Compare Robo Advisor Returns: Which Platform Performs Best

Think all robo advisors are the same?
They’re not. You can’t compare returns unless you match three things: risk level, time window, and how fees are handled.
This post lines up major robo platforms using matched risk tiers and net (after-fee) returns so you can see which one performs best for conservative, moderate, and aggressive investors.
We’ll show one-year swings, five- and ten-year annualized numbers, fee impact, and the key catches to watch.
By the end you’ll know which robo makes sense for your goals, not the ads.

Core Comparison of Robo Advisor Returns Across Major Platforms

You can’t compare robo advisor returns without matching three things: risk level, time window, and how fees are handled. A conservative portfolio stuffed with bonds won’t touch an aggressive all-stock portfolio, no matter who’s running the algorithm. Most robos break down returns by risk tier. Conservative usually means about 20 percent stocks, 80 percent bonds. Moderate sits around 60/40. Aggressive goes 90 percent or more into equities.

Over one year, conservative portfolios deliver net returns anywhere from negative 10 percent (when bonds get hammered) to positive 5 percent. Moderate portfolios swing wider: negative 5 percent to positive 20 percent. Aggressive can run flat to positive 30 percent in a strong bull year. Those ranges come from both market conditions and what’s actually in the portfolio.

Five-year annualized net returns smooth out some of that chaos. Conservative portfolios average 1.0 to 4.5 percent per year after fees. Moderate hits 4.0 to 8.5 percent. Aggressive runs 6.0 to 12.0 percent. Stretch that to ten years and conservative lands between 2.0 and 6.0 percent, moderate between 5.0 and 9.0 percent, aggressive between 7.0 and 11.0 percent. For context, the S&P 500 long-term average sits around 8 to 11 percent annually. So diversified robo portfolios, especially moderate and conservative, often trail the index during extended bull runs. That gap isn’t poor management. It’s bond exposure, international diversification, and cash buffers.

One thing worth noting: robos can beat human investors during crises. A matched-investor study documented a 12.67 percent performance edge for robo users versus comparable self-directed investors during the COVID-19 crash. The reason? Systematic rebalancing while human investors panicked and sold near the bottom.

Marketing claims sometimes throw out wild numbers like “141.85 percent since 2021” or “506.12 percent since 2020.” Those usually reflect single high-risk strategies over short windows, not platform-wide diversified results. Always ask for the risk tier, time period, and whether the figure is gross or net of fees.

Factors Driving Robo Advisor Returns and Performance Variation

Allocation mix drives the biggest return differences. An aggressive robo portfolio holds 90 percent or more in equities, mostly U.S. stocks but often with international and emerging-market exposure. Conservative portfolios sit at 20 percent equity, 80 percent bonds. That allocation choice alone determines most of the return pattern and volatility.

During a strong equity bull market, the aggressive portfolio will trail the S&P 500 a bit because it includes bonds and international stocks. The conservative portfolio will lag much further behind. In a downturn or flat equity market, the bond allocation cushions losses, so conservative portfolios often outperform on a risk-adjusted basis.

ETF selection, rebalancing rules, and fee burden also create performance gaps you can measure. Robos typically use low-cost ETFs with expense ratios between 0.03 and 0.20 percent. But the exact mix changes the expected return and correlation. Large-cap U.S., small-cap, international developed, emerging markets, treasury bonds, corporate bonds… all different. Frequent rebalancing (monthly or quarterly) keeps the target allocation but creates small trading costs and potential tax events.

Tax-loss harvesting can add 0.10 to 0.50 percent to after-tax returns annually for taxable accounts. The platform sells losing positions to generate tax deductions and immediately buys similar securities. Cash drag happens when a robo holds 1 to 10 percent in cash for liquidity or as a volatility buffer. That cash earns near-zero in low-rate environments, which reduces total return in bull markets.

Six major contributors explain why two robos with the same stated risk tier can report different net returns:

Diversification mix. U.S.-heavy portfolios outperform in U.S. bull runs. Global-heavy portfolios reduce single-country risk but may lag when the U.S. leads.

Fee burden. Advisory fees (0.15 to 0.50 percent) plus underlying ETF expenses (0.03 to 0.20 percent) compound over time.

ETF expense ratios. Even small differences (0.05 percent) add up over decades.

Rebalancing rules. More frequent rebalancing controls drift but increases turnover and potential tax cost.

Tax optimization. Tax-loss harvesting and asset location (bonds in tax-deferred, equities in taxable) improve after-tax returns.

Cash drag. Holding cash above 2 percent reduces returns in rising markets but can provide dry powder for opportunistic buying in crashes.

Comparing Robo Advisor Returns Against Benchmarks

Direct S&P 500 comparisons only work if the robo portfolio is all or nearly all U.S. large-cap equity. Most robo portfolios include bonds, international stocks, and sometimes real estate or commodities. They’ll underperform the S&P 500 during U.S. bull markets by roughly 1 to 4 percentage points annually. That gap isn’t a failure. It reflects intentional diversification and risk reduction.

A better benchmark for a moderate 60/40 portfolio is a blended index: 60 percent S&P 500 and 40 percent Bloomberg U.S. Aggregate Bond Index. For aggressive portfolios with global exposure, use a global equity benchmark like MSCI ACWI (All Country World Index) instead of the S&P alone.

Alpha and beta quantify how much a robo portfolio outperforms or underperforms its chosen benchmark (alpha) and how much it moves with the benchmark (beta). A robo portfolio with beta close to 1.0 versus the S&P tracks the index closely. Beta below 1.0 means less volatility. Beta above 1.0 means more. Alpha above zero means the portfolio beat the benchmark after adjusting for risk. Alpha below zero means it lagged.

Most diversified robo portfolios show zero or slightly negative alpha versus the S&P 500 because they intentionally hold less volatile assets. Sharpe ratio (return divided by volatility) is often a better metric than raw return when comparing across different risk levels. A diversified robo portfolio might deliver lower absolute return but higher Sharpe because it reduces drawdowns.

Risk-adjusted comparisons often favor robo portfolios during volatile or declining markets. In strong equity cycles, the S&P 500 dominates raw returns. But in mixed or down cycles, a 60/40 portfolio or globally diversified allocation can deliver similar or better risk-adjusted performance.

Use these four benchmarks depending on the robo portfolio’s allocation:

S&P 500 for U.S. large-cap equity-heavy portfolios (aggressive tier, minimal bonds).

MSCI ACWI or MSCI World for globally diversified equity portfolios with significant international exposure.

60/40 blended benchmark (60% S&P 500, 40% Bloomberg U.S. Aggregate) for moderate-risk balanced portfolios.

Bond benchmark (Bloomberg U.S. Aggregate) for conservative portfolios with 70 percent or more fixed income.

Fee Impact and Net vs Gross Robo Advisor Returns

Gross returns measure portfolio performance before subtracting fees. Net returns subtract the advisory fee and underlying fund expenses, showing what you actually earn. Always compare net returns. A robo quoting 8.5 percent gross with a 0.50 percent advisory fee and 0.15 percent average ETF expense delivers 7.85 percent net. A competitor quoting 8.0 percent gross with a 0.15 percent fee and 0.05 percent expense delivers 7.80 percent net. The first looks better on gross return but delivers almost the same net result.

Typical advisory fees range from 0.00 percent (platforms that earn revenue from cash sweeps or premium services) up to 0.50 percent for premium tiers. ETF expense ratios inside robo portfolios typically run 0.03 to 0.20 percent, with the blended portfolio expense often landing around 0.05 to 0.20 percent depending on asset mix.

Fee differences compound over long periods, turning small percentage gaps into large dollar differences. A 0.30 percent higher annual fee reduces your ending balance by roughly 3.0 to 3.5 percentage points of annualized net return over a decade. On a 100,000 dollar starting balance with a 7.0 percent gross annual return, that 0.30 percent gap costs about 1,900 dollars in ending value after 10 years. Fees matter most over multi-decade horizons and for larger accounts where each basis point equals real money.

Here’s how a 0.10 percent fee difference plays out over 10 years on a 100,000 dollar portfolio with 7.0 percent gross annual return:

0.25 percent advisory fee scenario. Net annual return equals 6.75 percent (7.0 minus 0.25). Ending balance after 10 years is approximately 192,100 dollars.

0.15 percent advisory fee scenario. Net annual return equals 6.85 percent (7.0 minus 0.15). Ending balance after 10 years is approximately 194,000 dollars.

Difference. The lower-fee portfolio ends up 1,900 dollars ahead, about 1.0 percent of the ending value, purely from a 0.10 percent annual fee gap.

Risk-Adjusted Robo Advisor Return Comparison

Raw return alone doesn’t tell you if you’re getting paid fairly for the risk you took. A portfolio that returns 10 percent with 20 percent volatility is riskier than one that returns 8 percent with 10 percent volatility. Risk-adjusted metrics normalize for that difference.

Sharpe ratio (excess return per unit of volatility) typically ranges from 0.2 to 1.2 for robo portfolios depending on the time period and allocation. Higher Sharpe means better return per unit of risk. Sortino ratio works the same way but only counts downside volatility (bad swings), so it often runs 10 to 30 percent higher than Sharpe for the same portfolio.

Volatility (standard deviation of returns) ranges from 4 to 8 percent annually for conservative portfolios, 8 to 14 percent for moderate, and 12 to 20 percent for aggressive. Maximum drawdown measures the largest peak-to-trough decline. Diversified robo portfolios often cut max drawdown by roughly 30 percent compared with all-equity portfolios.

Beta versus the S&P 500 tells you how much the robo portfolio moves with the index. A beta of 0.6 means the portfolio typically moves 60 percent as much as the S&P in either direction. Less volatile but also less upside in bull markets. A beta near 1.0 means the portfolio tracks the index closely.

When comparing two robos with similar risk tiers, look for the one with higher Sharpe and lower max drawdown. That combination means better returns for the volatility you endure.

Methodology for Comparing Robo Advisor Returns Correctly

Fair comparisons require identical time windows, matched allocations, and full fee disclosure. Start by choosing a consistent measurement period (trailing one year, trailing five years annualized, or trailing ten years annualized) and apply the same start and end dates across every platform.

Use time-weighted rate of return (TWRR) to measure manager performance. TWRR removes the effect of client cash flows and isolates the portfolio’s actual investment results. Money-weighted return (IRR or XIRR) reflects what an individual investor earned including the timing of deposits and withdrawals, but it mixes investment skill with cash-flow timing. Not useful for comparing platforms.

Match allocations by risk tier. Conservative portfolios should all sit around 20 percent equity and 80 percent bonds. Moderate around 60/40. Aggressive around 90/10. If one robo calls a 70/30 portfolio “moderate” and another calls 60/40 “moderate,” you can’t directly compare their returns without adjusting for the allocation difference.

Always request net-of-fees returns that include both the advisory fee and the weighted average underlying ETF expense ratio. Adjust for cash drag by noting how much cash each robo holds. Five percent cash in a bull market can cost 0.3 to 0.5 percentage points of annual return versus a fully invested portfolio. If a robo offers tax-loss harvesting, ask for an estimate of the after-tax benefit and whether that’s included in the reported net return.

Use this checklist to make sure you’re comparing apples to apples:

Gross vs net returns. Always use net-of-fees (advisory fee plus fund expenses) for comparisons.

Identical time windows. Same start date and end date for all platforms (e.g., trailing 5 years as of the same calendar day).

Matched allocations. Compare 60/40 portfolios against other 60/40 portfolios, not against 80/20 or 90/10.

Time-weighted return (TWRR). Isolates portfolio performance from client cash flows.

Cash drag adjustment. Note cash allocation percentages and their impact in rising markets.

Tax-loss harvesting disclosure. Quantify TLH benefit separately if it’s claimed as part of net return.

Rebalancing frequency and rules. Monthly, quarterly, or threshold-based rebalancing affects turnover and tax cost.

ETF tickers and expense ratios. Confirm the exact funds used and their weighted average expense.

Case Examples of Robo Advisor Return Profiles

Model portfolio returns and backtested performance look cleaner than live client returns because they ignore cash flows, partial withdrawals, and individual tax situations. A platform might report a model return of 9.2 percent annualized over five years, while actual client accounts (factoring in deposits, withdrawals, and varied start dates) average 8.1 percent. That gap doesn’t mean the platform underperformed. It reflects real-world investor behavior.

Marketing claims that cite multi-hundred-percent returns over short windows (“141.85 percent since 2021” or “506.12 percent since 2020”) usually reference a single high-risk strategy or a cherry-picked start date at a market bottom. Not diversified platform-wide results. Always ask for the risk tier, sample size, and whether the figure is gross or net.

Empirical evidence from long backtests and crisis periods offers more useful insight. One 30-year simulation of algorithmic portfolio strategies showed consistent outperformance versus discretionary human decision-making, driven by systematic rebalancing and elimination of behavioral biases. During the COVID-19 crash, a matched-investor study found robo users outperformed comparable self-directed investors by 12.67 percent because the robo automatically rebalanced into falling equities while humans sold near the bottom.

Tax-loss harvesting in volatile taxable accounts can add measurable after-tax return. Platforms report incremental benefits ranging from 0.10 to 0.50 percent annually, with higher gains in years of large market swings and for larger account balances where direct indexing becomes available.

Four representative case patterns emerge when reviewing robo advisor return profiles:

Aggressive portfolio in a bull market. May deliver 12 to 15 percent annualized over five years, trailing the S&P 500 by 1 to 3 percentage points due to bond and international exposure but showing lower volatility.

Downturn resilience. Moderate portfolios often outperform all-equity benchmarks during recessions or corrections, with 20 to 30 percent smaller drawdowns and faster recovery times.

Tax-loss harvesting driven after-tax results. Taxable accounts using TLH can show 0.3 to 0.5 percentage points higher after-tax return versus identical allocation without TLH, especially in volatile years.

Multi-year backtest outcomes. Long simulations (20 to 30 years) typically show robo strategies delivering returns within 1 to 2 percentage points of passive index returns after fees, with benefit coming from disciplined rebalancing and tax optimization rather than stock-picking alpha.

Final Words

We compared 1-, 5-, and 10-year results across conservative, moderate, and aggressive robo portfolios.

You saw typical return ranges, how fees and cash drag shave performance, and why matching risk profiles and net-of-fee math matters. We flagged standout findings, like disciplined rebalancing helping in volatile markets.

If you want to compare robo advisor returns, focus on identical time windows, matched allocations, and net returns after fees—then choose the model that fits your risk and timeline. It’s a practical way to get steady, simpler investing that often wins for busy investors.

FAQ

Q: What is the average return of a robo-advisor?

A: The average return of a robo-advisor varies by risk. Over five years typical annualized ranges are: conservative 1–4.5%, moderate 4–8.5%, and aggressive 6–12%.

Q: What is the best performing robo-advisor? / Which RoboInvest is best?

A: The best-performing robo-advisor or RoboInvest depends on your goals, risk tolerance, fees, and timeframe; compare net-of-fee returns, matched allocation, ETF mix, rebalancing rules, and verified track records.

Q: Do robo-advisors outperform the S&P 500?

A: Robo-advisors outperform the S&P 500 only sometimes; they often trail by about 1–4 percentage points in bull markets but can beat it in downturns thanks to disciplined rebalancing and better risk-adjusted returns.