Page numbers refer to the print edition but are hyperlinked to the appropriate location in the e-book.
AB InBev, and SABMiller: consolidation story case study, 193–96, 194, 195, 196; valuing synergy in SABMiller deal for, 195
accuracy versus precision, 41
acquisition effects on narratives and numbers, 193
acquisitions: feedback loop, 160–61; investment news in, 192; narrative breaks in, 170; Valeant, 201
advertising: online, 104; product stories in, 28; storytelling in, 14; Super Bowl Sunday, 29
aging company, management of: case study, 260–62; challenges for, 260; Yahoo, 260
Alibaba: business narrative, 88–90; China story case study, 88–90, 120, 121, 146–48, 147; in Chinese market, 90, 122; counternarrative, 146; as global player, 122; global player case study, 108–9; the global story of, 148; Monte Carlo simulation case study, 155–57, 156; operating margins, 156; plausible counterstory of, 120; pricing feedback of, 160; revenue, 89, 120; scenario analysis for, 154–55; from story to numbers case study, 120, 120–22, 121, 122; Taobao site, 88; valuation, 146; valuation case study, 154–56, 155, 156
allure of storytelling, 2–3
Amazon, 83, 238; alternative narratives case study, 108, 142; business narrative, 87, 146; consistent narrative Amazon lesson case study, 251; consistent narrative for, 119, 251; cost of capital, 144–45; counternarrative, 119; Field of Dreams case study, 87–88; Field of Dreams story line of, 88, 117, 140, 142–44; market breakeven points case study, 158, 158–59; pricing feedback, 159; revenues, 87; stockholder doomsday, 144; from story to numbers case study, 117–19, 118, 119; valuation of, 87; valuing Field of Dreams case study, 142, 142–46, 143, 144, 145; world domination, 145
analysis: biases in, 60–61; in business, 39; herding in, 49; of mature companies, 73; of pharmaceutical companies, 64; of young companies, 73
analyst expectations game, 189
annual returns on stocks, and bonds, 43
Apple: debt decision case study, 197–98; meh chronicles case study, 180, 180–81, 181, 182; narrative shifts for, 181; price, and value of, 180; revenues, operating income and price reaction, 181; smartphone and device sales, 182; transitions for, 262
Apple keynote presentations, 15–16
Aristotle, 35; Poetics, 25
assets: fundamentals of, 110; investment news restructuring of, 191
auto companies: developed markets of, 79; emerging markets of, 79; Fiat, 85; Land Rover, 192; luxury market for, 79; market capitalization profitability of, 78; mass market for, 79; operating margin for, 76; reinvestments of, 77; return on invested capital for, 78; revenue of, 76; Tata Motors, 192; Tesla, 82, 192; Volkswagen, 75, 200–201. See also Ferrari
biases, 44–45, 50–51; in data collection, 55; in data presentation, 63; within number crunching, 44
big stories, 81; cycles, 213–15, 214, 215; predictability, 214–15; strategies, 216–17; Vale 3C company case study, 217–21, 218, 219, 220
Brazil, Russia, India, China (BRIC), 223; CDS spreads, 215
break-even revenues: Facebook, 103; online advertising companies, 105
business: aging of, 207; analysis in, 39; story structure in, 24
business education, 14–15
business narrative, 71, 90; AB InBev, 194; Alibaba, 88–90; Amazon, 87, 146; audience, 95, 152; big stories of, 81; company history for, 71, 72; credibility of, 109; criticism with, 161; disruption model of, 82; feedback loop for, 151, 167; Ferrari, 85–86; financing news altered by, 196; finite life for, 82; founder stories difference of, 70; hubris problems for, 151; IBM, 199; of low capital intensity, 114; macro narrative relationship of, 206; macro story of, 205; macro variables for, 213; market place of, 72; micro story of, 205; Netflix, 134; news impacting, 184; Nintendo, 134; reassessment of, 197; starting place for, 71–72; terminal value connected to, 131; Tesla, 192; Uber, 83; valuation in, 113, 137; for young companies, 72. See also corporate story
business stories, 30, 31; dual narratives of, 31; good ingredients for, 34; investment pitch of, 31; life cycle of, 32; show, don’t tell in, 33; special case of, 14–15
business storytelling, 14, 27; as fairy tale, 20; as runaway story, 20
capital intensity, 85, 176
case study: AB InBev, and SABMiller, consolidation story, 193–96; aging company, management of, 260–62; Alibaba China story, 88–90, 146–48; Alibaba Monte Carlo simulation, 155–57; Alibaba story to numbers, 120–22; Alibaba valuation, 154–55; Amazon alternative narrative, 108; Amazon Field of Dreams story line, 87–88; Amazon market breakeven points, 158–59; Amazon story to numbers, 117–19; Amazon valuing Field of Dreams story line, 142–46; Apple meh chronicles, 180–81; auto companies, 75–79; consistent narrative, 251; equity risk premium, 42–44; ExxonMobil oil price exposure, 67–68; ExxonMobil valuation, 209–13; Facebook earning report, and narrative changes, 187–91; Ferrari duty-free catalog, 166; Ferrari narrative, 86–87; Ferrari story to numbers, 116–17; Ferrari valuing exclusive auto club, 107–8; GoPro valuation, 234–37; IBM buyback decade, 198–200; JCPenney valuation, 238–40; LTCM, 47; Lyft versus Uber, 251–56; narrative breaks, 171; numbers bias, and equity risk premium, 45; online advertising, big market delusion, 103–6; Petrobras, roadmap to destroying value, 68–69; pharmaceutical companies, 64–67; pricing feedback (Uber, Ferrari, Amazon, and Alibaba), 159–61; quant investing, 39–40, 50–51; Theranos, 21–23; Uber Gurley counternarrative, 162–66; Uber narrative, 83–85; Uber narrative differences, 240–43; Uber news and value, 173–77; Uber ride-sharing, 79–80; Uber story to numbers, 114; Uber urban car service company, 137–39; Under Armour, and Kevin Plank, 30; Vale 3C company, 217–21; Valeant drug business model, 201–3; Vale meltdown, 222–26
Chinese market: Alibaba hold on, 122; Alibaba in, 90
Chinese online retail, 120
competitive advantages, 174
competitive landscape, 74
Confessions of a Capital Junkie (Marchionne), 75
con game storytelling with a subversive edge case study, 20–21
consistent narrative, 251
continuum of skepticism, 95
corporate finance, equity risk premium input, 42
corporate governance, 259
corporate governance stories, 200–204
corporate life cycle, 8, 8–9, 266; aging of a business, 228–31; business idea, begins with, 228; determinants for, 229–331, 230; drivers for, 230, 232–33; growth phase of, 230; for management culture, 249; narrative and numbers across, 231–46, 246; for non-tech and tech companies, 231; number crunching in, 227; problems, 233; start-up phase of, 232; stories of, 9, 233; storytelling in, 227; for tech companies, 231; transitions of, 229, 256; Yahoo, 261
corporate scandal and misconduct: of American Express, 15; stories damaged by, 200; Valeant drug business model case study, 201–3, 202; Volkswagen, 75, 200–201
corporate story, micro and macro factors, 222
correlation coefficient, 58
credit default swap (CDS), 215, 223
Damodaran narrative versus Gurley narrative, 165
dangers of numbers, 40; fall of quant investing case study, 50–51; illusion of control, 46–47; illusion of objectivity, 44–45; illusion of precision, 41, 41–42, 42; intimidation factor, 48; intimidation problem, 48–49; lemming problem, 49; noisy historical equity risk premium case study, 42–44, 43, 44; numbers and bias with equity risk premium case study, 45; sad (but true) story of long-term capital management case study, 47; storytelling as antidote, 49–51
dangers of storytelling, 17; con game storytelling with a subversive edge case study, 20–21; emotional hangover, 18–19; fickleness of memory, 19; numbers, the antidote, 19–20; Theranos case study, 21–23
data age, storytelling in, 16–17
data analysis: biases in, 61–62; observations of, 60–61; PE ratio for U.S. companies, 59; S&P bond ratings for U.S. companies, 58; tools for, 57; trailing PE versus expected growth in EPS, 60
data collection, 53; biases in, 55–56; choices in, 54–55; financial data choices in, 54; noise and errors, 56–57
data-driven analysis, 4–5
data presentation, 53; biases and sins, 63–64; choices, 61–62; demonstration tables in, 62; equity risk premiums and Treasury bond rate, 63; ExxonMobil oil price exposure case study, 67, 67–68; Petrobras case study, 68; pharmaceutical companies case study, 64–67, 65, 66; reference tables in, 62
deconstructing value, 149
developed markets: of auto companies, 79; emerging markets difference of, 78
discounted cash flow valuation (DCF), 96; dynamic discount rates with, 135; flexibility of, 134; as intrinsic valuation model, 111; limitations of, 112; stories reversal of, 149; terminal value role with, 129–30; value use of, 125; venture capitalists against, 135
disruption model: business narrative, 82; Tesla, 82
dividends, buybacks, and cash balances, 197–99, 198; IBM buyback decade case study, 198–200, 199
dynamic discount rates, 135
emerging markets: of auto companies, 79; of BRIC, 215; developed markets difference of, 78
entrepreneurs: big market for, 100, 101; lessons for, 265–66; overconfidence of, 100
equity risk premium, 63, 63; case study, 42–44; corporate finance input for, 42; country risk incorporated with, 221; estimates of, 43, 44; numbers and bias case study with, 45; utilities effects of, 45; valuation input for, 42
establishment versus disruption, 81–82
estimate value, value drivers, 128
estimation process, 41, 125
ExxonMobil: finite life of, 83; management culture for, 248; normalized oil price and value per share, 211; oil price distribution, 211; oil price exposure case study, 67, 67–68; oil price-neutral valuation of, 209; operating income of, 67, 210; regression of, 67, 212; simulations for, 212; valuation of, 67, 210–13; valuing case study, 209, 209–13, 211
Facebook: break-even revenues of, 103; changing look of, 190; earning report, and narrative changes case study, 187–91, 188, 189, 190; earning report for, 187, 189; as Google wannabe, 188; market response for, 189; narrative shifts for, 190; online advertising for, 104; valuation for, 191
feedback loop, 166; Alibaba Monte Carlo simulation case study, 155–57, 156; Amazon market breakeven points case study, 158; for business narrative, 151, 167; business narrative problems of, 151–52; crowdvaluing for, 161; for Ferrari, 166; get out of the echo chamber, 152–55; pricing feedback case study - Uber, Ferrari, Amazon, and Alibaba, 159–60; pricing feedback in, 157–58; in storytelling, 6; valuation transparent for, 160. See also alternative narrative
Ferrari, 1–2; business narrative of, 85–86; exclusive auto club case study, 107–8; exclusive nature of, 86; feedback from a duty-free catalog case study, 166; feedback loop for, 166; iron triangle of value of, 107; narrative case study, 85–87, 86; pricing feedback of, 159; revenue breakdown of, 86; rev it up strategy for, 117, 139–41, 141; from story to numbers case study, 116, 116–17, 117; terminal value for, 140–41; valuation inputs of, 116, 139; valuation of, 75, 76; valuing exclusive auto club case study, 139–42, 140
Field of Dreams story line: of Amazon, 88, 117, 142–44; as valuation inputs, 118
financing news, 196; Apple’s debt decision case study, 197–98
five Ws (who, what, where, when, why) of journalism, 33
Food and Drug Administration (FDA), 64
founders: lessons for, 265; overconfidence of, 105
founder stories: business narrative difference of, 70; dangers of, 30; listener connections with, 30; types of, 29–30
framing: effect of, 42; of numbers, 41–42
frequency distribution, 57, 58
Freytag’s storytelling structure, 25–27, 26
GoPro: corporate life cycle of, 236; history of, 235; inputs for, 237; management culture for, 248; potential markets for, 235; profit margins for, 236; projected earnings of, 239; valuation case study, 234–37; valuation simulations for, 237
herding: in analysis, 49; numbers problem with, 50
IBM: buyback decade case study, 198–200, 199; buyback decade of, 198–99; operating and share count history of, 199; transitions for, 262
implausible stories: big market delusion, 99–102, 100, 101; Facebook break-even revenues of, 103; market dynamics, 99; online advertising big market delusion case study, 103–6, 105, 106
impossible stories: bigger than the economy, 96–97; bigger than the market, 96–98; costless capital in, 98–99; growth rate in, 97; intrinsic value in, 96; of market growth, 97–98; more than 100% profit margins, 98
improbable stories, 106; Alibaba global player case study, 108–9; Amazon alternative narrative case study, 108; Ferrari exclusive auto club case study, 107–8; iron triangle of value, 106
the information effect, 184–85; earnings reports and narratives, 185–87, 186; Facebook earning report and narrative changes case study, 187–91, 188, 189, 190
ingredients in a good story, 34–35
inputs: business narrative reflected in, 113; for GoPro, 237; stories connecting to, 112–14, 113; uncertainty of, 155
inputs to value: Alibaba China story case study, 146–48, 147; Amazon valuing Field of Dreams case study, 142, 142–46, 143, 144, 145; Ferrari valuing exclusive auto club case study, 139–42; Uber valuing the urban car service company case study, 137, 137–38, 138; valuation basics, 129, 129–30; valuation diagnostics, 135–36; valuation loose ends, 131–34; valuation refinement, 134–35
intermediate variable, 127
intrinsic valuation model: changes over time, 168; DCF model as, 111; expectations of, 159
intrinsic value: basics of, 110–11; in impossible stories, 96; theory of, 111; of Yahoo, 261
investment news, 192; as asset restructuring, 191; for corporate news, 191
investment philosophy, 15
investor composition, 203–4
investors: buybacks for, 198; dividend for, 198; lessons for, 264–65; listener connection of, 70; overconfidence of, 105; quants importance to, 37; simulations for, 213; skill sets of, 244–45; storytelling for, 14; tools for, 244–45
iron triangle of value, 106, 107
JCPenney, 234; corporate life cycle of, 238; revenues of, 238, 240; valuation of, 246; valuing a declining company case study, 238–40, 239, 240
Jobs, Steve, 15, 262; master storyteller case study, 15–16
Kalanick, Travis, 84, 254
Keynes, John Maynard, 172
lessons for entrepreneurs, business owners, and managers, 265–66
Lewis, Michael, 38; Flash Boys, 40; Moneyball, 3
life cycle perspective on managerial imperatives, 248–49, 249
listener, 2; founder stories connection with, 30; investors connection of, 70; in stories, 13; storyteller connections with, 14, 91; tailor stories for, 35; understanding of, 32–33
Long-Term Capital Management (LTCM) case study, 47
low-capital intensity, 85, 114
Lyft, 81, 166, 204; managerial imperative for, 256; narrative differences of, 253; small narratives for, 256; story of, 255; Uber contrast of, 252; versus Uber case study, 251–56, 252, 253, 255
macroeconomic forecasting, 215–17
macro narrative: bringing it together, 208; bring it together, 208; economic cycles in, 14; ExxonMobil valuing case study, 209, 209–13, 211; macro evaluation, 206, 207; micro assessment, 207. See also macro story
macro variables: for business narrative, 213; cycles of, 213–14; evaluation of, 207; micro assessment for, 207–8; of Vale, 207, 222
management culture: corporate life cycle for, 249, 249–51; for ExxonMobil, 248; for GoPro, 248; for Uber, 176; of Yahoo, 260
managers, 247; challenges for, 250; consistent narrative for, 251; constants for, 250; corporate life cycle for, 248; lessons for, 265–66; narrative shift for, 249; tests for, 248
marginal return on capital, 135, 136
market, 184; analysis of, 73; breakeven points of, 158; capitalization of, 78; dominance story of, 113; dynamics of, 99; effect of, 241; entry of, 229; focus of, 78; growth of, 80, 97–98; response of, 189; size of, 79, 102
mature companies, analysis of, 73
micro and macro factors, 222
micro assessment, 207; for composite story, 208; for macro variables, 208
narrative and numbers, 231; constraints and story types, 233–34; GoPro valuation case study, 234–37, 235, 236, 237; implications for investors, 244; investor skill sets, 244–45; JCPenney valuing a declining company case study, 238–40, 239, 240; narrative drivers across the life cycle, 232, 232–33; Uber narrative differences case study, 240, 240–43, 241, 242
narrative and numbers, management lessons across the life cycle, 249–51; Amazon consistent narrative Amazon lesson case study, 251; Lyft versus Uber case study, 251–56, 252, 253, 255
narrative breaks, 7; acquisitions as, 170; for Aereo, 171; for Ashley Madison, 171; capital squeeze as, 170; case study, 171; disasters as, 169; government expropriation as, 170; legal action as, 169; payment failure as, 170; risk exposure for, 170; stories end by, 183
narrative changes, 7, 167, 172; cost structure of, 175; sources for, 168, 171–72; status quo problems of, 174, 176; for Uber, 173; valuation inputs relate to, 173–74; with qualitative news, 168; with quantitative news, 168. See also narrative breaks
narrative differences: divergent values of, 240; Lyft, 253; Uber, 240, 253
narrative drivers across corporate life cycle, 232
narrative pre-work: auto business case study, 75–79, 76, 77, 78, 79; the company, 71–72; the competition, 73–74, 74; the market, 72–73, 73; ride-sharing companies landscape case study, 79–80
narrative shift, 7; Apple, 181; Facebook, 190; for managers, 249; narrative framework for, 179; tweaks as, 179–80; Valeant, 203
narrative to numbers: Alibaba from story to numbers case study, 120, 120–22, 121, 122; Amazon from story to numbers case study, 117–19, 118, 119; breaking down value, 110–12; connecting stories to inputs, 112–14, 113; Ferrari from story to numbers case study, 116, 116–17, 117; Uber story to numbers case study, 114, 115
narrator-based stories case study, 30
narrator-based stories Under Armour and Kevin Plank case study, 30
negative value for equity, 136
networking advantages, 174
noisy historical equity risk premium case study, 42–44, 43, 44
normalization: dangers of, 225; of macro story, 217
number crunching, 1; biases within, 44; in corporate life cycle, 227; in data age, 16–17; developments in, 4; expansion of, 37; numbers intimidation for, 48
numbers: acquisition effects on narrative and, 193; as antidote, 19–20; base systems for, 36–37; and bias with equity risk premium case study, 45; constrained by, 3; control illusion of, 46; control indicator of, 39; corporate life cycle connection with, 9; dangers of, 40–41; decision making by, 48; delusions of, 44; framing of, 41–42; herding problem with, 50; history of, 36–37; for intimidating number crunching, 48; intimidation factor of, 48; limitations of, 4; power of, 3–5, 36–38; precision of, 38; scientific nature of, 38; stories connection of, 110; story process to, 6; unbiased nature of, 39; valuation bridge for, 5
objectivity illusion, 44–45
oil price distribution, 211
online advertising big market delusion case study, 103–6, 105, 106
operating results: commodity price link to, 212; value estimation as, 212
overconfidence: of entrepreneur, 100; of founders, 105; of investor, 105; overpricing degrees of, 102; overvaluation links to, 102; of VC, 100
PE ratio for U.S. companies, 59
pharmaceutical companies case study, 64–67, 65, 66
plausibility, 92–95; in corporate life style, 232; implausible stories levels of, 99; of rev it up strategy, 117; Uber test for, 94
plausible counterstory, Alibaba, 120
power of numbers, 3–5; numbers and bias with equity risk premium case study, 45; numbers are objective, 38–39; numbers are precise, 38; numbers indicate control, 39; power of quant investing case study, 39–40
power of quant investing case study, 39–40
precision illusion, 41–43
presentation choices, 62–64
pretax operating margin, 112
price-earnings (PE), 58, 59; expected growth correlation between, 60; ratio of, 59
pricing: earning reports involved with, 185; feedback, 157; steps in, 125; valuation divergence of, 158
pricing a story: connecting stories to prices, 126; dangers of pricing stories, 127; essence of pricing in, 124–26; venture capital valuation in, 126
pricing feedback case study - Uber, Ferrari, Amazon, and Alibaba, 159–60
qualitative factors: number cruncher problems with, 123; quantitative factors connection with, 124; storyteller problems with, 123; value affected by, 123
qualitative meets quantitative, 123–24
quant investing: case study, 39–40; fall of, case study, 50–51
quantitative factors, 124
quants, 4, 9; investor importance of, 37; power of, 39; strategy biases in, 50–51; strategy storytelling connection with, 51
regression: advantage of, 60; of ExxonMobil, 67, 212
regulatory commission, 46
research and development (R&D), 65; revenue growth in, 66
return on invested capital, and cost of capital, 78
revenue: of JCPenney, 238, 240; for online advertising, 106
revenue, operating income and price reaction, 181
revenue growth: of Alibaba, 120; for Amazon, 144; auto companies of, 76
ride-sharing companies: competition for, 175; cost structure of, 175; landscape case study, 79–80; Uber, 79; Uber versus Lyft case study, 251–56
sad (but true) story of long-term capital management case study, 47
scenario analysis: for Alibaba, 155; for uncertainty, 153
Security Analysis (Graham), 264
simulations: for Alibaba, 156; for ExxonMobil, 212; for investors, 213; mean in, 157; median in, 157; for uncertainty, 154
Standard and Poor’s (S&P), 55; bond ratings for U.S. companies by, 58
statistics: estimation process of, 41; importance of, 4, 57; mean in, 57; median in, 57; mode in, 57; standard deviation in, 57; standard error in, 41
steps for storytellers, 32–34
Steve Jobs master storyteller case study, 15–16
stock-based compensation, 133
stories, 19–20; basic plots of, 28; as business critical, 23; changes within, 7; continuum of skepticism breaking down of, 95; corporate life cycle, 9, 233; DCF reversal of, 149; emotions appeal to, 2; inputs connecting to, 112, 113; listeners of, 2, 13, 30; narrative breaks end for, 183; number combination of, 50; numbers connection of, 110; patterns in, 3; plausibility of, 92–93; possibility of, 92; power of, 11–12; practice importance of, 35; pricing, 124, 126–27; printing press growth of, 11; probability of, 92–93; product, 28–29; remembrance of, 13; Uber, 138; valuation bridge for, 5; valuation into, 149; value link to, 110. See also business narrative
stories, power of, 11–12; special case of business stories, 14–15; Steve Jobs master storyteller case study, 15–16; stories connect, 11–13; stories get remembered, 13; stories spur action, 14
the story, 80, 91; Alibaba China story case study, 88–90; Amazon Field of Dreams case study, 87–88; big versus small, 81; establishment versus disruption, 81–82; Ferrari narrative case study, 85–87, 86; going concern versus finite life, 82–83; growth spectrum, 83; Uber narrative case study, 83–85, 84
story structure: in business, 24; Freytag’s storytelling structure, 25–27, 26; ingredients for, 25
storytellers: case study Jobs, 15–16; of improbable stories, 106; listener connections with, 14, 91; number crunchers and, 263–64; qualitative factors problems for, 123; steps for, 32; understanding business for, 32
storytelling: 3P test of, 5; in advertising, 14; allure of, 2–3; as antidote, 49–50; as art, 24; aspects of, 3; in business, 14; in business education, 14–15; case study con game, 20–21; chemical connections of, 12; as con games, 20–21; in corporate life cycle, 227; as craft, 24; dangers of, 17–23; emotional appeal of, 10; emotional hangover of, 18; expanded scope of, 17; feedback loop importance in, 6; five Ws of journalism for, 33; through history, 10–11; importance of, 2; in information age, 10; investment philosophy integral part in, 15; for investor, 14; neural coupling in, 12; pricing connecting to, 124; quant strategy connection with, 51; religion connection with, 11; runaway telling dangers of, 10; in a technology/data age, 16–17; weakness of, 2
story to numbers process, 6, 6
story types: business stories, 30–32, 31; founder stories, 29–30; general types, 28; narrator-based stories Under Armour and Kevin Plank case study, 30; product stories, 28–29
strong networking benefit story, 113
synergy: of AB InBev, 195; of SABMiller, 195
terminal value, 96; for Amazon, 144–45; business narrative connected for, 131; DCF role with, 129–30; equations for, 97; for Ferrari, 140–41; price appreciation as, 130; of Uber, 138
Tesla: business narrative of, 192; disruption model of, 82
Theranos case study, 21–23
Thinking, Fast and Slow (Kahnemann), 18
3P (plausibility, probability, possibility) test, 5, 92–95; narrative and investor type, 96
trailing PE versus expected growth in EPS, 60
transitional tectonics: corporate governance and investor activism, 259–60; easy transitions, 256–57; misfit CEOs, 257–59; Yahoo challenges of managing aging company, and Marissa Mayer, 260, 260–62, 261
transition points: of corporate life cycle, 229; tests for, 229
transitions: for Apple, 262; for CEOs, 257–59; of corporate life cycle, 256; for IBM, 262; scenarios for, 256–57; for Yahoo, 261
truncation value: with truncation risk, 112
22 Rules to Phenomenal Storytelling (Coats), 35
Uber, 81, 233; bad news for, 176; big narratives of, 254; bottom line for, 174; business market of, 240; business model of, 84; business narrative of, 83; capital intensity for, 176; capital investment model of, 242; competitive advantages of, 241; counternarrative for, 162; first-mover status of, 114; as global logistics company, 178; global logistics company of, 177; Gurley counternarrative case study, 162–66, 163, 165; input changes for, 177; landscape of, 79–80; Lyft case study, versus, 251–56; Lyft contrast of, 251–56, 252, 255; management culture for, 176; managerial imperative of, 252; market effect of, 241; narrative, and valuations for, 242; narrative case study, 83–85, 84; narrative differences case study, 240, 240–43, 241, 242; narratives for, 163, 173, 240–43, 253; networking advantages of, 241; news and value case study, 173–77; potential market of, 240; pricing feedback of, 159; ride-sharing case study, 79–80; story to numbers case study, 114, 115; terminal value of, 138; 3P test for, 94; valuation inputs for, 114, 115, 137, 242; valuation of, 84–85, 179; valuing the urban car service company case study, 137, 137–38, 138
uncertainty: decision trees for, 154; of inputs, 155; in overpricing, 102; scenario analysis for, 153; simulations for, 154; in valuation, 153; with counternarrative, 161
Under Armour, and Kevin Plank narrator-based stories case study, 30
Vale, 219; country risk, 217–21, 218, 220, 223, 224; dark side beckons, 220; debt effect of, 226; macro variables of, 207, 222; meltdown case study, 222–25, 223, 224; political risk for, 226; regrets of, 224; stock price collapse of, 218, 219; 3C company case study, 217–21, 218, 219, 220; value changes, 223
Valeant: drug business model case study, 201–3, 202; Philidor relationship with, 202
valuation, 1, 6–7; of Alibaba, 146; of Amazon, 87; of Apple, 180–81; basics of, 129–31; business narrative with, 137; in China story, 120; confronting weakness of, 166; cross holdings in, 132; deconstructing of, 149, 150; divergences in, 157; equity risk premium input of, 42; expectations of, 159; of ExxonMobil, 67, 210; for Facebook, 191; fears about, 243; feedback loop transparent for, 160; of Ferrari, 75; for GoPro, 234; information of, 135; of JCPenney, 238–40; lessons for, 243; macro factors driven of, 221; of macro-neutral, 208; net debt in, 131–32; numbers bridge as, 5; of plausibility, 93; of possibility, 93; pretax operating margin of, 112; pricing difference of, 124; pricing divergence of, 158; of probability, 93; process for, 131; refinements of, 134–35; sensitivity analysis addendum for, 46; steps in, 129; stock-based compensation in, 133; into stories, 149; stories bridge as, 5; of Uber, 84–85, 179; uncertainty in, 153; U.S. tax codes in, 132; of Vale, 220; valuation inputs for, 128; of Volkswagen, 75; with subsidiaries, 134; for young companies, 234
valuation as a bridge, 5, 5–7, 6
valuation inputs, 127; for Amazon, 142; connecting stories to, 113; of Ferrari, 116, 139; Field of Dreams story line as, 118; interconnectedness of, 129; narrative changes relate to, 173–74; for Uber, 114, 137; for valuation, 128
valuation simulations: of Alibaba, 156; for GoPro, 237
value: assessment of, 6; breaking down of, 110–12, 149; DCF use in, 125; deconstruction of, 150; drivers of, 110, 128; estimation of, 125, 212; growth of, 111; inputs of, 6; qualitative factors of, 123; synergy of, 195
venture capitalists (VC): against DCF, 135; entrepreneur connection with, 100; overconfidence of, 100
venture capital valuation, 126
volatile stock returns, 43
Volkswagen, 75; corporate scandal of, 200–201
Yahoo: case study challenges of managing aging company, and Marissa Mayer, 260, 260–62, 261; intrinsic value of, 261; management culture of, 260; operating history of, 260; transitions for, 261
young companies: analysis of, 73; assessment of, 73; business narrative for, 72; failure risk of, 112; valuation for, 234