Wednesday, October 26, 2016

US Elections 2016: Sentiment, Lies, and Videotapes

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US elections 2016 sentiment

The 2016 US Presidential campaigns have degenerated to the level of a daytime talk-show, and the media is playing a big role in influencing the remaining voters. With just under two weeks to go, we’ve been able to generate a wealth of big data analytics on both Hillary Clinton and Donald Trump that captures the overall media sentiment in the US.  With this, we can survey the lay of the election land and offer some analysis on the impact of media coverage for each candidate in the run-up to the November 8th elections. 

As it stands at the moment, we see a very strong correlation between the media sentiment and poll numbers when comparing each candidate with each other.  As already widely reported, the data confirms that the release of a damaging video of Mr. Trump making lewd comments in early October was a clear turning point for his campaign.  His subsequent accusations that the election will be rigged are not working in his favor either.  Mr. Trump’s media sentiment continues to fall with recent polls, translating into better polling numbers for Mrs. Clinton with just over two weeks until the election.

Tuesday, October 4, 2016

A Textual Analysis of Economic and Geopolitical News Events for Crude Oil

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Big Data Analytics Predict Crude Oil Prices
Oil prices are notoriously hard to explain and predict. The academic literature has suggested anything from real prices of oil following a random walk without drift, to the explanatory power of oil pricing factors to vary over time and with different importance at different time horizons. The ever changing nature of this predictive relationship contributes to the difficulty of forecasting oil prices. Furthermore, oil prices are not only related to economic fundamentals but also to geopolitical events that are much harder to quantify. However, with the emergence of big data analytics, datasets have become more easily available that quantify both macroeconomic and geopolitical events - offering new avenues to explore for “alpha capture”.

"Brandt & Gao (Duke University / University of Luxembourg) use RavenPack's Big Data analytics to predict oil prices"

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In a recent study, Brandt (Duke University) & Gao (University of Luxembourg) used RavenPack data to compare and contrast the importance of macroeconomic and geopolitical information for crude oil.

Monday, September 26, 2016

Achieving High Capacity Strategies Trading Economically-Linked Companies

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When evaluating a strategy, its capacity is usually overlooked, but its performance might be considerably affected by the invested capital. For instance, strategies involving small cap companies can appear very profitable at first sight, but their performance can deteriorate fast as assets under management (AUM) scale. The main reason being that small cap companies are usually associated with more stringent liquidity constraints, which makes them more difficult to trade without moving their stock price…  A potential solution? Propagate the signal across economically-linked companies to increase portfolio size!

Supply-chain and competitive landscape information can help create more scalable portfolios from infrequent or sparse signals.

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Uncovering business relationships among companies can help build profitable investment strategies. No company operates in isolation; they are all part of an ecosystem consisting of suppliers, competitors, customers, and partners, also known as economically-linked companies. Understanding a company’s business relationships informs investors about their business opportunities and risk exposure. For example, looking at the sentiment of one company can tell you how the market might perceive its suppliers or competitors, and this can be exploited in order to get larger and more scalable investment portfolios.

Tuesday, August 16, 2016

Gearing Up for the US Elections: Lessons from our Successful Brexit Prediction

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Here at RavenPack, we are currently developing our next-generation news analytics platform which includes many exciting new features and enhancements. To showcase the power of our new platform, we wanted to look at a current high profile economic and political event, and what better case than the US Presidential Elections taking place on November 8th?

To test the methodology of our planned blog series, we felt that the recent Brexit referendum provided the perfect context for a test of the type of analysis we’ll be performing in the run up to the US Presidential Elections. In this post, we are going to walk through our methodology and analysis of key entities and media sentiment throughout the Brexit campaign in an effort to understand how good our next-gen news analytics platform is at predicting the outcome of political events.

Friday, July 22, 2016

Event take-aways, slides & videos - Reshaping Finance with Alternative Data

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RavenPack Event takeaways

Recently, RavenPack hosted its 4th Annual Research Symposium in New York titled “Reshaping Finance with Alternative Data”. The feedback on the street is that it is a must attend event for quantitative investors and financial professionals that are serious about Big Data. Over 200 guests attended the event.

I wouldn’t want anyone to miss out, so take a look at the video recordings and access PDF slides of each session. Watch the event highlights below.

Tuesday, April 12, 2016

Pairs Trading: Using Structured News to Reduce Divergence Risk

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A study by Deutsche Bank

The principle of pairs trading is remarkably simple. An investor finds assets whose prices moved together historically, open a trade by shorting the winner and buying the loser when the spread between them widens. The trade is closed when the spread converges. Although, it may sound simple…the Devil is in the detail!
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Over the years, pairs trading has become one of the most popular statistical arbitrage strategies. The strategy exploits temporary anomalies between prices of assets that have some equilibrium relationship. While methods may differ in sophistication, all implementations rely on the use of statistical analysis of historical prices to identify pair candidates with stable inter-relationships.

The main challenge in building such strategies is that, often, cointegration between two assets breaks down out-of-sample – making the trade a losing proposition.

Tuesday, April 5, 2016

Can Earnings Sentiment Give Investors an Edge over Consensus?

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Tracking company earnings is a key element of any fundamental trading strategy. Often, a short-term trading opportunity arises following the realization of a company either missing or beating market expectations. Gaining an edge on such information, however, is very difficult since there are literally millions of eyes on Wall Street looking at the same information. In a recent study, we considered whether earnings sentiment could provide the edge that everyone so much desires.
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Well, to address this question, we considered RavenPack Big Data Analytics derived from real-time news and social media content. More specifically, we leveraged RavenPack’s elaborate event taxonomy to track the sentiment of news tagged as being related to earnings, revenues or dividends, i.e. spanning anything from changes to analyst’s estimates or company guidance, to reported figures being better or worse than expected. This allowed us to move beyond the “hard numbers” to consider how earnings information is portrayed in the news from a sentiment perspective and how to weigh potentially conflicting information, i.e. positive historical results vs. negative future guidance.