added dcf growth (#2244)

Co-authored-by: Jeroen Bouma <jer.bouma@gmail.com>

openbb_terminal/stocks/fundamental_analysis/dcf_view.py

@@ -34,6 +34,7 @@ class CreateExcelFA:
         len_pred: int = 10,
         max_similars: int = 3,
         no_filter: bool = False,
+        growth: bool = False,
     ):
         """
         Creates a detailed DCF for a given company
@@ -49,9 +50,11 @@ class CreateExcelFA:
         len_pred : int
             The number of years to make predictions for before assuming a terminal value
         max_similars : int
-            The maximum number of similar companies to show, will be less if there are not enough similar companies
+            The maximum number of similar companies to show, will be less if there are not enough
         no_filter : bool
             Disable filtering of similar companies to being in the same market cap category
+        growth : bool
+            When true this turns the revenue assumption from linear regression to percentage growth
         """
         self.info: Dict[str, Any] = {
             "len_data": 0,
@@ -64,6 +67,7 @@ class CreateExcelFA:
             "no_filter": no_filter,
         }
         self.letter: int = 0
+        self.growth = growth
         self.starts: Dict[str, int] = {"IS": 4, "BS": 18, "CF": 47}
         self.wb: Workbook = Workbook()
         self.ws: Dict[int, Any] = {
@@ -215,12 +219,13 @@ class CreateExcelFA:
         )
         dcf_model.set_cell(self.ws[1], f"{dcf_static.letters[col]}4", "m")
         dcf_model.set_cell(self.ws[1], f"{dcf_static.letters[col+1]}4", "b")
-        self.get_linear("Date", "Revenue")
-        self.get_linear("Revenue", "Cost of Revenue")
+        rev_pred_type = "growth" if self.growth else "linear"
+        self.get_growth("Date", "Revenue", pred_type=rev_pred_type)
+        self.get_growth("Revenue", "Cost of Revenue")
         self.get_sum("Gross Profit", "Revenue", [], ["Cost of Revenue"])
-        self.get_linear("Revenue", "Selling, General & Admin", True)
-        self.get_linear("Revenue", "Research & Development", True)
-        self.get_linear("Revenue", "Other Operating Expenses")
+        self.get_growth("Revenue", "Selling, General & Admin", True)
+        self.get_growth("Revenue", "Research & Development", True)
+        self.get_growth("Revenue", "Other Operating Expenses")
         self.get_sum(
             "Operating Income",
             "Gross Profit",
@@ -231,10 +236,10 @@ class CreateExcelFA:
                 "Other Operating Expenses",
             ],
         )
-        self.get_linear("Revenue", "Preferred Dividends")
-        self.get_linear("Revenue", "Interest Expense / Income")
-        self.get_linear("Revenue", "Other Expense / Income")
-        self.get_linear("Operating Income", "Income Tax")
+        self.get_growth("Revenue", "Preferred Dividends")
+        self.get_growth("Revenue", "Interest Expense / Income")
+        self.get_growth("Revenue", "Other Expense / Income")
+        self.get_growth("Operating Income", "Income Tax")
         self.get_sum(
             "Net Income",
             "Operating Income",
@@ -245,27 +250,27 @@ class CreateExcelFA:
             "Preferred Dividends",
             "Preferred Dividends are not important in a DCF so we do not attempt to predict them.",
         )
-        self.get_linear("Revenue", "Cash & Equivalents", True)
-        self.get_linear("Revenue", "Short-Term Investments", True)
+        self.get_growth("Revenue", "Cash & Equivalents", True)
+        self.get_growth("Revenue", "Short-Term Investments", True)
         self.get_sum(
             "Cash & Cash Equivalents",
             "Cash & Equivalents",
             ["Short-Term Investments"],
             [],
         )
-        self.get_linear("Revenue", "Receivables", True)
-        self.get_linear("Revenue", "Inventory", True)
-        self.get_linear("Revenue", "Other Current Assets")
+        self.get_growth("Revenue", "Receivables", True)
+        self.get_growth("Revenue", "Inventory", True)
+        self.get_growth("Revenue", "Other Current Assets")
         self.get_sum(
             "Total Current Assets",
             "Cash & Cash Equivalents",
             ["Receivables", "Inventory", "Other Current Assets"],
             [],
         )
-        self.get_linear("Revenue", "Property, Plant & Equipment", True)
-        self.get_linear("Revenue", "Long-Term Investments", True)
-        self.get_linear("Revenue", "Goodwill and Intangibles", True)
-        self.get_linear("Revenue", "Other Long-Term Assets")
+        self.get_growth("Revenue", "Property, Plant & Equipment", True)
+        self.get_growth("Revenue", "Long-Term Investments", True)
+        self.get_growth("Revenue", "Goodwill and Intangibles", True)
+        self.get_growth("Revenue", "Other Long-Term Assets")
         self.get_sum(
             "Total Long-Term Assets",
             "Property, Plant & Equipment",
@@ -279,10 +284,10 @@ class CreateExcelFA:
         self.get_sum(
             "Total Assets", "Total Current Assets", ["Total Long-Term Assets"], []
         )
-        self.get_linear("Revenue", "Accounts Payable")
-        self.get_linear("Revenue", "Deferred Revenue")
-        self.get_linear("Revenue", "Current Debt")
-        self.get_linear("Revenue", "Other Current Liabilities")
+        self.get_growth("Revenue", "Accounts Payable")
+        self.get_growth("Revenue", "Deferred Revenue")
+        self.get_growth("Revenue", "Current Debt")
+        self.get_growth("Revenue", "Other Current Liabilities")
         self.get_sum(
             "Total Current Liabilities",
             "Accounts Payable",
@@ -303,7 +308,7 @@ class CreateExcelFA:
                 "nceinstitute.com/resources/questions/model-questions/financial-modeling-plug/"
             ),
         )  # This is the plug
-        self.get_linear("Revenue", "Other Long-Term Liabilities")
+        self.get_growth("Revenue", "Other Long-Term Liabilities")
         self.get_sum(
             "Total Long-Term Liabilities",
             "Long-Term Debt",
@@ -316,7 +321,7 @@ class CreateExcelFA:
             ["Total Long-Term Liabilities"],
             [],
         )
-        self.get_linear("Revenue", "Common Stock")
+        self.get_growth("Revenue", "Common Stock")
         col = self.info["len_data"] + 1
         rer = self.title_to_row("Retained Earnings")
         nir = self.title_to_row("Net Income")
@@ -328,7 +333,7 @@ class CreateExcelFA:
                 num_form="[$$-409]#,##0.00;[RED]-[$$-409]#,##0.00",
             )
 
-        self.get_linear("Revenue", "Comprehensive Income")
+        self.get_growth("Revenue", "Comprehensive Income")
         self.get_sum(
             "Shareholders' Equity",
             "Common Stock",
@@ -771,38 +776,27 @@ class CreateExcelFA:
         )
 
     @log_start_end(log=logger)
-    def get_linear(self, x_ind: str, y_ind: str, no_neg: bool = False):
+    def get_growth(
+        self, x_ind: str, y_ind: str, no_neg: bool = False, pred_type: str = "linear"
+    ):
+        """Add growth to a column. Usually this is linear but other options are allowed
+
+        x_ind: str
+            The x variable to use. This is unused if growth is the pred_type
+        y_ind: str
+            The y variable to use
+        no_neg: bool
+            Whether or not the value can have negative numbers (profit can be revenue cannot)
+        pred_type: str
+            This is assumed to be linear but growth is allowed as well
+        """
+
         x_type = "IS" if x_ind in self.df["IS"].index else "BS"
         y_type = "IS" if y_ind in self.df["IS"].index else "BS"
         x_df = self.df["IS"] if x_type == "IS" else self.df["BS"]
         y_df = self.df["IS"] if y_type == "IS" else self.df["BS"]
-        pre_x = (
-            x_df.columns.to_numpy() if x_ind == "Date" else x_df.loc[x_ind].to_numpy()
-        )
-
-        vfunc = np.vectorize(dcf_model.string_float)
-        pre_x = vfunc(pre_x)
-
-        if x_ind == "Date":
-            pre_x = pre_x - np.min(pre_x)
-        x = pre_x.reshape((-1, 1))
-        pre_y = y_df.loc[y_ind].to_numpy()
-        y = vfunc(pre_y)
-        model = LinearRegression().fit(x, y)
-        r_sq = model.score(x, y)
-        r = abs(r_sq**0.5)
-
-        if r > 0.9:
-            strength = "very strong"
-        elif r > 0.7:
-            strength = "strong"
-        elif r > 0.5:
-            strength = "moderate"
-        elif r > 0.3:
-            strength = "weak"
-        else:
-            strength = "very weak"
 
+        analy_message = ""
         row1 = (
             y_df.index.get_loc(y_ind)
             + 1
@@ -810,38 +804,101 @@ class CreateExcelFA:
         )
 
         col = self.info["len_pred"] + self.info["len_data"] + 3
-        dcf_model.set_cell(
-            self.ws[1], f"{dcf_static.letters[col]}{row1}", float(model.coef_)
-        )
-        dcf_model.set_cell(
-            self.ws[1], f"{dcf_static.letters[col+1]}{row1}", float(model.intercept_)
-        )
-        dcf_model.set_cell(
-            self.ws[1],
-            f"{dcf_static.letters[col+2]}{row1}",
-            dcf_static.letters[self.letter],
-            font=dcf_static.red,
-        )
-        dcf_model.set_cell(
-            self.ws[3],
-            f"A{self.letter+4}",
-            dcf_static.letters[self.letter],
-            font=dcf_static.red,
-        )
-        dcf_model.set_cell(
-            self.ws[3],
-            f"B{self.letter+4}",
-            (
+        if pred_type == "linear":
+            pre_x = (
+                x_df.columns.to_numpy()
+                if x_ind == "Date"
+                else x_df.loc[x_ind].to_numpy()
+            )
+
+            vfunc = np.vectorize(dcf_model.string_float)
+            pre_x = vfunc(pre_x)
+
+            if x_ind == "Date":
+                pre_x = pre_x - np.min(pre_x)
+            x = pre_x.reshape((-1, 1))
+            pre_y = y_df.loc[y_ind].to_numpy()
+            y = vfunc(pre_y)
+            model = LinearRegression().fit(x, y)
+            r_sq = model.score(x, y)
+            r = abs(r_sq**0.5)
+
+            if r > 0.9:
+                strength = "very strong"
+            elif r > 0.7:
+                strength = "strong"
+            elif r > 0.5:
+                strength = "moderate"
+            elif r > 0.3:
+                strength = "weak"
+            else:
+                strength = "very weak"
+            analy_message = (
                 f"The correlation between {x_ind.lower()} and {y_ind.lower()}"
                 f" is {strength} with a correlation coefficient of {r:.4f}."
-            ),
-        )
+            )
+            dcf_model.set_cell(
+                self.ws[1], f"{dcf_static.letters[col]}{row1}", float(model.coef_)
+            )
+            dcf_model.set_cell(
+                self.ws[1],
+                f"{dcf_static.letters[col+1]}{row1}",
+                float(model.intercept_),
+            )
+            dcf_model.set_cell(
+                self.ws[1],
+                f"{dcf_static.letters[col+2]}{row1}",
+                dcf_static.letters[self.letter],
+                font=dcf_static.red,
+            )
+            dcf_model.set_cell(
+                self.ws[3],
+                f"A{self.letter+4}",
+                dcf_static.letters[self.letter],
+                font=dcf_static.red,
+            )
+        elif pred_type == "growth":
+            pre_y = y_df.loc[y_ind].to_numpy()
+            fin_y = []
+            for item in pre_y:
+                try:
+                    fin_y.append(float(item))
+                except TypeError:
+                    fin_y.append(0)
+            overall_growth = (fin_y[-1] - fin_y[0]) / fin_y[0]
+            yearly_growth = overall_growth / len(fin_y)
+            analy_message = (
+                f"Assuming growth percentage of {(yearly_growth * 100):.2f}%"
+            )
+            dcf_model.set_cell(
+                self.ws[1], f"{dcf_static.letters[col]}{row1}", yearly_growth
+            )
+            dcf_model.set_cell(
+                self.ws[1],
+                f"{dcf_static.letters[col+2]}{row1}",
+                dcf_static.letters[self.letter],
+                font=dcf_static.red,
+            )
+
+        dcf_model.set_cell(self.ws[3], f"B{self.letter+4}", analy_message)
 
         col = self.info["len_data"] + 1
         for i in range(self.info["len_pred"]):
-            if x_ind == "Date":
+            if pred_type == "growth":
+                row_n = (
+                    y_df.index.get_loc(y_ind) + 1 + self.starts["IS"]
+                    if y_type == "IS"
+                    else self.starts["BS"]
+                )
                 base = (
-                    f"(({dcf_static.letters[col+i]}4-B4)*{dcf_static.letters[col+self.info['len_pred']+2]}"
+                    f"({dcf_static.letters[col+i-1]}{row1}* (1+"
+                    f"{dcf_static.letters[col+self.info['len_pred']+2]}{row1}))"
+                )
+
+            elif x_ind == "Date":
+                base = (
+                    f"(({dcf_static.letters[col+i]}4-B4)*"
+                    f"{dcf_static.letters[col+self.info['len_pred']+2]}"
                     f"{row1})+{dcf_static.letters[col+self.info['len_pred']+3]}{row1}"
                 )
             else:
@@ -851,7 +908,8 @@ class CreateExcelFA:
                     else self.starts["BS"]
                 )
                 base = (
-                    f"({dcf_static.letters[col+i]}{row_n}*{dcf_static.letters[col+self.info['len_pred']+2]}{row1})"
+                    f"({dcf_static.letters[col+i]}{row_n}*"
+                    f"{dcf_static.letters[col+self.info['len_pred']+2]}{row1})"
                     f"+{dcf_static.letters[col+self.info['len_pred']+3]}{row1}"
                 )
             dcf_model.set_cell(

openbb_terminal/stocks/fundamental_analysis/fa_controller.py

@@ -1262,17 +1262,26 @@ class FundamentalAnalysisController(StockBaseController):
             default=6,
             help="Number of similar companies to generate ratios for.",
         )
+        parser.add_argument(
+            "-g",
+            "--growth",
+            action="store_true",
+            dest="growth",
+            default=False,
+            help="Whether to replace a linear regression estimate with a growth estimate.",
+        )
         ns_parser = self.parse_known_args_and_warn(
             parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
         )
 
         if ns_parser:
             dcf = dcf_view.CreateExcelFA(
-                self.ticker,
-                ns_parser.audit,
-                ns_parser.ratios,
-                ns_parser.prediction,
-                ns_parser.similar,
+                ticker=self.ticker,
+                audit=ns_parser.audit,
+                ratios=ns_parser.ratios,
+                len_pred=ns_parser.prediction,
+                max_similars=ns_parser.similar,
+                growth=ns_parser.growth,
             )
             dcf.create_workbook()