## 2 Literature Review
### Analysis Methodology in Current U.S. Market Research  
Text-based analysis is a frequent-used method in U.S. market research. [PERSON] et al. [6] demonstrated a news-based stock clustering algorithm which was verified through the Taiwanese stock market. They made a codebook for defining the business scopes that include the agriculture, biology, medical, manufacturing, transport, energy, and information technology sectors. [PERSON] [7] introduced a temporal document retrieval model for business news archives; this model helps researchers cluster companies' business scopes through different time intervals. With the development of information technologies, the company's business scope could change every year or even every quarter of a year, and text-based market analysis can hardly adapt to such a rapidly changing market. Indeed, the time series data of stocks, such as closing price, is believed to be highly related to the operation of a certain company. [PERSON] [8] demonstrated a pattern discovery method in his paper. Using self-organizing maps, the authors could discover the pattern character of a stock series. [PERSON] [9] carried out time series clustering experiments on stocks, and proved that the stock pairs with similar time series pattern also shared some similar business scopes. [PERSON] [10] demonstrated a fuzzy time series forecasting model combined with ant colony optimization and auto-regression, which could be used to accelerate the efficiency of time-series based stock clustering. Each new discovery in this area has introduced methodology based on previous studies. To discover stocks with similar movement in stock prices, [PERSON] [11] demonstrated an algorithm assuming that similarity queries can be classified into certain categories. [PERSON] [12] introduced clustering arrangement in which neighboring stocks were as similar as possible. This algorithm prevented text from obscuring the structure of the diagram. [PERSON] [13] made an evaluation upon sentence similarity using the company's annual report, and companies could be clustered more precisely in business scopes. [PERSON] [14] built semantic kernels for text classification using Wikipedia data. This method was then widely used in text similarity analysis. [PERSON] [15] calculated a daily market return by summing the abnormal returns; these analysis considered a bank's average text similarity and tail co-movement with all other banks. Since news may contain more information, also full of diverse expressions, [PERSON]'s method cannot be guaranteed in terms of validity. The data mining and machine learning community have made a great efforts with respect to time-stamped data [16; 17]. This is not surprising as time-stamped data is commonly seen in our daily lives. For example, many smart cities now monitor hourly traffic flow rate to better understand the traffic patterns [18]. The authors also monitor heights of ocean tides every day to understand the possible climate change. Analysts monitor daily closing prices of stocks to understand the market. Doctors monitor patients' monthly weights to gain insights into the condition of the patient. It follows that the data mining tasks over this data aim to discover patterns and knowledge from the collected temporal sequences [19]. However, the patterns of time series data are usually influenced by other events that cannot be easily obtained or derived from the real-valued time series data. In order to better understand the behavior of time series data, the authors could incorporate some external knowledge, such as concurrent events. As an illustration, new iPhone release has had a significant impact on the stock prices of Apple Inc. (Cupertino, CA, USA) [20]. Specifically, analysts observed that, in the last few years, the stock of Apple has been driven by anticipation of the next iPhone release.