S&P 500 Valuation Scenarios¶

This chart shows potential S&P 500 price levels and returns for the next 12 months. It uses a scenario matrix driven by two factors: Forward P/E ratios (vertical axis, from historical averages) and Forward EPS growth (horizontal axis, centered on consensus). Each cell displays the resulting index price and percentage return from the current price. This helps investors visualize risk and potential upside under different market conditions.

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# BQL Code to fetch SPX valuation data
import polars as pl

from polars_bloomberg import BQuery

query = """
    let(
        #last_price = px_last();
        #12m_trail_eps = headline_eps_market(fpt=LTM);
        #12m_fwd_eps = headline_eps_market(fpt=BT, fpo=1);
        #epsg_consensus = #12m_fwd_eps/#12m_trail_eps - 1;
        #current_pe = headline_pe_ratio(fpt=BT, fpo=1);
        #5y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-5Y, 0D, frq=W)));
        #10y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-10Y, 0D, frq=W)));
        #20y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-20Y, 0D, frq=W)));
        #35y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-35Y, 0D, frq=W)));
        #max_pe = max(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-35Y, 0D, frq=W)));
    )
    get(
        #last_price,
        #12m_trail_eps,
        #12m_fwd_eps,
        #epsg_consensus,
        #current_pe,
        #5y_avg_pe,
        #10y_avg_pe,
        #20y_avg_pe,
        #35y_avg_pe,
        #max_pe
    )
    preferences(dropCols=['CURRENCY', 'DATE', 'AS_OF_DATE', 'REVISION_DATE', 'PERIOD_END_DATE'])
    for('SPX Index')
"""  # noqa: E501
with BQuery() as bq:
    res = bq.bql(query)

df = res.combine()
df
# BQL Code to fetch SPX valuation data
import polars as pl

from polars_bloomberg import BQuery

query = """
    let(
        #last_price = px_last();
        #12m_trail_eps = headline_eps_market(fpt=LTM);
        #12m_fwd_eps = headline_eps_market(fpt=BT, fpo=1);
        #epsg_consensus = #12m_fwd_eps/#12m_trail_eps - 1;
        #current_pe = headline_pe_ratio(fpt=BT, fpo=1);
        #5y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-5Y, 0D, frq=W)));
        #10y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-10Y, 0D, frq=W)));
        #20y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-20Y, 0D, frq=W)));
        #35y_avg_pe = avg(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-35Y, 0D, frq=W)));
        #max_pe = max(headline_pe_ratio(fpt=BT, fpo=1, fill=PREV, dates=range(-35Y, 0D, frq=W)));
    )
    get(
        #last_price,
        #12m_trail_eps,
        #12m_fwd_eps,
        #epsg_consensus,
        #current_pe,
        #5y_avg_pe,
        #10y_avg_pe,
        #20y_avg_pe,
        #35y_avg_pe,
        #max_pe
    )
    preferences(dropCols=['CURRENCY', 'DATE', 'AS_OF_DATE', 'REVISION_DATE', 'PERIOD_END_DATE'])
    for('SPX Index')
"""  # noqa: E501
with BQuery() as bq:
    res = bq.bql(query)

df = res.combine()
df

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shape: (1, 11)

ID	#last_price	#12m_trail_eps	#12m_fwd_eps	#epsg_consensus	#current_pe	#5y_avg_pe	#10y_avg_pe	#20y_avg_pe	#35y_avg_pe	#max_pe
str	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
"SPX Index"	6753.61	269.207986	307.417324	0.141932	22.120614	20.041525	18.825588	16.331631	16.771892	25.532804