By: Koburn Weisman
Traditional sources of information for active traders include analyst estimates, news stories, measures of market volatility and various trading tools. Recently, “social sentiment” investing tools have become a common resource as well, designed to aggregate and analyze social media data to produce predictive market analytics. While other types of investment analysis should be included in investment decisions, social media data can assist in providing investors with indications of future market and economic performance along with, in some cases, positive or negative ratings of stocks and potential trading and investment strategies.
Social Market Analytics, Inc.:
Founded in 2011, Social Market Analytics, Inc. (SMA) goal is to harness the massive amount of unstructured financial data across both Alternative and Traditional sources into machine readable feeds and market intelligence. Over the last decade, SMA has become the leader in providing APIs for quantitative systemic trading firms and market makers. SMA’s patented processes provide the financial and marketing communities with new data sources to evaluate financial data sets, enhance returns, and reduce risk, allowing SMA to operate as a predictive analytics FinTech firm. SMA has partnered with Lightspeed Financial Services Group (Lightspeed) to provide data as a visual, updated every minute.
SMA Factor Performance:
The graph above shows Open-to-Close returns for US Equities that have an S-Score (Sentiment) greater than +2.000 or less than -2.000 at 9:10 AM ET, highlighting the predictive nature of SMA’s data.
The green line represents the performance of all stocks with a S-Score greater than +2.000 at 9:10 AM ET (20 minutes before the market open). Data suggests that this group of stocks would outperform the market due to the significantly high sentiment surrounding these stocks.
The orange line shows the performance of all stocks with a S-Score less than -2.000 at 9:10 AM ET. Data predicts an underperformance from this group of stocks because of their significantly negative mentions on Twitter.
The position is entered on the Open and is then liquidated on the Close. This strategy does not take into account trade costs, bid/offer spreads, or trade slippage.
How Traders Use SMA on Lightspeed:
SMA covers the broad U.S. Equity market across small, medium, and large cap stocks, filtering through roughly 1 billion Tweets a day to capture all conversations relating to US Equities. SMA’s technologies provide a live Top/Bottom 10 Watchlist, live Market Sentiment and Tweet Volume on individual securities, and once a day snapshot on Sentiment from the most accurate Twitter accounts. Oftentimes news breaks on Twitter ahead of traditional media outlets, allowing traders to track sentiment spikes as news breaks. As SMA captures ongoing Twitter sentiment for U.S. equities, Lightspeed lets you know by the minute.
SMA helps traders monitor open positions by updating Sentiment every 60 seconds. SMA’s Odometer on Lightspeed lets traders monitor Sentiment in near real time and understand why Sentiment on a stock reports as positive, negative, or neutral.
Trade Cost Analysis (TCA) plays a large part in understanding how execution effects returns (alpha), especially for large institutional firms. TWAP (time weighted) and VWAP (volume weighted) algorithmic execution models, especially, have become very popular over the past decade. Depending on the sentiment of a stock and the change in sentiment, a trader might be inclined to TWAP or VWAP their execution. For instance, if a trader wants to buy AAPL and the stock’s sentiment is positive, the trader may choose to buy at the market. However, if AAPL’s sentiment is negative, the trader might choose to buy over a period of time during the session while monitoring Sentiment*. SMA’s Sentiment Trend Chart can help track these sentiments in real time.
Accurate Accounts are the SMA version of the Starmine product by Refinitiv that ranks Equity Analysts. SMA rates Twitter accounts talking about stocks with a 12-factor algorithm. One of those factors is the accuracy of accounts when they talk positively or negatively about a stock over different holding periods. SMA measures this over 1 day, 2 days, 1 week, and 1 month holding periods. Every day SMA extracts the 100 most accurate accounts for each holding period and display what percentage of those accounts are speaking positively or negatively about securities. This widget is updated every day.
About SMA: SMA has developed Intellectual Property in four areas FinTech Areas:
For more information on SMA’s integration with Lightspeed Trader, watch Lightspeed’s Social Media Predictive Data webinar outlining SMA’s real-time metrics available for active traders on the Lightspeed Trader platform.
*Disclaimer: SMA is just one of many information points and although helpful, other factors should be considered when trading.
Active Trading with Lightspeed
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About Social Market Analytics, Inc.
Founded in 2011, Social Market Analytics, Inc. (SMA) goal is to harness the massive amount of unstructured financial data across both Alternative and Traditional sources into machine readable feeds and market intelligence. SMA’s DNA goes back to its predecessor firm Quantitative Analytics Inc. (QA Direct) which was acquired by Thomson Reuters (Refinitiv). SMA provides the financial and marketing communities with new data sources to evaluate financial data sets, enhance returns, and reduce risk. SMA’s Patented Process is unique in the emerging field of AI, Machine Learning, Natural Language Processing, NLP Sentiment, Textual Parsing, Topic Modeling, and Source Rating. SMA data is specifically designed and developed to integrate with existing quantitative models commonly used in the financial industry. SMA delivers quantitative metrics through RESTful JSON APIs. SMA data is delivered at the Security (Ticker) level across all Asset Classes.
For more information, visit www.socialmarketanalytics.com.
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