In the study, Saeed Amen, Quant Strategist at The Thalesians and a former Executive Director in Quantitative Strategy at Nomura, uses RavenPack News Analytics Global Macro data to show how news-based filters can alleviate drawdowns during times of risk aversion on FX carry trades.
Specifically, he finds that the filters:
- Considerably improve risk-adjusted returns for a G10 FX carry basket when compared to an unfiltered basket (or a generic VIX filter), with risk-adjusted returns of 0.84 compared to 0.41 since 2002
- Cut drawdowns for FX carry in half, compared to either an unfiltered or VIX filtered strategy
- Reduce drawdowns for long-only S&P500 futures strategies to 26.1% from 57.1%, whilst increasing risk-adjusted returns to 0.44 from 0.31
Saeed’s research also suggests the aggregated RavenPack filter created likely has applications to more broadly filter high beta strategies elsewhere.
Carry the news trade: Filtering FX Carry Using RavenPack News Analytics [White Paper]
Whilst historically having long exposure in risky assets such as S&P500 and FX carry has been profitable, it has been accompanied by drawdowns during periods of risk aversion. In this paper, we investigate how RavenPack news data can be used to create a filter for risky assets. In Figure 1, we show how it can be used to improve the risk adjusted returns for G10 FX carry and reduce drawdowns by more than half, when compared to long only FX carry exposure or when using a VIX filter. Later, we shall also demonstrate how it can be used to trade S&P500.
As we remarked earlier, FX carry has been a popular strategy in FX for many years. Indeed, using an extended sample going back to the 1970s, using developed market currencies, we can see that FX carry has broadly been profitable (see Figure 2). Essentially, FX carry involves buying higher yielding currencies and funding these purchases through selling low yielding currencies. In our case, we have simply bought the four highest yielding currencies in G10 and sold the four lowest yielding currencies, reweighting our basket on a monthly basis.
A carry investor collects the carry differential between the high yielding and low yielding currencies in his or her carry basket. The trade is profitable provided there are not large depreciations of the high yielding currencies vs. the lower yielding currencies in the basket.
Unfortunately, carry trades can be subject to large drawdowns during periods of risk aversion when investors’ priorities shift from seeking return from higher yielding assets to safe guarding capital. Indeed, we note the significant drawdowns in carry have often coincided with those in equities, notably in 2008 (see Figure 2). In Figure 3, we present the total returns of being long AUD/JPY, which is a classic carry trade. Generally Australian yields have been higher than Japanese yields. Hence, a carry trader would generally be long AUD/JPY throughout history.
We note that total returns (which include carry) have been much higher over time than spot returns. The difference between the two is the carry, which actually constitutes the bulk of returns. Hence, we see that over a long period of time a large amount of carry can accrue. Thus, if we wish to profit from a carry trade, we need to be holding it for a long period of time.
The main challenge facing FX carry traders is finding methods of reducing drawdowns associated with the strategy. Typically these involve filters which give an indication of risk sentiment. When the indicator signals “risk aversion” the carry basket is usually exited. In this paper, we shall investigate a filter which is based upon RavenPack news data. We shall compare it to using VIX as a risk filter which tends to be one of the most prevalent ways to filter exposure to risky assets such as FX carry and S&P500.
Outlining steps for creating a news based trading rule
Before we discuss any sort of trading rule, we need to understand the general ideas behind news analysis. The process behind trading news data is more involved than the process of creating a carry basket, which is somewhat simpler. We note that there are many steps involved in analysing news data for trading purposes, which we list below.
- Analysing text from news articles/headlines (RavenPack does this step!)
- Aggregate RavenPack data by time frequency
- Create a sentiment index for specific areas of interest
- Apply a trading rule to the sentiment index
Next, we shall endeavour to discuss...
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