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Relevant bibliographies by topics / OMX Baltic Benchmark

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Journal articles

Academic literature on the topic 'OMX Baltic Benchmark'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "OMX Baltic Benchmark"

1

Dzikevičius, Audrius, and Svetlana Šaranda. "EMA versus SMA Usage to Forecast Stock Markets: The Case of S&P 500 and OMX Baltic Benchmark." Business: Theory and Practice 11, no. (3) (2010): 248–55. https://doi.org/10.3846/btp.2010.27.

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The academic literature is showing a growing interest in such trading rules as Moving Average. The majority of researches were made using simple moving average. Although semi-professional traders use the technical analysis methods to predict the future stock prices, to identify the stock trend changes, OMX Baltic Benchmark Index was never tested. Previous researches on the S&P 500 Index using the most widely used method of technical analysis – Moving Averages are more or less appellative. Technical analysis is opponent to classical economic theory but investors use it widely all over the w

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2

Belovas, Igoris. "Modeling Baltic market benchmark index: a comparison of models." Lietuvos matematikos rinkinys 60 (December 5, 2019): 6–10. http://dx.doi.org/10.15388/lmr.b.2019.15207.

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In this paper we perform a statistical analysis of the returns of OMX Baltic Benchmark index. We construct symmetric α-stable, non-standardized Student’s t and normal-inverse Gaussian models of daily logarithmic returns of the index, using maximum likelihood method for the estimation of the parameters of the models. The adequacy of the modeling is evaluated with the Kolmogorov-Smirnov tests for composite hypothesis. The results of the study indicate that the normal-inverse Gaussian model outperforms alternative heavy-tailed models for long periods of time, while the non-standardized Student’s

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3

Norvaišienė, Rasa, and Jurgita Stankevičienė. "The Month Effect in the Baltic and Nordic Stock Markets at Market-Level and Sector-Level." Engineering Economics 33, no. 5 (2022): 473–85. http://dx.doi.org/10.5755/j01.ee.33.5.28183.

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The purpose of this study is to analyze and compare the trends in the expression of the month effect in the Nasdaq Baltic and Nasdaq Nordic stock markets, as well as to examine whether the seasonal stock price fluctuations occur in all industrial sectors of these markets or are specific to certain sectors only. The OMX Baltic Benchmark, OMX Baltic, and OMX Nordic 40 indexes, which reflect the situation in the Baltic and Nordic stock markets, were used in the study to assess seasonality at the market level. To assess the seasonality in separate sectors of the Baltic and Nordic markets, we used

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4

Keliuotyte-Staniuleniene, Greta, and Julius Kviklis. "Assessing the reaction of the Baltic stock market to the spread of the COVID-19 pandemic." Technium Social Sciences Journal 25 (November 9, 2021): 260–72. http://dx.doi.org/10.47577/tssj.v25i1.4995.

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This research aims to assess the impact of the spread of the COVID-19 pandemic on the Baltic stock market. To reach this aim, the methods of bivariate (OLS) regression and VAR-based impulse response functions are employed. We use daily new cases of COVID-19 as well as the cumulative number of COVID-19 cases as independent and OMX Baltic Benchmark GI index as dependent variables for our research. The research period, covering data from 2020 March 1st to 2020 November 21st, is divided into three separate periods, reflecting the different phases of the spread of the COVID-19 pandemic. The results

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5

Dzikevičius, Audrius, and Svetlana Šaranda. "EMA versus SMA Usage to Forecast Stock Markets: The Case of S#38;P 500 and OMX Baltic Benchmark." Verslas: teorija ir praktika 11, no. 3 (2010): 248–55. http://dx.doi.org/10.3846/btp.2010.27.

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6

Dzikevičius, Audrius, and Svetlana Šaranda. "SMOOTHING TECHNIQUES FOR MARKET FLUCTUATION SIGNALS." Business: Theory and Practice 12, no. 1 (2011): 63–74. http://dx.doi.org/10.3846/btp.2011.07.

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The financial crisis of 2008–2009 caused lots of discussions between Academia and as a result researches on financial crisis and bubble prediction possibilities appeared. Academia shows its growing interest in the issue during the last decade. The majority of researches made are based on different forms of forecast used. Some of previous studies claim that the trend of the stock market can be forecasted using moving average method. After the finance market crashed, a need to forecast further possible bubbles arises. As the economics of the Baltic States is very sensitive to such bubbles it is

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7

Dzikevičius, Audrius, and Svetlana Šaranda. "Smoothing Techniques for Market Fluctuation Signals." Business: Theory and Practice 12, no. (1) (2011): 63–74. https://doi.org/10.3846/btp.2011.07.

Full text

Abstract:

The financial crisis of 2008–2009 caused lots of discussions between Academia and as a result researches on financial crisis and bubble prediction possibilities appeared. Academia shows its growing interest in the issue during the last decade. The majority of researches made are based on different forms of forecast used. Some of previous studies claim that the trend of the stock market can be forecasted using moving average method. After the finance market crashed, a need to forecast further possible bubbles arises. As the economics of the Baltic States is very sensitive to such bubbles it is

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8

Rudzkis, Rimantas, and Roma Valkavičienė. "The impact of sectoral economy indicators on the stock market in the Baltic countries." Lietuvos matematikos rinkinys 55 (December 15, 2014). http://dx.doi.org/10.15388/lmr.a.2014.11.

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The article examines the dependencies of individual sectoral stock price indices of OMX Baltic security market on sectoral indicators of Lithuania economy, using econometric methods. Regression models are constructed using quarterly time series of 2005–2013 years. VAR models obtained in the [3] paper have been extended to verify if the inclusion of sectoral economy indicators improves the ability to provide a higher level of accuracy in estimating the growth of sectoral price index. These indicators significantly improve the predictive power compared with the benchmark VAR model. The short-ter

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