At Social Market Analytics (SMA) we create predictive
signals by aggregating the intentions of professional investors as expressed on
Twitter. We have accumulated eight years
of out-of-sample data illustrating the predictive nature of the data. We publish sentiment metrics to illustrate the
tone of the current conversation relative to historical conversations. One of our key metrics is S-Score. S-Score is effectively a Z-Score, the measure
of deviation from the mean. An |S-Score|
> 2 means the current conversation is two standard deviations from the mean
over the predefined lookback period.
In the prior blog we
explored the benefits of SMA patented machine learning algorithms on return
characteristics. In this blog we
incorporate rolling back tests on the predictive signal to select portfolio securities.
SMA data is predictive across sectors
and industries but as with any factor there are securities that react more
predictively than others.
For chart below we use a rolling one-year accuracy metric for
predicting subsequent O-C return. The
faded lines are S-Score values only. Bolded
lines represent a theoretical portfolio with accuracy filters overlaid with
S-Score values. Only select |S-Score|
> 2 securities that have moved in the predicted direction 60% of the time over
the last year (bold lines). This is all
S-Score > 2 return values are very similar for accuracy filter
and S-Score only. S-Score < -2 had a large
impact from the accuracy filter. Securities
reacting negatively to negative Twitter conversation as measured by S-Score continued
to underperform relative to sentiment only.
This is another example of using sentiment combined with other metrics
leading to statistically significant predictive signals.
To see how sentiment can be used in your models ContactUs@SocialMarketAnalytics.com.
Social Market Analytics has extensive Intellectual Property in three distinct areas: Topic model creation, account filtering and natural language processing (NLP). I have written blog post about SMA topic model creation capabilities and the impact of our account filtering algorithms. This blog answers the question – “Do your machine learning algorithms really add value to the NLP process?”. Answer -> Yes. The chart below illustrates the statistically significant benefits of Social Market Analytics Machine Learning Algorithms in isolation.
Start date for this analysis is 11/20/2018 and the end date is 4/30/2019. This period was chosen because of the significant market draw down in December. We use dictionaries with three distinct rule sets. We use a static dictionary as of the start and end dates and compare resulting predictive returns with a point-in-time dictionary (production). Our patented NLP scores Tweets using the dictionaries at each time, S-Scores are calculated from the generated Tweet scores. The point-in-time dictionary represents word additions, phrases, and grammatical logic as they are made.
We isolate the impact of our NLP process by turning off account filtering applied to the Twitter stream. To ensure we are pulling Tweets only discussing companies and securities, we are using our topic model filtering algorithms. We regularly publish our full return charts to illustrate the impact of our entire process.
Let us start by defining the lines in our chart.
Red Line = Tweets are scored using our dictionary of words and phrases as of 11/20/2018. This illustrates the performance with no machine learning applied on a go forward basis. This is the base case. This line represents the least amount of learned information.
Black Line = Tweets are scored using words and phrases
applied Point-In-Time. This is the
production feed SMA customers receive. We
use Supervised and Unsupervised Machine Learning. There are impacts from both during this
Line = Represents the Perfect Information scenario. Take the most up to date
dictionary of words and phrases (4/30/2019) and apply them backwards. All information learned during the volatile
period is included. This represents the
values expected to be received on a go forward basis.
The charts below represent the cumulative Open to Close return of securities selected based on S-Score 20 minutes prior to market open. S-Score measures the tone of the current conversation relative to historical benchmarks. We select securities with an |S-Score| > 2. Securities with S-Score > 2 are purchased on the open. Securities with S-Score < -2 are sold short on the open. SMA Chart lines represent a theoretical long/short portfolio. Isolated long and short sides are available upon request.
For comparison purposes S&P 500 open to close chart for the analyzed period is below.
The chart below illustrates the cumulative O-C performance illustrating the impact of our ML algorithms. As expected, the lowest performance is the red line representing the dictionary at start date. The back line represents SMA production data and green line represents the perfect information case.
Sharpe and Sortino Ratios
for our test period are below. SP Return
= 3.3%, Sharpe = .58 and Sortino = .96.
Again, this only looks at the impact of SMA NLP and does not include account filtering. At SMA we believe it’s not just what is being said but who is saying it. We employ a twelve variable algorithm to score and filter all Twitter accounts Tweeting about companies/securities to identify our approved account universe. As you can see SMA NLP is a learning system with demonstrable impact. To learn more please contact us at contactUs@SocialMarketAnalytics.com.
Social Market Analytics (SMA) the leader in predictive social media data feeds has added the ‘Crypto Fast’ to its suite of API data feeds. SMA’s S-Factor and Activity Cryptocurrency data feeds have been in production since December 2017. “Clients asked for a bespoke feed for a shorter baseline with 1-Hour price projections. Although clients can create their own baselines and metrics with our Activity Feed, clients wanted SMA to do the development work and produce and support the product which has been named ‘Crypto Fast’.”
The SMA Crypto Fast Feed provides faster moving signals than the
SMA S-Factor feed. The S-Factor Feed is a 24H lookback with a 20 baseline with
decay which supports intraday out to 2-3 trading days. SMA’s Activity Feed is
in isolation of what happened in each minute, which can be narrow to HFT or
customized to any period including W, M, Q.
Like the calculation of SMA’s S-Score, the ‘Crypto Fast’ is a
normalized sentiment score with a shorter 1H lookback period with a 12H
baseline to better take into account the high volatility in the cryptocurrency
market. For each crypto asset, SMA makes a 1-hour price projections based on
its Crypto Fast and price momentum. SMA provides a projected return, as well as
a projected range on the return with a 95% confidence interval. The accuracy
field reflect how often the subsequent return has fallen within the projected
return range historically.
Photo credit: SMA has partnered with TheTie to power sentiment www.thetie.io
SMA APIs went into production in 2011 for U.S
Equities and have grown to include UK Equities, ETFs, FX, Futures, and
Cryptocurrencies. SMA produces over 25 distinct APIs across 6 asset classes www.socialmarketanalytics.com
Coin Metrics and Social Market Analytics (SMA) announced today a partnership to incorporate SMA’s Crypto Currency Data Feed into the Coin Metrics Market Data Platform.
Alternative data such as social media platforms and data feeds have become a vital source of information for traders, particularly in the Crypto Currency Markets. The SMA Crypto Currency Sentiment Feed will offer the Crypto Currency community a tool for including social media sentiment data in their trading and portfolio strategies and expand Coin Metrics market leading Crypto Asset market and network data products.
“As the Crypto Investing market continues to mature, institutional investors are demanding data from trusted partners. These institutions are looking to make data-driven decision by accessing sources of data that they understand from their legacy investing frameworks. We believe that the power of combining sentiment data with granular network and market data is fundamental to building a deeper understanding of crypto assets. Coin Metrics is excited to partner with SMA, who has a long history of providing sentiment data to traditional capital markets participants and share Coin Metrics’ principles and values. The ability to provide an all-in-one Crypto Financial Data solution is a huge convenience for institutions.” Comments Tim Rice Co-Founder and CEO of Coin Metrics.
“Artificial intelligence and Natural Language Processing are moving into our everyday lives at light speed, and perhaps into financial markets even faster than that. We feel strongly at SMA that participants in Crypto Currency markets will benefit from our unique process in this emerging field, both in its approach to filtering social media data and in the analytical methodology used to develop our proprietary metrics. We’re excited to partner with the Coin Metrics team to offer this service through a versatile industry leading platform” said Joe Gits, Co-Founder and CEO of SMA.
About Coin Metrics
Coin Metrics was founded in 2017 as an open-source project to provide the public with actionable and transparent network data. Today, Coin Metrics delivers market and network data, analytics and research to its community and wider industry. https://coinmetrics.io/
About Social Market Analytics, Inc.
Social Market Analytics quantifies social media data for traders, portfolio managers, hedge funds and risk managers using patent pending technology to detect abnormally positive or negative changes in investor sentiment. SMA produces a family of quantitative metrics, called S-Factors™, designed to capture the signature of financial market sentiment. SMA applies these metrics to data captured from social media sources to estimate sentiment for indices, sectors, and individual securities. A time series of these measurements is produced daily and on intraday time scales. For more information, including a User Guide to S-Factors™, please visit www.socialmarketanalytics.com