Economic Modeling for Forecasting

Retirement Accumulation Strategies with Real Assets and Inflation Risk New publication from Amundi Investment Institute. With Benjamin Bruder, Camille Schittly, and Jiali Xu, we explore the optimal design of retirement solutions and glide paths. Over time, longevity has become a systemic risk for PAYG and DB pension plans, and an idiosyncratic risk for individuals. For example, life expectancy is projected to reach 82 years by 2100, up from 46 years in 1950. This increase has contributed to the growth of DC pension plans. Before individuals can effectively decumulate in retirement, they must first accumulate sufficient wealth, highlighting the central role of dynamic asset allocation in retirement planning. This paper provides both a theoretical framework, empirical insights and practical consideration. Here are the main key findings. First, the optimal allocation can be interpreted as a leveraged version of the constant-mix strategy, where human capital plays a key role in determining the leverage ratio. Understanding the human-to-financial capital ratio paves the way for personalized retirement solutions. Second, we solve a fundamental puzzle in retirement planning: Why do practitioners implement concave glide paths, even though theory predicts convex allocation patterns? Third, we identify the conditions under which the two-stage approach (combining Markowitz optimization with Merton leverage) produces the same solution as the multi-asset stochastic optimal control problem. Fourth, we compare glide path implementations using traditional assets with those that include real assets. Our results show that extending the investment universe to real assets adds value, even after accounting for transaction costs and liquidity risk management. Finally, we analyze retirement solutions under inflation risk, showing that the optimal dynamic solution consists of a performance portfolio and a liability-hedging portfolio. This aligns DC strategies with the liability-driven investment principles used by DB plans. Importantly, the hedging demand may be positive or negative depending on whether the objective function incorporates an inflation discounting component (reflecting the investor’s time horizon and myopia) and the correlation between assets and inflation. This analysis revisits the classic debate on inflation risk (expected vs. unexpected inflation, level vs. variability) and demonstrates how different inflation components influence dynamic asset allocation. While this paper is technical, we provide a 15-page non-technical introduction and conclusion that clearly summarize the main issues and key findings of the accumulation period. Here are the links to the paper: https://lnkd.in/ezvCAqSm https://lnkd.in/emPtHHTx https://lnkd.in/eSMSMSgD #retirement #assetallocation

What can we learn from the US yield curve today? The chart below shows the US yield curve today (orange) and one year ago (blue). Before we jump into the conclusion we can derive from the change in curve shape, it's important to understand ''what'' yield curve are we looking at. This is the Overnight Index Swap (OIS) yield curve, not the standard yield curve derived using government bond yields - why? Government bond yields incorporate two dimensions: the risk-free rate set by the Fed (and investors' expectations about where it will be in the future), and something called ''asset swap spread'' on top. The asset swap spread (ASW) represents the compensation that investors require to warehouse US Treasury bonds on their balance sheet rather than simply expressing their long duration view via swaps. Being interest rate derivatives, swaps are a cash-light instrument requiring a small amount of margin to be executed while Treasuries either require the full cash amount (unlevered bond purchase) or balance sheet capacity (repo-funded purchase). Regulation has made balance sheet capacity quite scarse in the US, and so Treasury yields trade at a marked premium to swaps. This is why looking at the Overnight Index Swaps (OIS) curve gives us cleaner signals about investors' expectations for risk-free rates. So, where do we stand today? 1️⃣ The Fed has delivered a cutting cycle which has mildly exceeded expectations from a year ago 2️⃣ Yet, long-end rates today are 20+ bps higher than a year ago 3️⃣ This is because the market now prices in a higher ''neutral rate'' at around 4% Today's OIS curve (orange) sends a clear signal. The Fed is expected to cut rates to around 4%, and that's broadly considered to be the interest rate at which the US economy can operate delivering its potential growth - no overheating, no recession. This is why the yield curve is flat as a pancake, and the Fed is reinforcing this message via their ''long pause'' Fedspeak. Productivity seems to be slowly increasing, but on the other hand the US housing market is showing some early signs of distress - unsold houses are on the rise, and the construction sector stopped hiring. Do you think the new neutral rate in the US is 4%? P.S. If you enjoyed this post, follow me (Alfonso Peccatiello) to make sure you don't miss my daily dose of macro analysis.

What gets measured gets managed. If you don't know what to measure, you don't know what to manage. This is one of my trackers for managing operating and financial drivers, KPIs and metrics. Here's what it does: Let's assume you have a $50 million company that's realizing 28% gross margins (revenue less direct costs). This means you're making $14 million in gross profit. But you think you can do better. Examining the business, you observe 4 problems with direct costs: 1⃣ Problematic suppliers The companies is finding it difficult to manage uncertainty around the operations of its 20 overseas suppliers. The unreliable supply chain led to substantial delays and unexpected costs. To mitigate this, the company has decided to reduce the number of suppliers to 11 to ensure tighter control and more reliable operations. If the company can reduce complexity in its overseas supply chain, it may realize up to $353K in incremental profit. 2⃣ High variable costs / low contribution margin Inflation has led to skyrocketing material costs. Last-minute orders have led to higher material and freight costs. If the company can purchase in bulk and plan further in advance, variable costs can decline. This would lead to an estimated increase of contribution margin from 38% to 40% and incremental profit of $1.9 million. 3⃣ Manufacturing inefficiency Dated machinery and suboptimal scheduling has led to manufacturing inefficiency, worse that what it was in prior years. If the company can manage its manufacturing inefficiency from 13% to 8%, it can realize $616K in incremental profit. 4⃣ High rate of error The company has been dealing with quality control issues. Continuous complaints from customers about product quality have been traced back to inferior components. If the company can address its quality issues from 10% to 2%, it can realize $616K in incremental profit. --------------- Weighting the drivers and KPIs: Through an operational restructuring and process improvement, we believe we can bring an additional $3.525 million in profit (bringing margin up to 35% from 28%). But not all drivers are equal. This is how we weighted the impact of each initiative. 1⃣ Problematic suppliers - 10% 2⃣ High variable costs - 55% 3⃣ Manufacturing inefficiency - 17.5% 4⃣ High rate of error - 17.5% Therefore, improvements in direct variable costs are expected to bring the greatest benefit to profit, more than 3x as much as improving manufacturing inefficiency or errors and more than 5x as much as reducing the supplier base. What's this mean? If you're going to improve your company's financial position, you need to understand the strategic mapping and financial drivers. And you need to know which drivers move the needle the most. If you want to learn more about strategic financial mapping: https://lnkd.in/eRPRJf8N What questions do you have? #seidmanfinancial

The #Fed cut 50bps, but how low will they go? To us, this is the big question. Chair Powell stated that the neutral rate – the policy rate at which Fed policy is neither restraining not supporting economic activity – has likely moved higher since the pandemic. Whether that's the case, and how high a higher neutral rate would be, is hotly contested. The FOMC's median estimate of the neutral rate is 2.9%, but the range is 2.2 to 4.1%... a big difference. Our own best guess is that neutral is around 3%. This range of long-term interest rate estimates makes sense to us, because the path of #inflation from here depends heavily on what happens in November. A sweep in either direction is likely to bring higher spending, firmer inflation, and modestly higher #rates. It may also bring more interest rate volatility as investors grow wary of interest payments exceeding major U.S. government budget line items like defense. A split government allays these fears and points to modestly lower inflation and rates. 

Macro Outlook for Capital Allocators to Consider in 2026 (Part 2 of 2): 1. Rates: Front-end lower, long-end anchored The front end of the curve should continue to rally as the Fed eases policy and maintains purchases of short-term Treasuries. By contrast, longer-dated yields are likely to remain sticky, with the 10-year UST fairly valued in a 4.00%–4.25% range. A meaningful break lower in long rates would likely require the onset of QE program or inflation to settle in at ~2%. A dove will the reins in May who will make it his mission to lower rates in an effort to allow the economy to heat up. Rate volatility should continue to compress: the MOVE index, which has oscillated between 60 and 150 over the past three years, is likely to trade below 50 later this year. I expect the Fed to cut rates three times in 2026, bringing the policy rate towards its neutral level of ~3.0%. The risk to this scenario is higher inflation. 2. Equities: High valuations, range-bound returns, subdued volatility The S&P 500 enters the year trading at roughly 23.5x forward earnings, an elevated multiple by historical standards. As a result, returns are likely to be more range-bound despite a constructive macro backdrop. I expect the S&P 500 to trade between 6,500 (-5%) and 7,666 (+12%) over the course of 2026. Equity volatility should remain well behaved, with the VIX largely confined to a 10–20 range, likely drifting lower in the coming months. This contrasts sharply with last year, when the VIX spent most of the time between 15–25, spiking above 50 in April. In this environment, volatility-selling strategies are likely to underperform. Earnings growth of ~10% is achievable given continued economic strength, but upside equity returns may be limited given starting valuation levels. The two major risks to this scenario are significantly slower GDP growth and/or AI bubble that bursts. Conclusion: 2026 is shaping up less as a year for directional conviction and more as a test of portfolio construction discipline. With policy easing expected to be incremental and asset prices already reflecting a benign macro regime, the opportunity set will increasingly favor credit selection over beta. Capital allocators should prepare for an environment where carry, relative value, and structural inefficiencies matter more than broad market exposure, and where resilience to policy or inflation surprises is as important as upside participation. If volatility continues to compress, the real edge may come from building portfolios that can compound steadily, while remaining positioned for regime shifts that markets may be underpricing today.

Jeff retired at 60 with £500,000 but he had one big problem… Jeff had worked hard for 40 years and was finally ready to enjoy retirement. ✔️ £500,000 in pensions & savings ✔️ No mortgage ✔️ Plans to travel, play golf, and spend time with family But when he sat down to plan his finances, one big question loomed over him… Would his money last? Jeff planned to withdraw £30,000 per year from his pension. That seemed reasonable—until he looked at the impact of: ⚠️ Inflation – £30,000 today won’t buy the same lifestyle in 20 years ⚠️ Market downturns – A few bad years could reduce his pot faster than expected ⚠️ Living longer than planned – What if he lived to 90+? Would he still have enough? At that rate, his pension could run out in his mid-80s, just when he might need it most for care costs or extra support. How Jeff fixed it (using Cashflow Modelling) Instead of guessing, Jeff worked with a financial planner who used cashflow modelling to map out his retirement finances. Here’s what it showed him: 📊 If he withdrew £30,000 per year without a strategy, his money could run out by age 83 📊 If he adjusted withdrawals, invested wisely & minimised tax, he could have enough until 95+ With a clear picture of how long his money could last, Jeff made smart changes: ✅ Adjusted his withdrawal strategy – Taking a flexible approach rather than a fixed amount each year ✅ Maximised tax efficiency – Withdrawing from different pots to reduce unnecessary tax ✅ Kept part of his pension invested – Allowing his money to grow even in retirement ✅ Planned for later-life costs – Factoring in potential care expenses so he wouldn’t be caught off guard Now, instead of worrying about running out, Jeff has a long-term plan based on real numbers… giving him peace of mind and the freedom to enjoy retirement. Key lesson… A big pension pot doesn’t always mean financial security. Without a clear plan, it’s easy to: 🚨 Withdraw too much, too soon 🚨 Pay more tax than necessary 🚨 Run out of money later in life Cashflow modelling helps you see the bigger picture, so you can make confident financial decisions for retirement 🙌

Have you heard of "the 4% rule"? It's a retirement drawdown strategy. Here are the basics: → spend 4% of your portfolio's starting balance annually → every year, adjust the withdrawals upward for inflation → and you have a low risk of running out of money over 30 years It's a popular way to determine your retirement spending capacity. But there's a BIG problem... It's designed to handle a "worst case" scenario. 📈 What if investment returns during retirement are better than "worst case"? Your portfolio will run up in value while your spending stays too low. 📉 What if returns are worse than history has ever seen? You'll run out of money earlier than 30 years. *sad trombone* If you look at historical simulations of the 4% rule, the "average" scenario resulted in a retiree ending up with nearly 3x their starting balance! 💰💰💰 and there's only a 10% chance they die with less than their starting principal. That means missed opportunities to travel early in retirement - while young and healthy. And foregone chances to "give with a warm hand" - when family or charities can use the money earlier. There's a solution to this underspending problem 👇 💡 Use a dynamic withdrawal strategy. One method is called "guardrails". It maximizes the amount you can take from the golden goose... and reduces the odds that you kill it too early. ☠️ ⬆️ Guardrails provides higher income when your portfolio is doing well ⬇️ As long as you're willing to tighten the belt - just a little - when it's not The result is higher retirement income and a greater probability of plan success. What does that mean in English? 👉 More travel, more spending, more giving, and more living! Guardrails is the best strategy I've found to avoid BOTH types of retirement plan failure. ------------ Now... leaving a legacy for your family is a noble goal. But there's no prize for being the richest person in the graveyard. You built wealth. Retirement is the time to enjoy it. Make sure you're not blindly following a rule of thumb like the 4% rule! ------------ I'm Allen Mueller, a financial advisor who helps Aerospace & Tech professionals build wealth, win the tax game, and make work optional. If you want your money to work as hard as you do → Visit my website to book a complimentary meeting! **This post is general education, not financial advice.**

Ever wondered how a small change in any of your cost variables could completely transform your bottom line? The Profitability Calculator I've been using these past months shows exactly this: how adjusting costs - fixed and variable - directly impacts your margins. Wish someone had shared it with me, so I'm passing it along here. I think it’s a really helpful dynamic way to immediately understand how the variation of one (or more) of your current costs will impact your overall profitability. You can play with your core metrics (AOV, Order volume, Returns rate, Product costs, CAC, etc - ‘Input Parameters’ in the screenshot) and see how the whole picture quickly changes knowing exactly: - Marginality per order - Monthly profit - Breakeven orders - Total revenue estimated BUT that’s just the first part… You automatically get additional insights and functionalities such as: - The possibility to save the different scenarios in PDF and compare them - A detailed Order Breakdown with all variable and fixed costs, understanding how changing one or more variables can impact your results - The marginality trend graph which can be a game-changer for pricing discussions, you immediately see how profit margins change at different price points and better understand pricing flexibility - A detailed cost breakdown visualizing your largest cost drivers and opportunities for optimization - The sensitivity analysis showing you exactly which variables move the needle most. In this case, as an example, a 10% increase in selling price could boost margins by 15% -- 👉🏼 Drop a comment to try the Calculator (just make sure we're connected first) + I’ll be sending over a video where I go through the details and show you how I’ve been using it. I hope it’ll help speed up your decision-making :) P.S. More screenshots with examples in the comments #EcommerceAnalytics #Profitability #DataDriven

One of my OCDs is financial planning. And a subset of that is retirement planning. Typically a retirement planner will ask you to add your corpus and monthly expense. You might also add some expected returns on investment and some expected inflation. Using this data, the planner might give you an estimate of either how long your fund will last or how much additional corpus you need to retire. However, there is a problem with this approach. Neither inflation nor returns are uniform year on year. They follow a distribution. A good way to plan retirement, therefore, is to create multiple scenarios using Monte Carlo simulations. I have built an app that allows for such planning. Each trace in the chart below is a possible outcome. The median trace (50th) is the most likely outcome. But if unlucky (5th), you might end up finishing your corpus very early. Or, if you are very lucky (75th), you might do very well. I will appreciate technical feedback and some help in getting this only. But more importantly, I would appreciate it if finance geeks here critiqued the usefulness of this approach and the implementation. GitHub link in comments.

So to better understand the impacts of the ongoing tariff/duty conversation, I worked up a few scenarios – the new cycle and the numbers used have the ability to distort the potential outcomes and I needed, for myself, to see the numbers in a way that makes sense to me. I thought there may be others in the network here who would appreciate the same view – again, just to ensure clarity and manage distortion. Let’s invent a product that sells for $100 at retail. This means the retailer probably paid $50 to the brand for said product to achieve what’s commonly referred to as a ‘keystone’ margin (50%). The brand perhaps seeks the same keystone margin, so looked to land the product in the US for $25. The Landed Cost (LDP) is the cost of the product (FOB) + freight + duty + occasional other variables (ie. FSO, Agent Fees, etc). In my model, the FOB for the $100 retail product is $20, and it has a 20% duty and $1 freight, which equals a $25 LDP. Let’s take the same product and apply a different duty schedule to it.  If the duty moves to 40%, the landed cost moves to $29 all else staying the same. This would give the brand a 42% margin if the retailer is keeping the 50% margin. Ok, fair enough. But what if the brand is running a <10% net profit. If COGs essentially increases by 8%, we’re approaching dangerous territory as far as net profit is concerned. If the brand wants to maintain its health, they would have to reprice the wholesale price for the product from $50 to ~$60 to maintain a 50% gross margin. Now that $60 becomes $120 at retail – remembering the retailers’ need for a 50% margin as well. So doubling the tariff in this case increased the retail price by 20%.  This is a VERY simple scenario, with very simple math – in reality there are many, many more variables. But it's important to understand the basics and the impacts. And let me be very clear: a 20% price increase not based on an act of God, like COVID or a GFC, is a certain path to deeply unfortunate outcomes. I don't think most people can easily absorb that kind of hit to their family budgets.