In volatile markets, portfolio managers are often faced with the challenge of whether to rebalance, rotate tactically into underperforming sectors, or stay the course. While broad market corrections (>10%) are widely analyzed, the dynamics of sector-level drawdowns offer an untapped opportunity for more tactical and efficient capital deployment. This paper explores a rule-based sector strategy using real, investable data from SPDR sector ETFs, aiming to evaluate how different sectors recover after temporary drawdowns.

Instead of trying to predict market direction, we simulate a strategy that deploys capital into each sector whenever its price falls 5% or more from a recent high. Therefore, the focus of the analysis is on understanding recovery behavior and is not intended to create an investment strategy nor tactical tilts: how quickly and strongly each sector rebounds after such pullbacks, and what practical implications this holds for asset allocators and tactical portfolio managers.

1. Methodology and Data

• The strategy uses monthly Adjusted Close prices for the 11 SPDR sector ETFs: XLB, XLC, XLE, XLF, XLI, XLK, XLP, XLRE, XLU, XLV, and XLY.
• Data begins in July 2015, which is the earliest month with complete historical coverage for all ETFs.
• For each sector, we allocate an initial capital of $1,000,000, divided into 20 equal tranches of $50,000.
• A new tranche is invested only when the ETF's price drops 5% or more from its most recent peak.
• Assume not deployed capital is invested at risk free rate of 2%.
• The peak is updated each month if the ETF reaches a new high, ensuring a dynamic drawdown threshold.
• Once a drawdown is triggered, one tranche is deployed at that month’s closing price. This continues until all 20 tranches are fully allocated.
• The same strategy is applied to SPY, the S&P 500 ETF, to provide a benchmark.

2. Results and Analysis

The most striking finding is the substantial variation in recovery potential across sectors. While some showed moderate and steady gains, others exhibited significant compounding after downturns.

One of the key drivers of when capital is deployed in this strategy is sector volatility. Sectors with higher historical volatility tend to reach the 5% drawdown threshold more frequently and sooner, enabling faster capital activation. For example, Energy (XLE, 33.5%), Consumer Discretionary (XLY, 23.0%), and Technology (XLK, 21.5%)are among the most volatile sectors, meaning they tend to trigger entries early and often. In contrast, more stable sectors like Consumer Staples (XLP, 13.3%), Health Care (XLV, 15.3%), and Utilities (XLU, 16.1%) take longer to correct by 5%, leading to slower capital deployment. Communication Services (XLC, 19.6%) and Financials (XLF, 21.3%) fall somewhere in the middle, offering a blend of correction frequency and rebound potential. These differences underscore the need to calibrate tactical strategies not just by threshold size, but also by each sector’s intrinsic volatility.

Here is the chart showing how quickly capital was deployed across each sector under the 5% correction threshold strategy (where it can be seen that all capital was deployed):

At the top of the performance spectrum is XLK (Technology), which reached a final value of approximately $4.02 million, more than four times the original capital deployed. This result was driven by deep but fast-recovering drawdowns, especially in the years following the COVID-19 crash, when early tranches saw exceptional gains.

XLF (Financials) and XLI (Industrials) also delivered strong results, finishing with $2.67 million and $2.54 million respectively. These sectors demonstrated frequent moderate drawdowns and meaningful recoveries, ideal conditions for the incremental tranche approach used in the simulation.

In contrast, more defensive sectors like XLP (Consumer Staples) and XLU (Utilities) ended with significantly lower final values, reflecting their more muted volatility and slower post-correction recoveries. While these sectors may provide stability, they appear less suited for tactical deployment strategies focused on rebound potential.

Below is the chart comparing all sector results to the SPY tactical benchmark:

At the top of the performance spectrum is XLK (Technology), which reached a final value of approximately $4.02 million, more than four times the original capital deployed. This result was driven by deep but fast-recovering drawdowns, especially in the years following the COVID-19 crash, when early tranches saw exceptional gains.

XLF (Financials) and XLI (Industrials) also delivered strong results, finishing with $2.67 million and $2.54 million respectively. These sectors demonstrated frequent moderate drawdowns and meaningful recoveries, ideal conditions for the incremental tranche approach used in the simulation.

In contrast, more defensive sectors like XLP (Consumer Staples) and XLU (Utilities) ended with significantly lower final values, reflecting their more muted volatility and slower post-correction recoveries. While these sectors may provide stability, they appear less suited for tactical deployment strategies focused on rebound potential.

Below is the chart comparing all sector results to the SPY tactical benchmark:

2.1. Key Takeaways for Portfolio Managers

• Technology (XLK) significantly outperformed all other sectors, turning $1 million into over $4 million with an annualized return of 14.9%. This comfortably outpaced the tactical S&P 500 benchmark of 8.8%, driven by deep corrections and fast recoveries that allowed early tranches to compound substantially.
• Financials (XLF) and Industrials (XLI) also exceeded the SPY benchmark, delivering 10.3% and 9.7% annualized returns, respectively. Their higher volatility and consistent cyclical corrections made them well-suited for systematic reentry strategies.
• Consumer Discretionary (XLY) and Health Care (XLV) generated solid returns of 8.1% and 7.6%, slightly below the S&P 500 benchmark. These sectors recovered well but exhibited less frequent or shallower corrections compared to the top performers.
• Defensive sectors such as Utilities (XLU, 6.8%) and Consumer Staples (XLP, 5.7%) underperformed within this framework. Their lower volatility and more muted recovery profiles limited both the frequency of entries and the magnitude of subsequent rebounds.
• Applying the strategy across sectors independently introduces natural diversification of entry timing, as different sectors hit drawdown thresholds at different times, smoothing overall capital deployment.
• Recovery analysis at the sector level offers a practical roadmap for portfolio managers to identify where tactical allocation is most likely to produce excess returns, especially in periods of market stress or rotation.

2.2. Testing the robustness of results: Start the strategy just before COVID Crash

To test the sensitivity of the strategy to its starting point, we re-ran the simulation beginning in January 2020, just before the COVID-19 market crash (applying the same strategy as before). This period represents an extreme stress test, as markets experienced sharp and synchronized drawdowns followed by rapid recoveries across most sectors.

Despite the abrupt volatility, the results closely mirrored those of the full-period analysis:

• Top-performing ETFs during this period were:
  • Energy (XLE): 17.0% annualized return
  • Technology (XLK): 14.4% annualized return. - compararlo contra buy and hold
  • Financials (XLF): 13.3% annualized return
• Worst-performing ETFs were:
  • Consumer Staples (XLP): 1.4% annualized return
  • Health Care (XLV): 2.1% annualized return
  • Real Estate (XLRE): 4.0% annualized return
• Capital deployment was completed in all sectors except Consumer Staples (XLP), which deployed only 16 out of 20 tranches. This partial deployment limits upside potential, as some capital remained uninvested during strong rebounds. In contrast, sectors that reached all 20 tranches (like XLK, XLF, and XLE) were better positioned to fully capitalize on post-COVID recoveries.
• Comparison to the original full-period simulation:
  • Technology (XLK) returned 14.9% in the full period vs. 14.4% in the COVID-era. This shows remarkable consistency in recovery behavior.
  • Financials (XLF) returned 10.3% over the full period vs. 13.3% post-COVID, indicating acceleration in sector recovery dynamics after 2020.
  • Energy (XLE) was not among the top performers in the full sample, but led during the COVID window with 17.0%, reflecting the unique rebound conditions after the energy price collapse and reopening rally.
• Volatility increased across nearly all sectors during the COVID-era window, contributing to faster drawdown triggers and quicker capital deployment. The most notable increases were observed in Energy (XLE: +2.1 percentage points), Real Estate (XLRE: +1.5pp), and Consumer Discretionary (XLY: +1.5pp). These shifts reflect heightened market sensitivity and sector-specific shocks during the pandemic recovery. Understanding these changes is critical for adjusting correction thresholds and deployment pacing under elevated volatility regimes.

This case study highlights that while some sector patterns (like Tech) remain stable across timeframes, others (like Energy) can rise sharply depending on macro conditions. For portfolio managers, this underscores the value of adjusting tactical exposure dynamically based on prevailing recovery catalysts.

3. Conclusion

This article highlights how a rule-based, correction-triggered investment strategy can be used not only to time entries but to differentiate sectors based on their recovery strength and volatility characteristics. By simulating tactical deployment across 11 SPDR sector ETFs using a 5% drawdown threshold, we gain valuable insight into which sectors tend to generate stronger post-correction performance, and how that can inform real-world allocation decisions.

For portfolio managers considering tactical exposures, several actionable insights emerge:

• Sectors like Technology, Financials, and Industrials consistently displayed strong rebound behavior, making them prime candidates for tactical entry following market corrections. These sectors offer a favorable mix of volatility and recovery potential that supports capital deployment when markets are under pressure.
• Energy showed strong performance in specific macro environments, such as the post-COVID recovery. Its high volatility makes it highly responsive to tactical strategies, but managers should pair it with macro context to avoid drawdown traps.
• Defensive sectors such as Consumer Staples, Utilities, and Health Care tend to underperform in correction-based strategies, largely due to lower volatility. These sectors correct less frequently, leading to delayed or incomplete capital deployment and shallower recoveries.
• When constructing tactical overlays, it is crucial to account for each sector's volatility profile. Higher-volatility sectors reach drawdown thresholds more quickly, enabling faster capital allocation, while lower-volatility sectors may require looser thresholds or complementary strategies to avoid underinvestment.
• Tactical models can be improved by blending correction-based entry logic with macro indicators or relative strength signals, particularly in more volatile sectors where timing is more critical.
• Finally, diversifying correction triggers across sectors creates natural staggering in capital deployment. This not only reduces portfolio-level timing risk but also provides multiple paths to capture asymmetric upside during recovery phases.

In short, sector recovery behavior is not just a theoretical exercise, it can and should be a core input in tactical allocation frameworks. Portfolio managers seeking to improve entry discipline and boost post-correction performance would benefit from incorporating these insights into their sector rotation, rebalancing, and tactical allocation processes.

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