Invest

Fund Comparison: The First Will Be The Last

Pinterest LinkedIn Tumblr

When comparing funds, investors usually choose the actively managed equity funds that have delivered the highest returns in the recent past. But these supposed winning funds will often develop below average in the future. Investors do better when they systematically select loser funds. Anyone who wants to invest in actively managed equity funds usually aims to generate a higher return than the market average. But only a few fund managers manage that. The problem for investors is therefore having to find out when comparing funds which managers will outperform their benchmark index in the future.

But this endeavor usually fails, as various studies suggest. For example, the British competition authority CMA found in a study that even professional investment advisors who sell their expertise at high prices to institutional investors such as pension funds, insurance companies and other corporations are unable to systematically identify funds that will perform above average in the future. The methods they use to compare funds do not seem to work. The main reason for this misery is likely to be the excessive focus on past performance. Both private and institutional investors mostly choose funds that have recently outperformed their benchmark index and / or performed better than their competitors. Fund managers who were particularly successful with their stock selection and market timing will continue to perform in the future.

TODAY’S WINNERS ARE TOMORROW’S LOSERS

That seems intuitively plausible, but it is fundamentally wrong. Today’s winners are not tomorrow’s winners, but the clear losers. The majority of investors buy actively managed equity funds that have generated the highest returns in the recent past. These funds also usually have a good rating. They are at the top of fund rankings because the fund ratings are primarily based on past returns.

ANYONE WHO FALLS BEHIND IN THE FUND COMPARISON WILL BE FIRED

After fund purchases, investors regularly check whether the managers are achieving the expected above-average return. If this is not the case within a certain period of time, the investors sell the fund again. You are replacing it with a new fund that has outperformed its benchmark in the recent past. To do this, they put together an equally weighted fund portfolio. At the start, it contained the 10 percent of equity funds that had outperformed their benchmark indices by the most in the past 3 years. The winning portfolio was reviewed every three years. The researchers replaced funds that were no longer among the top 10 percent with new funds that were now in the current top group (winning strategy).

For comparison, the analysts constructed a portfolio that consisted of average funds (average strategy) and one that contained the loser funds using the same pattern. That was the 10 percent of equity funds that, measured against their benchmark index, had performed worst in the past 3 years (losing strategy).

THE LOSING STRATEGY WINS

The researchers narrowed the population of all US equity funds according to two criteria: They only considered funds that managed at least one billion US dollars, since smaller funds are avoided by institutional investors. They also excluded the 10 percent of funds with the highest administrative costs. It is known from other studies that particularly expensive funds usually achieve below-average returns. The results of this fund comparison are likely to surprise many investors: the losing strategy produced the highest returns and the winning strategy the lowest. The average strategy is in between. While the losing strategy generated a profit of 10.04 percent per year, the winning strategy only brought in 7.43 percent – a difference of 2.61 percentage points annually.

These results cannot be shaken, no matter which methods are used to measure returns. The losing strategy was also ahead of the game in terms of profit, measured by the risk taken. Tests with different strategy variants and different populations from which the funds were selected confirm the results.

SMALL FUNDS HAD MORE POTENTIAL FOR RETURNS

The reverse conclusion from these empirical results is that when selecting active funds, you should consistently rely on the losers of the more recent past who have more price potential. However, this strategy, which is unlikely to be widely accepted by investors, does not necessarily lead to above-average returns. In almost all the calculated variants, the losing strategy lagged behind the market average. The yield spread was 0.11 to 1.13 percentage points per year. Only when funds with less than one billion US dollars of investment capital were included was the losing strategy 0.4 to 0.48 percentage points better than the benchmark indices annually. This shows that even if investors do not allow themselves to be absorbed by the winning funds of the past and instead choose the more successful losing strategy, in many cases they should do better with exchange-traded index funds (ETF). They deliver the average market return after deducting costs and taxes- and keep that promise.

Hello, I have been working as an investment consultant and author for more than 20 years. I love what I do and I have enriched everyone around me. A lot of money is not important, the main thing is how you use the money.

Write A Comment

  • bitcoinBitcoin (BTC) $ 23,850.00 3.91%
  • ethereumEthereum (ETH) $ 1,883.91 1.86%
  • tetherTether (USDT) $ 1.00 0.05%
  • usd-coinUSD Coin (USDC) $ 1.00 0.06%
  • bnbBNB (BNB) $ 321.32 3.84%
  • xrpXRP (XRP) $ 0.374030 2.54%
  • cardanoCardano (ADA) $ 0.529366 2.37%
  • binance-usdBinance USD (BUSD) $ 1.00 0.12%
  • solanaSolana (SOL) $ 42.82 4.16%
  • polkadotPolkadot (DOT) $ 9.27 2.14%
  • dogecoinDogecoin (DOGE) $ 0.070797 3.93%
  • avalanche-2Avalanche (AVAX) $ 28.72 3.79%
  • staked-etherLido Staked Ether (STETH) $ 1,829.90 1.77%
  • shiba-inuShiba Inu (SHIB) $ 0.000012 2.54%
  • daiDai (DAI) $ 1.00 0.33%
  • matic-networkPolygon (MATIC) $ 0.919509 3.16%
  • tronTRON (TRX) $ 0.069873 1.55%
  • wrapped-bitcoinWrapped Bitcoin (WBTC) $ 23,820.00 3.7%
  • ethereum-classicEthereum Classic (ETC) $ 42.26 0.51%
  • okbOKB (OKB) $ 20.70 7.3%
  • leo-tokenLEO Token (LEO) $ 4.75 0.8%
  • nearNEAR Protocol (NEAR) $ 5.87 0.38%
  • chainlinkChainlink (LINK) $ 9.37 2.74%
  • litecoinLitecoin (LTC) $ 61.26 3%
  • uniswapUniswap (UNI) $ 9.02 2.51%
  • ftx-tokenFTX (FTT) $ 30.34 6.53%
  • crypto-com-chainCronos (CRO) $ 0.152247 1.65%
  • cosmosCosmos Hub (ATOM) $ 11.70 3.27%
  • stellarStellar (XLM) $ 0.124690 1.76%
  • flowFlow (FLOW) $ 2.93 4.02%
  • moneroMonero (XMR) $ 162.40 2.85%
  • bitcoin-cashBitcoin Cash (BCH) $ 140.25 3.69%
  • algorandAlgorand (ALGO) $ 0.362027 1.83%
  • vechainVeChain (VET) $ 0.033173 0.46%
  • filecoinFilecoin (FIL) $ 8.51 2.05%
  • apecoinApeCoin (APE) $ 6.85 3.23%
  • internet-computerInternet Computer (ICP) $ 8.13 3.6%
  • decentralandDecentraland (MANA) $ 1.05 2.7%
  • chain-2Chain (XCN) $ 0.087104 1.23%
  • hedera-hashgraphHedera (HBAR) $ 0.081068 0.02%
  • tezosTezos (XTZ) $ 1.89 2.44%
  • the-sandboxThe Sandbox (SAND) $ 1.32 2.62%
  • quant-networkQuant (QNT) $ 124.45 2.81%
  • axie-infinityAxie Infinity (AXS) $ 18.22 3.54%
  • theta-tokenTheta Network (THETA) $ 1.59 1.09%
  • aaveAave (AAVE) $ 110.35 0.02%
  • elrond-erd-2Elrond (EGLD) $ 64.39 5.35%
  • fraxFrax (FRAX) $ 1.00 0.23%
  • lido-daoLido DAO (LDO) $ 2.56 10.15%
  • eosEOS (EOS) $ 1.29 2.32%