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Digital Technologies Witness Bull Run in Capital Markets
Capital Markets have always been on the forefront of adoption of new technology. Effective technology deployment in this industry has a direct impact on the bottom line of the organizations. Whether it is technology deployment for coming up with low latency applications where the trade is executed in less than a milli second or an intelligent analytical model which is used to price complex financial products, capital markets are always innovating the application of technology in business.
In the following paragraphs, I discuss in detail how innovation in technology has resulted in a material impact on the bottom line in this industry. As a result, what appears to be a futuristic trend in technology seems to find a real world application in this industry.
This industry has always re-defined the limits of imagination in terms of application of technology. Faster execution of trades, algorithmic trading and complex analytical tools are just a few examples where technology has been critical to staying ahead of competition.
We are now experiencing another innovative use of technology in this industry i.e. the use of the digital platform and social media. The current article deals with some very interesting and innovative trends that are emerging in terms range of applicability and impact of digital technology and social media on this industry.
Predicting Stock Prices: Research studies have indicated a strong correlation between social mood and stock prices. Social mood is found to be a lead indicator of the stock prices. The volume of data posted on a daily basis on the social media is huge.
For instance, Twitter alone has over 65 million tweets a day. The correlation between collective thoughts and moods of millions provide an indication of the stock markets in the near future. Academic studies have tried to classify tweets in six different mood states i.e. calm, alert, sure, vital, kind and happy. The calm state has been found to have an 86.7 percent correlation with Dow Jones Industrial Average (DJIA).
Online brand popularity has also been found to be a lead indicator of the stock prices. Social media popularity of the brand on Facebook, Twitter and You Tube has shown correlation with the stock price.
A study conducted in association with Famecount (which tracks brand popularity on social media), has demonstrated that popularity of the brand on social media can be used to predict the stock price. This study focused on three brands- Starbucks, Coca Cola and Nike, over a period of 10 months. Social media popularity of these three brands was tracked against the daily stock price movement.
Interestingly, this correlation is not just restricted to the academic world. London based Derwent Capital has launched a 25 million pound hedge fund which will utilize the sentiment derived from real time social media sentiment analysis. Another example of industry adoption is Penson Financial Services Inc, which will be providing both retail and institution brokers quantitative trading signals that incorporate social media sentiment.
Social Media in Retail Trading: Retail trading is adopting social media in a big way. Unlike professional traders who do not want the market to know their positions and trading history, the online retail traders treat information sharing as a valuable asset. Social networking is an important resource to obtain ideas and tips. Retail investors are using social media for trading in multiple asset classes.
Online FX trading website like www.currensee.com allows the users to build communities to share and discuss trades. On this platform the retail forex traders around the world can share their real trades in real time. It allows you to build trading friends based on trading profile. The users can then collaborate with their trading friends.
On similar lines, Zecco's Wall Street, which is a Facebook application, gives the users the ability to like a stock, thereby giving the ability to stay tuned to stocks latest development. One can also see the stocks which are liked by friends etc. In addition, the users can place the orders right from Facebook.
Stocktwits allows the users to participate in a global community of investors and traders. Get information and ideas on stocks. The users can connect with investors and traders of similar profile and follow them. In addition, the user can share ideas and trades with a wider community and get feedback.
Analysis of the Trend: On the face of it, prediction of stock prices based on the collective sentiment on social media sounds very futuristic. The concept is still in its infancy stage and needs a detailed study covering multiple stocks from multiple sectors over a long period of time so as to test different market conditions.
In addition, the extraction and interpretation of data from social media has to be made more intelligent. Information available on social media is unstructured data. All information in the semi-structured / unstructured domain is not useful to analyse sentiment, as a result intelligent filters have to be applied to extract the relevant feeds. Enhancing intelligence in extracting the relevant feeds will be of critical importance.
Analysis of the social media feed has to be context sensitive. This requires an application which is able to understand the association between words. The application should be intelligent enough to create the association between words over time which indicate a particular mood state. This will be a key differentiator between stock price predictive models.
Location is another factor that has to be taken into account. Tweets from a user in Bhutan may not be used as a lead indicator of the sentiment to predict the Dow Jones Industrial Average (DJIA). On similar lines, demographic profile of the user needs to be considered while extracting the social media feeds.
The social media feed extraction logic and feed analysis should be in conjunction with the news feed analysis. For instance, commodity prices have an impact on the currency movements so news feed impacting the oil producing countries in a positive manner should be used in conjunction with the sentiment analysis of social media feeds from the region. Interpreting the news in conjunction with the social media feed will validate the output of sentiment analysis.
Predicting stock price based on social media popularity may work for brands in the public media. However, brands which are less consumer facing might not demonstrate the level of correlation that has been seen for consumer facing brands.
Correlation of the popularity on social media with stock price needs to be studied in all market conditions as fans may not cease to be fans in a bad market.
To conclude, in terms of predictive analytics, convergence of social media and capital markets is a promising area for research. Final verdict on this area of research is not out as yet, so for now there are no right or wrong answers. However, with the right level of investment, accuracy of output can be enhanced and more importantly confirmed.
The application of social media in retail trading is only going to increase as it enhances the efficiency in retail trading. Fundamentally social media caters to information dissemination through viral propagation, which is of great value in the world of retail trading. In the near future, more retail brokers would be offering social media based trading platforms to their clients on which the clients can collaborate and trade effectively.