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Social Media and Smart Beta

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Smart Beta Sentiment Enhanced ETF Performance Analysis

At SMA we continuously research our data.  Below we discuss modifying weights of the SPDR SPY ETF based on sentiment values and examine the impact on return.  Please contact SMA (info@SocialMarketAnalytics.com) to learn more.

The SPDR SPY ETF is a cap-weighted ETF which closely replicates the performance of the S&P 500. Our objective is to develop a “smart beta” strategy using the social media sentiment levels of individuals ETF constituents and amplify or accentuate the weights of the constituents in the ETF while keeping the Assets under Management constant. The transaction cost assumption is ignored for both the original and the enhanced ETF.

One of the strategies explored was looking at the sentiment levels an hour before the close (2:55 PM Eastern Time) and re-balancing the weights according to that. The stocks were bought or sold (to reduce position as per new weight only, NO short selling) at the close of the day and the positions were maintained until the next day when the re-balancing was performed again. To explore the weight modification methodology please contact SMA.

Our re-balance strategy keeps the AUM constant with no need for additional funds. Another strategy explored was to use a “lagged” sentiment. The lag being a day. So, for adjusting the weights today, we looked at the sentiment at 2:55 PM yesterday, and changed the positions based on that.

The results for the cumulative returns calculated over the period extending 7/31/2013-8/31/2015 are summarized below.  Chart 1 shows the cumulative returns over the period for the “Original” which calculates fund returns using positions and closing price data. The “500% PM” makes the calculations using enhanced weights based on the pre-close sentiment. The “500% PM Lagged” has enhanced performance using pre-close sentiment from previous (trading) day.

Chart 2 shows the cumulative out performance, for the 2 “smart beta” strategies.  As you can see both strategies track the SPDR SPY ETF while outperforming performance.  You see the benefit of adding sentiment to your calculation process without increasing risk.

Chart1

Chart2

This is preliminary research we will be enhancing and updating over the coming weeks.

Regards,

SMA

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Social Topic Conversations: What You Want, When You Want It, How You Want It

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SMA is an analytics company with unique IP for filtering and quantification of social media.   SMA to date has been primarily focused on the capital markets given our extensive knowledge of this industry.   On deck for us is the natural expansion of our capabilities to a “Topic Model” format.  Right now, we use our proprietary technology to filter and quantify the conversations around stocks, commodities and foreign exchange.  But the world cares about much more and we can help.

Early on we recognized the trans-formative value of Twitter as the next frontier for breaking and disseminating news.  Its high noise to signal ratio represented an opportunity for us to apply our knowledge to generate value.   We founded SMA in early 2012 to help people in the capital markets make sense of Twitter without having to weed through individual tweets.  We could see the explosive growth trajectory of tweets – now at 750 million a day – and realized it would soon become impossible to use traditional tools to really understand the market pulse around these social conversations.   We learned to convert the Twitter fire hose into real-time streams of high signal predictive data.  We also learned that the methodology used to generate these data streams let us filter the fire hose for specific conversations in very valuable ways.

First, we filter accounts for quality based on programmatic algorithms.  We started this process to eliminate the spammers, scammers and pump-and-dump schemers.   It’s a critical step in finding the quality information.  Even with this filtering, we current certify 65,000+ Twitter accounts for capital markets conversations alone, more than one person could reasonably manage to follow.   Each approved account is then rated and weighted, again diagrammatically.  This step is interesting for a Topic Model format in that you can certify accounts for different topics to create an expert stream of signals on any topic.

Next, we generate our S-Factor metrics.  SMA Dashboard Clients are familiar with our S-Factor Alerts.  Let’s talk about what these can really do.  Let’s say you’d like to track conversations on new products, but you really only need to know when the excitement is extremely high/low, rapidly turning negative, very volatile or going viral.  By building a list of custom alerts on your specified Topics, you get only what you want, when you want it.   If you want to know of the slightest hint of trouble, you can specify tight thresholds.  Or you can set your threshold levels much higher and only get notified of extremely unusual conversation trends.   You can then drill down to the individual tweet level to get more granular level content.  You can also search on individual tweet scores and view just those tweets with high/low scores.

SMA will send you an e-mail or text alert when your specified alert limits are hit.  You can also track all changes in real-time on the Dashboard. Of course, we still have our high-powered API and all of this capability can be directly integrated into any client system.  From the start, we designed our technology to be source and search agnostic and given client demand, we’ve added additional data sources.   As we start tackling the conversations outside of finance, we welcome your interest in new Topics.

By Kim Gits, CFO

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Social Media and Investing for a New Generation

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By Kim Gits,  CFO of Social Market Analytics

Are you prepared for the shift in attitudes and expectations of the next generation of investors? What is the next step for social media use in the capital markets?  How far will you go in implementing a social strategy to retain/attract investors?  Once in house, how will you communicate with them?

Much has been said about this new generation of investors – the Millennials.  Their generation is larger than even the Boomer generation.  They will be recipients of the largest wealth transfer in history.  They grew up with cellphones and instant access to information via the internet.  They are social and mobile.

But what does this mean to firms who wish to court this generation of investors?

In the last few years, we have seen Twitter and other social media sites like StockTwits and Seeking Alpha come of age as reliable data sources.  At Social Market Analytics, we are incredibly excited about these questions and the changes in investing that are on the horizon.

I’d break down the market response to this paradigm shift in waves.  The first wave many years ago was to develop a consistent firm-wide policy for employee use of social media.  Much legal angst went into creating these edicts.  Many of the Boomer and even GenX population saw it as a fad that would pass and certainly never saw themselves as Twitter users (Deja-vu for me when thinking about the adoption of the internet in the late 80s).  This first wave marked the beginning of a new means of communication.  For us at SMA it represented a new source of data as “smart money” finally had the clearance to Tweet their thoughts.

I’d describe the second wave as the re-posting of individual Tweets – something we began seeing as early as 2012.  A few firms began streaming Twitter posts as they related to stocks and the markets.  But let me ask you – with over 500,000 tweets, about stocks, a day (and growing) is it really possible to read all of those Tweets and still get any work done?  Do you really care that your brother-in-law’s third cousin thinks Apple is going up (unless of course he’s Warren Buffet)?

The third wave was adoption of social media data by hedge funds and quant traders.  Always on the lookout for new ways to generate alpha, this group has been adding various forms of social media data to their trading strategies.  SMA and its partners have been at the forefront of research in alpha generation strategies using S-FactorsTM, our social media metrics.  Growth at this level continues with more advanced strategies using multiple asset classes.

The final wave as I see it is the native integration of social media and its rich knowledge sharing capabilities into the investing platform.  Not only will investors be alerted to what they want to know in real-time, they will also have the ability to communicate socially with their brokers and execute transactions on a mobile platform based on either alerts or social communications from their broker.

As you address these coming changes, we are prepared to help.  SMA finds the real-time “smart-money” conversations in social media.  Knowledge sharing is known to be important to this new generation of investors.  SMA’s differentiation is that our proprietary filtering eliminates the spammers, scammers and naïve-user conversations.  Our metrics are based on the social media posts of “smart-money”.  Also, SMA’s unique normalization process helps users find hidden stock conversations that might otherwise be overwhelmed by the likes of Apple, Google and Facebook.  Our data and metrics are engineered to perform at the highest levels and we offer fine-grained customization to meet the needs of your specific customer base.  User-defined thresholds of our metrics let investors listen to only the conversations that are meaningful to them.  To learn more, please contact us at info@socialmarketanalytics.com

Thanks,

Kim

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