臺灣博碩士論文加值系統

本文首先建立了委託單驅動市場資訊交易機率理論模型，並以此模型分析資訊交易與資產報酬之關連性，買賣－價格效果。其次，我們應用資訊交易機率理論模型，建構了能分析護盤基金及套利交易之委託單驅動市場套利交易機率理論模型，探討政府護盤是否有其必要和探討護盤進場時點是否符合下跌時進場，上漲時不介入之穩定基金設立精神。最後，我們則是建立一個能分析資訊交易者、非資訊交易者日內各交易區間交易規模的交易者交易實證模型，並利用此模型分析交易頻率改變時，市場各類型投資人之日內交易規模變化，瞭解市場績效之成因，主要實證結果分述如下：
在研究資訊交易與資產報酬及買賣－價格效果相關分析部分，我們發現1)短期(日內、日)資訊交易機率與資產報酬無關，但中期(週)資訊交易機率與資產報酬有關，但其影響程度並未如先前研究(Hasbrouck (1991a, b), Glosten and Harris (1988))預期般高。2)好消息交易日之日內資訊交易明顯高於壞消息，此結果顯示市場存在買賣資訊交易不均衡之現象。
在探討護盤進場時點是否符合下跌時進場，上漲時不介入之穩定基金設立精神部分，其結果主要為1)在護盤基金介入個股波動稍微變小、效率稍微變差、報酬變得較佳及流動顯著變大。2)護盤基金介入標的之套利交易機率與其他公司套利交易機率並無顯著差異，且兩者間績效(包含波動、效率、流動及報酬)也無明顯差別。3)護盤基金與套利者傾向開盤期間交易，此結果與Schwartz(1988)之主張相符。4)我們還發現相較於其他套利交易，護盤基金交易與市場漲跌較為緊密，而與個股漲跌較為疏遠。
在分析交易頻率改變時，市場各類型投資人之日內交易規模變化部分，則是發現1)交易頻率變慢，造成了開盤的日內交易比率變小與績效變差，而提高了收盤時的日內交易比率與績效，尤其是在高流動性公司之變化特別顯著。2)交易次數增加，能增加高、中流動性公司之流動性。對低流動公司而言，雖有提升流動性之幫助，但卻會增加其波動，並降低其價格發現速度。
另外，本文模型主要貢獻在資訊交易機率理論模型部分，首先是補先前未有委託單集合競價理論模型之不足；其次，模型設定加入資訊交易者可採限價委託，因此與實際市場現象較相符；第三，模型能計算交易日日內區間之資訊交易機率，因此能分析資訊交易者及市場日內及週內行為或現象；第四，模型是以成交資料而非委託資料來估計資訊交易機率，避免了委託單成交風險造成資訊交易機率估計誤差。第五，模型是在區隔好、壞消息後，計算個股資訊交易機率，因此能分析買、賣資訊交易行為。在套利交易機率理論模型部分，則是提供了能分析市場是否存在自行穩定機制－套利交易的方法，來探討穩定基金存在之必要性及其日內交易行為。最後在資訊交易機率實證模型部分，由於本文是藉由模擬市場非資訊交易者日內交易行為策略，以迴歸分析萃取出日內交易區間成交量變異被非資訊交易者日內行為變異解釋比率，來計算區間資訊及非資訊交易比率，因此能避免先前委託單資訊交易實證模型之各筆交易量被認定僅來自單一交易者缺失。

This paper firstly constructed an order-driven market probability model of informed trading to analyze the correlation between informed trade and return of assets and the trade-price effect. Secondly, using the probability model of informed trading, we constructed a probability model of arbitrage trading in order-driven call market, which could analyze the stabilization fund and the arbitrage trade, to investigate whether the government’s interference measures were necessary and whether the intervened timepoints conformed to the set-up spirit of the stabilization fund—to intervene while falling and not to while rising. Finally, we set up a ratio empirical model of informed trading which could analyze the intraday trade scale of each trade section of informed traders and uninformed traders, to analyze the change of intraday trade scale of each type of investors while trade frequency changed to explore the factors of market performance. The main results are as follows respectively:
Regarding the correlation analysis of informed trading and return of assets and trade-price effect, we found that (1) in the short-term (intraday, day) there was no relationship between probability of informed trading and return of assets, whereas in the mid-term probability of informed trading was correlated with return of assets although the influence impact was not as high as prior researches (Hasbrouck (1991a, b), Glosten and Harris (1988)) expected. (2) The intraday probability of informed trading of good news days was obviously higher than that of bad news days, which indicated that unbalanced buy-sell informed trade phenomenon existed in the market.
Regarding the investigation of whether the intervened timepoints of stabilization fund conformed to the set-up spirit of the stabilization fund—to intervene while falling and not to while rising, the main results are: (1) the individual stocks intervened by the stabilization fund had slightly smaller volatility, slightly worse efficiency, better returns and significantly larger liquidity. (2) There was no significant difference in the probability of arbitrage trading between the targets intervened by the stabilization fund and the other companies, nor in the performance (including volatility, efficiency, liquidity and return) between both. (3) The stabilization fund and arbitragers tended to conduct transactions in the opening period, which corresponds with the proposition of Schwartz (1988). (4) We also found that compared with other arbitrage trade, the trade of the stabilization fund was more correlated with the price up-down of the market, but not with that of individual stocks.
In the analysis of the intraday trade scale change of each type of investors while trade frequency changed, the main findings are: (1) the slowdown of trade frequency caused smaller intraday trade ratio and worse performance in the opening, but it increased the intraday trade ratio and performance of the closing period, which was especially significant in the high-liquidity companies. (2) The increase of trade frequency could raise the liquidity of the high-liquidity and middle-liquidity companies. As to the low-liquidity companies, although the increase of trade frequency increased the liquidity, it raised their volatility and decreased their price finding speed.
The main contributions of this paper’s models are indicated as follows. Regarding a probability model of informed trade: first, it improves the prior ones by bringing the order-driven call market model; second, the addition of informed traders’ possibility to use limit order in the model set-up better corresponds to the real market; third, the model can calculate the probability of informed trading of intraday trade section and thus can analyze the intraday and intraweek behavior or phenomenon of informed traders and the market; fourth, the model estimates the probability of informed trading using trade data, not order data, and thus avoids the probability of informed trade estimation error caused by order trade risk; fifth, the model calculates the probability of informed trade of individual stock after separating good and bad news and thus can analyze buy-sell informed trade behavior. Regarding the probability model of arbitrage trading, it provides a method to analyze whether self-stabilization mechanism-arbitrage trade exists in the market to investigate on the necessity of the stabilization fund and its intraday trade behavior. Finally, regarding the ratio empirical model of informed trading, since this paper calculated the section informed and uninformed trade ratio by simulating uninformed traders’ intraday trade strategy and by extracting the ratio of the trade volume variation of intraday trade section explained by uninformed traders’ intraday behavior variation using regression analysis, it can avoid the deficiency that every trade volume was regarded as from a single trader in the prior order empirical model of informed trading.

第一章 緒論 10
1.1 研究動機 10
1.2研究目的 17
1.3 內容簡介、研究貢獻及論文架構 18
第二章 文獻回顧 23
2.1資訊交易與價格變動相關研究 23
2.2資訊交易與資產報酬相關研究 23
2.3買賣－價格效果相關研究 24
2.4穩定基金相關研究 25
2.5交易頻率相關研究 25
2.6市場投資人交易行為相關研究 26
2.6.1 資訊交易者行為 27
2.6.2 雜訊交易者行為 27
2.6.3 流動性交易者行為 28
第三章 資訊交易機率模型及資產報酬與買賣－價格效果分析 29
3.1 前言 29
3.2 資訊交易機率模型 29
3.2.1 模型假設 30
3.2.1.1 交易機制假設 30
3.2.1.2公開資訊資產評價假設 30
3.2.1.3 私人資訊假設 31
3.2.2 交易者行為 32
3.2.2.1 資訊交易者交易決策 32
3.2.2.2 非資訊交易者交易決策 33
3.2.3 符號設定 34
3.2.4 資訊交易機率 35
3.3 交易者策略模擬 37
3.3.1 第t個交易區間非資訊交易者之買、賣單比率模擬 38
3.3.2 第t個交易區間非資訊交易者之限、市價單比率模擬 38
3.3.3 資訊交易者第t個交易區間之限、市價單比率模擬 39
3.3.4 區間狀況s成交於買、賣價之機率模擬 40
3.4 資訊交易機率估計 40
3.5 樣本資料及研究說明 43
3.6 實證分析 44
3.6.1 資訊交易機率 44
3.6.2 資訊交易與價格變動 48
3.6.3 資訊交易與資產報酬 48
3.6.4 資訊交易與買賣－價格效果 49
3.6.5 資訊交易之穩健度分析 51
第肆章 套利交易機率模型及921地震護盤績效及行為分析 54
4.1 前言 54
4.2 套利交易機率模型 55
4.2.1 交易機制假設 55
4.2.2 一般時期資產評價 55
4.2.2.1 公開資訊資產評價假設 55
4.2.2.2 私人資訊假設 56
4.2.3非經濟因素重大不利事件發生時期資產評價 56
4.2.4 交易者行為 58
4.2.4.1套利交易者交易決策 58
4.2.4.2 非套利交易者交易決策 59
4.2.5 符號設定 59
4.2.6 套利交易機率 59
4.3 樣本資料及研究說明 61
4.4 實證分析 63
4.4.1 套利交易機率 63
4.4.2 績效分析 63
4.4.3 套利日內行為分析 65
4.4.4 交易者護盤期間交易時點分析 66
第伍章 各類交易者交易比率模型及交易頻率改變對市場交易者交易行為影響分析 69
5.1 前言 69
5.2 各類交易者交易比率模型 69
5.2.1 市場投資人設定 69
5.2.2模型設定 70
5.2.2.1 交易過程 70
5.2.2.2 成交量 72
5.2.3 日內交易機率模擬分析 72
5.2.3.1 交易者交易策略變數模擬 73
5.2.3.2 日內各類交易者交易比率模擬 74
5.3 研究假設 75
5.4 檢定方法 76
5.4.1 不同時期及日內區間差異檢定 76
5.4.2 交易頻率與日內交易績效的關係 77
5.5樣本資料及研究說明 79
5.6 實證分析 81
5.6.1 交易者基本性質檢定 82
5.6.2 敘述統計分析 82
5.6.3 不同交易頻率時期及日內區間差異檢定 83
5.6.4 不同交易頻率時期日內型態差異大小及方向 85
5.6.5 交易頻率與日內交易績效的關係 89
第六章 結論 93
參考文獻 97

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