Bond Over Big Data Trading Bond Futures With Ravenpack News Data

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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 acro 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.

Transcript of Bond Over Big Data Trading Bond Futures With Ravenpack News Data

  • THE THALESIANS Cross Asset / Quant Strategy

    1

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

    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 RavenPacks 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 saeed@thalesians.com 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 Figure 2: Bond spreads with news data

    Source: Thalesians, RavenPack, Bloomberg

    Source: Thalesians, RavenPack, Bloomberg

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    2001 2003 2005 2007 2009 2011 2013

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

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

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

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    2001 2003 2005 2007 2009 2011 2013

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

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

    Saeed Amen

    Quantitative Strategy

    +44 20 3290 9624

    saeed@thalesians.com

    @thalesians

    http://www.thalesians.com

  • THE THALESIANS Cross Asset / Quant Strategy

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

    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 Citis 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 Citis 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).

  • THE THALESIANS Cross Asset / Quant Strategy

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

    Figure 3: UST 10Y yields vs Citi US ESI Figure 4: Regressing macro (T stats)

    Source: Thalesians, Bloomberg

    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.

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    2006 2009 2012

    Citi US Economic Surprise Index (LHS)

    3M Chg UST 10Y yields (RHS)

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    UST 10Yfuture

    UST 5Yfuture

    UST 2Yfuture

    UST 2-10Y

    future

    EURUSD S&P500

  • THE THALESIANS Cross Asset / Quant Strategy

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

    Figure 5: G4 bond futures returns Figure 6: G4 bond futures correlations

    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 d