Tarun Sanghi
The StarMine Combined Credit Risk Model is a powerful tool to evaluate corporate credit risk. We discuss how this model can be extended to add value to equity portfolios.
- The StarMine Combined Credit Risk Model (CCR) blends the strengths of the other three StarMine credit risk models to generate an estimate of public company credit risk.
- Strong mean reversion is observed among low-credit-risk securities and enhanced momentum is observed among high-credit-risk securities in a combined value-momentum alpha model based top and the bottom decile portfolio.
- The momentum effect is found to be stronger over one month while mean reversion is found to be stronger over 12 months.
StarMine credit risk models
The StarMine suite includes four credit risk models:
- StarMine Structural Credit Risk Model (SCR) evaluates credit risk from the equity market’s view using a proprietary extension of the Merton structural default prediction framework that models a company’s equity as a call option on its assets.
- The StarMine SmartRatios Credit Risk Model (SRCR) uses financial ratio analysis for credit risk assessment and incorporates both reported information and forward-looking estimates using StarMine SmartEstimate.
- The StarMine Text Mining Credit Risk Model (TMCR) mines the language in textual data from multiple sources (Reuters News, StreetEvents, conference call transcripts, corporate filings, and select broker research reports) to evaluate companies’ potential financial distress.
- StarMine Combined Credit Risk Model (CCR) combines StarMine SCR, StarMine SRCR and StarMine TMCR to generate a single, final estimate of public company credit risk. It significantly outperforms the three individual credit models as well as alternative approaches such as Altman Z-Score-based estimates.
Using StarMine CCR in equity selection
Although StarMine CCR was trained to predict company default events, it can also be used to complement a fundamental or quantitative equity selection strategy.
We use the StarMine Value Momentum (ValMo) model, which combines StarMine’s two valuation models (StarMine Intrinsic Valuation and StarMine Relative Valuation), along with StarMine’s two momentum models (StarMine Analyst Revision, and StarMine Price Momentum), into one powerful stock-selection model, as the representative alpha model.
As StarMine ValMo is already a combination model, it has proved to be a robust and steady performer in all regions.
To study the joint existence of the momentum and mean reversion effect in ValMo portfolios, we construct two low-credit-risk groups from within the ValMo score-based top and bottom deciles.
StarMine provides model scores as ranks between 1 and 100 with 1 representing a “bearish” and 100 representing a “bullish” score.
- LowValMo-HighCredit: This group is created by identifying low-default-risk securities (CCR score >=90) from within the ValMo score-based bottom decile (ValMo score <=10). This group represents securities that are negative-past-losers that also appear overvalued (low ValMo score) but have very low credit risk.
- HighValMo-HighCredit: This group is created by identifying low-default-risk securities (CCR score >=90) from within the ValMo score-based top decile (ValMo score >90). This group represents securities that are positive-past-winners that also appear undervalued (high ValMo score) and have very low credit risk.
The two groups are reconstituted monthly using the month-end date ValMo and CCR model scores.
Tables 1 and 2 below compare the forward-twelve-month return (f12M_ret) of the two groups with the remaining securities in their respective deciles (ExGrp) and equal-weighted market (Mkt) returns from January 2008 to September 2021.
We include the largest 3,000 U.S. equity securities by market capitalization as our analysis universe.
| Group | Period | Statistics | f12M_ret group | f12M_ret ExGrp | f12M—ret Mkt |
|---|---|---|---|---|---|
| LowValMo-HighCredit | 2008 to 2021 | Mean | 16.02% | 9.08% | 11.70% |
| StdErr | 1.97% | 2.54% | 1.91% | ||
| x1-x2 | 6.94% | 4.32% | |||
| StdErr x1-x2 | 3.21% | 2.75% | |||
| t-stat x1-x2 | 2.16 | 1.57 | |||
| 2012 to 2021 post-GFC | Mean | 17.25% | 10.08% | 12.35% | |
| StdErr | 2.24% | 2.96% | 2.03% | ||
| x1-x2 | 7.17% | 4.89% | |||
| StdErr | 3.71% | 3.02% | |||
| t-stat x1-x2 | 1.93 | 1.62 | |||
| 2008 to 2018 pre-Covid | Mean | 15.78% | 8.23% | 10.96% | |
| Std Err | 1.78% | 2.18% | 1.84% | ||
| x1-x2 | 7.56% | 4.82% | |||
| StdErr x1-x2 | 2.81% | 2.56% | |||
| t-stat x1-x2 | 2.69 | 1.88 |
| Group | Period | Statistics | f12M_ret group | f12M_ret ExGrp | f12M_ret Mkt |
|---|---|---|---|---|---|
| HighValMoHighCredit | 2008 to 2021 | Mean | 7.04% | 14.65% | 11.70% |
| StdErr | 1.44% | 2.37% | 1.91% | ||
| x1-x2 | -7.61% | -4.66% | |||
| StdErr x1-x2 | 2.77% | 2.39% | |||
| t-stat x1-x2 | -2.75 | -1.95 | |||
| 2012 to 2021 post-GFC | Mean | 7.73% | 15.04% | 12.35% | |
| StdErr | 1.61% | 2.56% | 2.03% | ||
| x1-x2 | -7.31% | 4.63% | |||
| StdErr x1-x2 | 3.02% | 2.59% | |||
| t-stat x1-x2 | -2.42 | -1.79 | |||
| 2008 to 2018 pre-Covid | Mean | 7.56% | 12.88% | 10.96% | |
| StdErr | 1.44% | 2.31% | 1.84% | ||
| x1-x2 | -5.32% | -3.40% | |||
| StdErr x1-x2 | 2.72% | 2.34% | |||
| t-stat x1-x2 | -1.96 | -1.46 |
Table 2: Comparison of forward 12-month return (f12M_ret) of the HighValMo-HighCredit securities with the remaining high ValMo decile securities (ExGrp) and the overall market (Mkt). StdErr is the standard error of the mean, x1-x2 is the difference between the Group (x1) and ExGrp/Mkt (x2) returns. StdErr x1-x2 is computed as [(StdErr(x1)^2+StdErr(x2)^2]^0.5.
The main findings of the above empirical analysis are:
- Low-default-risk (high CCR score) securities are exhibiting strong mean reversion while ExGrp securities are exhibiting weak momentum over twelve months in both the top and the bottom ValMo deciles.
- The strength and the duration of the momentum and mean reversion is different among securities within the same decile. The mean reversion effect (in low-default-risk securities) is much stronger than the momentum effect (in ExGrp securities) over twelve months and seems to develop quicker in both the top and the bottom deciles.
Momentum and mean reversion effects are also observed over one month. However, unlike the twelve-month case, the momentum effect among high-default-risk securities is found to be stronger than the mean reversion among low-default-risk securities over one month.
Further, our research shows that the explicit inclusion of the mean reversion and momentum effects through CCR-ValMo interaction terms can quantitatively help in forecasting both longer-term (forward twelve months) and shorter-term (forward one month) holding period excess returns.
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