Sirvi Autor "Eratalay, Mustafa Hakan, juhendaja" järgi

Nüüd näidatakse 1 - 14 14

Assessing robustness of temporal disaggregation technique Denton in economic forecasting under anomalous conditions
(Tartu Ülikool, 2024) Adgozalli, Sabina; Voskovtsov, Kostiantyn; Eratalay, Mustafa Hakan, juhendaja ; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Cultural moderation in ESG performance and the environmental Kuznets curve: a cross-country perspective
(Tartu Ülikool, 2024) Chukwuemeka, Franklin Izuchukwu; Eratalay, Mustafa Hakan, juhendaja ; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Deep diving into the S&P 350 Europe index network and its reaction to the COVID-19
(Tartu Ülikool, 2021) Cortes Angel, Ariana Paola; Eratalay, Mustafa Hakan, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Early warning system for financial crisis: application of random forest
(Tartu Ülikool, 2020) Wanyama, Geofrey; Eratalay, Mustafa Hakan, juhendaja; Alfieri, Luca, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
The study identiﬁes important variables in detecting the likely occurrence of a ﬁnancial crisis 1 to 3 years from its onset . We do this by implementing random forest on Macroeconomic Historical time series data set for 16 developed countries from 1870-2016. By comparing the misclassiﬁcation error for logistic regression to that obtained for random forest, we show that random forest outperforms logistic regression under the out-of-sample setting for long historical macroeconomic data set. Using the SMOTE technique, we show that minimising class imbalance in the data set improves the performance of random forest. The results show that important variables for detecting a ﬁnancial crisis 1 to 3 years from its onset vary from country to country. Some similarities are however also observed. Credit and money price variables for instance emerge as very important predictors across a number of countries.
Effect of real estate news sentiment on stock returns of Swedbank and SEB Bank
(Tartu Ülikool, 2019) Puzanova, Yuliia; Eratalay, Mustafa Hakan, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
The effects of the higher education-related factors on digital skills in the European region
(Tartu Ülikool, 2021) Chukhlebova, Daria; Vadi, Maaja, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Evaluation and comparison of machine learning and classical econometric AR model on financial time series data
(Tartu Ülikool, 2020) Mikeliani, Roza; Kavlashvili, Nino; Eratalay, Mustafa Hakan, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
This paper examines the effects of time series data behaviour on the predictive performance of classical econometric univariate autoregressive and machine learning autoregressive models. The research aims to understand which forecasting approach would perform better in extreme scenarios. Even though some empirical studies demonstrate the superiority of machine learning methods relative to classical econometric methods, it is still arguable under what conditions one method can be constantly better than the other. And if there are any cases when econometric models are preferable than machine learning. Data is derived from simulation, ensuring the presence of different outlier and error distributions in small and relatively larger samples. The simulation results show that the machine learning approach outperforms econometric models in most of the cases. However, the existence of outliers worsens the performance of machine learning on small datasets. Even with the presence of outliers, as the sample size grows, the result improves so much for machine learning that it dominates the econometric model. The same models were used to forecast with rolling sample approaches on real financial data.
Evaluation of alternative weighting methods for the selection of portfolio optimization model
(Tartu Ülikool, 2023) Rahimov, Nabi; Mammadov, Ismayil; Eratalay, Mustafa Hakan, juhendaja; Sharma, Rajesh, juhendaja; Lapitskaya, Darya, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Is there a significant effect of e-residency on Estonia's GDP?
(Tartu Ülikool, 2021) Erkan, Berk; Tasar, Demer; Eratalay, Mustafa Hakan, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Market manipulation in cryptocurrencies through social media: the role of influencers
(Tartu Ülikool, 2023) Rahimov, Kamran; Rahimov, Elchin; Lapitskaya, Darya, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Sharma, Rajesh, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
The performance of a momentum-based equity portfolio on the example of the Nasdaq-100 (NDX)
(Tartu Ülikool, 2021) Mirski, Sten; Kantšukov, Mark, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Predicting stock return and volatility with machine learning and econometric models— a comparative case study of the Baltic stock market
(Tartu Ülikool, 2021) Nõu, Anders; Sharma, Rajesh, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Lapitskaya, Darya, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Predicting the stock market is a widely researched area of study that is a challenging task. The nature of the problem lies in correctly forecasting the direction and the magnitude of the stock market movement. The severity of the problem exists due to the stock market being impacted by a multitude of factors. There are numerous ways to analyse the stock market and make appropriate investment decisions, but it is challenging to decide the best approach. Here we show which approach is more effective in predicting the returns and volatility of the Baltic stock market: the machine learning or econometric approach. There is a low amount of research on using machine learning or econometric models to predict the Baltic stock market. However, there are no comparative researches that offer a fair comparison between the different approaches for the Baltic stock market. Regarding results, the lowest symmetric mean absolute percentage error for the support vector regression model is 61.90%, and for the autoregressive moving average model, it is 165.43%. The lowest symmetric mean absolute percentage error for GARCH is 51.05%, and for the GARCH-ANN model, it is 61.65%. Overall, the machine learning models outperform the econometric models in most of the evaluated metrics. However, the econometric models’ results are comparable to the machine learning models’ results in most cases.
Spillover transmission and the effects of innovation and FDI on systemic risk
(Tartu Ülikool, 2020) Arhin, Samuel Acquaah; Claros, Gabriela; Eratalay, Mustafa Hakan, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Stock price relation to news: the case of the Tallinn stock exchange
(Tartu Ülikool, 2025) Lass, Kristin; Pulk, Kristjan, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
This article examines the relationship between media sentiment and stock price movements using the example of 12 companies listed on the Tallinn Stock Exchange. The study uses sentiment analyzes of Estonian-language financial news using the ARMAX model to quantify the impact of news sentiment on daily stock returns. The dataset comprises 1,930 news articles and trading data from 2019 to 2023. The results show that media sentiment has a statistically significant impact on short-term stock price movements, especially in the case of strongly negative news. This supports the view that market movements cannot be explained solely by fundamental analysis, but also indicate behavioral factors, such as attentional bias and overreaction, that affect stock price formation. The results contribute to media-based market analysis in small markets and highlights the need for considering the influence of media sentiment in financial modeling.