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TDRs與原上市地股票價格關係之探討 / Price transmission dynamics between TDRs and the underlying foreign securities張韡華 Unknown Date (has links)
台灣存託憑證(TDRs)從2009年起在台盛行,至今有三十五檔TDRs在台灣證劵交易所掛牌,各檔TDRs交易期間至今約半年至三年不等,目前TDRs檔數和交易時間已足以做比較和計量檢定,因此本文以在台灣證劵交易所掛牌之TDRs為對象,選取13檔TDRs做實證研究,資料期間為各檔TDRs上市日至2012年2月17日的日資料。研究內容包含兩部分:1.透過單根檢定、共整合檢定、VAR模型、Granger關係檢定和誤差修正模型等實證分析,觀察兩地股價是否具有長期的穩定關係和價格發現功能,以及股價波動是否具有因果關係;2.近年來台灣的相關法規是否對TDRs交易機制有正向幫助。實證結果主要如下:1.大部分TDRs結果顯示股價、匯率和大盤指數間具有長期的穩定關係,即是有共整合現象;誤差修正模型(VECM)中各變數的係數大多不顯著,也就是說各變數在短期並無法透過此模型來預測股價;2.在因果檢定方面,樣本中有5檔TDRs的原股股價對TDRs股價有領先效果,另外8檔TDRs的原股股價和TDRs股價間並沒有顯著的領先或落後效果;3.台灣近年陸續發布的相關法規確實改善市場的供需問題
和價格偏離現象,對投資人有正面幫助。
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濱海新區在天津經濟發展中的角色 / The role of the Binhai new area in Tianjin economic development劉嘉玲, Liu, Chia Ling Unknown Date (has links)
昔天津的經濟發展都落後於在中國內地的其他一些城市,提到天津,人們往往聯想到,她只是北京光環下的一個毗鄰城市。為了在經濟成長中保持天津經濟的穩定增長,天津採取了吸引大規模工業建設項目的發展策略,再加上近年濱海新區的蓬勃發展,不僅讓該區的經濟表現成績亮眼,也進而讓天津此一城市向國際化大都市腳步邁進。
再者,地處環渤海經濟圈沿海區位之天津濱海新區對其經濟發展推動也著實重要,主因係大陸將開發天津濱海新區納入國家十一五規劃和國家發展戰略,並批准濱海新區成為國家綜合配套改革試驗區,相關重大工業項目都相繼落戶於濱海新區,進而促使天津濱海新區成爲國家級新區中發展最爲迅速的新區,所以本研究係從文獻探討回顧探討影響天津經濟發展的決定因素,並利用樣本資料與實證模型來推估假設變數與天津經濟成長率之間的關係與影響程度,俾利提供學術上與實務上濱海新區在天津經濟發展中所扮演角色之參考。
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國際學生來臺趨勢、擇國擇校過程及滿意度之研究 / Tendency, decision-making processes, and satisfaction of international students in Taiwan張琦, Chang, Chi Unknown Date (has links)
近來,臺灣教育部為招收更多國際學生而實行新政策。本研究分析政策實行的效果、學生來臺原因及在臺學生的滿意度。研究一以時間序列分析1954至2008年的國際學生人數,ARIMA模式的預測效果良好(RER=0.83%),未來三年內國際學生人數將突破二萬人。研究二以問卷調查分析學生選擇留學國家及學校的選擇因素,同時分析學生來臺動機、所遇困難及在臺滿意度間的關係 (N=210)。結果顯示容易的簽證程序及臺灣的學術聲望影響學生來臺讀書;此外,財務、後勤及生活條件影響學生選擇留學學校。學生因臺學術聲望來臺且無食物適應困難者,願意再次來臺念書;學生因具吸引力課程及方便生活環境來臺且無硬體設備及學習適應方面困難者,願意建議他人來臺留學。根據研究結果,政府應加強國內教學品質,並協助提升學校學術聲望;增加赴海外任教教師,以教學專業提升我國國際形象。有興趣吸引國際學生的學校應可從健全學校行政體制著手,並聚焦於完善學生招生、入學諮詢輔導工作及協助生活適應。 / Recently, the Taiwanese Ministry of Education has implemented a new policy to recruit more international students. This study analyzes the policy’s effects and the reasons motivating students to study in Taiwan. The number of international students in Taiwan from 1954 to 2008 was used to generate a time series model to estimate the number of international students before and after the policy implementation. This ARIMA model (RER=0.83%) demonstrated a dramatic increase in the number of international students over the years. Using a questionnaire designed to examine determinants that affect international students’ decisions for choosing Taiwan and their respective schools (N=210), this study then study the relationship between the students’ motivations, the challenges they faced, and their degree of satisfaction with their experiences. Results demonstrated that an easy visa application process and Taiwan’s acclaimed academic reputation attracted students. Furthermore, financial, logistical, and living reasons affected the students’ choices in schools. Students who found living and educational conditions favorable and who felt intellectually stimulated would choose again to study in Taiwan and would recommend others to do so. Based on these results, the government should strengthen the quality of instruction and further promote Taiwan’s universities abroad. Schools interested in attracting international students should develop structured administrative systems focused on recruiting students and helping them transition into a new environment.
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Adaptive decomposition of signals into mono-componentsWang, Yan Bo January 2010 (has links)
University of Macau / Faculty of Science and Technology / Department of Mathematics
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基於 EEMD 與類神經網路方法進行台指期貨高頻交易研究 / A Study of TAIEX Futures High-frequency Trading by using EEMD-based Neural Network Learning Paradigms黃仕豪, Huang, Sven Shih Hao Unknown Date (has links)
金融市場是個變化莫測的環境,看似隨機,在隨機中卻隱藏著某些特性與關係。不論是自然現象中的氣象預測或是金融領域中對下一時刻價格的預測, 都有相似的複雜性。 時間序列的預測一直都是許多領域中重要的項目之一, 金融時間序列的預測也不例外。在本論文中我們針對金融時間序列的非線性與非穩態關係引入類神經網路(ANNs) 與集合經驗模態分解法(EEMD), 藉由ANNs處理非線性問題的能力與EEMD處理時間序列信號的優點,並進一步與傳統上使用於金融時間序列分析的自回歸滑動平均模型(ARMA)進行複合式的模型建構,引入燭型圖概念嘗試進行高頻下的台指期貨TAIEX交易。在不計交易成本的績效測試下本研究的高頻交易模型有突出的績效,證明以ANNs、EEMD方法與ARMA組成的混合式模型在高頻時間尺度交易下有相當的發展潛力,具有進一步發展的價值。在處理高頻時間尺度下所產生的大型數據方面,引入平行運算架構SPMD(single program, multiple data)以增進其處理大型資料下的運算效率。本研究亦透過分析高頻時間尺度的本質模態函數(IMFs)探討在高頻尺度下影響台指期貨價格的因素。 / Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.
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