make the following better: "Into to Credit Rover

The purpose of credit rover is to systematically search the IG market for relative value ideas and flag them to PMs/traders/analysts who can apply their judgement to filter further for actionable ideas. It is a tool designed to enable FIG to generate new sources of alpha within the funds, a one-stop shop for Relative Value (RV) - for PMs, Credit analysts and Traders, a centralized warehouse for all the data needed to make investment decisions, and a common place for Research and Trading to signal trade ideas and discuss them.

Credit Rover is part of a bigger platform called the Insights Platform. It aims to enable efficient market insights through intelligent data aggregation, interactive modules, and configurable alerts. Over time, the Insights platform should expand to support additional applications, especially given the volume of proprietary information we have—which grows every day.

RV & Signal

RV stands for Relative Value. It is a quantitative analytical approach and is a systematic way to address the fundamental question: what’s expensive and what’s cheap? It allows us to identify relative misvaluations between and among securities, and if they improve the quality of the predictions we make about the future richening and cheapening of these securities. Credit RoVer uses mainly price adjusted OAS to generate relative value signals. The aim is to have one centralized place for all data RV everyone would agree upon.

Curve fitting is needed as the starting point for relative value. It allows us to judge which bonds are cheap or rich on an issuer's curve, as well as generate a long history of 5y, 10y, 30y, etc. points to make apples to apples comparisons across issuers for the purpose of relative value. In RoVer, what we effectively try to do is fit thousands of curves automatically and every day. Each issuer or benchmark has a specific curve. Not time efficient to build it out manually for each of them. Curve fitting can be used as an aid for data visualization (term structure), to infer values of a function where no data is available (new issues), and to summarize the relationship among two or more variables (maturity and model OAS).

What we effectively do here is generate signals for the desk that help them narrow down potential opportunities to capture alpha. In RoVer, all signals have mean reversion as an underpinning assumption.

There are two types of signals, one is spread/standard RV, which focus on CUSIP vs curve signal; the other is regression RV, which illustrates curve vs curve signal. Both signals are generated based on price adjusted OAS.

Spread RV looks at distance of latest value of a specific point w.r.t. mean of that same point over a specific window. Each CUSIP has 10 different options of benchmark curves, either issuer curve, or 9 other benchmarks consisting of combination of sector and ratings. This method is typically used on the Bond Selection Page.

For example, the graph below looks at AAPL CUSIPs against AAPL issuer curve. The spread to curve (STC) is called the residual. We then take all STC/residuals from AAPL and find their mean. The RV signal/z-score is expressed is expressed in number of standard deviations away from mean. If the latest value is below mean, spread is considered expensive, and the RV signal is negative. If above mean, spread is considered cheap, and the RV signal is positive. Always with mean reversion as a base case.

Regression RV look at how the spread of a specific point on a fitted curve compares with the same point on the benchmark curve historically. This method is shown under ‘RV Z Score’ on the Issuer Selection and Forecasting page.

Taking AAPL as an example, the spread between the AAPL issuer curve and the Technology A and Above curve is the spread to benchmark (STB). We take all STB and find their mean. If latest point of STB is trading below the mean (negative RV signal), it’s considered expensive (positive RV signal). Conversely, when above the mean it is considered cheap.

Both Spread and Regression RV signals have different options of look-back window, meaning the signal is built based on 10 days, 1 month, 3 months, 6 months, 1 year, 2 years of historical data.

Back-testing

The underpinning assumption of the RV signals is that all signals follow mean reversion. For back-testing, we want to see if the underpinning assumption holds in different regions. We want to evaluate if the spread is moving in the same direction or not as indicated by the RV signal.

Let’s assume we choose look-back window of 3 months and expect the mean-reversion happens in 30 days (holding period = 30 days). As shown in the graph below. If the current CUSIP is trading at px adj OAS of 100 bps, while it’s issuer curve (after curve fitting) at the same tenor shows 90 bps of px adj OAS, it means that the CUSIP is currently trading cheap relative to its issuer curve. If the signal of this CUSIP has a RV z-score of 1, which means that this CUSIP is currently trading at 1 stdev above the 3 months historical STC mean. If we hold this CUSIP for 30 days (expect the mean-reversion happens in 30 days), if the RV signal is the right indication, we are expecting it to be mean reverted, which means that the STC should becomes smaller regardless of the CUSIP or the IC moves. At T + 30, both scenarios shown in the graph on the right-hand side follow mean reversion, where we are expecting this CUSIP to become rich. Likewise, the opposite scenario exhibits a divergent dynamic.

To perform a thorough back-testing, we tested RV signals with all available look-back periods that the signals are based on, 10 days, 1 month, 3 months, 6 months, 1 year, and 2 years. We are expecting the signals to be mean reverted, but mean reversion can happen at different time frame. So we look at different holding periods to see how mean reversion occurs, at 2 weeks, 1 month, 2 months, 3 months, and 6 months, which can also be extended in the future as needed.

In addition, to better understand the performance of RV signals, we performed bucket analysis on RV signals as well as maturities of the bonds. Bin the signals in buckets of (-Inf, -3.0], (-3.0, -2.5], (-2.5, -2.0], …, (2.5, 3.0], (3.0, Inf], and maturity (yrs) in buckets of 0-3, 3-5, 5-7, 7-10, 10-15, 15-20, 20-30, 30+.

For the back-testing results discussed below, we are testing with 2 years of historical data from 2020-2022. And we breakdown the analysis by currency as well.

The results above show the statistics of the residual (STC) movement of CUSIPs in the format of average, median, standard deviation, and P-value. To be more specific, on the far right, we can specify currency, RV signal look-back window, maturity bucket, and historical dates. In this case, we are looking at USD with look-back window of 90 days (3 months). To better understand what the result means, if we look at the upper left cell, where RV z-score bucket is in (3, Inf) with a holding period of 2 weeks, it means that for all bonds in the historical testing data with z-score of 3 and above, on average, residual (STC) tightened 5.79 bps over 2 weeks of holding period. It indicates mean reversion preserves for this specific z-score bucket with this holding period, as positive z-score indicates that the bonds are currently trading cheap (above the mean) and expected to become rich (expensive – smaller px adj oas) over time. From the result, we can see that the monotonical increasing in the residual movements (bps) with the decreasing in the z-score buckets, which indicate overall mean reversion occurs in this scenario. The stronger the signals are, the larger residual movements occurred. A small p-values give stronger support statistically.

We also look at how hit rate performs over holding periods by buckets over time. The graph above shows the daily average hit rate of CUSIPs that exhibited residual change in right direction over the holding period. Similarly, we find that the stronger the signals are (more stdev away from the mean), the higher the hit rates are over time.

Throughout the back-testing, we’ve found that RV signals strongly follow mean reversion in USD, with different look-back windows and holding periods. The longer the look-back window (2yr) as well as the holding period (6 months), the stronger the signals are. CAD also tends to follow mean reversion, but not as strong as USD. For European and Australian market (EUR, GBP, AUD), mean reversion is minimally from the observation. The main reason is that the bulk of EUR and GBP issuers have very few debts outstanding, and as a result, the curves fitted are less reliable compared with USD. The quant team is working on improving the curve fitting for the next step.

Additional highlight on OAS Analysis

Apart from back testing the RV signals, we did analysis on OAS in addition to the residual (STC). The reason behind it is that we want to have a better understanding of the market overall. We’ve noticed that the average daily OAS movement is monotonically increasing with the decreasing of RV z-score during 2020-2022. This indicates that rich bonds cheapen more than cheap bonds over time. The yields on rich bonds are increasing at a faster rate than the yields on cheap bonds. This OAS analysis gives us a better understanding of the impact of beta with given period.

We have the back-testing results built into the Tableau report, and plan to have it refreshed quarterly to keep track of the behavior of RV signals. On top of that, the report also has a version to select by issuers based on need"