Franklin is the founder and chief investment officer of Directional Advisors. Since beginning his career in 2007, Franklin has been dedicated to helping investors of all kinds achieve their goals using financial markets.
A CFA charterholder and active researcher, Franklin is an international speaker and author of dozens of peer-reviewed articles and trade publications, including the 2023 book with Wiley, Goals-Based Portfolio Theory. In 2016, he was the winner of a Quora knowledge prize, and in 2017 Franklin was recognized by the National Association of Active Investment Managers for his work incorporating business cycle analysis to help individuals achieve their goals.
Franklin serves on the advisory board of the Journal of Wealth Management, and his written work has appeared in Enterprising Investor, Forbes, Financial Planning Magazine, RealClear Markets, CityWire, Foundation for Economic Education, HuffPost, Journal of Wealth Management, Journal of Impact & ESG Investing, Journal of Behavioral Finance, International Family Offices Journal and many others.
Franklin is an adjunct professor for the American College of Financial Services, and he is the incoming Season 4 host for the CFA Society of Dallas-Ft Worth Podcast. Franklin is also a member of the Sons of the American Revolution.
Franklin enjoys playing guitar and piano with his free time, and expressing his creativity in the kitchen. More than anything, he enjoys good food and good wine with good friends.
Investors are expecting a rate cut at this meeting, followed by a series of cuts into next year — with an expectation for rates to end up around 3.25% by end of summer 2026. Some data last week may make this a harder decision than investors would like, and markets will be watching Powell’s press conference very closely. If Powell expresses any doubt about the future path of cuts, that will likely push markets around.
Other than the Federal Reserve meeting, there is not much data on tap. Investors are wrapping up their years and getting their portfolios in position for taxes and quarter-end window dressing. As I have repeatedly discussed, my view is that a recession is brewing out there, though markets seem not to care. I am urging investors to evaluate their portfolio holdings in light of that likelihood.
Chart of the Week
There has been lots of talk about the “white collar recession” brewing as companies introduce AI and have need of fewer human workers. October’s spike in job cut announcements was taken as a sign that this has finally come to pass. However, in context (with COVID layoffs, for example), October’s jump was negligible. It was however, part of a larger trend in 2025 — companies have increased layoffs, and this may be an indication of normal economic slowing ahead of a recession.
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
Ambiguity is still the main theme for markets today.
Earnings season is over, with companies growing earnings by about 13% over this time last year. Those are very solid earnings, yet we’ve seen markets trade mostly sideways. This is largely due to the underlying economic data, which has been getting progressively worse. Consumer confidence, business indexes, inflation, and unemployment have all sapped investor confidence.
This week we see important data on personal expenditures, consumer debt, and data on the health of the services sector. Early indications of black Friday retail sales were positive, but whether that momentum is maintained into the end of the year remains to be seen.
The Federal Reserve meeting is approaching with the FOMC somewhat divided over whether to continue the path of interest rate cuts into the end of the year. Chair Powell has indicated that the data they see is murky with the risks of inflation still lingering, yet several committee members have indicated they want to continue cutting. Markets see a 90%+ chance that the Fed cuts at their December meeting — a change to that expectation could push markets around quite strongly.
Overall, I am still cautious. The underlying economic data has worsened despite strong corporate earnings. That said, a rally into the end of the year would be a normal seasonal occurance, so we do have that tailwind for markets. 2026, however, may get interesting very quickly.
Chart of the Week
One challenge over the past month and a half has been the delay in data releases (or their outright cancellation). Corporate earnings, therefore, have taken on a more important role than normal. Looking more closely at corporate earnings we see that, despite strong earnings growth, the US stock market is more expensive than about 90% of its history. That should give investors pause about future returns as high valuations tend to foretell lower future returns over 3-, 5-, and 10-year periods.
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
With earnings season mostly done and the government shutdown putting a lid on new data releases, there isn’t too much to talk about.
Earnings have been mostly good for US companies, and more evenly distributed than in past quarters. That said, the AI bubble appears to be deflating a bit. The sky-high valuations given to AI companies have come down, with Nvidia and Amazon’s valuations coming back to earth and Tesla’s valuation replacing them in space (see Chart of the Week).
The big problem right now is that investors are mostly flying blind. We are seeing job cuts on the rise, with over 153,000 job cuts announced in October alone — bringing the year-to-date total to over 1 million (last year at this time we had 653,000 job cuts). Without figures for job creation, we are struggling to understand if these job-seekers are now joining the ranks of unemployed or if they are finding new jobs. Recall, job creation has been very slow this year, as well, so my estimate is that these folks are now unemployed. But, again, without official employment reports it is hard to know.
On that front, the US Senate has cleared a procedural hurdle to re-open and fund the government. There is still some wrangling to come, but investors are celebrating the milestone.
Overall, there are positives and negatives weighing on the economy. I see the balance of risks to the downside, but it is difficult to know which trigger might break investor confidence and push markets over a cliff. The deteriorating labor market is a serious concern and if it has gotten considerably worse while investors sat in the dark, I suspect markets will react negatively. I am still recommending caution to our investors, but your goals will determine which risks are appropriate for you.
Chart of the Week
Today’s chart shows the valuation of the “magnificent seven” stocks (NVDA, AAPL, META, GOOGL, MSFT, TSLA, and AMZN) as measured by each company’s price-to-earnings ratio. The more extreme valuations have come back to earth, but Nvidia and Amazon’s 2023 valuations have been replaced by Tesla’s of almost $300 for every $1 of profit!
The challenge with valuations, however, is that they are not a very good timing indicator. Sometimes extreme valuations can still lead to above-average returns. NVDA is a good example. Despite watching their valuation fall from 250x to 54x (an 80% contraction!), the price of their stock still moved higher, tripling over the same period! Valuations are a tricky business.
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
Traditional investment wisdom says “buy and hold forever.” Don’t worry when markets sell off because they will come back. I remember early in my career repeating these lines often — it was 2008 and market losses were all anyone wanted to call and talk to me about.
However, what I learned the hard way was that for investors with a goal to achieve (like retirement), losses do matter. And they can matter quite a lot. It isn’t because I worry that markets won’t recover. Historically, US markets have recovered and gone on to new highs. Rather, it is a worry about whether they will recover in time for you to hit your goal.
This is something I have written about extensively, including in my book Goals-Based Portfolio Theory. In this post, I want to use python to help us build an intuition around when investment losses become too great to recover from.
A quick background
In traditional investment theory, we look to maximize the following function
$$ u = m – \frac{1}{2}gs $$
where m is the expected return of your portfolio, s is the expected standard deviation of those returns, and g is how averse you are to volatility.
What should be clear is that this equation assumes that the only thing you are averse to is volatility — that is why your financial advisor gives you a risk-tolerance questionnaire. But that isn’t really true, is it?
You are probably investing because you want to do something with that money at some point in the future. You are, most likely, a goals-based investor because you have, well, goals to achieve. That means you aren’t averse to volatility per se, but rather averse to “not having the money you need when you need it,” to quote my friend, Martin Tarlie.
I’ll spare the entire background, but it should be clear that this changes the nature of the investment problem entirely. You now want to maximize the probability of achieving your goal:
$$ u = P( R | m, s ) $$
where P is the probability that your portfolio meets your required return, R, given your portfolio’s expected return and volatility, m and s. In this equation, volatility is an input to the equation, of course, but it isn’t what you are ultimately caring about. You ultimately care about the probability that you get the return you need to accomplish your goal in time.
A simple way to think about losses
Since what you really care about is achieving your return requirement, let’s dig into that a bit further. We can use the time-value of money to set up this equation for thinking about the role of losses in your portfolio:
$$L = \frac{ W }{w(r+1)^{t-1} } – 1 $$
where L is your maximum allowable loss, W is the total wealth you need to achieve your goal, w is the wealth you have dedicated to this goal today, r is the return markets provide during a recovery (after a signficant loss), and t is the time horizon within which you’d like to achieve your goal.
Here’s the logic: we’ve assumed that we had a one-year loss in your portfolio meaning we have a new and higher return requirement. If we have some assumption about the recovery rate of markets, r, then we can rearrange the whole thing into the equation above, solving for the maximum loss we can sustain in a year so that our new required return is met by the recovery return of the market.
Using python, let’s visualise our portfolio’s maximum loss tolerance with various inputs (I’ll generate it based on time as L decreases with time horizon). We’ll start by loading our libraries:
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport mathfrom scipy.optimize import minimizeplt
From here we can build our L function and then input some variables to build some intuition about portfolio losses over time, and at various levels of funding.
# Build a simple max loss functiondefmaximum_loss(w, W, t, r):return W / (w * (r+1)**(t-1) ) -1# Make time a variable and return max allowable losst = np.arange(5, 20, 0.5)# Assumes I have $650,000 (or $500,000) with a need for $1,000,000 in t years.# Portfolio recovery return is 12%MAL_1= maximum_loss( 650000, 1000000, t, 0.12)MAL_2= maximum_loss( 500000, 1000000, t, 0.12)# Arrange into a dataframedf = pd.DataFrame({'Time Until Goal': t, 'Maximum Allowable Loss, 65%': MAL_1,'Maximum Allowable Loss, 50%': MAL_2})# Generate visualizationplt.plot(df['Time Until Goal'], df['Maximum Allowable Loss, 65%'], label='Max Loss, 65% Funded', color='blue')plt.plot(df['Time Until Goal'], df['Maximum Allowable Loss, 50%'], label='Max Loss, 50% Funded', color='black')plt.xlabel('Years Until Goal')plt.ylabel('')plt.title('Maxmium Allowable Loss Over Time')plt.legend()
And this yields a plot showing how time erodes our portfolio loss tolerance in an exponential way. Also of interest is how your funding level also erodes your portfolio’s loss tolerance.
Upside risk versus downside risk
The method above, however, only looks at downside risk. This is, of course, the most common type of risk investors think about. However, goals-based investors must also balance upside risk against downside risk. That is because you are unlikely to achieve your goal unless you gain the return that financial markets provide. Upside risk is, in summary, the risk that you are in cash when markets run higher.
A simple framework for thinking about this balance might be
$$I = \frac{S-B}{P-B}$$
where I is the percent of our portoflio we need to invest, S is the price at which we sold and moved to cash, B is our desired breakeven price, and P is our buy-back-in price.
Hereβs what is going on: our breakeven price is the price at which we must buy back in lest we fall behind because markets moved higher without us. By default, that price is the price at which we exit. When we buy back in at a lower price (or higher price), that breakeven level moves higher (or lower). We can set an arbitrary value here, but something like 10% to 20% higher than our exit price would give us some comfortable room to breathe. If we sold as markets went down, then bought back in at a lower price with some of our portfolio, then our breakeven price is higher than the level we sold at — we gained some advantage in the sale.
Assuming, then, that we have some market outlook (if we donβt, then this whole exercise is moot), we can balance our downside risk with the same variables:
$$I = \frac{L}{G}$$
where L is the expected portfolio loss, I is the percent we reinvest at this new price, and G is the expected portfolio loss still to go. Using these two equations we can begin thinking about the balance between upside and downside risk. Let’s set up the problem in python:
# Build our % invested for downside riskdefinvested_downside(L,G):return L/G# Build our function for % invested upside riskdefinvested_upside(S,B,P):return (S-B)/(P-B)# Build our visualization using some assumptionsP = np.arange(0.65, 1.00, 0.01) # Price is our variable# Percent to invest to mitigate downside riskdownside = invested_downside( maximum_loss(0.65, 1, 6, 0.12), 0.50- P)# Percent to invest to make our breakeven 20% higher (we exit at 1.00)upside = invested_upside( 1.00, 1.20, P )# Generate visualization
What this plot is showing us is when upside risk outweighs downside risk, and vice versa. As this demonstrates, we could invest a little under 50% of our portfolio when price drops to about $0.77, and thereby neutralize both upside and downside risk at the same time (that is the point where the two lines cross). Then, so long as we reinvest the remainder of our portfolio at $1.20 or less (we exited at $1.00), then we are ahead on the trade.
Of course, our market outlook could be wrong! Such is the travesty of investing: generally, all you can do is move risks around — you can never eliminate them. That said, the goals-based approach at least helps us quantify which risks you can afford to take, and which risk you can’t afford to take. Ideally, we move risks from the places you cannot afford it to the places where you can.
Again, this is a simplistic way to think about the problem, but it does at least offer us some framework for thinking. And, notice, none of this has anything whatsoever to do with your psychological risk tolerance (whatever that is). It is quantitatively derived from your goals.
Paying for hedges
When we talk about protecting against downside risk, hedging is an obvious solution. The next obvious step is to ask, “what are you willing to pay for those hedges?” And therein lies the rub.
So, what are we willing to pay for hedges, as goals-based investors? It turns out that is a quantitative question that we can answer.
The full derivation of this solution is a bit too long to post here, but you can read about it in chapter 5 of the book. But let’s sketch the basic idea.
We have two axes of things we care about. First, what is the probability of having a loss year verses a typical year (call this p)? And second, for each of those scenarios, what is the return of the hedged versus non-hedged porfolio? To visualize:
Hedged Portfolio
Non-Hedged Portfolio
Typical year, 1 – p
R_H
R
Bad year, p
R_H&L
R_L
Where each return scenario has a different definition. The hedged portfolio’s return in a typical year, for example, is the return of the portfolio in a typical year minus the cost of the hedge. Drawing from our probability equation in the top section, we can then set up our problem in python.
# Build our probability function - use left-tail logistic cdfdefphi(x, location, scale):return1-1/(1+ math.exp( -(x - location)/scale) )# build our optimizer functiondefoptimization_objective( cost ):# In this function, R_L is the recovery needed in a non-hedged portfoliio that experienced a loss# R is the recovery return needed in a non-hedged portfolio that experiences a typical year# R_HL is the return needed after a loss with a hedge.# R_H is the return needed after a typical year where you paid for a hedge.R_L= (required_wealth / (1+ loss_return * initial_wealth) )**(1/ (time_until_goal -1)) -1 R = (required_wealth / ( (1+ typical_return) * initial_wealth ) )**(1/ (time_until_goal -1)) -1R_HL= (required_wealth / ( (1+ amount_of_max_loss) * (initial_wealth + cost) ) )**(1/ (time_until_goal -1)) -1R_H= (required_wealth / ( (1+ typical_return) * (initial_wealth + cost)) )**(1/ (time_until_goal -1)) -1# Probability of attaining the long-term return requirement in each scenario. phi_L = phi(R_L, recovery_return, recovery_vol) phi_R = phi(R, typical_return, typical_vol) phi_HL = phi(R_HL, recovery_return, recovery_vol) phi_H = phi(R_H, typical_return, typical_vol) output =abs(p * phi_HL + (1-p) * phi_H - p * phi_L - (1-p) * phi_R)return output# Probability of a bad year as our input variableprob = np.arange(0.01, 0.65, 0.01)# Look at how different variable values change FV cost to hedge# Scenario Aloss_return =-0.40# loss in a bad yearamount_of_max_loss =-0.25# what is our maximum loss toleranceinitial_wealth =1.00required_wealth =1.35time_until_goal =10typical_return =0.08typical_vol =0.11recovery_return =0.10recovery_vol =0.09p =0.45c = []for i inrange(0, len(prob)): p = prob[i] c.append( minimize(optimization_objective, -0.01).x )# Scenario Bloss_return =-0.30# Change loss return to -30%amount_of_max_loss =-0.15# change hedge protection to -15%c2 = []for i inrange(0, len(prob)): p = prob[i] c2.append( minimize(optimization_objective, -0.01).x )# Scenario Ctime_until_goal =5# Change time horizon to 5 yearsloss_return =-0.40# change loss return back to -40%c3 = []for i inrange(0, len(prob)): p = prob[i] c3.append( minimize(optimization_objective, -0.01).x )# Generate visualization
Giving us the plot below. As the plot demonstrates (and the numbers bear out in our modeling), how much you are willing to pay for a hedge is dependent on numerous factors. But, all of those factors are quantitative, bringing together your goals, your market outlook, and what the hedge does for us.
Closing thoughts
Despite the relatively simple framework, this analysis at least gives us some idea of how to think about hedging in light of our goals. Once we input your goal variables we get a simple number — 2.5% of your portfolio, let’s say — and we can then look at the market price of a hedge. If the market price is lower than our fair-value, we should hedge the portfolio. If it is higher than our fair-value, we do not hedge (or we hedge in another way!).
In any event, it is worth thinking through for investors with goals to achieve. Those goals are valuable. We should protect them.
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
Trade tensions, earnings, and economic data (or lack thereof) are on investors minds this week.
The US-China trade spat broke out again on Friday with Chinese officials declaring a dramatic increase in controls around rare-earth mineral exports. The Trump administration threatened a 100% tariff on Chinese imports unless that was walked back. All eyes are on a potential Trump-Xi meeting in Korea later this month.
Earnings season begins this week with major banks reporting such as JPMorgan and Bank of America. Cash set aside to offset bad loans will be an important figure to watch, as that tends to be a bellwether for the economy. Overall, however, investors expect a good earnings season with earnings coming in about 13% higher than this time last year.
Finally, investors are waiting on important data, such as the unemployment rate, retail sales, and inflation — all of which have been delayed due to the government shutdown. Investors tend to get jittery when flying in the dark, so the longer important data is delayed, the more risk that tends to build up in markets.
Overall, I still see ample weakness in the underlying economic data: all of the classic recession signals are flashing red. However, earnings continue to be strong and, despite high valuations in US stocks, I expect a good earnings season could create a tailwind through the end of the year. There are lots of risks to that view, of course, including the sudden failure of some large companies in recent months, trade tensions, and a drawn-out US government shutdown. In the end, the risks you take in your portfolio are entirely dependent on your goals. If you are unsure what that should look like, let’s talk about it.
Chart of the Week
This week’s chart is courtesy of LSEG and Reuters, showing expected earnings growth by sector. Technology is yet again expected to outperform (largely driven by darlings like Nvidia, Oracle, and other AI names), with the market expecting to average about 8.8% higher earnings than this time last year. Factset suggests that this figure could be closer to 13%. In any case, earnings have been the one bright spot. As investors listen to earnings calls, they will be listening carefully for any sign of future weakness. Valuations are very high, and that can create quite a bit of fragility.
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
Last week the Federal Reserve lowered interest rates by 0.25%, which was widely expected, and indicated that two more rate cuts are likely before year-end. In his press conference, chairman Powell pointed to the very bad jobs figures that came in through the summer as reasons for their cut. This week we see some data on durable goods orders, consumer sentiment, and personal consumption expenditures, but it is overall a light data week.
Cryptocurrencies sold off hard over the weekend — bitcoin fell 2.5% and ether fell 6.9% — in what looks like the unwinding of a significant number of positions (Bloomberg reports that over 400,000 traders liquidated in a 24-hour period). While crypto markets are subject to their own dynamics, this could well be simple profit-taking after a strong run upward. In related news, we have seen gold rally substantially, moving higher by 10% in the last month.
Overall, the underlying economic data is still weak. Employment is deteriorating, and the restatement of employment figures over the past year has indicated that employment is far worse than previously thought. That said, corporate earnings have been the little engine that could — powering forward no matter the underlying data. As this quarter comes to a close, investors are watching earnings reports very, very closely for any sign of weakness.
Chart of the Week
I came across another indicator that I am adding to my lineup — a thank you to LSEG’s Worskspace team for this one! This indicator is, essentially, an indicator of how fragile the current market is. Of course, fragility is a difficult indicator; just because something is fragile does not mean that it will break. That makes this indicator a poor market timing tool — but it does tell you to be cautious and keep your eyes open for something that may rattle markets too much. As the indicator shows, this market has entered a fragile state.
In other words, be careful out there.
source: LSEG Workspace
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
I am a big fan of extracting market expectations on various questions. It can give a starting point for your own analysis, giving a sort of βbenchmark of knowledgeβ on a question. Here is what the market thinks, do I agree with that? If I do not agree, why not? Do I have more evidence/insight than the market or should I just adopt that view?
One use-case for this idea is extracting the market’s implied earnings growth rate from a company’s price to earnings ratio. While it isn’t obvious at first, a P/E ratio does carry some information about expected earnings growth. Here’s how.
The Concept
Since equity holders are owners of a company, they are entitled to its profits. Therefore, we can think of current price simply as the sum of all future cashflows discounted back to today:
$$ P = \sum_i^T \frac{E_i(1+g)^i}{(1+d)^i} $$
If we adjust today’s earnings, E, back to $1, that reduces the “price” side of our equation down to the price-to-earnings ratio, so, really
$$ P/E = \sum_i^T \frac{(1+g)^i}{(1+d)^i} $$
where g is the growth rate of earnings, d is the discount rate, and T is the number of years we care about.
What is great about this way of thinking is that we have all of the variables except one: the growth rate of earnings. We know P/E, we know the discount rate (usually it is the yield on the treasury maturing in the year we are discounting for). However, because of the dicounting summation, there is not a closed form solution (at least, I haven’t taken the time to derive it). Which means, we need to use a numerical method to solve for the g that will make the formula match our observed P/E.
Enter python.
Analysis: How Markets Infer a Growth Rate in P/E
After loading the requisite libraries, we will define the equation above as a function in python:
import numpy as npimport pandas as pd # not used, but I always importfrom scipy.optimize import minimizeimport matplotlib.pyplot as plt# Define Price Function# Inputs the Growth Rate expected, the discount rate, and the time horizon# Outputs the expected Price/Earnings ratiodefprice_function( growth_rate, discount_rate, time ): cashflows = [] # Prime the cashflows variablefor t inrange(1, time+1): # Build discounted cashflows over the time interval cashflows.append( (1+ growth_rate)**t / (1+ discount_rate)**t )returnsum( cashflows )
This is helpful, but it is not a function that we can use our minimizer for, so using this function, let’s build a function that we can minimize:
# Create a function that we can minimizedefsolver_function( growth_rate ):return ( PE- price_function( growth_rate[0], discount_rate, time ) )**2
Now, we input our known variables. I’ve solved for two time horizons, 10-years to breakeven and 15-years to breakeven, both of which are nested in the for loop:
# Input the known variablesdiscount_rate =0.045# 10-year UST yield. Could use different yields for different time horizons.time =15# initialize the variablePE=20# initialize the variable# Now we iterate through various P/E ratios to determine what growth rate the # market expectspe =list( range(1,250+1) )g_15 = []g_10 = []for i inrange(0, len(pe)):PE= pe[i] time =15 g_15.append( minimize( solver_function, 0.09 ).x[0] ) time =10 g_10.append( minimize( solver_function, 0.09 ).x[0] )
Which gives us two arrays of implied growth rates — one for a 15-year breakeven (g_15), and one for a 10-year breakeven (g_10). Finally, let’s illustrate our results:
As our plot demonstrates, as P/E ratios get above, say, 50 or so, the growth rate required to just break even from earnings gets to be a stretch. At a P/E of 50, we can infer that the market expects a growth rate of 19.4% every year for 15 years, or a growth rate of 34% every year for 10 years! At a P/E of 200, the implied growth rate is 60% for 10 years.
Analysis: How the Discount Rate Affects P/E
We’ve seen how we can derive an implied earnings growth rate from P/E, but what about the discount rate? How does that affect implied growth rates? Again, let’s continue with the code above to find out.
# Analysis of the discount ratedr = [0.025, 0.045, 0.065] # Test various levels of discount rate# Create an empty array with pe in rows and discount rates in columnsg_15 = np.empty((len(pe), len(dr)), dtype=float) g_10 = np.empty((len(pe), len(dr)), dtype=float)# Iterate through different discount ratesfor j inrange(0, len(dr)): discount_rate = dr[j]# Iterate through pefor i inrange(0, len(pe)):PE= pe[i] time =15 g_15[i,j] = minimize( solver_function, 0.09 ).x[0] time =10 g_10[i,j] = minimize( solver_function, 0.09 ).x[0]
And, if we then visualize the results, we find something to talk about.
What we can begin to see in this plot is that as discount rates move higher, growth rates must also move higher to support the same levels of P/E. We can say the same thing a different way: if discount rates move higher, P/E ratios will move lower assuming earnings growth expectations stay the same.
This, by the way, is why bond markets matter to stock markets! US Treasuries are typically the discount rate applied to earnings in coming years. As US Treasury yields rise, stock markets will tend to fall because the discount rate increases. In fact, let’s look at the problem from that perspective:
# Analyze how discount rates affect P/E ratiosdr = np.linspace(0.001, 0.100, num=100) # Various discount ratesgr = [0.10, 0.15, 0.20, 0.25] # Various Growth ratespe = np.empty( (len(dr), len(gr)) ) # Empty array with rows as dr and columns as grfor g inrange(0, len(gr)):for d inrange(0, len(dr)): pe[d, g] = price_function( gr[g], dr[d], 10 )
Visualizing the results:
And here we see that P/E rates move downward in a subexponential way as discount rates increase, and vice versa — assuming that expected earnings growth remains the same (which is often not the case).
P/E Ratios are Speaking to You
What is important about this idea, and slicing it in different ways, is that P/E ratios are telling you something about what the market expects. From there, we can ask ourselves, is this reasonable? Do I agree with this expectation?
Isolating each angle can help us understand the magnitude of the affect of a particular variable. For example, moving from a P/E ratio of 200 to 150 only implies a decrease from 60% growth to about 55% growth. However, moving from 200x earnings to 150x earnings means a price drop of 25%. This is an exponential affect: a lowering of growth by 8% means a lowering of price by 25%.
For more extreme scenarios, we might seriously question whether the market is overestimating (or understimating) what might happen. I’m not aware of a company that averaged 60% compounding earnings growth over a 10-year period, but that is what a P/E ratio of 200 implies! And, as we just mentioned, there is a lot of price risk in a small adjustment of expectations.
Of course, there are other things going on, too. Movement in discount rates implies a change in P/E ratios, all else equal. Of course, all else is rarely equal, so think about all the angles.
All that said, I’ve included the full code below so you can play with your own expectations and see how their changes might affect price multiples in the company you care about.
But, P/E ratios are speaking to you. You can hear them if you know how to listen.
Full Code
import numpy as npimport pandas as pdfrom scipy.optimize import minimizeimport matplotlib.pyplot as plt# Define Price Function# Inputs the Growth Rate expected, the discount rate, and the time horizon# Outputs the expected Price/Earnings ratiodefprice_function( growth_rate, discount_rate, time ): cashflows = [] # Prime the cashflows variablefor t inrange(1, time+1): # Build discounted cashflows over the time interval cashflows.append( (1+ growth_rate)**t / (1+ discount_rate)**t )returnsum( cashflows )# Create a function that we can minimizedefsolver_function( growth_rate ):return ( PE- price_function( growth_rate[0], discount_rate, time ) )**2# Input the known variablesdiscount_rate =0.045time =15PE=20# Now we iterate through various P/E ratios to determine what growth rate the # market expectspe =list( range(1,250+1) )g_15 = []g_10 = []for i inrange(0, len(pe)):PE= pe[i] time =15 g_15.append( minimize( solver_function, 0.09 ).x[0] ) time =10 g_10.append( minimize( solver_function, 0.09 ).x[0] )# Create visualization# Analysis of the discount ratedr = [0.025, 0.045, 0.065] # Test various levels of discount rate# Create an empty array with pe in rows and discount rates in columnsg_15 = np.empty((len(pe), len(dr)), dtype=float) g_10 = np.empty((len(pe), len(dr)), dtype=float)# Iterate through different discount ratesfor j inrange(0, len(dr)): discount_rate = dr[j]# Iterate through pefor i inrange(0, len(pe)):PE= pe[i] time =15 g_15[i,j] = minimize( solver_function, 0.09 ).x[0] time =10 g_10[i,j] = minimize( solver_function, 0.09 ).x[0]# Create Visualization# Analyze how discount rates affect P/E ratiosdr = np.linspace(0.001, 0.100, num=100) # Various discount ratesgr = [0.10, 0.15, 0.20, 0.25] # Various Growth ratespe = np.empty( (len(dr), len(gr)) ) # Empty array with rows as dr and columns as grfor g inrange(0, len(gr)):for d inrange(0, len(dr)): pe[d, g] = price_function( gr[g], dr[d], 10 )# Create Visualization
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
The Fed stole the show last week. Investors got what they hoped for from Powell’s speech at the central bank’s Jackson Hole retreat — a more dovish Powell paves the way for a September rate cut, which is now mostly expected. With Trump’s pressure on the Fed ramping up, some analysts suspect a “jumbo” cut may be on the offering (0.50%) by the end of the year.
The market’s run to yet another new high, however, is overshadowed by growing concern about the labor market which is sending mixed signals. Job creation has slowed to a crawl and layoffs have begun, yet the unemployment rate has held remarkably steady in the low 4% range.
Though it is very difficult to disentangle, this is likely due to the significant drop in immigration. For the first time in recent memory, the US may see a net negative immigration flow (that is, more people leaving than coming). There are arguments for whether this is good or bad, but in the short term it has shrunk the labor market. Though there are fewer jobs, but there are also fewer people looking for jobs. So, the labor market appears to be in an unusual equilibrium at the moment.
Overall, the fundamental data is still poor, but corporate earnings — especially among retailers, which are a good guage for US consumers — have continued higher. While I still see a recession, it appears my calls have made me early. I recognize that this is just as dangerous as being late, so I suggest, just as I always have, that you balance your upside risks and downside risks based on the goals you are trying to achieve. Investors within 5 years to a goal have less ability to weather downside, while investors with more than 10 years to a goal probably shouldn’t worry too much in the short term about a recession.
Chart of the Week
One benefit to consumers we have seen since 2024 has been a reversal of the damaging trend of price growth outpacing wage growth. The almost 1% faster growth of wages over the past year is helping workers get back ahead, though inflation has remained stubbornly high. We are, unfortunately, still a ways away from the “glory days” of 2018 – 2019 when wages grew about twice as fast as prices — though, it is very unlikely we will see the 2010 – 2020 era again in our lifetime.
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
This week we see the all-important inflation figure, expected around 2.8%. Investors have been whipsawed a bit by on-again off-again expectations about the Fed cutting rates. Because inflation is one of the core inputs into rate expectations, this week’s data could push markets around. This week we also see retail sales figures. Since consumers have largely kept the US economy afloat over the past year, these figures will be scrutinized closely.
Earnings season is coming to a close with 90% or so of US companies having reported earnings. Overall, earnings posted better than expected, with profit growth around 12% over this time last year. Additionally, tariffs have been less of a concern among both analysts and business executives now that policy is (mostly) ironed out.
As I have mentioned many times before, all of the traditional recessionary indicators are flashing red. The yield curve, the unemployment rate, PMIs, and several others have been indicating contraction for many months now. Yet, the market powers higher and companies continue to earn higher profits. While I have urged caution up to now, I admit to being at a crossroads. It is possible that the traditional signals are simply too distorted by any number of things to be reliable, in which case it may make sense to turn back up the risk in your portfolio.
Before doing that however, we should assess the costs of being wrong. If the signals are indeed accurate and the market enters a recessionary phase in the coming months, the costs of that is likely higher for individuals within about 5 years to a goal. For investors with 10 years or more until their goal, that cost may not be so high. In any case, your goals will determine the types of risks we can afford in your portfolio. Discussing this with an advisor just makes sense in this confusing environment.
Chart of the Week
Breaking down US GDP into its component pieces we see the volatility in trade that has characterized the last six months. However, looking past that (the pink and blue bars in the chart below) reveals an ongoing trend in personal consumption that has been concerning. Before 2025, personal consumption had been adding some 2.5 percentage points of growth. In the past two quarters it has added less than half of that. Trade volatility may be hiding the real problem: are US consumers reaching exhaustion?
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
This week is a big week for markets. The Federal Reserve meets this week, we see data on employment and economic growth, and we are in the thick of earnings season. A lot to digest!
We are about a third of the way through earnings season. It appears companies will grow earnings by about 7%, which is right about average. Investors are listening to earnings calls for hints about coming quarters as the trade war and deteriorating economic data are both taking their toll on forecasts (more on this in this week’s chart of the week).
This past weekend, the Trump administration reached a deal with the European Union, settling on 15% tariffs on EU imports to the US. Steel and aluminium will be taxed at a 50% rate, however, and there remains some provisions to still be hammered out. The EU agreed to purchase $750 billion worth of energy products (oil, natural gas, and nuclear fuel) and invest $600 billion in US infrastructure and military equipment over the next three years. To put these figures into perspective:
Total US oil & gas production totals around $480 billion per year. Assuming that most of the EU’s committed purchases are of oil & gas, this commitment represents about half of total US production per year — a substantial increase in demand for US producers.
The US exports around $118 billion worth of military equipment every year. If we assume that around half of the EU’s committed $600 billion figure is slated for military equipment, that would represent an almost doubling of military exports over the coming three years.
There are several investment takeaways from this deal, and we will begin implementing those in our portfolios over the coming weeks, though many questions still remain (not the least of which: how can these numbers possibly work?).
Investors expect the Fed to hold rates steady at their meeting this week, though all ears will be tuned to hear any changes in the pace of cuts. At the moment, the market is split between a cut in September or a cut in October.
And, lastly, we are watching the employment very closely this week. So far, US employment has been getting steadily worse, with more people leaving the labor force than finding jobs, and several prominant job cuts coming up.
Overall, while the recent trade deals may be a boon for certain sectors, we still see higher prices to consumers at a time when consumers are strained. The economic data is still negative, but markets have continued to climb to new highs. In our view, this is a time to evaluate where and how you are taking risks.
Chart of the Week
This week’s chart demonstrates the impact of tariffs on global companies. By far, the most common action in the US has been to cut and withdraw earnings guidance, with many companies simply stating they expect to make less money.
source: LSEG and Reuters
This document is a general communication being provided for informational purposes only. It is educational in nature and not designed to be taken as advice or a recommendation for any specific investment product, strategy, plan feature or other purpose in any jurisdiction, nor is it a commitment from Directional Advisors to participate in any of the transactions mentioned herein. Any examples used are generic, hypothetical and for illustration purposes only. This material does not contain sufficient information to support an investment decision and it should not be relied upon by you in evaluating the merits of investing in any securities or products. In addition, users should make an independent assessment of the legal, regulatory, tax, credit, and accounting implications and determine, together with their own financial professionals, if any investment mentioned herein is believed to be appropriate to their personal goals. Investors should ensure that they obtain all available relevant information before making any investment. Any forecasts, figures, opinions or investment techniques and strategies set out are for information purposes only, based on certain assumptions and current market conditions and are subject to change without prior notice. All information presented herein is considered to be accurate at the time of production, but no warranty of accuracy is given and no liability in respect of any error or omission is accepted. It should be noted that investment involves risks, the value of investments and the income from them may fluctuate in accordance with market conditions and taxation agreements and investors may not get back the full amount invested. Both past performance and yields are not reliable indicators of current and future results.
