蘋果公司在過去短短幾年徹底的改變了數位攜帶裝置的面貌，2019年第四季的服務營收更創下第四季度新高記錄達到125億美元，在蘋果官方網站每年公布全球前 200 大供應商名單，這些供應商佔全球蘋果公司產品的材料、製造及組裝採購支出高達 97%。屬於台灣的供應廠商有半導體、零組件、面板相關、光學鏡頭、聲音元件、組裝相關、包裝材料等產業公司。隨著蘋果公司不斷擴大影響力之下，蘋果概念股成為過去十年台股每年的焦點，每年iPhone或相關產品上市，相關供應鏈股票就是市場上投資人的重要投資標的。
2018年5月的蘋果概念股共110家公司，本研究從此110 家公司中擷取67家上市公司作為研究對象，樣本期間為2015年到2019年台股每個交易日之日內連續競價之交易資料，來探討買賣盤之委託單失衡，是否隱含各股價未來趨勢之資訊。實務上，優先得到資訊的投資人，會在大眾得知前慢慢買進股票，因此在股價反應資訊前的一段時間，成交量會有顯著增加的情況，本文將年度交易日的資料以每30分鐘及每日作為區間，探討30分鐘內及日內的委託單失衡，是否可以預期未來公司股價趨勢。研究結果發現，以不同時間長短來劃分，委託單失衡的筆數與成交值對股價報酬率具有預測能力，且對於電子上中下游不同廠商，敏感度也不同。

In the past few years, Apple has completely changed the face of digital portable devices. In 2019, service revenue reached a new record of US$12.5 billion. As Apple continues to expand its influence, Apple's concept stocks have become the focus of Taiwan stocks every year over the past decade. Each year iPhone or related products are listed. Related supply chain stocks are an important investment target for investors in the market.
In May 2018, there were a total of 110 Apple concept stocks. This study extracted 67 listed companies from the 110 companies as the research object. The sample period is the consecutive bidding transactions for each trading day of Taiwan stocks from 2015 to 2019. Information, to explore whether the order imbalance in the trading order implies information on the future trend of each stock price.
In practice, the investors who get priority information will slowly buy stocks before the public knows it. Therefore, in the period before the stock price reflects the information, the number of bids or trading volume has increased significantly. The results of the study found that divided into different lengths of time, the number of orders and transaction value imbalances have different levels of predictive power on stock price returns, and the sensitivity is different for a different manufacturer.

摘要 1
ABSTRACT 2
CONTENTS 3
CHAPTER 1 INTRODUCTION 4
1.1 BACKGROUND AND MOTIVATION 4
1.2 RESEARCH OBJECTIVE 5
1.3 RESEARCH METHOD 5
CHAPTER 2 LITERATURE REVIEW 7
CHAPTER 3 PROBLEM DEFINITION 12
3.1 DATA SOURCE AND DATA SELECTION 12
3.2 SELF-REGRESSION ANALYSIS AND SINGLE ROOT TEST 13
3.3 ANALYTICAL METHOD 14
CHAPTER 4 EMPIRICAL ANALYSIS 16
4.1 DESCRIPTIVE SATISTICS 19
4.2 ORDER IMBALANCE AND STOCK RETURNS (30-MINUTE INTERVAL) 21
4.3 ORDER IMBALANCE AND STOCK RETURNS (DAILY INTERVAL) 24
CHAPTER 5 CONCLUSION 26
REFERENCE 28

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