| Summary: | 碩士 === 世新大學 === 財務金融學研究所(含碩專班) === 104 === This study analyzes articles and comments among netizens from 16 financial planning/wealth management focused fan pages to explore critical interactions between these fan pages and their members via research methodologies such as Text Mining.
Starting from the attributes of fan pages, we found that even with overlapping participants, there is very low similarity among prevailing key words across 4 major types of fan pages according to Chi-square Test analyses. In addition, there is a significant linear correlation between types of articles published on fan pages and TSE index, suggesting that the number of articles published increases as market performs better. Those fan pages aim to promote their respective proposition and philosophy whenever the market is relative active.
Further analyzing the characteristics of the netizens, a difference in investment key word selection was identified between those who participated in contests offering prizes and the overall participants. When analyzing the linear relationship between these key words and Google Tread, TSE index, and trading volume, a negative correlation was found between topic/trading-centric keywords and trading volume. The research also indentified a negative correlation between key words such as “security”, “ETF”, “insurance” and the trading volume, suggesting that when market is relative more active, the discussions on these key words on fan pages becomes quieter.
The results of the study indicate that, the higher TSE index is, the more comments and articles on the fan pages will be. Administrators from these fan pages can leverage these opportunities to interact with netizens by increasing the number of article published. When the trading volume is low, netizens present their anxiety and pay special attention to their investment. They tend to leave more comments on the fan pages and search for relevant key words more actively. A recommendation to security brokerage companies to manage their fan pages is to analyze the key words used in comments and articles, together with multiple external variables, trading information of derivatives, to indentify individual customer’s preference, and to promote their product and service more efficiently.
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