9 Reading the results
After the deadline, the Results page shows how each fund did in the round just completed: fund performance before and after fees, AuM and flows, satisfaction, the firm’s financials, and the ranking against the other teams in the universe.
For more detail, the Analyst page shows the performance ratios, factor coefficients, and performance attribution. The strategic allocation analysis compares your strategic allocation with the optimal allocation for the client.
Read the page in this order. Start with the ratios: the active return and the information ratio tell you whether the tactical tilt worked. Then read the attribution to see which decision was responsible. Then read the strategic allocation analysis, which tells you whether the γ estimate is right. The first two are adjustments you make every round; the third is an adjustment you make once.
9.1 Micro attribution: diagnosing the source of the active return
The Analyst page has three attribution tabs: Active return, Allocation, and Selection. Allocation is the effect of how much you put in each segment (the tactical tilt); Selection is the effect of which assets you held inside each segment (the security selection). Together they decompose the active return by source, so you can see which decision (currency, asset class, industry, maturity, factor) was responsible for the outcome. This is micro attribution.
9.1.1 The three questions micro attribution answers
When the IR is negative, the first question is always: which decision went wrong? The attribution tabs answer three questions, in order:
- Was it the allocation or the selection? The Allocation tab shows the effect of where you were positioned (the tactical tilt). The Selection tab shows the effect of what you held (the security selection). If the allocation effect is negative and the selection effect is zero, the tactical tilt was wrong. If the allocation effect is zero and the selection effect is negative, the security selection was wrong. If both are negative, both were wrong.
- Was it the currency or the asset class? Within each tab, the breakdown by Currency and Asset class tells you whether the problem was a currency bet (e.g., overweight euro when the euro fell) or an asset-class bet (e.g., overweight equities when equities fell). If the currency effect is large and the asset-class effect is small, the problem was the currency.
- Was it the industry, the maturity, or the factor? If the currency and asset-class effects are small but the industry, maturity, or factor effects are large, the problem was at the security level. This is where the security selection shows up. A large negative industry effect means the fund was overweight an industry that underperformed; a large negative factor effect means the fund was tilted toward a factor that underperformed (e.g., value when growth was winning).
9.1.2 A worked example: diagnosing a negative IR
Suppose the Orange fund has an active return of −0.8 % in a round, and so a negative IR. The Analyst page shows:
| Tab | Category | Effect |
|---|---|---|
| Allocation | Currency | −0.5 % |
| Allocation | Asset class | −0.1 % |
| Selection | Industry | −0.2 % |
| Selection | Factor | 0.0 % |
The diagnosis. The total active return is −0.8 % (the sum of the effects). The allocation effect is −0.6 % (currency −0.5 % + asset class −0.1 %), which is the larger part. Within the allocation, the currency effect is the dominant term. The selection effect is −0.2 %, all from industry. The factor effect is zero.
What this tells you. The problem was primarily a currency bet that went wrong, not the asset-class allocation and not the security selection. The tactical tilt was in the wrong currency; the industry selection was a secondary problem; the factor tilt was neutral.
What to do next round. The currency bet is the first thing to fix. Go back to the view that produced the currency tilt (Chapter 5) and ask whether the view was wrong or the currency movement was a surprise. If the view was wrong, lower its confidence or drop it. If the currency movement was a surprise, the view may have been right but the outcome was unlucky; keep the view but check whether the tracking-error budget was large enough to accommodate the surprise.
The industry selection is a secondary problem. It is a smaller effect, and it may be an artifact of the value-weighting rule rather than a deliberate security selection. If the fund is on automatic allocation, do not try to fix it; if it is on manual allocation, review the industry overweights.
The factor tilt is neutral. Do not change it.
9.1.3 What micro attribution cannot tell you
Micro attribution is a diagnostic tool, not a decision tool. It tells you where the active return came from, but it does not tell you whether the decision was right in expectation. A currency bet that lost money this round may have been the right bet given the information at the time. The attribution tells you the outcome; the record of the views tells you whether the outcome was consistent with the process.
The rule of thumb: change the decision only if the attribution shows a systematic problem, not a one-off loss. A currency bet that lost money once is a draw. A currency bet that has lost money three rounds in a row is a systematic error, and the attribution tells you to fix it. This is the same principle as the IR: one round is not evidence of skill, and one round is not evidence of failure either.
9.1.4 The connection to the performance ratios
The micro attribution and the performance ratios are two sides of the same coin. The ratios tell you whether the fund added value; the attribution tells you where it came from. Read them together:
- A negative IR with a large currency effect means the currency bets were wrong.
- A negative IR with a large asset-class effect means the tactical tilt was wrong.
- A negative IR with a large industry or factor effect means the security selection was wrong.
- A positive IR with a large allocation effect means the tactical tilt was right.
- A positive IR with a large selection effect means the security selection was right.
The ratios give you the verdict; the attribution gives you the reason. Use both.
9.2 Defining the performance ratios
The game reports four ratios per fund. They measure different things, and a fund can look good on one and bad on another.
9.2.2 Sortino ratio
Excess return per unit of downside volatility:
\[ \text{Sortino} = \frac{R_p - R_f}{\sigma_{\text{downside}}} \]
It does not penalise upside volatility, so a fund with large gains and small losses scores much higher on Sortino than on Sharpe. Useful when the client cares more about losses than gains.
9.2.3 Treynor ratio
Excess return per unit of market risk:
\[ \text{Treynor} = \frac{R_p - R_f}{\beta_p} \]
It isolates systematic risk, which makes it useful for funds held as part of a diversified portfolio. Note: if the fund’s beta is negative, the Treynor is negative even when the return is positive. Read it alongside the beta.
9.2.4 Information ratio
Active return per unit of active risk (tracking error):
\[ \text{IR} = \frac{\text{Active Return}}{\text{Tracking Error}} \]
It is the ratio that matters for the tactical tilt, because it measures the skill of the active decisions, not the return of the fund as a whole.
The game’s information ratio compares the fund’s return after fees with the benchmark’s return before fees. A fund that charges a fee therefore shows a negative information ratio whenever its tilt earned less than the fee, even if the tilt added return. To judge the tilt, compare the fund’s return before fees with the benchmark’s. The rules in this chapter refer to that comparison.
9.3 Reading the factor coefficients
The game reports five coefficients per fund, from a four-factor model. They tell you what kind of risk the fund is taking.
- Alpha: return not explained by the factors. Positive is good.
- Market beta: sensitivity to the market. Negative means the fund hedges against downturns.
- HML beta: value (positive) vs. growth (negative).
- MOM beta: momentum (positive) vs. contrarian (negative).
- RMW beta: quality (positive) vs. junk (negative).
A fund with positive alpha, HML, MOM and RMW is a value-momentum-quality fund with skilled management. A fund with negative alpha and RMW but high MOM is a momentum-junk fund with poor management.
9.4 Reading the strategic allocation analysis
The lower half of the Analyst page compares your strategic allocation with the allocation the game’s parameters hold as optimal for the client, and gives a recommendation. The recommendation compares your weight to the target range for that segment, which is the range consistent with the client’s γ. A weight outside the range means the γ estimate or the reading of a rule is off.
- “Balanced allocation.” Your weight is inside the target range. No change.
- “Overweighted.” Your weight is above the range.
- “Underweighted.” Your weight is below the range.
The direction tells you which way to adjust γ. Overweighted in equities means your γ is too low; move it up. Overweighted in government bonds means your γ is too high; move it down.
9.5 Actionable takeaways: what to do with the numbers
The ratios and the attribution tell you what happened. To decide what to do next round, you need to translate them into actions. This section says how.
9.5.1 If the information ratio is below 0
What it means: The active return was negative relative to the risk taken. The tactical views were wrong, or the tracking-error budget was too large for the quality of the views.
What to do: Reduce the size of the tactical tilts. Either use fewer views, lower the confidence on the views you have, or narrow the maximum deviation band in station 4. If the IR is negative for three rounds in a row with high confidence, stop entering that view entirely.
9.5.2 If the information ratio is between 0 and 0.5
What it means: The active return was positive, but modest relative to the risk taken. The views were slightly right, or the tracking-error budget was too loose.
What to do: Keep the views, but check whether the confidence levels are calibrated. If views you entered with high confidence keep producing a small IR, lower the confidence next round.
9.5.3 If the information ratio is above 0.5
What it means: The views were right relative to the risk taken. The tactical process is working.
What to do: Keep doing what you are doing. Do not increase the tracking-error budget just because the IR is high; the budget is there to protect the client, not to maximise the score.
9.5.4 If the Sharpe ratio is high but the information ratio is low (or zero)
What it means: The fund generated good absolute risk-adjusted returns, but the tactical decisions did not add value. The high Sharpe comes from the strategic allocation, not from the tactical tilts.
What to do: Check the attribution. If the selection effect is zero and the allocation effect is zero, the tactical allocation is identical to the strategic allocation. That is fine if you had no views, but if you entered views and the IR is zero, the views were neutralised by the model (e.g., because the confidence was too low). Raise the confidence or do not enter the view.
9.5.5 If the Sortino ratio is much higher than the Sharpe ratio
What it means: The fund’s volatility is skewed to the upside. The fund has large positive returns and small negative ones.
What to do: Nothing. This is a good outcome. The client is getting upside volatility without the corresponding downside, which is exactly what a risk-averse client wants.
9.5.6 If the Sortino ratio is much lower than the Sharpe ratio
What it means: The fund’s volatility is skewed to the downside. The fund has large negative returns and small positive ones.
What to do: Check the attribution for a large negative allocation effect. If the fund is taking concentrated positions that have gone wrong, pull them back. The client’s loss aversion makes downside volatility particularly costly.
9.5.7 If the alpha is positive but the IR is negative
What it means: The fund is generating excess return relative to the factor model (good management), but the tactical allocation is underperforming the benchmark (bad tactical decisions). The two are unrelated: alpha is measured against factors, IR is measured against the benchmark.
What to do: Fix the tactical views, not the strategic allocation. The strategic allocation is generating alpha; the tactical tilts are destroying value.
9.5.8 If the market beta is negative
What it means: The fund moves opposite to the market. It is a hedge.
What to do: Check whether this is intended. If the client is risk-averse and the fund is holding defensive assets, a negative beta is fine. If the client is growth-oriented and the fund is hedging unintentionally, reduce the defensive weight.
9.5.9 If the strategic allocation analysis says “Overweighted”
What it means: The fund is holding more of a segment than the optimal allocation for the client’s γ.
What to do: If the overweight is in equities, raise the γ estimate (the client is more risk-averse than you thought). If the overweight is in bonds, lower the γ estimate. Change γ once, early, and record why.
9.5.10 If the satisfaction is falling but the IR is positive
What it means: The fund is beating the benchmark, but the client is unhappy. The most likely cause is that the fund is too far from the guidance bands (the client wants a specific allocation, not just a good return), or the fee is too high.
What to do: Compare the fund’s weights to the guidance bands. If the fund is outside the bands, pull it back. If the fund is inside the bands, check the fee.
9.6 Satisfaction and AuM
Satisfaction moves with three things: the distance to the optimal allocation, the risk the fund took against what the client expects, and the fee. AuM follows satisfaction and risk-adjusted performance with a lag.
9.7 Feeding it back
For the next round, in this order:
- Pull back any extreme position.
- Revise the γ estimate if the strategic allocation analysis says so, once.
- Revise the confidence of the views that were wrong; add new views from the new outlook.
- Revisit the fee only if AuM moved for no investment reason.
Then Chapter 5 again.
9.8 Deliverable of this step
A short written review per fund in the team forum: attribution by effect, verdict on each view, distance to the optimal allocation and what you conclude for γ, satisfaction and AuM, the decision for the next round.