Ranking Backtest

Methodology

The simulated historical performance was generated using the same ranking system and factor weights as the current live rankings published on ClarityVesting. Rankings cover all sectors currently available on the platform, with factor weights defined on a per-sector basis.

For each historical period, a ranking was built using only the historical data that was available at that point in time. No future information was used, which avoids look-ahead bias (i.e. using data that would not have been available to an investor at the time).

Companies were selected based on their ranking and then combined into a market-cap-weighted portfolio. As a result, large- and mega-cap stocks have significantly greater weight in the historical portfolio than mid- and small-cap stocks.

A three-month lag was introduced between the ranking date and portfolio construction to further reduce the risk of look-ahead bias resulting from late regulatory filings, reporting delays, or financial restatements.

Each portfolio was then held for five years without rebalancing. At the end of each five-year period, a new ranking was generated and a new portfolio was constructed using the same methodology. This process was repeated throughout the 20-year historical period. The simulated historical return represents the compounded performance of these successive five-year holding periods.

Survivorship bias

The data provider used by ClarityVesting has limited historical coverage of delisted and bankrupt companies prior to 2018. This may introduce survivorship bias, which occurs when historical performance is calculated using only companies that still exist today.

However, the impact of this bias is substantially reduced by the market-cap-weighted nature of the portfolio.

To estimate the potential impact of survivorship bias, we can consider an intentionally pessimistic scenario:

Assume that 1% of the total U.S. equity market capitalization disappears each year through complete bankruptcies, which is likely an overstatement.
Assume that 50% of this disappearing market capitalization is selected in the top 3% of stocks by the ranking system, an intentionally conservative assumption that implies the model performs particularly poorly at identifying financially distressed companies.

Based on this pessimistic scenario, the potential overstatement of the backtested annual returns caused by missing delisted and bankrupt companies has a conservative upper bound of 0.5% per year.

Avoiding overfitting

Overfitting happens when a model is tuned so carefully to past results that it looks brilliant in a historical test, yet fails when markets change. Simply maximizing backtested returns is an easy way to create that illusion. ClarityVesting takes a different approach: ranking weights are calibrated carefully, but under rules designed to keep the system robust rather than to chase the single best historical number.

Weights are set separately for each sector covered by ClarityVesting, so that what matters most in Technology is not forced to look identical to what matters in Utilities or Healthcare. At the same time, each sector must still use a balanced mix of the main investment dimensions (value, growth, quality, and momentum). No single factor is allowed to dominate the ranking, and short-term price momentum is deliberately capped so the system cannot collapse into a pure trend-following model.

Calibration is also judged on whether the ranking behaves in a coherent way — not only on how the top tranche performed. Higher-ranked groups are expected to have delivered stronger results than lower-ranked ones over time. A setup that produces an impressive top slice while the rest of the ranking looks random is treated as a warning sign, not a success.

To reduce the risk of fitting the past too closely, weights are tuned primarily on an earlier part of the historical record and then checked on a later period that was not used to choose them. In other words, the model has to prove itself on data it did not “see” during calibration. ClarityVesting also tests nearby variations of the chosen weights: a sound configuration should sit in a stable region where small changes still produce sensible results, rather than on a fragile peak that works only for one exact combination.

These safeguards do not eliminate uncertainty. Markets change, and no historical process can guarantee future performance. They do, however, reflect a deliberate methodology: sector-aware calibration, hard limits against extreme weightings, coherence checks across the full ranking, and out-of-sample validation — rather than unconstrained optimization of a backtest curve.

Limitations to consider

Backtested returns do not include transaction costs, brokerage fees, taxes, or other investment expenses. These costs vary depending on the investor's broker, country of residence, and individual tax situation.

In many jurisdictions, long-term investing may benefit from more favorable tax treatment than short-term trading.

Overall Backtest Performance

The ranking system has been backtested over the last 20 years. Historical portfolios were constructed from different portions of the ranking (top 3%, top 20%, top 30%, and others), combining stocks from all sectors. Stocks were weighted by market capitalization so that larger companies had a greater influence than smaller ones. Each portfolio was held for five years without rebalancing. The analysis uses only information that would have been available to investors at the time.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 3%	17.41%	75	58.04%	27.24%
Top 10%	15.74%	236	52.44%	25.30%
Top 20%	12.85%	465	54.92%	24.96%
Top 30%	11.96%	697	52.99%	23.76%
Top 40%	11.33%	927	54.87%	24.48%
Top 50%	11.19%	1158	53.84%	24.24%
SPY (S&P 500)	10.42%	—	55.19%	19.07%
Bottom 40%	-13.30%	929	98.67%	4028.38%

The chart below shows the hypothetical growth of a $10,000 investment in the top 3% of ranked stocks compared with an investment in the S&P 500 Index.

Technology Backtest

The ranking system has been backtested on the Technology sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	25.63%	30	60.04%	40.42%
Top 20%	19.82%	60	56.86%	29.67%
Top 30%	18.33%	90	55.46%	28.46%
XLK (Technology)	14.68%	—	53.04%	22.49%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Technology stocks compared with the sector benchmark.

Communication Services Backtest

The ranking system has been backtested on the Communication Services sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	16.22%	9	65.35%	32.88%
Top 20%	15.76%	17	64.67%	31.64%
Top 30%	14.72%	26	64.38%	31.15%
VOX (Communication Services)	7.84%	—	57.18%	20.73%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Communication Services stocks compared with the sector benchmark.

Consumer Cyclical Backtest

The ranking system has been backtested on the Consumer Cyclical sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	13.16%	30	67.76%	34.54%
Top 20%	13.42%	59	50.12%	30.83%
Top 30%	12.07%	88	48.49%	27.08%
XLY (Consumer Cyclical)	11.21%	—	59.05%	22.01%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Consumer Cyclical stocks compared with the sector benchmark.

Financial Services Backtest

The ranking system has been backtested on the Financial Services sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	11.57%	40	65.51%	34.01%
Top 20%	9.13%	79	79.43%	43.01%
Top 30%	8.05%	118	79.70%	43.11%
XLF (Financial Services)	5.23%	—	82.69%	29.30%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Financial Services stocks compared with the sector benchmark.

Energy Backtest

The ranking system has been backtested on the Energy sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	6.44%	14	70.21%	41.11%
Top 20%	7.74%	27	62.40%	41.27%
Top 30%	7.79%	41	65.01%	40.71%
XLE (Energy)	7.17%	—	71.54%	29.54%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Energy stocks compared with the sector benchmark.

Healthcare Backtest

The ranking system has been backtested on the Healthcare sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	11.30%	24	40.88%	21.58%
Top 20%	9.07%	47	40.81%	20.54%
Top 30%	9.40%	71	39.59%	18.43%
XLV (Healthcare)	9.79%	—	39.17%	16.66%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Healthcare stocks compared with the sector benchmark.

Industrials Backtest

The ranking system has been backtested on the Industrials sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	9.97%	34	58.11%	27.32%
Top 20%	11.31%	67	57.22%	26.55%
Top 30%	11.03%	100	57.69%	26.64%
XLI (Industrials)	10.22%	—	62.26%	20.90%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Industrials stocks compared with the sector benchmark.

Consumer Defensive Backtest

The ranking system has been backtested on the Consumer Defensive sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	14.94%	12	37.61%	21.69%
Top 20%	9.99%	24	37.55%	18.88%
Top 30%	10.23%	36	37.88%	17.63%
XLP (Consumer Defensive)	9.26%	—	32.39%	14.09%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Consumer Defensive stocks compared with the sector benchmark.

Utilities Backtest

The ranking system has been backtested on the Utilities sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	11.77%	8	40.68%	21.82%
Top 20%	10.71%	16	39.47%	21.52%
Top 30%	9.60%	24	41.23%	22.46%
XLU (Utilities)	9.32%	—	46.48%	18.57%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Utilities stocks compared with the sector benchmark.

Basic Materials Backtest

The ranking system has been backtested on the Basic Materials sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	8.77%	18	78.54%	51.15%
Top 20%	6.21%	35	74.86%	36.51%
Top 30%	6.08%	53	74.04%	35.43%
XLB (Basic Materials)	8.11%	—	59.83%	23.21%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Basic Materials stocks compared with the sector benchmark.

Real Estate Backtest

The ranking system has been backtested on the Real Estate sector using the same rules as the overall backtest.

Ranking ⓘ	Ann. Return ⓘ	Nb. Stocks ⓘ	Max Drawdown ⓘ	Ann. Volatility ⓘ
Top 10%	7.05%	17	62.43%	45.32%
Top 20%	6.00%	34	67.40%	45.67%
Top 30%	5.07%	50	71.99%	47.22%
VNQ (Real Estate)	5.79%	—	73.07%	28.74%

The chart below shows the hypothetical growth of a $10,000 investment in the top 10% of ranked Real Estate stocks compared with the sector benchmark.

Backtested performance is not indicative of future results.

To go further

The backtested returns and rankings are based exclusively on quantitative metrics. They do not consider a company's business model, competitive advantages, risks, growth catalysts, management quality, or other qualitative factors.

The rankings should therefore be viewed as a starting point for research rather than followed blindly. Investors who complement the quantitative rankings with their own research, market knowledge, and understanding of the businesses may identify opportunities that are not fully captured by the ranking system alone.