Utilities: Crowding Data

研报英文原文证据摘录

Utilities: Crowding Data

Citi’s most/least crowded sectors

• We use Citi’s European quant team’s crowding scores to help assess stocks exposed to sentiment shifts. The team uses five

factors to determine crowding: alignment to the most crowded overall factor (quality), valuation relative to history, inverse

short interest ratio, skewness in sell-side ratings distribution, and macro exposure.

• While conventional thinking is to avoid buying crowded stocks, as it may become harder for them to find marginal investors,

crowding is not intended as a buy/sell signal. There may be times when stocks become crowded and remain so for good

investment reasons. There could be a development or change in view by fundamental analysts that causes a stock to attract

a larger investor base. Similar logic can be applied to non-consensus/low crowding stocks.

90% 25%

80%

20% 70% (Long) (Long)

60% 15%

50% score score 10%

40%

30% 5% 20% crowding 0% Crowding 10% in

0% -5%

-10% change Estate

3M

Real Staples Energy Care Energy Financials IT Industrials Materials CareHealth Utilities Discretionary StaplesCons. Services Utilities Financials Industrials Materials Services IT EstateReal Health Comm. Cons. Discretionary Cons. Comm.

Composite Crowding (Long) Composite Crowding (Long) 3M Ago Cons.

80% 4%

0% (Short) 60%70% (Short) 2% -2%

-4% score 50%40% score -6%

30% -8%

20% -10%

-12% 10% crowding Crowding -14% 0% in

IT -16%

Estate Care -18% Staples change Utilities Materials Industrials Energy Financials Real 3M Health Services Estate Discretionary Services Energy Utilities Materials IT Care Cons. Real Health Comm. Financials Industrials Discretionary StaplesCons. Cons. Comm.

Composite Crowding (Short) Composite Crowding (Short) 3M ago Cons.

Source: Citi Research – Quant Team