The Quantitative News Feed is a predictive news sentiment tool useful for trading strategy building and risk management. In previous research, our focus centered on shorter-term sentiment — specifically, a 24-hour positivity ratio for companies based on news sentiment. This was calculated as the ratio of aggregate positive mentions to total indicative sentiment hits. In this study, however, we extend our approach to look at sentiment trends over a 30-day period.  

Dive into an analysis of the Futures market, leveraging sentiment insights from our feed to uncover actionable trading strategies.

Context Analytics’ S-Factor feed can be used to generate alpha. Whether you’re analyzing sentiment or shifts in sentiment, CA’s tools provide a granular view of social sentiment, helping investors capture opportunities that traditional data sources might miss. In today’s fast-moving markets, understanding how sentiment is trending—rather than just where it stands—can give traders and investors a significant edge. With the Twitter S-Factor feed, CA empowers its clients to stay ahead of the curve, turning sentiment data into actionable signals for superior performance

United States defense and military weapons manufacturers are publicly traded companies. Consequently, when global tensions escalate securities in this sector become more volatile and followed. Several defense stocks have seen an uptick in value due to current Middle East tensions.

Context Analytics’ S-Factor feed is the company’s most mature product. Our offering of 10+ years of sample data and 15 different sentiment factors makes this dataset unique and ideal for back testing. With this dataset, users can harness the power of social media in financial markets.

As a follow-up to last week's 8-K Negativity Percentage blog last week, we developed a threshold strategy utilizing the Negativity Percentage. Instead of categorizing companies into tertiles, we employed a static threshold of 60% to identify companies with exceedingly negative filings. We found there is a correlation between negative 8-K filings and subsequent poor market performance.

With current market volatility, we are frequently asked how our data is performing. This blog goes over charts YTD Open-To-Close return of common stocks with an S-Score > 2 or S-Score <-2 prior to Market Open.

Our S-Score is predictive across all universes and market caps. For daily holding periods, a strategy with a universe of all Market Caps and only S&P 500 stocks both outperform the overall market.

Our S-Score creates a once a day Long/Short trading strategy that outperforms the market by over 30% (with minimal risk) since the start of 2020. With messages sourced from Twitter and StockTwits, these two distinct data feeds unveil hidden alpha from the social masses.

Using SMA Classifications feed we created a Daily Long/Short strategy that has outperformed SPY by over 4.5% annually over the past 5 years.

Due to climate and social challenges, companies have begun shifting priorities to include Environmental, Social and Governance (ESG) initiatives into their business practices. ESG reports inform investors on how companies are implementing ESG policies.

This blog is an analysis of the 8-K filings from SMA's Machine Readable Filings (MRF) dataset.  We look at the predictive power of the sentiment values of the 8-K filings towards the companies' future equity returns.

In this blog, we look at a Crypto Currency broad market indicator built with SMA sentiment data that is predictive of future Bitcoin prices.  We call this indicator the Crypto Market Score (CMS).

The SMLCW Index is a Long Only Index that has outperformed since it was released and has continues to outperform in the recent market volatility and sell-off. In the chart below the S&P500 is flat for the year and SMLCW is up nearly 5% YTD.

With the SMA team continuously expanding and improving the technology, SMA recently received a second patent as an extension of the original patent. The first patent was on the three-component system for extraction, evaluation and publication of metrics on social media feeds. SMA now has two granted U.S. Patents.

SMA covers the Top 1000 securities on LSE ranked by market cap. This blog is about the predictive nature of our LSE security universe.  We calculate our custom metrics on the top 1000 market cap securities listed on the LSE.  Our LSE data starts on 1/1/2016. The quintile analysis confirm the predictive nature of dataset in LSE data. The spread between the top and bottom quintiles is 10% annualized and sharpe and sortino numbers show a high return to risk ratio.

Now with five years of out-of-sample history, SMA created statistically significant signals with monthly and quarterly holding periods. This blog shows the use of sentiment data for monthly and quarterly holding periods, and also explores a trading system by looking at exhaustion in social media.

SMA researches modifying weights of the SPDR SPY ETF based on sentiment values and examines the impact on return.

SMA analyzes the predictive power on a close to close return basis based on the S-Score™ at 2:40 pm central time - giving a lead time of 20 minutes to make changes to the portfolio/alpha model - and buy at the close price of the day. This research, in addition to the pre-market open study, illustrates that sentiment is predictive and is not overly sensitive to timing.

SMA introduces two ways to access sentiment data without programming: Excel Add-in and Sentiment Dashboard.

SMA harnesses the power of social media signals to deliver up-to-the-minute golden nuggets of social sentiment.

SMA leverages over 2 years of its intraday sentiment factors on U.S. securities to establish evidence of trading signals born from social media, utilizing John Sweeney's Edge Ratio as a metric to validate signals derived from SMA S-Factors™.

This paper gives a third party illustration of SMA metrics in an automated trading strategy. It details how Deltix, a provider of analytics software and services, has used social media factors to create a profitable trading system.

SMA's original white paper. This piece introduces S-Factors™, a proprietary set of sentiment metrics. It also explores how they can be used to predict price movement in the capital markets.