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Social Trading – The Next Big FinTech Trend

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Social Trading – The Next Big FinTech Trend
  • Author(s)

    Chung-Chi Chen & Hsin-Hsi Chen
  • Biography

    Chung-Chi Chen is a PhD candidate in the Department of Computer Science and Information Engineering at National Taiwan University. He received his M.S. degree in Quantitative Finance from National Tsing Hua University, Taiwan. His research focuses on opinion mining and sentiment analysis in financial social media.
    Prof. Hsin-Hsi Chen is a distinguished professor in the Department of Computer Science and Information Engineering, National Taiwan University. He serves as director of MOST Joint Research Center for AI Technology and All Vista Healthcare. His research interests are natural language processing, information retrieval and extraction, and web mining.

  • Academy/University/Organization

    National Taiwan University
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Social trading platforms provide a forum for investors to share their trading ideas. They are one of the disruptive innovations in the recent FinTech (Financial Technology) trend. Sophisticated individual investors on the social trading platforms provide ponderable opinions for investment, and attract many followers. They corrode the value of industrial professionals. Someday soon, these investors will become the celebrities of the amateur investors just like the YouTubers in the entertainment industry.

Compared with the reports of traditional wealth managers and professional analysts, the opinions of the individual investors on social media are much cheaper and extremely easy to obtain. However, unlike the reports of the analysts which are well collected and properly converted into a structured form by market information providers such as Bloomberg and Reuters, the fine-grained opinions embedded in the posts on the social trading platforms are still unexplored. Along this line, our recent works aim to capture the fine-grained opinions on the social trading platforms, and attempt to summarize the textual data into indicators. With thorough experiments, we have found that the price targets of individual investors are comparable and complementary to the price targets of institutional investors. The success of the extracted opinions on market movement prediction challenge evidence that seizing the fine-grained information on the social trading platforms is possible and promising.

Differing from traditional social network platforms on which most users share lifelogs such as travel or meals, social trading platforms provide a forum for individual investors to share their trading ideas, strategies, and opinions. Such a special kind of social network platform is considered as the next big hit in the FinTech trend. Like the YouTubers who have eroded the value of the entertainment industry, the sophisticated investors on the social trading platforms have been corroding the value of the wealth management industry. Bloomberg and Reuters provide market information such as real-time market data, news, and research reports. The information in the reports of analysts has been well sorted out into a structured form. For example, price target (PT), the forecast price level that investors believe the price of certain financial instruments will achieve, is highlighted in the reports. However, the opinions of the crowd still remain at the sentiment level. Along this line, we attempt to sort out the fine-grained opinions of the crowd via numerical understanding techniques.

Numerals provide rich and crucial information in documents in many domains. For example, in clinical records, one important piece of information is dosage, expressed by numerals; numerals provide ingredient proportions in recipes; in financial narrative, numerals represent up to 17 meanings in the taxonomy of our previous work. These examples show that fine-grained analysis of numerals is worthwhile. In our demonstration, called CrowdPT, we focus on summarizing the crowd opinions into one numeral, price target, to show not only the bullish/bearish sentiment of the crowd, but also the exact price level that will be expected. 

Figure 1 illustrates the flowchart of price target extraction. Firstly, we monitor the latest tweets published on Twitter. Only the tweets mentioning the cashtags of the constituent stocks of the Dow Jones Index will be crawled. Secondly, the price target filter is performed to sort out the price target for each cashtag. Then, a classifier based on a convolutional neural network (CNN) is adopted to extract the price target. Finally, we collect the close price from Yahoo Finance and render the price chart.

Figure 1: Flowchart of CrowdPT
Figure 1: Flowchart of CrowdPT
Figure 2 shows the correlation between stock price and the crowd's PTs. The crowd’s PTs not only provide the bullish information, but also the bearish information. It also evidences that the crowd's PTs could be a leading indicator for stock market movement prediction.
Figure 2: Case study of the crowd’s PTs
Figure 2: Case study of the crowd’s PTs
We further compare the PTs of the crowd with the PTs of professional analysts. The upper part in Figure 3 shows the statistical results. The average differences between the analysts' PTs and the close price, and between individual investors’ PTs and the close price are 6.75% and 13.17%, respectively. This result shows that the forecasts made by individual investors may have a tendency toward progression. In other words, individual investors take a longer time to achieve the PT than professional analysts, and have a lower achieving rate. A total of 50.59% of the extracted PTs of individual investors and analysts take different views (bullish/bearish) of the same stock. These results show that the crowd’s opinion complements the analysts’ results from different aspects, which can eke out the missing part of the analysis.
Figure 3: Comparison of crowd’s PTs and analysts’ PTs.
Figure 3: Comparison of crowd’s PTs and analysts’ PTs.
The lower part in Figure 3 shows the backtest results of the trading strategy based on the PTs. With the narrow PTs, analysts achieve a higher winning ratio than individual investors do. However, the overall performance of analysts is worse than that of individual investors. On the one hand, the average return of analysts is lower than that of individual investors. This result indicates that following the conservative PTs of analysts may let investors close their positions too early. The comparison of the max profit and average profit also shows the evidence for this phenomenon. On the other hand, from the aspect of the risk, the average losses of both trading strategies are close, but the max drawdown of the strategy following the PTs of analysts is higher than that of the strategy following the PTs of individual investors. All evidence shows that leveraging the crowd’s opinions to predict the market movement may be better than using the opinions of the analysts.
In summary, investors can benefit from the information provided by our system. In particular, trading strategies combining the price targets of institutional investors and individual investors have great potential for further study. Potential research such as the impact on the volatility and the financial derivatives like futures and options can be extended based on our findings. Several kinds of fine-grained information from the crowd investors can be extracted with the same flow as our work, and the extracted information is expected to provide the additional information for both short-term and long-term trading. 


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