Economic Forecasting for Asset Managers

📊 Why Look at Hierarchical Forecasting for Sector-Style Asset Returns ❓ When macro conditions are unstable — inverted yield curves, “higher-for-longer” rates, sector rotations — traditional single-model forecasts often fail to capture the multi-level structure of asset behavior. Hierarchical forecasting allows us to: 🔷 Link top-down sector or macro forecasts with bottom-up asset predictions 🔷 Improve consistency across forecasts (sector ≈ sum of stock forecasts) 🔷 Leverage information sharing between related assets while preserving idiosyncratic detail 🔷 We can link Sector (Consumer Discretionary) with Style (Growth, Value) and raw Asset Returns (Growth: Amazon, Tesla, Nike, Value: HomeDepot, McDonalds, Starbucks) Over the past weeks, I’ve tested several hierarchical approaches for my own research — here’s what I found: 1️⃣ Bayesian Forecasting ✅ Strong, well-behaved uncertainty bands out of the box ❌ Can be rigid without careful hyperparameter tuning; better for longer-horizon scenarios than short-term idiosyncrasy 2️⃣ SARIMAX + Conformal Intervals ✅ Performed surprisingly well for me on both broad and idiosyncratic patterns ❌ Needs frequent refits to track rapid regime changes 3️⃣Gradient Boosting (LightGBM) + Conformal ✅ Fast on high-dimensional features; delivers consistently reliable forecasts, but tends to have symmetric forecast error (leaf splits nature) ❌ Intervals tended to be wide until I calibrated them more aggressively 4️⃣N-BEATS (Neural Basis Expansion for Time Series) + Conformal Intervals ✅ Accurate point forecasts and intervals; non-parametric and adaptable to complex structures ❌ Hyperparameter search can be extensive; still niche in finance relative to ML mainstays 💡 Takeaway: A hybrid stack — statistical + ML, with careful confidence interval calibration — has worked best for me so far. In volatile regimes, hierarchical forecasting can bridge macro-level structure and micro-level alpha. Of course, real rigorous backtesting would have been better here, but if you are curious, feel free to contribute or reach out to me for the same❗ 🔗 See links in comments ⬇️ 👉 Notebook – Bayesian Forecasting 👉 Notebook – SARIMAX + Conformal Intervals 👉 Notebook – N-BEATS + Conformal Intervals 👉 Notebook – Conformal LightGBM #QuantFinance #AssetReturns #HierarchicalForecasting #MachineLearning #SARIMAX #Bayesian #LightGBM #TimeSeries #NBEATS

Goldman Sachs Asset Management Fixed Income Outlook Q42024 Shift in Central Bank Policies: Central banks, including the U.S. Federal Reserve (Fed), are transitioning from inflation control to supporting growth. Global monetary easing is expected to benefit fixed income assets, particularly bonds, which serve as protection against downside growth risks. Economic Scenarios and Impact on Fixed Income: Four potential scenarios are outlined (acceleration, soft landing, softish landing, and hard landing), with each scenario reinforcing the strategic value of fixed income. Bonds, especially in investment-grade credit, are positioned to benefit from easing policies and protection from volatility. Investment Themes: -Dial Up Duration: Extending bond durations helps hedge against growth risks and captures income potential. Earn Income Amid Expansion: Despite growth risks, corporate and emerging market (EM) bonds offer income opportunities. -Go Global: Differences in central bank actions across regions create opportunities in both developed and emerging market bonds. -Monetary Easing and Market Reactions: The Fed's rate cuts and China's stimulus are creating favorable conditions for high-yield credit, EM debt, and local bond markets. Investors are advised to explore high-conviction opportunities amidst volatility. Interest Rates and Inflation: Global inflation is decelerating, and central banks are cutting rates. The U.S. election and geopolitical tensions, however, present uncertainties. Geopolitical and Political Risks: Geopolitical tensions, such as those in the Middle East, and potential tariff escalations in the U.S. could impact fixed income markets. Oil prices, in particular, may rise due to supply disruptions, impacting inflation. Fixed Income Sectors: The report emphasizes opportunities across different bond sectors, including high yield, investment-grade corporate bonds, municipal bonds, and securitized credit. Responsible Investing: Growth in green, social, and sustainability bonds (GSS) is highlighted, with an emphasis on the alignment of financial returns and sustainability goals.

“Tactical Asset Allocation with Macroeconomic Regime Detection” presents a novel approach to portfolio management by integrating macroeconomic regime detection into tactical asset allocation using machine learning techniques. The authors develop a three-stage model: (1) classifying macroeconomic regimes using a modified k-means clustering algorithm, (2) predicting asset returns and volatilities based on regime probabilities, and (3) mapping these forecasts into portfolio allocations using multiple sizing schemes. The model leverages over 100 monthly macroeconomic time series from the FRED-MD database, applying unsupervised learning methods to identify stable and volatile economic periods. Empirical results show that regime-based portfolios outperform traditional benchmarks, including equal-weight, buy-and-hold, and mean-variance optimization strategies. The linear ridge regression approach, particularly in its long-only implementation, achieves the highest Sharpe ratio (1.505) and the best drawdown protection (-4.389% max drawdown), suggesting machine learning methods can significantly enhance tactical asset allocation. The study also finds that simpler models (2-3 regime clusters) outperform more complex ones and that long-only strategies consistently generate superior returns compared to long-short variants. Additionally, the paper introduces a probabilistic framework for regime classification, incorporating transition probability matrices to model regime shifts dynamically. The results indicate that integrating macro-based regime detection with asset allocation models provides a statistically significant advantage, offering a more robust strategy for navigating different economic conditions.

📢 New Investment Strategy! "Dynamic Asset Allocation with Asset-Specific Regime Forecasts" How market regimes can improve multi-asset portfolios. Keep reading! 🔻 This paper presents a cutting-edge hybrid strategy for asset allocation that uses machine learning to enhance predictions and optimize portfolios. The main contributions and findings are as follows: 👉 Hybrid Regime Forecasting: This new framework applies both unsupervised and supervised learning techniques to identify and predict bullish or bearish market regimes for individual assets, improving accuracy and asset-specific insights. 👉 Customized Forecasting: By moving beyond broad economic regimes and focusing on specific asset dynamics, the method enhances the signal-to-noise ratio and allows for more precise portfolio adjustments in response to market shifts. 👉 Dynamic Asset Allocation: Integrating these asset-specific regime forecasts into the Markowitz mean-variance optimization model, this approach helps create optimal allocation weights, balancing return and risk more effectively. 👉 Proven Performance: Empirical studies show that this model outperforms traditional strategies like minimum-variance and naive-diversified portfolios, demonstrating stronger returns and lower risk across various asset classes. 👉 Adaptable to Market Volatility: This framework offers investors a dynamic tool for managing portfolios in fast-changing markets, making informed decisions to capture opportunities while mitigating risk. ----------------------- → Join 3000+ Asset Pricing & Quant Finance enthusiasts who receive top new research ideas weekly in their email: https://lnkd.in/dkxSDJpq ----------------------- Link to the paper: https://lnkd.in/dsS6m6ih