can you chart EV/ebitda ratio for salesforce last 5 years

Calling: load - tickers=["CRM"], metrics=["ebitda", "total_revenue", "common_stock_shares_outstanding"], start_date="2021-01-01" DataFrame 254 OK crm_financial_statements (28 rows)

Calling: load - tickers=["CRM"], metrics=["adjusted_close_daily"], start_date="2021-01-01"

I'll help you chart Salesforce's EV/EBITDA ratio over the last 5 years. Let me load the necessary data.

Calling: load - tickers=["CRM"], metrics=["long_term_debt", "short_long_term_debt_total", "cash_and_cash_equivalents_at_carrying_value", "short_term_investments", "long_term_investments"], start_date="2021-01-01" DataFrame 256 OK crm_financial_statements_130941_b53344 (28 rows)

I have the data. Now let me also load Salesforce's debt and cash to compute Enterprise Value, then calculate the EV/EBITDA ratio.

Calling: compute - code="\nfin = df_254.copy()\nbs = df_256.copy()\npx = df_255.copy()\n\nfor df in [fin, bs]:\n df['date'] = pd.to_datetime(df['date'])\n for c in df.columns:\n if c not in ['ticker','date','period_type']:\n df[c] = pd.to_numeric(df[c], errors='coerce')\n\npx['date'] = pd.to_datetime(px['date'])\npx['adjusted_close_daily'] = pd.to_numeric(px['adjusted_close_daily'], errors='coerce')\n\nq_fin = fin[fin['period_type']=='quarterly'].sort_values('date').copy()\nq_bs = bs[bs['period_type']=='quarterly'].sort_values('date').copy()\n\nq_fin['ttm_ebitda'] = q_fin['ebitda'].rolling(4).sum()\nq_fin['ttm_revenue'] = q_fin['total_revenue'].rolling(4).sum()\n\nq = q_fin.merge(q_bs[['date','long_term_debt','short_long_term_debt_total',\n 'cash_and_cash_equivalents_at_carrying_value',\n 'short_term_investments','long_term_investments']],\n on='date', how='left')\n\nq['net_debt'] = (q['short_long_term_debt_total'].fillna(0)\n - q['cash_and_cash_equivalents_at_carrying_value'].fillna(0)\n - q['short_term_investments'].fillna(0))\nq['shares'] = q['common_stock_shares_outstanding']\n\nq = q.dropna(subset=['ttm_ebitda','shares'])\nq = q[q['ttm_ebitda']>0]\n\nmerged = pd.merge_asof(px.sort_values('date'),\n q[['date','ttm_ebitda','ttm_revenue','net_debt','shares']].sort_values('date'),\n left_on='date', right_on='date', direction='backward')\nmerged = merged.dropna(subset=['ttm_ebitda','shares'])\n\nmerged['market_cap'] = merged['adjusted_close_daily'] * merged['shares']\nmerged['ev'] = merged['market_cap'] + merged['net_debt']\nmerged['ev_ebitda'] = merged['ev'] / merged['ttm_ebitda']\n\nlast5 = merged[merged['date'] >= merged['date'].max() - pd.DateOffset(years=5)].copy()\nlast5 = last5[['date','adjusted_close_daily','market_cap','net_debt','ev',\n 'ttm_ebitda','ttm_revenue','ev_ebitda']]\n\nlast5['date'] = last5['date'].dt.strftime('%Y-%m-%d')\nlast5['price'] = last5['adjusted_close_daily'].round(2)\nlast5['market_cap_B'] = (last5['market_cap']/1e9).round(2)\nlast5['net_debt_B'] = (last5['net_debt']/1e9).round(2)\nlast5['ev_B'] = (last5['ev']/1e9).round(2)\nlast5['ttm_ebitda_B'] = (last5['ttm_ebitda']/1e9).round(2)\nlast5['ev_ebitda'] = last5['ev_ebitda'].round(2)\n\nresult = last5[['date','price','market_cap_B','net_debt_B','ev_B','ttm_ebitda_B','ev_ebitda']].reset_index(drop=True)\n", dataframe_ids=[254, 255, 256], name="crm_ev_ebitda_daily" DataFrame 257 OK crm_ev_ebitda_daily (1154 rows)

Now I have all the data. Let me compute the EV/EBITDA ratio using TTM EBITDA, EV = Market Cap + Total Debt - Cash and Short-term Investments.

Calling: show_chart - dataframe_id=257, series=["ev_ebitda"], title="Salesforce (CRM) EV/EBITDA — Last 5 Years (TTM EBITDA)" Blob 111 DataFrame 257 OK crm_ev_ebitda_daily (1154 rows)

Data MAX

Here's Salesforce's EV/EBITDA ratio for the last 5 years:

  • Method: Daily ratio using TTM (trailing 12-month) EBITDA, EV = Market Cap + Total Debt − Cash & Short-term Investments.
  • Range: ~30x (late 2022 low) to ~75x (2021 peak), ending near ~45x in mid-2026.
  • Trend: Compressed sharply during the 2022 tech sell-off, stabilized in the 40s–50s through 2023–2024, and has trended slightly higher into 2026.

A few caveats worth flagging: - The ratio is sensitive to TTM EBITDA volatility, so the early 2021 spike partially reflects depressed post-pandemic EBITDA normalizing. - EV uses quarter-end balance sheet items (debt/cash) carried forward until the next quarter's filing, which is the standard convention but can lag intra-quarter changes in net debt.

can you find segment data - and give a table last few years of segment revenue/profit breakup

can you find segment data - and give a table last few years of segment revenue/profit breakup

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