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Accern Long Short Equity Strategy using Macro Insights - Russell 2000 Finance Edition

Dear Community Members,

Below is a long short equity Russell 2000 backtest conducted on Accern Institutional historical data.

About Accern

Accern is a fast-growing NYC startup that is disrupting the way quantitative hedge funds can gain a competitive advantage using news and social media data. It currently has the world’s largest financial news coverage – covering well over 300 million public news websites, blogs, financial documents, and social media websites such as Twitter. Furthermore, Accern derives proprietary analytics from each news stories to help quantitative hedge funds make accurate trading decisions. Accern consolidates multiple news data feeds into one to help both small and large funds reduce costs drastically. With Accern proprietary data filters, it’s able to deliver relevant articles to clients with a 99 percent accuracy rate. Accern’s delivery mechanism is a RESTful API where it delivers derived analytics from news articles, including the original article URLs so quantitative hedge funds can derive their own analytics in-house from the relevant articles.

Accern Institutional API Trading Metrics

Sentiment Analysis

Article Sentiment (1 to -1): Identifies the attitude the article is written in. This can be used as a directional signal.
Story Sentiment (1 to -1): Tracks the aggregated sentiment for a specific story. This can be used as a directional signal.

Source and Author Rankings

Overall Source Rank (1 - 10): Evaluates the credibility of a source based on its timeliness and re-post rate of releasing stories.
Event Source Rank (1-10): Evaluates the credibility of a source based on its timeliness and re-post rate of releasing stories on specific events.
Overall Author Rank (1 - 10): Evaluates the credibility of an author based on its timeliness and re-post rate of releasing stories.
Event Author Rank (1-10): Evaluates the credibility of an author based on its timeliness and re-post rate of releasing stories on specific events.
Impact Analysis

Market-Moving Event Ranks

Overall Event Impact Score (1-100): Probability that an event will have a greater-than-1% impact on any stock.
Entity Event Impact Score (1-100): Probability that an event will have a greater-than-1% impact on the mentioned stock.
Time and Exposure Analysis

Timeliness Of News

First Mention (TRUE/FALSE): Alerts you on unique story before they become exposed on the web.
Story Saturation (Low/Mid/High): Tracks the online exposure rate of a specific story. This can be used as an enter and/or exit signal.

The Strategy

This is an equity long-short strategy that is strictly based on trading finance public equities within the Russell 2000. Filtering for signals and executing trades were dependent on Accern derived metrics such as article sentiment and/or overall source rank in addition to various economic factors and conditions. Positions are entered 1 minute after the market has opened and closed 15 minutes before the market closes. More details on replicating this strategy can be found under Section D (“Replicating the Strategy”).

BACKTEST REPORT (PDF)

https://dl.dropboxusercontent.com/u/570185017/APIDocuments/Accern%20Long%20Short%20Equities%20w-%20Macro%20Indicators%20-%20Russell%202000%20Finance%20Edition.pdf

HISTORICAL DATA FILE USED

https://dl.dropboxusercontent.com/u/570185017/Accern_Backtest/rus_data_fin.csv

FOR QUANT FUNDS INTERESTED

Contact [email protected] to request access to our 4 years of historical data (15 million articles) and our low-latency API feed for research and backtesting.

Enjoy,

Kumesh Aroomoogan
Co-Founder and CEO, Accern
[email protected]

Clone Algorithm
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Backtest from to with initial capital
Total Returns
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Alpha
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Sortino
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Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 583de652249d575e797c71a4
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