Bond Over Big Data Trading Bond Futures With Ravenpack News Data

THE THALESIANS Θαλῆς ὁ Μιλήσιος

Cross Asset / Quant Strategy

Saeed Amen Quantitative Strategy +44 20 3290 9624 [email protected] @thalesians http://www.thalesians.com

Bond over Big Data Trading bond futures (& FX) with RavenPack news data

20 Jan 2015

Over the past few years, strategies which use news analytics have become more popular. Whilst the focus has been on equities, there is also significant news flow when it comes to macro assets. Here, we examine how RavenPack’s macro news analytics data can be used to trade bond futures (& FX). We create news based economic sentiment indices (NBESI) which mimic the behaviour of growth surprise indices. We use these news indices to create trading rules for bond futures. Our NBESI bond futures basket has risk adjusted returns of 1.14 and drawdowns of 7.7% since 2001, outperforming a passive basket with risk adjusted returns of 0.79. Our NBESI UST futures spreads basket has risk adjusted returns of 0.90 which outperforms a passive strategy with risk adjusted returns of 0.46. We also apply the same approach to trading FX, using news data. Our combined filtered G10 FX carry and G10 FX NBESI basket has risk adjusted returns of 1.11 and drawdowns of 6.7%. This paper has been kindly sponsored by RavenPack, a pioneer in financial news and sentiment analytics. Please contact [email protected] if you are interested in learning about this paper, our quant consulting services and more about our research at the Thalesians. Also see http://www.thalesians.com and follow us on Twitter @thalesians. Time series of the news based sentiment indices constructed here are available on request.

Introduction News analytics has emerged in the past few years as a rich new data source for traders to create systematic trading models. Much of the focus has been on equities. In this paper, we seek to extend this work into bond futures, where there tends to be less research on news analytics. Later, we also examine using news analytics data to trade FX. In Figure 1, we present returns for our RavenPack trading rule for bond futures and in Figure 2, for UST futures spreads. We find our trading rules based on news significantly outperform the long only case, both in terms of risk adjusted returns and the reduction of drawdowns. Figure 1: Bond futures with news data 225

Long Only Ret=4.3% Vol=5.5% IR=0.79 Dr=-9.5%

Figure 2: Bond spreads with news data 225

US NBESI Ret=4.8% Vol=4.2% IR=1.14 Dr=-7.1%

US NBESI Ret=4.7% Vol=5.2% IR=0.9 Dr=-10.1%

Local NBESI Ret=4.5% Vol=4.4% IR=1.03 Dr=-7.2%

175

175

125

125

75 2001 2003 2005 2007 2009 2011 2013 Source: Thalesians, RavenPack, Bloomberg

Long Only Ret=3.2% Vol=7% IR=0.46 Dr=-12.7%

75 2001 2003 2005 2007 2009 2011 2013

Source: Thalesians, RavenPack, Bloomberg

1

Thalesians Ltd. Non-independent investment research (see disclaimers)

THE THALESIANS Θαλῆς ὁ Μιλήσιος

Cross Asset / Quant Strategy

The link between bonds and broader economic data Before creating any sort of trading rule based on news data, we need to understand the relationship between markets and economic sentiment. It seems relatively intuitive that there should be a relationship between economic data and the price action in bonds. As economic data improves, we would expect central banks to adopt a more hawkish tone to keep inflation in check, which would be accompanied by rising yields as the market prices this in. By contrast as economic data gets worse, we might expect central banks to become more dovish, which would be reflected in lower bond yields. There is the obvious caveat, that there can be periods where high levels of inflation can occur during periods of poor growth, which is called stagflation.

What does the data tell us about this link? Can data confirm our hypothesis? We can take a look at economic surprise indices to help answer this question. Economic surprise indices are popular in the market. Many banks produce their own versions including Nomura, where I created their growth surprise indices. These measure the difference between actual data and economist expectations. Hence, we can use them as indicators of economic sentiment. Creating such indicators can be non-trivial from a data collection perspective. In Figure 3, we plot Citi’s US economic surprise index, which is the most well-known of the various surprise indices, against 3 month changes in UST 10Y yields. We find, at least on a stylized basis, there is a strong positive correlation between changes in bond yields and changes in economic surprises. We note that broadly speaking economic sentiment data has mean-reverting properties. This seems quite intuitive, if we consider how the market interprets economic data. As data improves, the market updates its expectations higher. Eventually, the expectations become so elevated that data starts to miss expectations. We then see a peak in market sentiment with respect to economic data, which coincides with the medium term peak in yields. At this point economic sentiment begins to mean-revert, as do yields. We see a similar process in reverse, when economic sentiment keeps worsening and it creates a trough, which coincidences with the local low in yields. In Figure 4, we look at the relationship in a more systematic manner, conducting a regression between daily changes in UST futures and Citi’s US economic surprise index. We report T stats, which are statistically significant. We note obviously, that the sign is negative, because bond futures have an inverse relationship with bond yields. As we might expect, S&P500 has a positive correlation with US economic surprises, whilst EUR/USD has a negative correlation (the rationale is that worse data results in lower UST yields which tends to be bearish USD, thus pushing EUR/USD higher).

2

Thalesians Ltd. Non-independent investment research (see disclaimers)

THE THALESIANS Θαλῆς ὁ Μιλήσιος

Cross Asset / Quant Strategy

Figure 3: UST 10Y yields vs Citi US ESI 150 100

Citi US Economic Surprise Index (LHS)

Figure 4: Regressing macro (T stats) 2

3M Chg UST 10Y yields (RHS)

0

1

50 0

0 -50

-2 -4 -6

-100

-1

-8 UST 10Y future

-150

-200 2006 2009 Source: Thalesians, Bloomberg

2

UST 5Y future

UST 2Y future

-2

UST 210Y future

EURUSD S&P500

2012 Source: Thalesians, Bloomberg

The idea behind creating news based economic sentiment indices, is that they will have a much richer dataset than economic data surprise indicators. Later, we shall discuss how we can use the relationship between economic sentiment and yields to enable us to create trading strategies to trade bond futures, when using news based economic sentiment indices. On a broad basis, there are two ways we can trade economic sentiment indices, one using a momentum based approach, which takes advantage of the fact that assets are correlated with economic sentiment. We can also take a longer term approach, fading economic sentiment, given that over the longer term, sentiment is mean-reverting and it tends to be bounded.

What about the relationship between various bond markets? So far we have only looked at UST futures. However, what is the relationship between USTs and other G4 bonds? In Figure 5, we plot the returns for UST 10Y, Bunds, long Gilts and JPN 10Y bond futures. We have adjusted for the differences in volatility. We see that there does appear to be a strong relationship between the various bond futures. In Figure 6, we compute weekly correlations between these various bond futures from 2001-present. We find that there are generally quite high correlations. We shall later use the highly correlated nature of G4 sovereign bond markets to enable us to use both US based and local news indicators. The rationale behind using US based news indicators, is that the US is likely to be a major driver for other bond markets.

3

Thalesians Ltd. Non-independent investment research (see disclaimers)

THE THALESIANS Θαλῆς ὁ Μιλήσιος

Cross Asset / Quant Strategy

Figure 5: G4 bond futures returns 350

250

Figure 6: G4 bond futures correlations

UST 10Y future vol adj Bund future vol adj Long Gilt future vol adj JPN 10Y future vol adj US10Y DE10Y GB10Y JP10Y

150

US10Y DE10Y GB10Y JP10Y 68% 65% 35% 68% 85% 38% 65% 85% 33% 35% 38% 33%

50 2001 2003 2005 2007 2009 2011 2013 Source: Thalesians, Bloomberg

Source: Thalesians, Bloomberg

Difference between unstructured or structured news data There are many different methods we can apply, when it comes to interpreting news data from a systematic viewpoint. The first step is to decide how we initially read news data. We have two choices:  

Unstructured news data – Read news articles, blogs etc. in their raw text form and then directly apply text based analysis to gauge sentiment Structured news data – RavenPack processes a large amount of news from numerous sources into a more manageable dataset for us to explore. In their news analytics dataset, RavenPack include important additional fields which measure concepts such as the relative sentiment of news and its relative novelty

Using unstructured news data can be hugely time consuming. Even before we have tried to interpret the news we have collected, we need to aggregate all our data sources and manage their storage. Once this has been done the language needs to be analysed to gauge sentiment, using a natural language processing technique, which is non-trivial. This contrasts to structured news data, which can be accessible via APIs (on an intraday or daily basis) or CSVs produced on a daily basis, together with precomputed sentiment scores. Using structured news data therefore frees up a significant amount of time for traders. They can therefore concentrate on creating effective trading rules and running risk, rather than spending that time dealing with massive quantities of unstructured news and text analysis.

Description of RavenPack structured news analytics data In this section, we briefly outline the structure of the RavenPack Macro 4.0 news dataset, which is available in three different sources listed below. We shall be using the Full Edition, which includes all three news sources, (later, we shall also be using some elements of RavenPack’s equity product):   

Dow Jones Edition – Dow Jones Newswires, regional editions of the Wall Street Journal and Barron's Web Edition – Business publishers, national and local news, blog sites, government and regulatory updates – 19,000 different sources PR Edition – 22 newswires and press release distribution networks – More than 100,000 press releases and regulatory disclosures processed every day 4

Thalesians Ltd. Non-independent investment research (see disclaimers)

THE THALESIANS Θαλῆς ὁ Μιλήσιος

Cross Asset / Quant Strategy

RavenPack classifies news events from these various sources, using their own proprietary algorithms. For each news event analysed, a record is generated which includes 34 fields. Below, we give a small selection of the fields recorded for each news event. For full details, please read the RavenPack news analytics guide.  



 



Timestamp of publication – In UTC time with a millisecond timestamp Focus of the publication – Includes details on the country and the general subject of the news – We shall use these fields later to filter news (for example for US news related to the economy) Positive/negative nature of news – Scaled from 0 to 100, where >50 is positive,