Monday, November 28, 2016

Data Hoarding and Alternative Data In Finance - How to Overcome the Challenges

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What is Alternative Data in Finance

Financial institutions have become data hoarders


Banks, hedge funds, and asset managers have become data hoarders. However, many of these firms find it difficult to make use of all of this data. They need tools that can be used to extract information from various internal unstructured content and to democratise its use: legal documents, emails, instant messages, news archives, analyst reports etc.

An increasing number of firms are now embracing the cloud making it easier for vendors to come in and analyse proprietary content on their behalf. This new trend is primarily driven by the more sophisticated hedge funds and assets managers, since banks are often more restricted by their compliance.

But it is challenging to make use of that data. Big data craze inspires firms to save every possible bit of data, with the misconception that the more data you have, the better. Firms must keep data (for compliance purposes) or often aren’t sure what information they need to keep. Having more data is not necessarily a good thing when you are not sure how it is going to accumulate or how to manage the data. There is hope that data hoarding, however, will eventually bear fruits when it comes to alpha generation - with the right help that is!

Tuesday, November 8, 2016

US Elections 2016: A Final Big Data Perspective

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US elections 2016 sentiment part 2

It's Polling Day!


We’re finally at the end of what has truly been a roller coaster ride of an election campaign.  Since our first update on the 26th of October, FBI Director James Comey has announced the reopening of the investigation into Hillary Clinton’s use of a private email server and subsequently confirmed that there are no grounds for charges against Mrs. Clinton.

It is doubtful that we’ll see the effect of Comey’s recommendation not to pursue charges against Mrs. Clinton, but the news sentiment for Mrs. Clinton spiked following Director Comey’s announcement, and the spread moved back in her favor after having moved in favor of Donald Trump for a short time.

As we did in the first article, we’re going to look at the 14-day rolling average of the sentiment spread vs. the poll spread. Unlike our first article, we’ll also take a look at the 3-day rolling sentiment and poll spread numbers to focus on the short term impact of media sentiment from Comey’s announcement. We’ll leave the final judgement to you on whether or not the sentiment reversal in favor of Mrs. Clinton will translate into a better margin at the polls today.

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.