Index
Active investments, 231–243
and blend portfolios, 233
passive investments vs., 226, 231–243, 269
returns of skilled active managers, 231–233
and stock picking, 233–243
Adler, Tim, 204
Against the Gods (Bernstein), 148
Agarwal, Vikas, 128
Allen, David, 212
“Allocating Assets in Climates of Extreme Risk” (Cuffe and Goldberg), 166
failure of diversification with, 127–131
portfolio construction with, 229–230
(See also Private assets)
Alternative betas, 179
American Express, 240
American Investment Council, 219, 220
Andretti, Mario, 263
Ang, Andrew, 131
Antidiversification, 123
Aon, 14
Arbelaez, Camila, 228
“Are Optimizers Error Maximizers?” (Kritzman), 212
Arnott, Robert D., 26, 34–35, 57
Asness, Cliff, 26, 27, 70, 182, 224–225
Asset allocation:
constant (fixed weight), 95
as implicit forecasting, 2
recommendations for, 134
scenario analysis in, 134
stocks vs. bonds in, 185–195
strategic (see Strategic asset allocations)
tactical (see Tactical asset allocation [TAA])
theoretical foundations of, 271–274
(See also Portfolio construction)
“Asset Allocation” (Pedersen, Page, and He), 129
“Asset Allocation Implications of the Global Volatility Premium” (Fallon, Park, and Yu), 103
Asset classes, 173–184
asset weights in market portfolio, 17–19
betas for, 19–22
changes over time in, 158–162
correlation forecasting for, 140–143
failure of diversification across, 125–132
influence of macro factors on, 64–66
left- and right-tail correlations for, 125–131
persistence of risk measures in, 112–119, 156
in portfolio construction, 173–184
and risk factor diversification, 130
risk factors models in portfolio optimization, 178–179
risk factors vs., 173–184
risk premiums, 179–184
in 2018 target rate hike, 133
three-year returns on, 73
underlying risk factors for, 162–165
(See also individual classes)
Asset weights:
fixed, 95
in market portfolio, 17–19
in portfolio optimization, 201, 207–209
Austin, Maureen, 218–219
Average yield, 40–42
Backtests:
exceptional, 30
managed volatility, 95–101
of risk parity, 213
for risk premiums, 182–184
of volatility risk premium strategies, 103
without return forecasts, 32
“Bad Is Stronger Than Good” (Baumeister et al.), 132
Bai, Yu, 235
Barclays Aggregate Index, 160–161, 163
Barclays Commercial Mortgage Backed Securities (CMBS) Index, 163–164
Barclays Global Aggregate, 16
Barclays U.S. Aggregate, 177
Barclays U.S. Government/Credit Index, 16
Baumeister, Roy F., 132
Bayesian shrinkage, 209
Beck, Noah, 183
Bekaert, Geert, 131
Ben-David, Itzhak, 237
Bernanke, Ben, 133
Bernstein, Peter L., 6
Bernstein, William, 34–35
Beta(s), 179–180
for asset classes, 19–22
calculating, 10–11
conditional, 133
defined, 10
equity market, 179
macro, 62
and relative returns of stocks and bonds, 10–11
in risk models, 166
in stock pricing, 18
“Betting Against Beta” (Frazzini and Pedersen), 180
“Betting Against Betting Against Beta” (Novy-Marx and Velikov), 181
Bhansali, Vineer, 128
Billio, Monica, 128
The Black Swan (Taleb), 8–9
Black-Litterman model, 23, 209
Blend portfolios, 233
Bloomberg Barclays U.S. Aggregate, 39, 40
BNY Mellon, 14
Bodie, Zvi, 187
“Bond Investing in a Rising Rate Environment” (Guo and Pedersen), 15–16
Bond Portfolio Investing and Risk Management (Bhansali), 128
Bonds:
beta and relative returns of stocks and, 10–11
CAPM expected returns for, 20
corporate, 127
correlation of stocks and, 132–134
forecasting returns for, 15–17, 39–42
government, 132
and human capital, 189
in market portfolio, 17–19
matching the liability with, 194
momentum for, 75–76
in portfolio construction, 185–195
relative valuation between stocks and, 27–28, 56–59
in retirement planning, 188
returns on stocks vs., 112, 117–119
risk premiums for, 40
riskiness of, 162
during US stock market sell-offs, 124
Boudoukh, Jacob, 115
Boyd, Johnrac H., 64
Bratslavsky, Ellen, 132
Brightman, Chris, 35
Buffett, Warren, 12
Building block valuation model, 28–39
buybacks in, 34–36
components of returns in, 28
T. Rowe Price survey process for, 37–39
theory behind, 28–29
and time horizons, 33
valuation change in, 28–34
variations of, 29
Buy and hold strategy, 99–100, 105
Buybacks, 34–36
Cahan, Rochester, 235
Cambridge Associates, 220
Campbell, John, 26
Capital asset pricing model (CAPM), 5–14, 273
for asset class returns, 19–22
and bond market return forecasting, 15
as building block approach, 11
equity-only examples of, 17–18
expected returns predictions with, 10–14
extensions to, 22
issues with and limitations of, 6–10
Capital asset pricing model (CAPM) (continued):
and mean-variance optimization, 7
risk definition in, 10
and shift in asset weights, 18–19
time horizon in, 14
usefulness of, 22–23
“The Capital Asset Pricing Model” (Fama and French), 8
Capital Ideas (Bernstein), 6
Capital Ideas Evolving (Bernstein), 6
Capital markets assumptions, 37–38, 45, 83–84
Carhart, Mark, 22
Central banks, 117
Chaves, Denis B., 57
Chen, Linda, 226
Chow, Tzee-man, 57
Chua, David, 122–123
Clements, Mark, 35
Clinton, Hillary, 238
Concentrated portfolios, 207–210
Conditional betas, 133
Conditional value at risk (CVaR), 143–144, 206, 207
Conditioning bias, 124
Constant conditional correlation (CCC), 140
Corporate bonds, 127
Correlations, 121–136
across risk factors vs. asset classes, 177
assumptions for, 213
in beta, 10
during crises, 121–123
failure of diversification across risk assets, 125–132
between fixed income and equity factors, 176
forecasting, 139–143
impact of regime shifts on, 131
on macro dashboards, 67
and myth of diversification, 122–125
recommendations for asset allocation, 134
stock-bond, 132–134
and tail-risk-aware analytics, 135–136
between trailing returns and forward returns, 74
unstable, 166
of valuation changes, 30–31
of volatility across months, 89
between volatility and data frequency, 112–113
Cotter, John, 152
Counterweight effect, 41–42
Covered call, 105
“Covered Call Strategies” (Israelov and Nielsen), 102
Covered call writing, 93–94, 102–104, 108, 181–182
Covid pandemic, 5, 21, 34, 153–154
Cremers, Jan-Hein, 204
Cross-sectional regression model, 31–34
Cuffe, Stacy, 166
Currency carry trade, 131
Currency hedging, 108
Cyclically adjusted price-earnings ratio (CAPE), 13, 25–28
Czasonis, Megan, 225
Da, Zhi, 235
Dalio, Ray, 85
Dashboards:
to map macro factors to expected asset returns, 64–67
scenario, 158
Data frequency:
in correlation forecasting, 142–143
and persistence in of risk measures, 114–115
in risk forecasting, 145
and time horizon, 112, 114–115
and volatility, 112–113
Data mining, 183–184
Davies, Shaun, 236
DCC–EGARCH model, 98
De Vries, Casper G., 125
“Demand for hedging” theory, 103
DeMiguel, Victor, 211
DeMuth, Phil, 17–19
Devarajan, Mukundan, 59
Dilution effect, 36
Dionne, Georges, 143
Direct utility maximization, 198–203 (See also Full-scale optimization)
Diversification:
across risk assets, failure of, 125–132
across risk premiums, 182
and correlations, 121 (See also Correlations)
during crises, 121–123
global, 249
myth of, 122–125
with risk factors vs. asset classes, 175–184
sources of, and stock-bond correlation, 132–134
time, managed volatility and, 100, 101
“Do Mutual Funds Trade on Earnings News?” (Chen, Huang, and Jiang), 226
“Does Academic Research Destroy Stock Return Predictability” (McLean and Pontiff), 184
Doeswijk, Ronald, 18
Dowd, Kevin, 152
Downside risk, 122
Downside volatility, 106
Duration risk, 132
Dynamic conditional correlation (DCC) model, 140
Dynamic risk-based strategies, 122, 135
Earnings, economic growth and, 36
“Earnings Growth” (Bernstein and Arnott), 34–35
Economic growth, 36
Edelen, Roger M., 227–228
Efficient market theory, 70
Efficient surface, 210
“Emerging, Markets Are Trapped by an Old Cliché” (Wheatley), 160
Emerging market stocks, 159–160
End-of-horizon risk, 168–170
Endowments, 194
Engle, Robert F., 91
Equilibrium, 5–23
betas for asset classes, 19–22
bond return forecasting, 15–17
capital asset pricing model for stock returns, 5–14
and market portfolio, 17–19
usefulness of CAPM, 22–23
Equities:
building block valuation model for, 28–37
global equity markets forecasts, 13–14
Equities (continued):
Mattu and Naik on returns on, 14
P/E ratio for, 25–26
private, 128–130
(See also Private assets; Public equities; Stocks)
Equity risk factors, 176
Equity risk premium (ERP), 14, 188
Excess kurtosis, 103
Exchange-traded funds (ETFs), 233–243
case studies with, 238–242
hidden costs of, 234
and stock picking, 233–237
Expected returns estimates, 2–3, 10 (See also Return forecasting)
“Expected Returns on Major Asset Classes” (Ilmanen), 10–11
Expected total market return, 19
Exposure to loss, 149
accounting for fat tails in, 149, 150
with CAPM, 9
measures of, 143–144
and probability distributions, 147
and risk tolerance, 149–150
Factor analysis, 162–165
“Factor Investing and Asset Allocation” (Naik et al.), 176
“Factor Investing and Asset Allocation” (Page et al.), 59
Fallon, William, 102–104
Fama, Eugene F., 8, 10, 18, 22, 62
Fat tails, 118
in modeling loss exposure, 149, 150
models for, 148
predictability of, 168
and risk regimes, 154–157
and risk tolerance, 149–150
in tail-risk estimation, 147–157
and 25-sigma probabilities, 151–152, 154
(See also High kurtosis)
Faulkner-MacDonagh, Chris, 62
Feynman, Richard, 86
Financial crises, scenario analysis and, 158–167
Financial Select Sector SPDR ETF, 239–240
Finkenauer, Catrin, 132
Fixed income markets, 15, 160–161
Fixed income risk factors, 176
Forbes, Steve, 70
Fortin, Alain-Philippe, 143
Foulke, David, 226
Four-factor model, 22
Franzoni, Francesco, 237
Frazzini, Andrea, 180
French, Kenneth R., 8, 10, 18, 22, 62, 116
Froot, Ken, 31
Full-sample correlations, 122, 134
Full-scale optimization, 134, 192, 201–207
Fundamental analysis, 2–3
broad economic environment in, 36–37
in earnings forecasts, 37–39
for stock picking, 242
GARCH model, 91
Garcia-Feijoo, Luis, 126
Garlappi, Lorenzo, 211
Gaussian assumptions, 9
GDP growth, 36–37
Geczy, Christopher C., 71–75
Genetic algorithms, 202
Getmansky, Mila, 128
Giamouridis, Daniel, 234
GIGO (garbage in, garbage out), 1–2, 83–84
Global equity markets, P/E ratio and, 13–14
“The Global Multi-Asset Market Portfolio, 1959–2012” (Doeswijk, Lam, and Swinkels), 18
Global tactical asset allocation (GTAA), 182
Glosten, Lawrence R., 236–237
Goldman Sachs, 152–153
Gottschlag, Oliver, 222–224
Government bonds, 132
Granger, Clive, 90–91
Grantham, Jeremy, 27
Growth, in building block valuation model, 28–30, 32–33, 36–38
Guo, Helen, 15–16
Hartmann, Philipp, 125
Harvey, Campbell R., 140, 141, 143
He, Fei, 129
Health Care Select Sector SPDR ETF, 238–239
Henneman, Charlie, 122
High kurtosis, 103, 112, 117–119, 144
added into objective function, 207
mean reversion of, 118–119
predictability of, 117–119, 147, 148
Samuelson on, 198
(See also Kurtosis; Skewness)
Hill, Joanne, 235–236
Histograms, 117
Historical data, 3
for bond returns, 16
and CAPM, 11–12
Historical models of risk, 91
Home bias, 249–250
“How to Combine Long and Short Return Histories Efficiently” (Page), 116
“How Unlucky Is 25-Sigma?” (Dowd et al.), 151–152, 165
Hsu, Jason, 183
Hu, Jian, 64
Huang, Jing-Zhi, 131
Huang, Wei, 226
Hubrich, Stefan, 97
on investment strategies, 93–94
on managed volatility, 100–101, 104
on mistaken causality, 58
on unconditional expected returns, 21
Humphrey, Chris, 152
Ibbotson, Roger, 35
Implicit forecasts, 2
“In Defense of Optimization” (Kritzman, Turkington, and Page), 211
“In Defense of Optimization: What If We Can Forecast?” (Allen, Lizieri and Satchell), 212
Income, in building block valuation model, 28, 29, 32–35
Indexes, 235–236
Inflection points (kinks), 201–203
Interest rate duration, 40–42
Interest rates:
adjusting P/E ratio for, 57
bond portfolio returns and rate shocks, 15–17
and stock/bond correlations, 133
Internal rates of returns (IRRs), 221, 222, 224
“Investor Flows and the Assessed Performance of Open-End Mutual Funds” (Edelen), 227–228
Investor utility, 199
Israel, Ronen, 115
Israelov, Roni, 102
Jacquier, Eric, 204
Jagannathan, Ravi, 64
Jensen, Gerald R., 126
Jiang, George, 226
Johnson, Nic, 132
Johnson, Robert R., 126
Jorion, Philippe, 209
Journal of Portfolio Management, 174–175
J.P. Morgan, 35
JPP (see Page, Jean-Paul)
Judgment, 84–85
Kamara, Avraham, 18
Kogelman, Stanley, 16
Kostka, Helge, 183
on biases with private equity, 225
on blended regime approach, 204
on career growth, 267
on “dilequants,” 113
on diversification, 122–123
on estimating volatility, 89–90
on Mahalanobis distance, 205
on mean-variance optimization, 204
and model comparison, 32
on risk regimes, 156
on risk tolerance, 149
and Samuelson, 197–198
and skewness joke, 118
on within-horizon and end-of-horizon risk, 168–169
Kurtosis, 118
mean reversion of, 118–119
persistence of, 117–119
(See also High kurtosis)
Lam, Trevin, 18
Lane, Hamilton, 218–220
Levenson, Alan, 58
Leverage, 229
Levered bias, 223
Li, Yuanzhen, 205
Liability-driven investors, 11
Lintner, John, 6
Liquidity, in public equity funds, 226–229
Liquidity risk premium, 229
Lizieri, Colin, 212
Lockups, 229
Longer-term risk forecasting, 111–119
conclusions from risk predictability tests, 113–115
higher moments (skewness and kurtosis), 117–119
mean reversion at longer horizons, 115–117
“Long-Horizon Predictability” (Boudoukh, Israel, and Richardson), 115
“The Long-Run Drivers of Stock Returns” (Straehl and Ibbotson), 35
Look-ahead bias, 30
Loss exposure (see Exposure to loss)
Low-risk anomaly, 180–181
Lowry, Kenneth, 204
Lynch, Hailey, 233
Macro betas, 62
Macro factors, 61–68
at 6- to 18-month horizons, 63–66
academic research on, 62–63
caveats with, 67–68
as drivers of asset returns, 62–63
in stock picking, 240–241
“Macroeconomic Dashboards for Tactical Asset Allocation” (Clewell et al.), 62
Madhavan, Ananth, 236
Mahalanobis distance, 205
Managed volatility, 93–94
backtesting of, 95–101
and buy and hold strategy, 99–100, 105
combined with covered call writing, 104, 105
and currency hedging, 108
and failure of diversification, 135
liquidity issues with, 107
and overreaction, 109
prior studies on, 96
risk parity vs., 105–106
risk-adjusted returns improved by, 99
and static portfolios, 100–101
and time diversification, 100, 101
trading costs for, 108
and value-based approach, 106–107
Market Efficiency (Markowitz), 6
Market portfolio, 17–19
Market risk premium:
for bonds, 15
for US T-bills, 19
Market volatility, 102
Markowitz, Harry, 6–9, 13, 85, 197–198
Marks, Howard, 224
Marra, Stephen, 91–92
Masturzo, Jim, 26
Materials Select Sector SPDR ETF, 242
Mattu, Ravi, 14
Maximum likelihood models, 156–157
McLean, R. David, 184
Mean reversion:
of higher moments, 118–119
at longer horizons, 115–117
for value investors, 180
Mean-variance optimization:
CAPM derived from, 7
debate over, 211
full-scale optimization vs., 198, 203–207
modified versions of, 204–207
peer group risk constraint with, 210
problems with, 203–204
“Mean-Variance Versus Full-Scale Optimisation” (Kritzman and Adler), 204
Merton, Robert C., 127–128, 187
Michaud, Richard, 208
Miller, Merton, 7
“The Mismeasurement of Risk” (Kritzman and Rich), 168–169
Modern Portfolio Theory and Investment Analysis (Elton and Gruber), 6
Moments, 118 (See also Higher moments)
for bonds, 75–76
and diversification, 130
and overreaction, 73–75
and time horizons, 71–73, 75–76
Monetary easing, 17
Morillo, Daniel, 236
Mortgage-backed securities, 127
Moskowitz, Toby, 182
Mossin, Jan, 6
Moussawi, Rabih, 237
MSCI World Index, 218
Mueller, Mark, 85–86
Multivariate distance, 205
“The Myth of Diversification” (Chua, Kritzman, and Page), 122–123, 127
Naik, Narayan Y., 128
Nallareddy, Suresh, 236–237
Negative skewness, 103, 104, 112
and exposure to loss, 144
frequency of, 118
positive skewness vs., 207
of volatility risk premium, 106
Nielsen, Lars N., 102
Nonnormal returns, 9
Northern Trust, 14
Novy-Marx, Robert, 181
Nowobilski, Andrew, 59
O’Connell, Paul, 31
“Optimal Hedge Fund Allocations” (Cremers, Kritzman, and Page), 204
“Optimal Portfolio in Good Times and Bad” (Chow et al.), 204–206
Option writing, Volatility Index and, 109
Outliers, 148
Overfitting, 183
Page, Jean-Paul (JPP), 265–269
on abnormal profits, 231
on accounting for risk, 217
on asset allocation, 247
on beta, 175
on efficiency of markets, 93
on finance, 61, 83, 265, 271–272
on financial analysis, 45
on hypotheses behind models, 1
on interest rates, 5
on market frictions, 69
on measuring risk, 89
on models, 197
on Modern Portfolio Theory, 173
on normal distributions, 147
on quality of models, 121
on standard deviation, 111
on theoretical foundations of asset allocation, 271–274
on utility theory, 185
on valuation, 25
Panariello, Rob:
and correlations, 140
and diversification, 122–125, 127–129
on fear, 234
and persistence of volatility, 113–114
and public vs. private equity, 228
and stock picking, 233
Park, James, 102–104
Passive investments, 269
active investments vs., 226, 231–243, 269
and blend portfolios, 233
mispricings and abnormal correlations with, 236
and stock picking, 243
“The P/B-ROE Valuation Model” (Wilcox), 28–29
Pedersen, Lasse Heje, 180, 182
Pedersen, Niels, 15–16, 59, 129, 132
Peer group risk constraint, 210
Pelizzon, Loriana, 128
“The Performance of Private Equity Funds” (Phalippou and Gottschlag), 222–224
Persistence in risk measures, 113, 148
and data frequency, 114–115
higher moments, 117–119
mean reversion at longer horizons, 115–117
Personal finance, 186–188
Phalippou, Ludovic, 222–224
Pioneering Portfolio Management (Swensen), 129
Pontiff, Jeffrey, 184
Poon, Ser-Huang, 90–91
Portfolio construction, 173–174, 268, 273
active vs. passive investments in, 231–243
with alternative assets, 229–230
asset class allocation in, 185–195
asset classes in, 173–184
full-sample correlations in, 134
private assets in, 217–229
rules of thumb for, 243–244
simplifying problem of, 203–207
single-period portfolio optimization, 197–215
stocks vs. bonds in, 185–195
strategic asset allocations for, 247–263
target-date funds in, 190–195
utility maximization in, 199–203
“The Portfolio Flows of International Investors” (Froot, O’Connell, and Seasholes), 31
Portfolio optimization, 173
allocation of stocks vs. bonds for, 193–194
methodologies for, 134
risk factors models in, 178–179
single-period, 194–195, 197–215
at T. Rowe Price, 192
and usefulness of optimizers, 211–212
Portfolio optimization models, 2
Portfolio theory, 85
Power utility, 199
Price-to-book (P/B) ratio, 38
Price-to-cash flow (P/CF) ratio, 38
Price-to-earnings (P/E) ratio:
compared to other ratios, 38
Price-to-earnings (P/E) ratio (continued):
debate over CAPE vs., 13, 25–26
and global equity markets forecasts, 13–14
and inflation, 13
inverse of, and real return for stocks, 12–13
as relative valuation signal, 58–59
and sector weights, 159
as short term timing signal, 57
and valuation change, 30–31
Principles (Dalio), 85
Private assets, 217–229
biases related to, 220–221, 223
diversification with, 128–130
footnotes and fine-print disclaimers with, 219–224
hype associated with, 224–226
in portfolio construction, 217–229
public equities compared to, 218–224
and public equity fund returns, 226–229
“Private Equity Performance” (Kaplan and Schoar), 221
Probability distributions, 117–118, 147, 152–153
Probability-weighted utility, 201
Prout, William, 218–219
Public equities:
fund returns for, 226–229
private equities compared to, 218–224
returns on private equities vs., 226–229
Public market equivalent (PME), 221–223, 229
Public pensions, 219
Publication bias, 91–92
Q Group conferences, 7
Qian, Edward, 213–214
Quantitative analysis, judgment and, 84–85
Quantitative data analysis, 2–3
Quantitative easing (QE), 17
Quantitative investing, momentum in models for, 70
Quantitative value-at-risk models, 165
Random walk model, 91
Real estate:
CAPM expected returns for, 20
diversification with, 128–130
private, 129–130
Real estate investment trusts (REITS), 18, 240
Real returns, inflation and, 11, 13
“Regime Shifts” (Kritzman, Turkington, and Page), 156, 157
Regime-switching dynamic correlation (RDSC) model, 140
Relative returns:
on dashboards, 64–66
and persistence of higher moments, 112, 117–119
on stocks vs. bonds, 10–11, 17–19, 112, 117–119
Relative valuation:
and CAPE, 27–28
macro factors confirming signals, 67–68
shorter-term signals of, 56–59
Resampling, 208
Retirement planning, 187–194, 249–253
Return forecasting, 1–3, 83–87, 267, 272
equilibrium, 5–23
momentum, 69–82
paradox of, 73
rules of thumb for, 86–87
shorter-term macro signals, 61–68
shorter-term valuation signals, 45–59
valuation, 25–42
“Return of the Quants” (Dreyer et al.), 94
“The Revenge of the Stock Pickers” (Lynch et al.), 233
Rich, Don, 168–169
Richardson, Matthew, 115
Ringgenberg, Matthew C., 236
Risk factor diversification, 130–131, 135, 176–177
Risk factors:
asset classes vs., 173–184
crowding of, 184
in portfolio construction, 174
in scenario analysis, 162–165
Risk factors models, 178–179
Risk forecasting, 89–92, 267–268, 272–273
basic parameter choices for, 144–145
CAPM definition of, 10
correlation forecasts, 139–143
correlations, 121–136
exposure to loss in, 143–144
fat tails, 147–157
goal of, 178–179
longer-term, 111–119
models of, 89–92
risk-based investing, 93–109
rules of thumb for, 170–171
scenario analysis, 157–168
within-horizon risk in, 168–170
“Risk Management for Hedge Funds” (Lo), 150
Risk parity:
and implicit return assumptions, 2
managed volatility vs., 105–106
in portfolio optimization, 212–215
Risk Parity Fundamentals (Qian), 213–214
Risk predictability tests, 112–119
Risk premiums, 179–184
backtest data for, 182–184
beta, 179–180
for bonds, 40
and currency carry trade, 131
diversification across, 182
low-risk anomaly, 180–181
and risk factors, 179–180
strategies for, 182–184
when rates are low, 12
Risk regimes, 131, 154–157, 168, 204
Risk tolerance, 149–150
Risk-based investing, 93–109
combination of strategies for, 104
covered call writing, 102–104
managed volatility backtests, 95–101
Q&A about, 105–109
(See also Managed volatility)
Risk-free rate, 11
Roll down, 40–41
Rossi, Marco, 131
Rules of thumb:
for portfolio construction, 243–244
for return forecasting, 86–87
for risk forecasting, 170–171
Samonov, Mikhail, 71–75
Sample bias, 223
Samuelson, Paul A., 186–187, 197–198
Sapra, Steve, 132
Satchell, Stephen, 212
Scenario analysis:
in asset allocation, 134
and asset class changes over time, 158–162
defensive use of, 157–167
defining scenarios in, 158
factor-based, 162–165
forward-looking scenarios in, 165–168
offensive use of, 167–168
Seasholes, Mark, 31
Sharpe, Bill, 6, 7, 9, 13, 151
Sharpe ratios, 150
Sharps, Rob, 228
Shiller, Robert, 13, 14, 25–26
“The Shiller CAPE Ratio” (Siegel), 26
Shive, Sophie, 235
Shkreli, Martin, 238
Shorter-term investments, macro factors for, 63–66
Shorter-term valuation signals, 45–59
for relative valuation between stocks and bonds and across bond markets, 56–59
for tactical asset allocation, 45–59
Simonato, Jean-Guy, 143
Single-period portfolio optimization, 194–195, 197–215, 268
issues with concentrated and unstable solutions, 207–210
mean-variance optimization, 198, 203–207
and risk parity, 212–215
and usefulness of optimizers, 211–212
Size of measurement errors, 148
Skewness, 118
of call options, 118
mean reversion of, 118–119
persistence of, 117–119
positive vs. negative, 207
and risk forecasting, 144–145
(See also Negative skewness)
“Skulls, Financial Turbulence, and Risk Management” (Kritzman and Li), 205
S.M.O.O.T.H. fund, 224–225
Smoothing bias, 128–130
Sovereign wealth funds, 37, 128–130, 194
S&P 500:
in March 2018, 12
P/E ratio of, 30–31
realized one-month volatility on, 103–104
recent earnings on, 27
sector weights in, 159
and tech bubble, 163
Spread duration, 40–42
Stock market:
used as market portfolio, 17–18
valuation changes in, 31–34
Stock picking, 233–243
“The Stock-Bond Correlation” (Johnson et al.), 132–133
Stocks:
beta and relative returns of bonds and, 10–11
correlation of bonds and, 132–134
of emerging markets, 159–160
and human capital, 189–190
international equity diversion, 125–126
in market portfolio, 17–19
P/E ratio and real return for, 12–13
P/E ratio vs. CAPE for returns on, 25–27
in portfolio construction, 185–195
relative valuation between bonds and, 27–28, 56–59
in retirement planning, 188
returns on bonds vs., 16–17, 112, 117–119
stock picking, 233–243
three-sigma days for, 94–95
Stonacek, Aaron, 39–40, 56, 75
Straehl, Philip, 35
Straetmans, Stefan, 125
Strategic asset allocations, 247–263
capital markets assumptions for, 45
forecast horizons for, 111 (See also Longer-term risk forecasting)
and mean reversion at longer horizons, 116
target allocation portfolios, 253–261
target volatility portfolios, 261–263
target-date portfolios, 248–253
Stress testing (see Scenario analysis)
Style premiums, 179
Sullivan, Rodney N., 235
Sustainable growth rate, 30, 37
Swensen, David, 129
Swinkels, Laurens, 18
T. Rowe Price, 191
Asset Allocation Committee, xii
capital markets assumptions approach at, 37–38
and global equity markets forecasts, 13–14
portfolios/products managed by, 248
scenario analysis at, 157–158, 163
and tactical asset allocation, 57–58
Target Date Fund series of, 250
Taborsky, Mark, 174
Tactical asset allocation (TAA), 45–59
best valuation signal for, 52–56
forecast horizons for, 111 (See also Longer-term risk forecasting)
global, 182
and macro dashboards, 67
in practice, 46–52
and relative valuation between stocks and bonds, 56–59
Tail dependence, 126
Tail risk:
combining volatility and, 206–207
and managed volatility, 99, 101
in optimization problem, 206
Tail-risk estimation:
fat tails, 147–157
scenario analysis, 157–167
Tail-risk hedging, 135
Tail-risk-aware analytics, 135–136
Tails:
and correlation asymmetries, 123
and correlations, 124–126, 134
and kurtosis, 118
and skewness, 118
(See also Fat tails)
Taleb, Nassim Nicholas, 8–9, 148, 150, 153
Target allocation portfolios, 253–261
conservative income, 253–255
diversified global portfolio, 257–259
diversified income, 255–257
Target allocation portfolios (continued):
growth portfolio, 261–261
specialized portfolios, 261–262
Target volatility portfolios, 261–263
Target-date funds (TDFs), 186, 187, 190–195, 248
Target-date portfolios, 248–253
Technology Select Sector SPDR ETF, 242
Thompson, Toby, 94
Three-factor model, 22
Three-sigma days, 94–95
Thurston, David, 218–219
Time horizon(s):
and bond returns, 16
and building block valuation model, 33
in CAPM, 14
and CAPM forecasts, 21
for cash return, 19
and correlation forecasting, 140–143
counterweight effect at, 41–42
differing between momentum and value investing, 180
for fixed income asset classes, 39–40
macro factors for short term tactical investments, 63–66
with managed volatility, 107
and persistence of risk measures, 113–117
in tactical asset allocation, 57
and volatility, 113–114
within-horizon and end-of-horizon risk, 168–170
Time series analyses, 159
Time series forecasts, 31
Tracking error, 210
Treasury Inflation-Protected Securities (TIPS), 186–188
Treynor, Jack, 6
Turing Pharmaceuticals, 238
Turkington, David, 156, 211, 225
“215 Years of Global Multi-Asset Momentum: 1800–2014” (Geczy and Samonov), 71
Tzitzouris, Jim, 186, 187, 189–192, 233
Unstable portfolios, 207–210
Uppal, Raman, 211
US Treasuries:
impact of QE on, 17
as safe-haven class, 161
stock correlations with, 132
volatility persistence for, 114
US Treasury bonds, 11
Utility function:
approximating, 203–207
inflection points in, 201–203
Utility maximization, 199–203
Utility theory, 199
Valeant Pharmaceuticals, 238
and bond future returns, 39–42
building block model for, 28–39
and CAPM model, 12
and equity risk premium, 14
fundamental analysis, 37–39
in market risk premium determination, 21
relative, between stocks and bonds, 27–28
shorter-term (see Shorter-term valuation signals)
of stocks, P/E ratio vs. CAPE for, 25–28
Valuation change:
in building block valuation model, 28–34
P/CF ratio correlation with, 38
“Value and Momentum Everywhere” (Asness, Moskowitz, and Pedersen), 182
Van Oordt, Maarten R. C., 126–127
Velikov, Mihail, 181
Viskanta, Tadas E., 140, 141, 143
Vohs, Kathleen D., 132
Volatility:
in beta, 10
and bond returns, 16–17
of cash, 11
combining tail risk and, 206–207
and mean reversion at longer horizons, 115–117
portfolio, 139–140
and time horizon, 113–114
as unstable through time, 95
Volatility clustering, 91, 101
Volatility Index (VIX), 109
Volatility risk premium, 102–104, 106, 181–182
“Volatility-Managed Portfolios” (Moreira and Muir), 100
Wang, Yuan, 131
“Warning” (Lo and Mueller), 85–86
Wealth managers, 128
Wheatley, Jonathan, 160
“When Diversification Fails” (Page and Panariello), 122, 129
Wilcox, Jarrod, 28–29
“Will My Risk Parity Strategy Outperform?” (Anderson, Bianchi, and Goldberg), 213
“Will Your Factor Deliver?” (Beck et al.), 182–183
Within-horizon risk, 168–170
Woodford Equity Income Fund, 225
Woods, Margaret, 152
Wurgler, Jeffrey, 235
Xiong, James X., 235
Yang, Sungsoo, 235
“Yes, the Composition of the Market Portfolio Matters” (Kamara and Young), 18
Yield to maturity, 15–17, 39–40
“You Can’t Eat IRR” (Marks), 224
Young, Lance, 18
Yu, Danny, 102–104
Zhou, Chen, 126–127
Zou, Yuan, 236–237