Smoothing techniques for market fluctuation signals
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 f...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Vilnius Gediminas Technical University
2011-03-01
|
| Series: | Business: Theory and Practice |
| Subjects: | |
| Online Access: | https://journals.vgtu.lt/index.php/BTP/article/view/8852 |
| _version_ | 1797332199505133568 |
|---|---|
| author | Audrius Dzikevičius Svetlana Šaranda |
| author_facet | Audrius Dzikevičius Svetlana Šaranda |
| author_sort | Audrius Dzikevičius |
| collection | DOAJ |
| description | 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 very important to forecast preliminary the trends of the finance markets ant to plan the right actions in order to temper such bubble influence on the national economics. Although economic theory is opposite to the technical analysis theory which is the main tool for traders in stock markets it is used widely. This paper examines whether a proper technical analysis rule such as Exponential Moving Average (EMA) has a predictive power on stock markets in the Baltic States. The method is applied to OMX Baltic Benchmark Index and industrial indexes as they are more or less sensitive to the main index fluctuations. The results were compared using systematic error (mean square error, the mean absolute deviation, mean forecast error, the mean absolute percentage error) and tracking signal evaluation, CAPM method and appropriate period of EMA finding for each market forecast. A graphical analysis was used in order to determine whether EMA can forecast the main trends of the stock market fluctuations. The conclusions made during the research suggest new research issues and new hypotheses for its further testing. |
| first_indexed | 2024-03-08T07:46:05Z |
| format | Article |
| id | doaj.art-971ce681616c4d4c9502e6f1f5c7aa01 |
| institution | Directory Open Access Journal |
| issn | 1648-0627 1822-4202 |
| language | English |
| last_indexed | 2024-03-08T07:46:05Z |
| publishDate | 2011-03-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Business: Theory and Practice |
| spelling | doaj.art-971ce681616c4d4c9502e6f1f5c7aa012024-02-02T16:21:20ZengVilnius Gediminas Technical UniversityBusiness: Theory and Practice1648-06271822-42022011-03-01121Smoothing techniques for market fluctuation signalsAudrius Dzikevičius0Svetlana Šaranda1Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, LithuaniaVilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, LithuaniaThe 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 very important to forecast preliminary the trends of the finance markets ant to plan the right actions in order to temper such bubble influence on the national economics. Although economic theory is opposite to the technical analysis theory which is the main tool for traders in stock markets it is used widely. This paper examines whether a proper technical analysis rule such as Exponential Moving Average (EMA) has a predictive power on stock markets in the Baltic States. The method is applied to OMX Baltic Benchmark Index and industrial indexes as they are more or less sensitive to the main index fluctuations. The results were compared using systematic error (mean square error, the mean absolute deviation, mean forecast error, the mean absolute percentage error) and tracking signal evaluation, CAPM method and appropriate period of EMA finding for each market forecast. A graphical analysis was used in order to determine whether EMA can forecast the main trends of the stock market fluctuations. The conclusions made during the research suggest new research issues and new hypotheses for its further testing.https://journals.vgtu.lt/index.php/BTP/article/view/8852technical analysisExponential Moving Averagebiasforecaststockmarket trend |
| spellingShingle | Audrius Dzikevičius Svetlana Šaranda Smoothing techniques for market fluctuation signals Business: Theory and Practice technical analysis Exponential Moving Average bias forecast stock market trend |
| title | Smoothing techniques for market fluctuation signals |
| title_full | Smoothing techniques for market fluctuation signals |
| title_fullStr | Smoothing techniques for market fluctuation signals |
| title_full_unstemmed | Smoothing techniques for market fluctuation signals |
| title_short | Smoothing techniques for market fluctuation signals |
| title_sort | smoothing techniques for market fluctuation signals |
| topic | technical analysis Exponential Moving Average bias forecast stock market trend |
| url | https://journals.vgtu.lt/index.php/BTP/article/view/8852 |
| work_keys_str_mv | AT audriusdzikevicius smoothingtechniquesformarketfluctuationsignals AT svetlanasaranda smoothingtechniquesformarketfluctuationsignals |