Academic literature on the topic '上市股票'

关联交易属于上市公司股票市场经济行为当中非常重要的组成部分，其在中国的上市公司当中是非常普遍的，会对股票市场造成严重的影响。从比较积极的方面来看，关联交易可以使交易成本降低，提升集团公司的运营效率以及资本运营能力，使公司的利润最大化。但是，关联方之间存在的关系具备复杂性以及特殊性，因此会时常出现关联方交易滥用的情况。近些年来，关联交易在中国的证券市场健康发展当中属于严重的绊脚石，从某种意义上来讲，它已经成为证券市场上市公司当中欺诈的代名词。因此，为了严格地对上市公司的关联交易进行规范，对其实施具体研究是非常重要的。

PurposeThis study examines the inter-linkages between Bitcoin prices and CEE stock markets (Hungary, the Czech Republic, Poland, Romania and Croatia).Design/methodology/approachThe dynamic contemporaneous nexus has been analyzed using both the multivariate DECO-GARCH model proposed by Engle and Kelly (2012) and quantile on quantile (QQ) methodology proposed by Sim and Zhou (2015). Our study is implemented using the daily data spanning from 6 September 2012 to 12 August 2019.FindingsFirst, the findings show that the average return equicorrelation across Bitcoin prices and CEE stock indices are positive, even though it is found to be time-varying over the research period shown. Second, the Bitcoin-CEE stock market association has positive signs for most pairs of quantiles of both variables and represents a rather similar pattern for the cases of Poland, the Czech Republic and Croatia. However, a weaker and primarily negative connectedness is found for Hungary and Romania, respectively. Furthermore, the interconnectedness between the co-movements in the Bitcoin market and stock returns changes significantly across quantiles of both variables within each nation, indicating that the Bitcoin-stock market relationship is dependent on both the cycle of the stock market and the nature of Bitcoin price shocks.Practical implicationsThe evidence documented in this study has significant implications for divergent economic agents, including global investors, risk managers and policymakers, who would benefit from a comprehensive knowledge of the Bitcoin-stock market relationship to build efficient risk-hedging models and to conduct appropriate policy reactions to information spillover effects in different time horizons.Originality/valueThis paper is the first study employing both the multivariate DECO-GARCH model and QQ methodology to shed light on the nexus between Bitcoin prices and the stock markets in CEE countries. The DECO model uses more information to compute dynamic correlations between each pair of returns than standard dynamic conditional correlation (DCC) models, declining the estimation noise of the correlations. Besides, QQ approach allows us to capture some nuanced features of the Bitcoin-stock market relationship and explore the interdependence in its entirely. Therefore, the main contribution of this article to the related literature in this field is significant.研究目的本研究旨在探討比特幣的價格與中東歐股市(匈牙利、捷克共和國、波蘭、羅馬尼亞和克羅地亞) 之相互聯繫.研究設計/方法/理念研究使用恩格爾與凱利(2012)(Engle and Kelly (2012)) 提出的多變量DECO-GARCH模型及Sim 與Zhou(2015)(Sim and Zhou ( 2015)) 研製的分位數-分位數方法來分析動態同期的聯繫。我們的研究使用由2012年9月6日至2019年8月12日期間取得的每日數據來進行.研究結果首先、研究結果顯示、跨比特幣價格與中東歐股價指數的平均回報當量關聯是正相關的，即使在研究期間被發現是隨時間而變化的。第二、比特幣與中東歐股市之聯繫在大多數兩變數分位數對而言出現正相關跡象，而且，這聯繫在波蘭、捷克共和國及克羅地亞而言表現一個頗相似的模式。唯就匈牙利而言、這聯繫則較弱、而羅馬尼亞則主要是負聯繫。研究結果亦顯示: 比特幣市場內的聯動與股票回報間之內在關聯會在每個國家內跨兩個變數的分位數而顯著地改變，這顯示比特幣-股市關係是取決於股市的週期和比特幣價格衝擊的本質.實際的意義本研究所記載的證據、對不同的經濟行為者而言極具意義 (這包括國際投資者、風險管理經理和政策制定者)，因他們會受惠於對比特幣-股市關係的全面認識，他們可建立有效的風險對沖模型、及在不同時間範圍對資訊溢出效應進行適當的政策反應.研究的原創性/價值本文為首個研究使用多變量DECO-GARCH模型和分位數-分位數(QQ)方法、來解釋比特幣價格與中東歐國家之股市的關係。這DECO模型使用比標準動態條件關係模型更多資訊，來計算每對回報間之動態關係，這能減少估測雜訊，而且，QQ方法讓我們可以取得比特幣-股市關係的一些細微特徵及全面地探索其相互依賴性。因此，本文的主要貢獻是在這學術領域內有關的文獻上.

在面对大型投资者日益科技化的投资行为的今天,由于普通的小型投资者存在着知识技术水平有限、设备资金限制等一系列的难点,难以获得摆脱主观投资而选择量化投资,导致普通小型投资者难以在立足于市场。因此本文立足于普通小型投资者的痛点,利用2012-2018年的历史数据,基于行业与周期轮动效应的交叉效应,构建一个能满足普通小型投资者自身条件的量化投资策略——TCPDMA投资策略,并使用2019年的股票收益率进行策略回测。结果表明,在股票正常运行的周期中,行业与周期轮动效应的交叉效应均存在于市场中,而基于此的TCPDMA投资策略的预期收益率均优于上证指数的实际收益率,表明TCPDMA投资策略具有盈利能力优以及操作简便的特点,适用于普通小型投资者的量化投资策略。

碩士
國立高雄第一科技大學
金融系碩士班金融組
104
How does the Taiwan futures exchange select, among all eligible stocks, the ones to trade futures contracts on? This thesis discusses three questions about this topic. First, the logistic model is used to explain the listing selection of single stock futures (SSF). Second, using the parameters estimated in the logit mdel, this thesis estimates the correct classification rate. Finally, the relationship between the predicted probability of being listed and SSF trading volume is examined. The logistic regression results find that market capitalization and stock volatility are significant explanatory factors for listing selection of SSF in 2010. Furthermore, stock turnover is significant predictors of a firm being listed in 2011. Next, based on the correct classification rate, the logistic model has captured the selection process quite well. Finally, in 2011 (the second batch of listings), the predicted probability of being listed is a significant and positive predictor of post-listing trading volume.