Coach Mary

A replication found earlier machine-learning fund results relied on look-ahead bias, because portfolio weights accidentally used future returns instead of realistic current information.
After the coding issue was corrected, the strongest algorithms saw annual performance ↓>1 percentage point, and no outperformance stayed statistically significant afterward.
Independent testing across long-only portfolios, multiple horizons, and risk models did not find statistically significant market-beating mutual fund selections using machine learning.
The clearest practical use was defensive: algorithms consistently flagged bottom-decile funds with meaningfully negative annual returns, helping investors screen avoidable risks better.
For most investors, the takeaway stayed simple: control costs, diversify, favor interpretable screening, and stay skeptical of backtested winner-picking claims in active management.

AI Helps Investors Avoid Fund Losers

A consumer finance expert's rule: raise your savings rate by one point every 6 mo, making progress feel gradual, automatic, and manageable.
$60K earner starting at ~3% saves $1.8K in year one; steady one-point bumps can reach $7.8K annually within 5 yr without pain.
After ~8.5 yr, the same path can reach $12K annually, showing how small automated adjustments can reshape retirement contributions over time meaningfully.
If an employer offers a 401(k) match, contribute enough to capture it first; a typical full match can add ~$3K on $60K pay.
Action plan: check your 401(k) contribution, confirm match formula, schedule semiannual one-point raises, and calculate total savings against gross pay for clarity.