最大跌幅投機型個股報酬率,波動性與買賣單不均衡之動態關係

Abstract

長久以來，股票市場中沒有資訊的投資人總是在找尋一個指標，能夠用來反映擁有資訊的投資人所握有的資訊，以期能準確的預測股價的走勢。而過去許多研究顯示，單日買賣單不均衡與單日股價報酬率間有顯著的關係存在，且在套利型股票中更是有momentum的情況。本文即是想延續前人的研究，先觀察買賣單不均衡與股價報酬率間是否真有顯著關係存在，並加入了波動性的因子，來探討買賣單不均衡與波動性間是否為正向關係，最後並嘗試發展出一套交易策略，期望能夠打敗大盤。 我們以GARCH(1,1)的模型來檢驗所取得的單日股價資料是否能與此模型符合，實證結果發現不論是否加入波動性的因素，GARCH(1,1)模型皆可成功的捕捉此時間序列資料的特性；此外，我們也發現當期的買賣單不均衡與股價報酬率間的確有正向的顯著關係。但在不考慮當期的買賣單不均衡下，前一期的買賣單不均衡與股價報酬率間卻有負向的顯著關係存在。 接著我們使用簡單迴歸模型，來研究買賣單不均衡和公司規模間，是否存在小型股效果。然而，實證結果發現，公司規模和買賣單不均衡間不僅不存在負向關係，且其結果甚至為正向關係。但由於此關係並不顯著，因此無法對小型股效果是否存在做出結論。 最後，本文嘗試發展出一套交易策略，當看到負向買賣單不均衡時就放空，而在看到第一個正向的買賣單不均衡時就回補，想測試是否能打敗所選的股票當日報酬率而賺取利潤。實證結果發現，在未經篩選交易量下，此策略是無法打敗所選股票當日報酬率的，然而在篩選99%的交易量下，超過九成的機率可以賺取正向報酬。因此我們認為此交易策略是可行的，不過必須排除白噪音，也就是小單量的影響，才能賺取利潤。

For many years, investors have been looking for a reliable indicator to predict the movement of stock prices. Many researches show that order imbalances have a significant relationship with stock returns, especially in speculative stocks. In this paper, we want to examine the relations between order imbalances, volatility and stock returns. Then, we try to find the predictability. Finally, we develop a trading strategy and see if it can earn profits. First, we apply GARCH (1,1) model with and without volatility to test whether it can fit our time series data. In our research, we find that GARCH (1,1) can capture the properties of our sample stocks in the two methods. Then, we use multi-regression model to see whether contemporaneous and lagged order imbalances have significant influences on stock returns. We find contemporaneous order imbalances have positive effect and lagged–one order imbalances have negative effect on stock returns. While controlling for contemporaneous order imbalances, only lagged- one order imbalances have significant and negative effects. Then, we want to test if there is a small firm effect on our data. After our empirical test, we can see that order imbalance and market capitalization have positive relation. However, the relation is not significant, thus we can’t say the small firm effect exist from our test. Finally, we develop a trading strategy and wish it can make profits. We short the stocks when order imbalance is negative and buy back when order imbalance is positive. In our test, we notice that if we don’t select the volume, there will be no abnormal returns. However, if we sift our data from trading volume, that is, above 99% volume, we can find that we will earn profits. Therefore, we can say that our trading strategy is useful.