Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan
碩士 === 國立政治大學 === 經濟學系 === 100 === The financial markets are usually affected by political, economic and social environment factors, and thus the volatilities of asset prices in these markets are subject to a lot of noises and shocks. To filter out noises and quantify shocks, this paper applies a da...
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ndltd-TW-100NCCU53890232016-02-21T04:19:34Z http://ndltd.ncl.edu.tw/handle/76743704116576804551 Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan 整體經驗模態分解在台灣期貨市場與選舉預測市場的應用 鄭緯暄 碩士 國立政治大學 經濟學系 100 The financial markets are usually affected by political, economic and social environment factors, and thus the volatilities of asset prices in these markets are subject to a lot of noises and shocks. To filter out noises and quantify shocks, this paper applies a data processing method, Ensemble Empirical Mode Decomposition (EEMD), and demonstrates its improved prediction to the futures and election prediction markets. While the first empirical application shows that the EEMD effectively filters out the noises in the futures market, the second one indicates that the Taiwanese election prediction using EEMD “residue” is not as accurate as that by original data from the prediction market. The reason why the residue cannot serve as a good predictor is that the market participants consider not only the long-term trend, but also shocks, especially those right before the elections. We then attempt to predict the election outcomes by the week trend series processed by EEMD. The prediction by the week EEMD trend series turns out to be more accurate than that by the poll and original prediction market. Based on this study, we can apply the EEMD to the next election prediction and improve its accuracy. 王信實 學位論文 ; thesis 73 zh-TW |
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碩士 === 國立政治大學 === 經濟學系 === 100 === The financial markets are usually affected by political, economic and social environment factors, and thus the volatilities of asset prices in these markets are subject to a lot of noises and shocks. To filter out noises and quantify shocks, this paper applies a data processing method, Ensemble Empirical Mode Decomposition (EEMD), and demonstrates its improved prediction to the futures and election prediction markets.
While the first empirical application shows that the EEMD effectively filters out the noises in the futures market, the second one indicates that the Taiwanese election prediction using EEMD “residue” is not as accurate as that by original data from the prediction market. The reason why the residue cannot serve as a good predictor is that the market participants consider not only the long-term trend, but also shocks, especially those right before the elections. We then attempt to predict the election outcomes by the week trend series processed by EEMD. The prediction by the week EEMD trend series turns out to be more accurate than that by the poll and original prediction market. Based on this study, we can apply the EEMD to the next election prediction and improve its accuracy.
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王信實 |
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王信實 鄭緯暄 |
author |
鄭緯暄 |
spellingShingle |
鄭緯暄 Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan |
author_sort |
鄭緯暄 |
title |
Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan |
title_short |
Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan |
title_full |
Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan |
title_fullStr |
Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan |
title_full_unstemmed |
Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan |
title_sort |
applications of ensemble empirical mode decomposition to future and election prediction markets in taiwan |
url |
http://ndltd.ncl.edu.tw/handle/76743704116576804551 |
work_keys_str_mv |
AT zhèngwěixuān applicationsofensembleempiricalmodedecompositiontofutureandelectionpredictionmarketsintaiwan AT zhèngwěixuān zhěngtǐjīngyànmótàifēnjiězàitáiwānqīhuòshìchǎngyǔxuǎnjǔyùcèshìchǎngdeyīngyòng |
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