ve piyasa katılımcıları için stratejik yaklaşımlar geliştirmeyi hedeflemektedir. ARIMA modeli, finans ve ekonomi literatüründe zaman serilerinin ortalama yapısının tahmin edilmesi için, GARCH modeli ise volatilitenin tahmin edilmesi için sıkça başvurulan metotlardır. Bu iki modelin entegrasyonu hem ortalama hem de volatilitenin kapsamlı bir analizini sağlamaktadır. Analiz sürecinde farklı volatilite modelleme teknikleri kullanılarak optimal model, Akaike (AIC), Schwarz (SIC) Bilgi Kriterleri ve Log likelihood değeri ile belirlenmiştir. Seçilen modelin performansı, gerçekleşen volatilite değerleriyle karşılaştırılarak değerlendirilmiştir. Sonuçlara göre, zeytin fiyatlarında belirgin bir düzeltme eğilimi gözlemlenmiş, bu da piyasa katılımcılarının fiyat hareketlerine hızla tepki verdiğini göstermiştir. Diğer taraftan zeytin piyasasında volatilitenin uzun süre devam edebileceği ve fiyat şoklarının uzun vadeli etkiler yaratabileceği belirlenmiştir. Kısa dönem tahminlerinde (3 ve 6 aylık), tahmin süresine bağlı olarak hata oranlarının arttığı, 1-2 aylık tahmin ufkunda modelin güvenilir sonuçlar verdiği saptanmıştır. Sonuçlara göre 9 aylık dönemde 2 aylık tahminler, orta vadeli planlamalar için güvenilir sonuçlar sunmuştur. 12 aylık tahminlerde ise, modelin uzun vadeli planlamalar için istikrarlı sonuçlar sağladığı belirlenmiştir.

show abstract

Analysis of Price Volatility in BIST 100 Index With Time Series: Comparison of Fbprophet and LSTM Model

Cited by 4 publications

References 14 publications

Improving Forecast Accuracy Using Combined Forecasts with Regard to Structural Breaks and ARCH Innovations

Improving Forecast Accuracy Using Combined Forecasts with Regard to Structural Breaks and ARCH Innovations

Application of machine learning algorithms to forecast prices of Sardinella brasiliensis commercialized in a South American supply center

Türkiye'de Sofralık Zeytin Fiyatlarındaki Dalgalanmalar: ARIMA-GARCH Yaklaşımıyla Volatilite Araştırması

Contact Info

Product

Resources

About