[av_section min_height='custom' min_height_px='400px' padding='default' shadow='no-border-styling' bottom_border='no-border-styling' bottom_border_diagonal_color='#333333' bottom_border_diagonal_direction='' bottom_border_style='' custom_arrow_bg='' id='child-cover' color='main_color' background='bg_color' custom_bg='' background_gradient_color1='' background_gradient_color2='' background_gradient_direction='vertical' src='https://contextanalytics-ai.com/wp-content/uploads/2018/05/child-cover.jpg' attachment='116' attachment_size='full' attach='scroll' position='bottom center' repeat='stretch' video='' video_ratio='16:9' overlay_opacity='0.5' overlay_color='' overlay_pattern='' overlay_custom_pattern='' av_element_hidden_in_editor='0' av_uid='av-jho4fu4g']
[av_heading tag='h1' padding='0' heading='Mission Statement' color='custom-color-heading' style='blockquote modern-quote modern-centered' custom_font='#ffffff' size='50' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' margin='0px,0px,25px ,0px' av_uid='av-tgjtu'][/av_heading]

[av_heading heading='‘Be the Leader in Unstructured Financial Data’' tag='h6' style='blockquote modern-quote modern-centered' size='20' subheading_active='' subheading_size='15' margin='0px,0px,25px ,0px' padding='0' color='custom-color-heading' custom_font='#ffffff' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' av_uid='av-k74yiigs' admin_preview_bg=''][/av_heading]

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SMA has built Intellectual Property in four Major FinTech Areas:

Natural Language Processing, Textual Parsing, Topic Modeling, and Source Accuracy

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[av_heading tag='h3' padding='0' heading='Products' color='custom-color-heading' style='blockquote modern-quote modern-centered' custom_font='#000000' size='40' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' margin=''][/av_heading]

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[av_heading tag='h3' padding='0' heading='Alternative Data Products' color='custom-color-heading' style='blockquote modern-quote' custom_font='#000000' size='24' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' margin=''][/av_heading]

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SMA was one of the first firms to normalize Social Media into Predictive JSON API data feeds.  Twitter and StockTwits are processed into two separate APIs. Out-of-sample point-in-time historical data for back testing goes back to December 1, 2011
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[av_heading tag='h3' padding='0' heading='Traditional Data Products' color='custom-color-heading' style='blockquote modern-quote' custom_font='#000000' size='24' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' margin=''][/av_heading]

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SMA is changing the way Traditional data sets are ingested by turning any document into machine readable textual data with quantitative metric overlays.  SMA delivers Real Time APIs and provides historical baselines for quantitative analysis go back as far as 1996
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[av_one_half first min_height='' vertical_alignment='av-align-top' space='' margin='0px' margin_sync='true' link='' linktarget='' link_hover='' padding='0px' padding_sync='true' border='' border_color='' radius='0px' radius_sync='true' background='bg_color' background_color='' background_gradient_color1='' background_gradient_color2='' background_gradient_direction='vertical' src='' attachment='' attachment_size='' background_position='top left' background_repeat='no-repeat' animation='' mobile_breaking='' mobile_display='' av_uid='av-k70mm3io']

[av_iconlist position='left' iconlist_styling='av-iconlist-small' custom_title_size='19' custom_content_size='16' font_color='custom' custom_title='#ffffff' custom_content='#ffffff' color='custom' custom_bg='' custom_font='#ffffff' custom_border='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' av_uid='av-jhpatatt' admin_preview_bg='']
[av_iconlist_item title='Social Media APIs' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-ns7xb6']

SMA supports Quantitative Trading across Long Only and L/S, Market Making, Algorithmic Execution, Asset Management, TCA, Risk, Research, and Market Intelligence

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[av_iconlist_item title='SMA S-Factor API Feed' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-mcv2o2']

SMA S-Factor API Feed aggregates Tweets and makes a comparison to baseline to deliver 15 metrics every minute.  Snapshots can be requested at different frequencies based on a 24H lookback with a 20D baseline with decay

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[av_iconlist_item title='SMA Activity API Feed' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-l01yhu']

SMA Activity API Feed isolates and aggregates information every minute and allows clients to create their own baselines and metrics.  Ideal for both HFT for Market Making and Algo Execution and longer baselines for both trading Weekly, Monthly, and Quarterly Long Only and L/S

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[av_iconlist_item title='SMA Tweet Level API Feed' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-jj6nz6']

SMA Tweet Level API Feed delivers SMA metrics at the Tweet level from our Natural Language Processing Sentiment Scoring and SMA’s Account Rating Algorithm.  Delivered in 300 milliseconds after receiving the Tweet from Twitter or StockTwits

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[av_iconlist_item title='SMA Lexical API Feed' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-gu02wi']

SMA Lexical API Feed is an enhanced version of the Tweet Level feed.  The Lexical Feed also contains a phrase level break down of each Tweet.  The data is delivered through the API in 300 milliseconds after receiving the Tweet from Twitter or StockTwits

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[av_iconlist_item title='SMA Dashboard' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-fkgmua']

SMA’s Dashboard is highly customizable across all asset classes.  The Dashboard supports a Watchlist with a Tweet stream, live Market Sentiment Snapshot, Daily Pre-Open and Pre-Close Reports, Sentiment and Tweet Volume Charts at the Security, Sector, and Index Level

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[av_iconlist_item title='Alerts and Accuracy Feed' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-62fbiq']

SMA delivers highly Customized Alerts 24/7 to SMS and e-mail on U.S. and LSE Equities, ETFs, Commodity and Financial Futures, FX Spot and Futures Pairs, and Crypto Currencies.  SMA curates the most impactful Tweets driving the Predictive Sentiment change in SMS text and e-mail

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[av_iconlist_item title='Consulting and Customized Products' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-d35m3m']

Not all Clients can ingest data via a JSON API.  SMA supports Macro, Credit, and Event Driven Funds through consulting and customized products, reports, and programmatic delivery of data visuals

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[av_iconlist_item title='Machine Readable Filings
(in Partnership with S&P Global Market Intelligence)' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-1ty7hu']
SEC Edgar Filings and International Reports Parsed Machine-Readable text released in near Real Time with a historical baseline going back to 1996 with Word Counts and Sentiment across Parts, Items, Subsections, and Footnotes.
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[av_iconlist_item title='Private Company Data' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-583poi']
In addition to Listed Securities, SMA covers large Multi-National Private Companies to very Early Stage Start-ups.  SMA’s Topic Model Engine produces Machine Learning ‘Inclusions’ and ‘Exclusions’. Downstream, SMA Sentiment NLP, Volume, and other metrics are applied
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[av_iconlist_item title='Advertising and Market Intelligence' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-3qxpcy']
SMA Data supports Advertising and Market Intelligence.  SMA’s platform was built for analyzing unstructured data on the Topic Model Engine to provide pinpoint Advertising and drilled-down Market Intelligence 
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[av_iconlist_item title='Indices' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-1xzyeq']
Custom Indices across Cboe, Long Only, L/S, ESG, Block Chain, CC Index, Regulatory Filings, Sectors, Market Caps
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[av_iconlist_item title='XBRL and iXBRL Filings' link='' linktarget='' linkelement='' icon='ue812' font='entypo-fontello' av_uid='av-7y9nwi']
Parsed tabular data using SEC Edgar’s new format for tagging Financial Filing Data
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[av_heading heading='Technology' tag='h2' style='blockquote modern-quote modern-centered' size='40' subheading_active='' subheading_size='15' margin='' margin_sync='true' padding='0' color='custom-color-heading' custom_font='#ffffff' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' av_uid='av-k724arip' admin_preview_bg=''][/av_heading]

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[av_heading tag='h2' padding='0' heading='SMA Sentiment NLP' color='custom-color-heading' style='blockquote modern-quote' custom_font='#ffffff' size='24' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' margin='' av_uid='av-88q1aa'][/av_heading]

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SMA started with Stanford Tokenization and discovered that we would have to build our own Machine Learning Natural Language Processing platform.  SMA uses a multi-pass approach combining techniques like stemming and lemmatization. SMA’s environment is supervised and unsupervised, and uses programmatic regression analysis

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[av_heading tag='h2' padding='0' heading='SMA Parsing Engine' color='custom-color-heading' style='blockquote modern-quote' custom_font='#ffffff' size='24' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' margin='' av_uid='av-5i3sn6'][/av_heading]

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SMA has built a general document parser to produce structured data in the form of machine-readable JSON.  Algorithms and historical baselines can be built to generate Alpha and actionable Market Intelligence 
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[av_heading tag='h2' padding='0' heading='SMA Topic Engine' color='custom-color-heading' style='blockquote modern-quote' custom_font='#ffffff' size='24' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' margin='' av_uid='av-44itdu'][/av_heading]

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To capture information related to a Security, Private Company, or Custom Topic, SMA has built a Real Time Machine Learning Topic Model Engine that uses Collocation to identify Inclusions and Exclusions.  Topics are constructed using a rules-based system grounded in qualitative knowledge. 
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[av_heading tag='h2' padding='0' heading='SMA Source Rating' color='custom-color-heading' style='blockquote modern-quote' custom_font='#ffffff' size='24' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' margin='' av_uid='av-2qerea'][/av_heading]

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It is not just what is being said in Traditional and Social Media, it is who is saying it. SMA has developed a twelve-factor algorithm to score each source before inclusions in sentiment and volume metrics. The ability to eliminate spam, bad bots, bad actors produces a pure data set. 
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[av_heading tag='h2' padding='0' heading='Patents' color='custom-color-heading' style='blockquote modern-quote modern-centered' custom_font='#000000' size='40' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' margin='' av_uid='av-ab1aq'][/av_heading]

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