• Asia pacific journal of information systems
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• 제24권4호
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• pp.559-575
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• 2014

Individual investors are increasingly flocking to message boards to seek, clarify, and exchange information. Businesses like Seekingalpha.com and business magazines like Fortune are evaluating, synthesizing, and reporting the comments made on message boards or blogs. In March of 2012, Yahoo! Finance Message Boards recorded 45 million unique visitors per month followed by AOL Money and Finance (19.8 million), and Google Finance (1.6 million) [McIntyre, 2012]. Previous studies in the finance literature suggest that online communities often provide more accurate information than analyst forecasts [Bagnoli et al., 1999; Clarkson et al., 2006]. Some studies empirically show that the volume of posts in online communities have a positive relationship with market activities (e.g., trading volumes) [Antweiler and Frank, 2004; Bagnoli et al., 1999; Das and Chen, 2007; Tumarkin and Whitelaw, 2001]. The findings indicate that information in online communities does impact investors' investment decisions and trading behaviors. However, research explicating the correlation between information on online communities and stock market activities (e.g., trading volume) is still evolving. Thus, it is important to ask whether a volume of posts on online communities influences trading volumes and whether trading volumes also influence these communities. Online stock message boards offer two different types of information, which can be explained using an economic and a psychological perspective. From a purely economic perspective, one would expect that stock message boards would have a beneficial effect, since they provide timely information at a much lower cost [Bagnoli et al., 1999; Clarkson et al., 2006; Birchler and Butler, 2007]. This indicates that information in stock message boards may provide valuable information investors can use to predict stock market activities and thus may use to make better investment decisions. On the other hand, psychological studies have shown that stock message boards may not necessarily make investors more informed. The related literature argues that confirmation bias causes investors to seek other investors with the same opinions on these stock message boards [Chen and Gu, 2009; Park et al., 2013]. For example, investors may want to share their painful investment experiences with others on stock message boards and are relieved to find they are not alone. In this case, the information on these stock message boards mainly reflects past experience or past information and not valuable and predictable information for market activities. This study thus investigates the two roles of stock message boards-providing valuable information to make future investment decisions or sharing past experiences that reflect mainly investors' painful or boastful stories. If stock message boards do provide valuable information for stock investment decisions, then investors will use this information and thereby influence stock market activities (e.g., trading volume). On the contrary, if investors made investment decisions and visit stock message boards later, they will mainly share their past experiences with others. In this case, past activities in the stock market will influence the stock message boards. These arguments indicate that there is a correlation between information posted on stock message boards and stock market activities. The previous literature has examined the impact of stock sentiments or the number of posts on stock market activities (e.g., trading volume, volatility, stock prices). However, the studies related to stock sentiments found it difficult to obtain significant results. It is not easy to identify useful information among the millions of posts, many of which can be just noise. As a result, the overall sentiments of stock message boards often carry little information for future stock movements [Das and Chen, 2001; Antweiler and Frank, 2004]. This study notes that as a dependent variable, trading volume is more reliable for capturing the effect of stock message board activities. The finance literature argues that trading volume is an indicator of stock price movements [Das et al., 2005; Das and Chen, 2007]. In this regard, this study investigates the correlation between a number of posts (information on stock message boards) and trading volume (stock market activity). We collected about 100,000 messages of 40 companies at KOSPI (Korea Composite Stock Price Index) from Paxnet, the most popular Korean online stock message board. The messages we collected were divided into in-trading and after-trading hours to examine the correlation between the numbers of posts and trading volumes in detail. Also we collected the volume of the stock of the 40 companies. The vector regression analysis and the granger causality test, 3SLS analysis were performed on our panel data sets. We found that the number of posts on online stock message boards is positively related to prior stock trade volume. Also, we found that the impact of the number of posts on stock trading volumes is not statistically significant. Also, we empirically showed the correlation between stock trading volumes and the number of posts on stock message boards. The results of this study contribute to the IS and finance literature in that we identified online stock message board's two roles. Also, this study suggests that stock trading managers should carefully monitor information on stock message boards to understand stock market activities in advance.

• 지능정보연구
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• 제25권1호
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• pp.63-83
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• 2019

투자자들은 기업의 내재가치 분석, 기술적 보조지표 분석 등 복잡한 분석보다 차트(chart)에 나타난 그래프(graph)의 모양으로 매매 시점을 찾는 직관적인 방법을 더 선호하는 편이다. 하지만 패턴(pattern) 분석 기법은 IT 구현의 난이도 때문에 사용자들의 요구에 비해 전산화가 덜 된 분야로 여겨진다. 최근에는 인공지능(artificial intelligence, AI) 분야에서 신경망을 비롯한 다양한 기계학습(machine learning) 기법을 사용하여 주가의 패턴을 연구하는 사례가 많아졌다. 특히 IT 기술의 발전으로 방대한 차트 데이터를 분석하여 주가 예측력이 높은 패턴을 발굴하는 것이 예전보다 쉬워졌다. 지금까지의 성과로 볼 때 가격의 단기 예측력은 높아졌지만, 장기 예측력은 한계가 있어서 장기 투자보다 단타 매매에서 활용되는 수준이다. 이외에 과거 기술력으로 인식하지 못했던 패턴을 기계적으로 정확하게 찾아내는 데 초점을 맞춘 연구도 있지만 찾아진 패턴이 매매에 적합한지 아닌지는 별개의 문제이기 때문에 실용적인 부분에서 취약할 수 있다. 본 연구는 주가 예측력이 있는 패턴을 찾으려는 기존 연구 방법과 달리 패턴들을 먼저 정의해 놓고 확률기반으로 선택해서 매매하는 방법을 제안한다. 5개의 전환점으로 정의한 Merrill(1980)의 M&W 파동 패턴은 32가지의 패턴으로 시장 국면 대부분을 설명할 수 있다. 전환점만으로 패턴을 분류하기 때문에 패턴 인식의 정확도를 높이기 위해 드는 비용을 줄일 수 있다. 32개 패턴으로 만들 수 있는 조합의 수는 전수 테스트가 불가능한 수준이다. 그래서 최적화 문제와 관련한 연구들에서 가장 많이 사용되고 있는 인공지능 알고리즘(algorithm) 중 하나인 유전자 알고리즘(genetic algorithm, GA)을 이용하였다. 그리고 미래의 주가가 과거를 반영한다 해도 같게 움직이지 않기 때문에 전진 분석(walk-forward analysis, WFA)방법을 적용하여 과최적화(overfitting)의 실수를 줄이도록 하였다. 20종목씩 6개의 포트폴리오(portfolio)를 구성하여 테스트해 본 결과에 따르면 패턴 매매에서 가격 변동성이 어느 정도 수반되어야 하며 패턴이 진행 중일 때보다 패턴이 완성된 후에 진입, 청산하는 것이 효과적임을 확인하였다.