Transform risk analytics into Competitive edge for investments and corporate loans

ALGOSAVE offers to bond – and credit – investors high-yielding and risk-return efficient bond issues and credit portfolios. Bundled with peace of mind and BANG ON their risk-return TARGET.

ALGOSAVE offers to bond – and credit – investors its proprietary selection of the most high-yielding and risk-return efficient bond issuers and credit portfolios.
Directly from ALGOSAVE proprietary issuer database, bundled with peace of mind and BANG ON investors risk-return target.

ALGOSAVE carefully – and fully automated – selected credit portfolios are based on ALGOSAVE proprietary FINancial TECHnology which leverages three innovative concepts, borrowed from the banking industry :

  1. Bond issuer Expected Credit Loss & Expected Return : ALGOSAVE proprietary financial technology selects the most risk-return efficient corporate bond issuers using their respective Expected Credit Loss & their respective Expected-Return term structure. ALGOSAVE ECL handily merges 2 credit-specific risk metrics : Probability and Default (PD) and Loss Given Default (LGD) term structure.
    Issuers must also first pass thru ALGOSAVE proprietary stringent selection process which protects investors while also allowing them to reach out for high-yielding assets with greater peace of mind. For instance, even a “CCC+” rated 5-year corporate bond – with its 11% credit spread above risk free rate – also appears on ALGOSAVE issuer radar screen.
  2. Portfolio Expected Loss : ALGOSAVE proprietary FINancial TECHnology builds risk/return efficient high-yielding bond portfolios using the Expected Loss concept. The EL is instrumental in (a) creating investor specific and optimal risk-return asset allocation as well as (b) nourishing an informed dialogue between investors and their investment/financial advisers.
  3. Putting issuers and portfolios thru ALGOSAVE proprietary stress-testing technology, ALGOSAVE offers high-yielding, diversified and risk-return efficient bond portfolios for investors with high, average and low risk appetite.

1 – Background.

Many tools and platforms have been developed to build efficient financial assets portfolios.
Those traditional tools are founded on some variation of Nobel prize winning Markowitz efficient frontier which selects asset portfolio based on their risk/return profile : the famous Efficient Frontier.

However, and in addition to typical Markowitz volatility of portfolio return, bond-portfolio investments are also sensitive to their asset-class specific risks : (1) a credit downgrade and  (2) a full-blown bankruptcy.

a – The first one – a credit downgrade – may force bond investors to sell.
Indeed, should a bond issuer credit rating move beyond their investment constraints (e.g high grade only) they will have to sell its bond and reinvest their proceeds – at a loss – into an investment policy compliant bond. This risk of downgrade is directly – and usually ahead of time – reflected in the bond-issuer Probability of Default term structure (PD)

b –  The second one – a full blown bankruptcy – will force bond investors to “go to” recovery together with the other stakeholders with equal seniority ranking. The loss here is BIG. It is best measured by issuer and seniority specific Loss Given Default term structure (LGD)

Some attempts to tackle these missing bond-specific risk metrics have been made where credit quality proxies such as credit rating or/and typical leverage ratio – NetDebt/Ebitda or FFO/NetDebt – are used as run-of-the-mill measures for credit and bankruptcy risk.

Unfortunately those proxies are not completely helpful : they are not easily translated into quantifiable risk-measures which in turn can be used in a risk-return bond selection and bond portfolio building.

2  – ALGOSAVE mission : bridge that gap and empower bond investors with a simple, turn-key and efficient bond-portfolio building platform.

Borrowing from BASEL banking regulation framework  – ALGOSAVE team is full of former bankers, so it is OK -, ALGOSAVE introduces bond-issuer specific Expected Credit Loss (ECL), and its portfolio-wide equivalent the Expected Loss (EL), as natural candidates for bond and bond portfolio tailor-made risk metrics.

First, let’s define each of them :
a – Expected Credit Loss = Loss Given Default x Probability of Default x Exposure At Default = LGD x PD x EAD
b – Expected Loss = the maximum bond portfolio loss with a chosen degree of confidence.

Nice things about ECL and EL ? Unlike rating or financial ratios, they are easily quantifiable and therefore, they can naturally be used to :
– (1) select risk-return efficient bonds and,
– (2) build risk-return efficient bond portfolio.

Indeed, the ECL merges together quantifiable PD and LGD term structure. This means that it can naturally be used as palatable single bond risk-metric.
In turn, the EL is a powerful tool for 2 reasons :
(a) ALGOSAVE EL in instrumental in creating investor specific and optimal risk-return asset allocation. Indeed, it directly determines the exact quantity of bonds to create optimal diversified portfolio by simply measuring bond portfolio diversification effect. The rest could be invested into other asset classes.
(b) By providing the maximum portfolio loss with a given degree of confidence (95%, 99% and even 99.9%) ALGOSAVE EL nourishes an informed dialogue between investors and their assets managers.

3.  Now that we have selected the right bond-specific risk metric, let’s build efficient bond portfolios with ALGOSAVE bond platform.

Now that investors have selected those precious bonds and bond-portfolio specific risk measures (respectively ECL and EL), let’s allocate investors’ hard earned moneys using those metrics.

First Step : Unlike share investors, beside pocketing their bond’s Yield To Maturity (YTM), bond investors enjoy little other upside. After all, as 1997 Nobel Prize Robert C. Merton showed it, bond investors are selling to shareholders a put option on the value of the assets of the bond issuer. Their maximum return is therefore the put option “premium” – a.k.a. the Bond Yield To Maturity – while their downside is unlimited – they can lose 100% of their investment. Doesn’t this requires extra care ?

4. This is why – at ALGOSAVE – bond issuers must go thru tough filters in order to become eligible for investors.

Every bond issuer first goes thru Algosave proprietary scoring technology which selects only those issuers where debt capital valuation and equity capital valuation simultaneously tell the same story. They must also adequately reflect the current risk profile of the corporate. The latter takes into account sales growth – with its past volatility and future sustainability – cash flow generation capacity – with its predictability and stability – operating and financial leverage – with their predictability and model-ability and many more risk parameters.

5. ALGOSAVE tough selection protects investors while also allowing them to reach out for high-yielding bonds with greater peace of mind.

Let’s start by selecting those bonds which will give investors the largest amount of yield per unit of risk. What we call the most efficient bonds.

To achieve this, ALGOSAVE computes the following ratio – also know as Sharpe Ratio – [Expected Bond return – Risk Free Rate] / Expected Bond Risk.

This graphs depicts a selection of 6 bond issuers from ALGOSAVE list of the 22 best risk/return 5-year maturity bonds (from ALGOSAVE bond platform) . The “22” will be explained later on. To the best of our knowledge, it has nothing to do with a “catch 22” 🙂

6. Now that we have sorted those bonds according to their risk-return efficiency, which, and how many bonds do we put in our 5-year portfolio ?

With ALGOSAVE bond platform, answering those 2 questions is simple and straightforward.

Which bonds come into our portfolio ?
We will build our proposed 5-year maturity portfolio by selecting those Sharpe ratio sorted bonds starting form the most efficient bond and going down the list.

How many bonds ?
The objective is to find the optimal number of bonds where the portfolio diversification effect is at its best. This graph shows that this optimal point resides on bond number 22. Of course, this all has to do issuer Probability of Default and LGD as well as with their correlation term structure (PD, LGD and PD-LGD correlation). We have written a few lines about these in those posts here, here, here,  and here.

The y-axis of the here-under  graph shows the expected credit loss within 5 years expressed in percentage of the initial investment with a portfolio of 1,2,3….123 bonds.


7 – ALGOSAVE proposed portfolio expected return above risk-free rate stands at 6.77% over the next 5 years.

For comparison sake, This is more than double the expected return above risk free rate on a portfolio of US banks over the same period

Let’s first examine the Expected Return of the equally weighted portfolio of those here-above 5-year maturity bonds. For each issuer, the expected return is computed as the survival rate weighted-average of its 5-year YTM. For instance when the 1-year PD = 1%, the survival rate is 99%, and the expected return becomes : Bond YTM x 99%.

As shown in the here-under sample table, the portfolio Expected Return above risk free rate – which is the is average of its constituent bonds Expected Return – is 6.77% over the next 5 years which is, for instance, more than double the expected return above risk free rate on a portfolio of US banks.

8 – From an efficiency point of view, the proposed 5-year portfolio is BANG on target.

The proposed 5-year portfolio is BANG on target. Indeed the portfolio 6.77% Expected Return exactly compensates the portfolio 6.78% Expected Credit Loss at the 99.9% confidence interval. This gives a Sharpe Ratio of 1.

Finally, ALGOSAVE palatable bond portfolio Expected Loss metric is far better suited to measure bond-portfolio Loss – at 50%, 95%, 99% or 99,9% even confidence interval – than credit rating, return volatility or any financial ratio for the matter.

Hence, the portfolio average (expected) credit loss for OECD consensus macro scenario and for ALGOSAVE stress-tested macro scenario are respectively 0.37% and 1.09%. The portfolio maximum losses (at 95% and 99% confidence interval) are respectively 2.66% and 4.27% (in OECD  consensus macro scenario) and 5.38% and 9.08% (in ALGOSAVE stress-tested macro scenario).

Conclusion : by mixing together issuer specific credit risk metrics (PD and LGD) as well as their scenario sensitive correlation term-structure , ALGOSAVE bond platform empowers bond-investors with a simple high-yielding and risk-return efficient bond-portfolio building platform. Bundled with peace of mind.

Of course, real-life bond portfolio must be tailor-made to investors risk and return objectives, time horizon, liquidity, tax, legal and unique circumstances.

For a complete list of ALGOSAVE most efficient corporate bond issuers as well as for ALGOSAVE risk-averse and risk-hungry credit portfolios, fill in the following form and send it ! We will graciously send them to you.

 

Transform risk analytics into Competitive edge for investments and corporate loans

Make your V.I.P. corporate client happy with TAILOR MADE COVENANTS, while watching out for COVENANT BUSTERS : meet ALGOSAVE unique PROBABILITY OF BREACH OF COVENANT.

Thanks to ALGOSAVE unique PROBABILITY OF BREACH OF COVENANT, it is now EASY to : 

  • Make V.I.P. corporate clients happy by giving them greater financial flexibility thru tailor-made financial covenants.
  • While, at the same time (since breaching a covenant means an IFRS9-related immediate hit to Net Income) watch out for potential covenant busters.

1 – Make V.I.P. corporate clients happy by giving them greater financial flexibility thru tailor-made financial covenants.

For instance, what if you could Increase your VIP corporate client financial flexibility – and even profitability – by granting them a 3.6 NetDebt-to-Ebitda covenant instead of an “automatic” 3.5 times ?

  • First good piece of news : by doing this you help your VIP corporate client increase its financial flexibility and debt capacity.
    This means a lot to its CEO  : more M&A, more Capex, more working capital…
  • Second good piece of news : you can also announce your VIP client that this means an $ XYZ additional  Return On Invested Capital – ROIC.
    This also means a lot and to a lot of people, starting – of course – with your VIP client’s shareholders.
  • Third REAL good piece of news : you have probably won the origination / underwriting contest, by making a REAL difference…
    And, this means a lot to YOU.

What does it mean from the bank’s perspective, for instance on a 2-year Revolver Credit Facility ?

Meet ALGOSAVE unique Probability of Breach of Covenant.

For instance, and in ALGOSAVE benign macro economic scenario, ALGOSAVE PROBABILITY OF BREACH OF COVENANT shows that, giving this additional leeway to BP PLC means that the Bank keeps 87% of the quality of its early warning bankruptcy protection.

Indeed, the probability of breaching the x3.5 NetDebt to EBITDA covenant stands at 8.09% and drops to 7.01% (a 13% drop) at x3.6. The Bank will “miss” 13% of the total covenant breaching events when granting a x3.6 instead of a x3.5 turn of leverage covenant.

In ALGOSAVE stressed macro economic scenario,  the Bank keeps 93% of the quality of its early warning bankruptcy protection.

This marked difference in probability of breach of covenant is not found in the case of Exxon. In both case, the quality of the bank bankruptcy hedge is kept at 96%.

Hence although granting a 3.6 leverage covenant – instead of a 3.5 leverage – to BP PLC. might be questionable, it is a “no brainer” in the case of Exxon Mobil.

2. Beside gaining a competitive edge, knowing the probability of breaching covenant becomes critical to the P&L of IFRS-reporting Financial Institutions.

 

  • Indeed, under IFRS9 rules, a breach of covenant triggers an immediate move from 12-month ECL – Stage1 – to lifetime ECL – Stage2.
  • This means a significant increase in Expected Credit Loss with its immediate hit to P&L.
  • As illustrated in the here-above table, behind the “one-size-fit-all” x3.5 NetDebt-to-EBITDA covenant, hides a broad range of probability of breaching this covenant within the next 2 years : from Statoil 3.18% to BP. PLC 36.42% goinf thru Royal Dutch Shell 13.51%.
  • Below the quiet and reassuring surface of  traditional “one-size-fit-all” covenants, hides a boiling reality : degrees of operating and financial leverage, free cash flow volatility, sensitivity to macro economic scenario – and many other parameters – explain this diversity.
  • Endowed with ALGOSAVE probability of Breach of Covenant, credit and risk management committees build a watch list of possible covenant busters. This simple – yet powerful – tool guides them to take action before a sudden breach of covenant immediately hits P&L. They have seen it coming.

Ask for your private access to ALGOSAVE CORPORATE UNDERWRITING PLATFORM, and see how you can SECURE UNIQUE COMPETITIVE edge with your VIP clients while BEING ON THE WATCH for possible COVENANT BUSTERS.

Transform risk analytics into Competitive edge for investments and corporate loans

What is the WACC we – analysts – should be using to value a corporate Free Cash flow, and ultimately a corporate Enterprise Value – EV – ?

 

What is the WACC we – analysts – should be using to value a corporate Free Cash flow, and ultimately a corporate Enterprise Value – EV – ?

Since there is a credit spread curve, do we need a WACC curve, or can we resort to using one single WACC ?

Also, we all know that – theoretically – Equity BETA should be deleveraged and re leveraged as a function of corporate financial leverage dynamics. Should we be using one-single Equity BETA to compute the cost of Equity, or do we have to build a Beta Curve ?

Lets’s examine a few examples, to better measure and understand this twin challenge.

Here are the WACC distribution (the top image) and Equity BETA distribution (the bottom picture) for 4 major and global retailers : WALMART, THE KROGER, AHOLD and CARREFOUR.

  • 1-year distribution in blue
  • 5 year distribution in red
  • 10-year distribution in green
  • First observation : WALMART is unique : a relatively narrow bandwidth both in its BETA as well as in its WACC
  • Second observation : in any case, what a world of difference between those corporates.
  • Third observation : what a world of difference between between 1-year, 5-year and 10-year WACC and BETA distribution for every corporate.
  • Final observation : even for a given year, what a broad distribution in the Equity BETA itself.

Can we answer the original question : for better issuer valuation, should we use a WACC and a BETA curve ? Probably so.

Ask for your private access to ALGOSAVE ISSUER DATABASE and check how you can increase the POWER AND UNIQUENESS of your financial analysis.

Transform risk analytics into Competitive edge for investments and corporate loans, Transform risk analytics into Competitive edge for Trading

Normal asset value return distribution : the normal tree that hides the risky forest

First the not-so-good piece of news.

By construction, most existing credit models assume normality of asset-return distribution in order to be able to compute their Distance to Default and other related credit attributes.

But do we really leave in a normal world ? No comment…

What if an innovative and sophisticated structural credit model could provide all borrower credit attributes based on real life asset return distribution ?

That is exactly what we have achieved at Algosave.

For instance, let’s examine Algosave latest forward-looking asset return distribution for a high grade global human resource and employment corporate borrower. Any hint of normality ? NOPE.

Now : the good piece of news.

This is the exact universe which Algosave FinTech uses in order to deliver all borrowers’ forward-looking credit attributes : multi-year, Point-in-Time, and scenario sensitive PDs and LGDs. Those are therefore based on a far richer and real-life set of possible scenarios than most current credit models.

Ditto for the here-above corresponding Asset Value distribution. Watch the fat – fat – tail !

The many – many – tails we see on those distributions are hidden away from you in most existing credit models. And, unfortunately, that is also – and precisely – where the “action” may happen. But, for its clients, Algosave FinTech goes the extra-mile and shows the unseen risk : the risky forest behind the normal tree.

As a courtesy, should you want to see all Algosave deliverables – which of course, include those ones – on 1 or 2 specific borrowers, do not hesitate to leave me a note – here in the comments. We will send them to you with pleasure.