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My initial reaction to this question was to accept the premise and attempt an explanation. Indeed, the three-year performance data you find on Yahoo or Morningstar are all quite poor. Here is a representative chart lifted from Yahoo.However, an anonymous twitter user (“@nonrelatednonsense”) pointed out that his analysis for QLEIX (one of AQR funds) showed better results than mine. I looked a bit deeper. Specifically: 1) I found returns data since inception for each fund; 2) I used total returns, with dividends fully reinvested; 3) I checked data quality against Morningstar, Bloomberg and AQR’s web site; 4) I used MSCI world as a benchmark (since this is the funds’ actual benchmark, not SP500).The funds for which I could find data are:QMNIX = AQR Equity Market Neutral I (perhaps the largest fund)QMNNX = AQR Equity Market Neutral NQMNRX = AQR Equity Market Neutral R6AQMIX = AQR Managed Futures Strategy IASAIX = AQR Multi-Strategy Alternative IQLEIX = AQR Long-Short Equity IQSPIX = AQR Style Premia Alternative IAQRIX = AQR Multi-Asset I (Risk Parity)ADAIX = AQR Diversified Arbitrage Fund Class IQCPIX = AQR Core Plus Bond Fund Class IFor performance, I did a simple regression on daily returns of the funds I could find against the MSCI World Index, which is used by AQR as the benchmark. The regression uses the entire fund data since inception. The intercept of the regression is Jensen’s alpha. In this simple one-factor model, the IR is defined as Jensen’s alpha divided by the volatility of the residuals. The alpha, Sharpe and Information Ratios in the table below are annualized assuming 252 trading days.There are four considerations in reading the results.First, there is no risk-free correction for returns (both AQR and benchmark), but this should be small given the low LIBOR rates post-2008, and immaterial for the low-beta funds (QLEIX gets a very small increase in reported performance because has beta~0.5).Second, regressing against an index is perhaps unfair, because it is not directly investable; if I used an ETF tracking this index AQR funds would perform very slightly better.Third, dividends can or can’t be taxed depending on the legal status of the investor. Taxation as long-term capital gains would lower the performance somewhat, but I think the results before taxes have validity as measure of actual performance.Lastly, I also considered the IR before AQR management fees, because this is instructive in measuring the true alpha of the funds.With all the caveats above, this is what I get.ADAIX is an event-based fund (merger arb, event arb and convertible arb). It is the best performed, but has limited capacity and closed to new investors. QCPIX is a bond fund, with low absolute returns, very recent, and not really comparable to MSCI World. The interesting funds come after. QLEIX has an IR of 1.35 before fees. The three equity funds (QMN*X) have very similar performance, and their returns are highly correlated (>0.98), so that they may be considered as one strategy underlying three funds, with an IR of 1. I think it makes sense to focus on the QLEIX and QMN*X because they are the largest, and are based on the best-known anomalies (cross-sectional momentum, quality, beta/res.vol, short interest). The questions in my mind are two. First, is this performance consistent with the historical performance of the well-known factors? And, if not, what could be the problem?In many papers and books, AQR partners (Asness, Frazzini, Pedersen, Moskowitz among others) have documented the historical performance of anomalies, making the case that they are robust to specifications, persistent over time, ubiquitous across asset classes, and monetizable at large scales. A good survey, with many references, is the book “Efficiently Inefficient” by L.H.Pedersen.In “Quality minus Junk”, Asness, Frazzini, and Pedersen report an Information Ratio (IR) of 1.2. In “Value and Momentum Everyhere” Asness, Moskowitz and Pedersen report IRs close to 1 for simple (50/50, i.e., not optimized) Value and and Momentum long-short portfolios, across asset classes and markets. A combined and optimized value and momentum portfolio across equity markets should yield an IR well in excess of it. In “Betting Against Beta”, Frazzini and Pedersen report an IR of 0.78 and 0.95 for US and international equities respectively. These portfolios are not uncorrelated but are obviously different. If we assume we just have these strategies with IRs of (1, 1, 1) for these three factors, with a pairwise correlation of 0.2, then the optimal portfolio will have an IR of sqrt(2.14) ~ 1.5. A pairwise correlation of 0.4 would reduce the optimal IR to sqrt(1.7) ~ 1.3 (I omit the proof of these facts). These numbers are very coarse. For starters, there is an intentional weighting to the market (which has a Sharpe of ~0.5); and then there are many other anomalies, some of which are quite robust. For example, Moskowitz, Ooi, Pedersen report an IR of 1.8 for combined time series momentum. Short-interest is a stalwart of quant equity strategies and has an IR of ~1.5; Low Volatility is another anomaly not entirely explained by Betting against Beta, and with a Sharpe of ~1. A well-constructed portfolio using all these non-exotic anomalies should exhibit at least an IR of 2. What is the probability of such a strategy to exhibit negative alpha after 1098/252= 4.35 years? Assuming gaussian returns, the three-year cumulative return would have mean=2*4.35*sigma and sd=sqrt(4.35)*sigma, where sigma is the one-year volatility. The probability of a observing an IR=1 is F(1*4.35, mu=2*4.35, sd=sqrt(4.35)), where F is the CDF of a gaussian with mean mu and stdev=sd. This is about 12%. What if the strategy had an IR of 1.5? Then prob (observing a IR=1|actual IR=1.5) ~ 15%. Much higher (and plausible). I’d guess that the true IR of the strategies is between these two extremes, 1.5 and 2, maybe around 1.5 for QMN*X and a bit closer to 2 for QLEIX. And of course the remaining funds (like QRPIX) fare somewhat worse. A solid IR of 1.5 is less than 2, but not dramatically less. I have a few hypotheses to explain the gap:Some of these anomalies have become less so over the years. Size used to be an anomaly and isn’t one know; momentum and value’s performance has worsened a great deal; and so on. Some papers suggest that publishing the anomalies weakens their effect.Data snooping is a much bigger problem than is currently acknowledged and shown by papers like “Searching the Factor Zoo” by Hwang and Rubsam, “The supraview of return predictive signals” by Green, Hang and Zhang, or the papers by Campbell Harvey.Transaction costs matter, when compared to the size of the anomalies. This is maybe contradicting “Trading Costs of Asset Pricing Anomalies” by Israel, Frazzini and Moskowitz.(really, 3b) Portfolio construction and risk management matter, a lot. Maybe AQR, which has a rather academic research culture, has overlooked these areas.AQR is betting on factors not documented in their papers. In 2018 Short Interest severely (and anomalously) underperformed (usually it loses money and is shorted; but last year it had positive returns). Maybe AQR was overweight short interest and suffered as a result.Going back to the original question: why have the funds done so poorly? Short answer: the premise is incorrect. There is no large underperformance, when we consider the data, even including the past year. Let’s cut AQR some slack. If they experience one or more anni horribiles after 2018, then I should edit this answer yet again.P.S.: My initial and naïve answer to this question was based on Yahoo statistics computed on the past three years performance. I extended the analysis to the entire lifetime of the funds, and included dividends in the returns. The results are very different and I have updated the the answer.

For tax purposes, how you handle this depends on the business structure and on when you make changes to the distribution of equity among the partners.If you are set up as an S-corporation, each shareholder's distributive share of the company's income has to be computed separately based on his/her individual holding in the company on each day of the tax year; see the specific instructions for Schedule K-1, Information about the Shareholder, in the Instructions for Form 1120S (2012). The extent to which this can become complicated depends on how often equity percentages change over the course of the tax year; I would think that the less often you make grants of equity, the easier it will be to keep track of this information for tax purposes, but that might defeat the intended purpose of the "Grunt Fund".If you are set up as a partnership, you can specify a method for computing each partner's distributive share of profits and losses in the partnership agreement - you could, in principle, specify that the percentages will be based on the dynamic equity relationship among the partners as of the end of the business's tax year, or some other date certain. You are only required to report the percentages as of the beginning of the tax year, and of the end of the tax year, on the partner's Schedule K-1. Whatever method you use must be consistent with the partnership agreement, and applied consistently from year to year. The IRS will respect partnership allocations as long as they reflect the true economic interests of each partner in the partnership, and since the "Grunt Fund" is an approach that attempts to be very accurate in quantifying each partner's contributions to the partnership, I can't imagine that the IRS would override any partnership decisions on allocation that were based on it.