最大漲幅投機型個股之買賣單不平衡、波動性、與報酬率之動態關係研究

Abstract

一直以來投資人努力找尋一個最適指標來預測股價走勢，獲得異常報酬。根據過去許多研究顯示，買賣單不平衡(由買方發起訂單或賣方發起訂單)對於股價變動有良好的解釋能力，其影響能力超越以往的股票交易量。本研究是延續先前的研究，以最大漲幅投機型個股為標的，觀察其買賣單不平衡、波動性、與報酬率之間是否真有顯著關係存在。 我們以GARCH(1,1)的模型來檢驗此模型是否能成功的捕捉到買賣單不平衡與股票報酬率之間的動態時間序列特性。實證結果顯示大部分的當期買賣單不平衡對於當期股價波動有良好的解釋能力，且此兩變數之間有正向的顯著關係。接著使用多元線性回歸模型，觀察同期或前幾期買賣單不平衡對於報酬率是否有影響。研究結果顯示當期的買賣單不平衡與股價報酬率間有正向關係。而不論是否考慮當期，前一期的買賣單不平衡與股價報酬率有負向的顯著關係存在。我們利用取樣資料期間，投機型各股股價持續性、股票經紀人穩定股票價格義務，來解釋這個負向關係。 接著我們使用簡單回歸模型，來探討買賣單不平衡和公司規模間，是否存在小型股效果。結果發現，雖然公司規模和買賣單不平衡間的確存在負向關係，但此關係並不顯著，因此無法對小型股效果是否存在做出結論。我們並嘗試發展出一套以買賣當不平衡為指標的交易策略，期望能夠獲得異常報酬。經過我們不斷改良的交易策略，此交易策略的平均投資報酬率由原本的負值變成正值。 最後利用GARCH (1, 1)模型捕捉買賣單不平衡和股票價格波動間的動態關係，發現買賣單不平衡和股價波動間有負向顯著關係，雖然和直覺的預測不同，我們利用預期理論和槓桿效果來解釋此的現象。

In order to earn profits, investors have worked hard for a long period of time to find the fittest indicator to predict the stock price movement. From former researches, order imbalance between buyer- and seller-initiated orders is a powerful determinant of stock price movements beyond trading volume. In this paper, we want to examine the relation between order imbalances, volatility and stock returns. Then, we try to find a profitable trading strategy. We apply GARCH (1, 1) model to see whether the model can capture the time variant property of sample returns and order imbalance. Most of current order imbalance has the explanatory power of current stock return. Then, we use multi-regression model to see whether contemporaneous and lag order imbalances have significant influences on stock returns. The contemporaneous order imbalances have positive influence and lag–one order imbalances have negative influence on stock returns. We use time span of data, momentum characteristic of speculative stocks, and price stabilization responsibility of market markers to explain this phenomenon. Then, we want to test if there is a small firm effect on our data. We fail to find a significant negative relationship between order imbalance and market capitalization. We also derive a trading strategy to see if it can make abnormal profits. Although the profit is not as good as we expect, we drive to the right direction of trading strategy. Finally, we care about the dynamic relation between order imbalance and stock volatility. GARCH (1, 1) model describes the relation well, but the large negative coefficients than positive ones seem inconsistent with our intuition. We introduce the “prospect theory” and “leverage effect” to interpret this phenomenon.