Using the Candas Data Science Library to redrive the Brokerage Sector models using the Goldman Sachs compensation ratio time series.

The following is an example of the use of Canalyst Fundamental Data using Candas, not intended to be a recommendation of action.

The Python Jupyter Notebooks supporting this analysis can be found at our Candas Github Repository.

Goldman Sachs (NYSE: GS) reported on January 18.

“On top of a weaker trading environment, generous compensation — the most costly expense at Goldman and other banks — has raised the ire of investors. Goldman’s compensation costs rose a whopping 33% to $17.7 billion in 2021, signaling big bonuses for a record year. But concerns about rising operating costs helped push JPMorgan shares down 6.2% on Friday, their most in over 18 months. Goldman didn’t fare any better, falling 8% on Tuesday, also the most in 18 months.”

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Consensus opinion suggests that compensation pressure would be endemic to the brokerage industry after JPM and GS results.

In this brief report we re-drive the brokerage industry models based on GS results and outline some surprising conclusions testing the compensation ratio read-across for the brokerage sector: Morgan Stanley (MS), Evercore (EVR), Piper Sandler (PIPR), and Stifel Financial (SF).

Observations:

  • The time series Compensation Ratio has a very low correlation among the brokers, so any read-across based on this time series is highly unreliable.
  • MS reported earnings this morning, and using that result we saw a mere .23 Correlation.  The following chart highlights the issue at GS:

  • When we apply the GS Compensation Ratio to the rest of the brokerage universe we similarly see very low r-squareds in the regressions:

  • As can also be seen by the chart of the fitted regression model GS vs EVR actual EVR vs predicted:

  • We can set new values from regressions and use the fit() function to redrive the models

  • And the output is highly unclear, because the fitted model suggests different directions

Conclusion

Compensation ratio is not a good read across between brokerage models.  The conclusion from JPM and GS – that compensation ratios must head higher industrywide – is not supported by either a regression analysis of comparable KPI nor by re-driving the models based on GS results.

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