Contrarian Investment Strategies
Page 55
expectations for, 307
See also specific strategy
structured investment vehicles (SIVs), 396
subprime mortgages
beginning of problems with, 386–88
characteristics of bubbles and, 19, 222
contrarian strategies and, 296–98, 299
ethics and, 400
Federal Reserve and, 387, 390, 391–94
as flawed industry, 390
forecasting of, 174, 213
government statistics and, 68
greed and, 4
Greenspan and, 390, 391–92
hindsight bias and, 78–79
interest on, 387
leverage and, 354
liquidity and, 7, 354, 398
ratings on, 79, 125, 394–97, 395n, 398–99
regulation of, 78, 387, 391–94
risk and, 392–93
shorting of, 397
standards for, 388
volatility and, 123, 124, 127, 132, 134
Subprime Mortgages: America’s Latest Boom and Bust (Gramlich), 392
Summers, Lawrence, 142, 384, 385, 396
Sun Microsystems, 193
Super Bowl XLII (2008): Giants-Patriots game for, 247–48
supply and demand, 159, 403
Supreme Court, Massachusetts, 359
Supreme Court, U.S., 293
surprises
additional earnings, 233
anxiety and, 217
“boot hills” of concept stocks and, 250, 251
and change in perception of stock, 229–33, 235, 241, 242
confidence and, 218, 230, 241
contrarian stocks and, 8
Dreman et al. study about, 219–33
earnings as, 216–43
effects over time of, 240–42, 243
EMH and, 145, 151–52
event triggers and, 229–33, 234, 235, 241, 242
favored stocks and, 8, 217–18, 219, 220–34, 237, 238, 239, 240–43, 248, 271, 272
forecasting and, 7, 217, 218, 219–33, 241
impact of, 216–43, 274
importance of, 243
IOH and, 272, 273, 274
negative, 217, 220–22, 226–29, 230, 231–32, 234, 237, 238, 239, 242, 243, 274
neuroeconomics and, 217–18, 226, 235–40
non-earnings, 231
optimism and, 219, 229
positive, 217, 220–22, 224–26, 230–31, 232–35, 237, 238, 239, 240–41, 242, 251, 274
price and, 8, 217, 218, 219–29, 230–32, 233, 234, 235, 241, 243, 248, 250, 274
Psychological Guidelines about, 224, 229–30, 242–43
regression to the mean and, 272
as reinforcing events, 230, 233–35, 236–37, 239, 242
series of, 241
unfavored stocks and, 217–18, 219, 220–33, 234, 237, 239–43, 248, 271, 272
valuation and, 229, 241, 242, 243
war and, 215–16
See also Durability Bias
Sussman, Adam, 330
Sutton, Willy, 394
Symantec, 193
Tabb Group, 330
takeovers: selling strategies and, 347
Taleb, Nassim, 393
tax credits, 412
Tax Reform Act (1986), 387
taxes
contrarian strategies and, 282, 285, 287
deduction of mortgage interest and, 387
risk and, 360, 361, 364–65, 364n, 367, 368, 370–71, 372, 373
Social Security, 405
Tea Party, 381
technical analysis, 88–89, 90, 91–93, 94, 95, 97, 99–100, 104, 133, 152–53, 321
technology
influence of, 212, 231, 386, 408, 409
See also Dot-Com Bubble/Crash; high-frequency trading; technology bubbles; technology stocks
technology bubbles, 23, 48, 57. See also Dot-Com Bubble/Crash
technology stocks, 19, 42, 44, 48, 77, 134, 183, 308. See also Dot-Com Bubble/Crash; technology bubbles
Templeton, John, 142, 348
Temporal Construal, 33, 43–45, 47
tender offers, 150–51
Terranova, Joe, 331
Thaler, Richard, 151, 271, 374
Thames River: prediction about flooding of (1524), 74
Thomas, Jacob, 145
three-factor model, 136–37, 137n, 138, 139, 259
3Com, 193
Timberwolf 1, 398, 399
time
risk and, 366–67
Temporal Construal and, 33, 43–45, 47
See also long-term return; short-term return
Time Warner, 36, 409
Tokyo Electric Power Company, 3
“too big to fail,” 116
Toys ‘R’ Us, 36
Tradeworx Inc., 330
trading
reversals in, 326, 332–33
See also high-frequency trading
transaction costs, 98, 141, 282, 285, 334, 339
Treasuries, U.S.
credit ratings on, 2, 125
HFT and, 339, 339n
high-yield strategy and, 286, 287
inflation and, 2, 415
interest rates on, 80, 216, 415–16
during July-August 2011, 2, 329
long-term, 387
risk and, 360, 361–74
S&P downgrading of, 2, 395n
as safe haven, 3, 80, 266, 287
Treasury Department, U.S., 2, 15, 126, 213, 297, 299, 381, 385, 414–15
Trichet, Jean-Claude, 16
Trope, Y., 44
Troubled Assets Relief Program (TARP), 126–27, 189, 381, 397, 414
Trump, Donald, 205, 354
Tulip Mania (1630s), 16, 21, 25, 26, 30
Tversky, Amos, 52, 52n, 53, 61, 64, 176, 177, 207
Two-Tier Market (1971–1974), 23, 37–39, 254
UBS Securities, 396
uncertainty, 32, 77, 171–72, 175, 183, 275, 359, 374
underwriting
ethics and, 401
mortgage, 388, 395, 398, 399–400, 399n
of mortgage-backed securities, 400
pressures on security analysts and, 188, 189, 205–6
salaries and fees for, 187–88, 189
unfavored stocks
Affect and, 271
contrarian strategies and, 248–67, 270, 277–79, 283–86, 318
dividends and, 280, 283
forecasting and, 272, 310
high-yield strategy and, 286–88
in industries, 311–16, 318
information processing and, 6
IOH and, 8, 271, 272, 273–75, 276, 279–82, 283–86
neuroeconomics and, 237, 239–40
price and, 272
price-to-book value and, 283–86
price-to-cash flow and, 282–83
price-to-earnings and, 279–82
reappraisals of, 248, 274
regression to the mean and, 272
risk and, 6
surprises and, 217–18, 219, 220–33, 234, 237, 239–43, 248, 271, 272
Unilever, 345
United Kingdom: Federal Reserve policies and, 415
United Technologies Corporation, 63
University of Chicago, 101, 105, 256, 260. See also specific person
University Computing, 180
University of Rochester Conference (June 1987), 110–11
USS Maddox attack, 216
utility stocks, 301–2
value
Affect and, 37–39, 41, 47, 48, 50
availability heuristic and, 56
characteristics of bubbles and, 20
Crash of 1987 and, 115
Dot-Com Bubble and, 37–39
EMH and, 128, 140, 141, 152–53
forecasting and, 199
fundamental analysis and, 93
insensitivity to probability and, 37–39
IOH and, 272, 273, 274, 275
of IPOs, 56
and markets as continually readjusting securities value, 275<
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and negative correlation of judgments of risk and benefits, 41
New Psychology and, 27
price and, 140, 141, 152–53
rationality and, 128, 152–53
and success of contrarian strategies, 260
surprises and, 229, 241, 242, 243
understanding bubbles and, 26
See also specific method
Value Line Investment Survey, 57, 177–78, 320, 321, 345
Value Line Mutual Fund Service, 341
Value Line New Issue Survey, 57
value strategies. See specific strategy
Vanderheiden, George, 308
Vanguard Group, 4, 341–42, 341n, 342
Vickrey, William, 159
Vietnam War, 216
Vishny, Robert, 260
VIX Index, 329, 331, 338–39
volatility
beta as measure of, 96–97, 96n, 135–37, 143, 351
of bonds, 286
bubbles and, 21
CAPM and, 96–97, 133, 134, 136, 138–40, 141n
as consistent over time, 120, 129, 134
contrarian strategies and, 257, 259
Crash of 1987 and, 106, 109, 110, 111–12, 116, 132, 134
Crash of 2000–2002 and, 134
dividends and, 151–52
EMH and, 106, 109–12, 116, 118–21, 123, 124, 127, 129, 131, 132–40, 142, 143, 152, 176, 257, 259, 276, 350, 358
ETFs and, 340, 341
flash crashes and, 2, 331
of futures trading, 120
HFT and, 328–34, 336–39
information and, 152
IOH and, 275
leverage and, 357
liquidity and, 134
long-term versus short-term, 351, 366–67
LTCM and, 118–19, 120–21, 123, 132, 134
MPT and, 134, 136
portfolio insurance and, 109
predictability of, 118
predictions about, 135–37
return and, 119, 129, 132, 133, 136, 137, 138–39, 140, 142, 176
risk and, 6–7, 96–97, 116, 119, 127, 131, 132–35, 276, 351, 352, 358, 366–67, 373, 374
subprime mortgage crisis (2007–2008) and, 123, 124, 127, 132, 134
VXX ETN, 338–39
Wachovia, 291, 388, 396
Waddell & Reed, 325–26
wages, 405, 407, 411, 413
Wal-Mart, 403, 406
Wall Street. See professionals, financial
Wall Street and the Financial Crisis Commission (U.S. Senate), 79
Wall Street Journal
and career pressures on security analysts, 204
HFT story in, 330
pessimism of professionals in, 60
professional performance poll by, 181
promotion of stock futures in, 107
subprime problem articles in, 391
Vanderheiden story in, 308
Wall Street Week (PBS-TV), 107
“wannabe gunslingers.” See “hot” analysts
war: inflation and, 359
Washington Mutual Bank, 79, 126, 299
The Wealth of Nations (Adam Smith), 402, 408
Weber, Elke, 34
Wedbush, Gary, 331
Wedbush Securities, 331
Weill, Sandy, 188–89
Welch, Ned, 34
Wells Fargo, 112
Wheatley, T. P., 43
Williams, John Burr, 199
Wilson, T. D., 43
Wilson, Woodrow, 215
Windsor Fund, 142
Womack, Kent, 151
Woodard, Nelson, 219
Woodward, Bob, 384
World War I, 215, 417
World War II, 16, 112, 156, 215–16
WorldCom, 189, 216, 283
XLF (ETF), 339–40
Yahoo!, 25, 44–45
Zacks, 190
Zeckhauser, Richard, 202
Zweig, Jason, 236
About the Author
DAVID DREMAN is regarded as the “dean” of contrarians by many on Wall Street and in the national media. Dreman is the chairman and managing director of Dreman Value Management, LLC, of Jersey City, New Jersey, a firm that pioneered contrarian strategies on the Street and manages over $5 billion of individual and institutional funds. The author of the critically acclaimed Psychology and the Stock Market and Contrarian Investment Strategy, Dreman is also a senior investment columnist at Forbes magazine. Articles dealing with the success of his methods have appeared in The New York Times, The Wall Street Journal, Fortune, Barron’s, Bloomberg Businessweek, Newsweek, and numerous other national publications. Dreman is also on the editorial committee of the Journal of Behavioral Finance. He resides with his wife and daughter in Aspen, Colorado, and on the family yacht, The Contrarian.
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1 Throughout the text I use the term “Wall Street” to refer only to the banks and investment banks and some of the hedge funds that were instrumental in causing the financial crisis, and not the majority of money managers, analysts, mutual funds, and scores of other parts of the financial industry that had nothing to do with the debacle and were, along with their clients, hurt by it.
2 Amazingly, an almost identical scheme was pulled off on a much larger scale in the 1996–2000 Internet bubble.
3 We will discuss cognitive heuristics in chapter 3.
4 An earnings discount model takes the analyst’s estimate of future earnings for a company well into the future (often thirty years or more) and applies a discount rate to each year’s earnings composed of the current price of long-term government bonds, then 5.9 percent, and then adds a 9.1 percent risk factor (which the author believed was quite conservative, given the staggering growth projected in markets which were in a very early stage of development and for which almost no attention was paid to future competition or a multitude of other factors that could limit growth over time).
5 As expected, the strength of the experts’ affective reactions was found to strongly influence the inverse relation toward the hazardous items being judged. In a second study, these same toxicologists were asked to make a “quick intuitive rating” for each of thirty chemical items (e.g., benzene, aspirin, secondhand cigarette smoke, dioxin in food) on an Affect scale (bad–good). Next, they were asked to judge the degree of risk associated with a very small exposure to the chemical, defined as an exposure that is less than 1/100 of the exposure level that would begin to cause concern for a regulatory agency. Rationally, because exposure was so low, one might expect these risk judgments to be uniformly low and unvarying, resulting in little or no correlation with the ratings of Affect. Instead, there was a strong correlation across chemicals between Affect and judged risk of a very small exposure. When the Affect rating was strongly negative, judged risk of a very small exposure was high; when Affect was positive, judged risk was small. Almost every respondent (ninety-five out of ninety-seven) answered in this manner.
6 This will be an important point to remember for chapter 7.
7 Approximately + or – 25 percent to 35 percent range above or below fundamental value, as noted previously.
8 Tversky served on the board of the Dreman Foundation.
9 Writer’s gripe: if it is not a United Express flight to a Colorado ski resort, which almost seems to be congenitally late.
10 The base rate’s trampling the case rate is also found with other important cognitive heuristics, as we shall see shortly.
11 Mortgage agencies raised their securities quality ratings enormously over where they should have been; this was an important cause of the financial crisis.
12 Also known as the Markowitz-Sharpe-Lintner-Mossin theory.
13 MPT looks to optimi
ze return for a given level of risk. CAPM states that the only way to receive higher returns is to take higher risk; lower risk will result in lower returns.
14 The late Paul Samuelson, a Nobel laureate, contributed one of the most important papers to this subject: “Proof That Properly Anticipated Prices Fluctuate Randomly,” Industrial Management Review (Spring 1945).
15 The initial work by Bachelier showed that similar movements of commodity prices were thought to be random, as were the particles in Brownian motion. The research did not show that stock prices themselves were random.
16 There are hundreds of such indicators. Price volume statistics measure whether stocks are moving up or down on increasing volume. A stock rising on increasing volume is considered bullish, and a stock going down is bearish. Nikolai D. Kondratieff was a brilliant Russian economist who is known for the Kondratieff wave, major self-correcting economic cycles that the European economies seemed to go through approximately every fifty years. After holding a major position under Lenin after the Russian Revolution, he disappeared into the Soviet Gulag system and was never heard from again.
17 A significant number of academics still believe in, or at least still stay employed, teaching these subjects.
18 Horses and cavalry were then unknown in the Americas.
19 Beta is calculated using regression analysis. Nonmathematically, think of beta as a number signifying the relative volatility of a stock compared with the broad market.
20 Later called the weak form of EMH.
21 As of 2011, he is the Robert R. McCormick Distinguished Service Professor of Finance at the University of Chicago Booth School of Business.
22 In 1992, Professor Fama himself “discovered” that contrarian strategies actually worked, after dismissing them for twenty years because of claimed methodological errors. They were then considered an “anomaly.”
23 In fact, numerous research studies, including some on contrarian strategies, were dismissed for one methodological cause or another—yet after they were redone, correcting for the methodological criticisms, the findings remained valid.
24 The Russell 2000 Index is the most widely followed small-company index.
25 For a definition and details, please see text below.
26 Shorting S&P 500 futures had the same effect on a portfolio as selling stocks directly and was believed to be much easier to do. If a money manager decided to lower his equity exposure by 10 percent, he could short the S&P futures 10 percent and get the same result.
27 Another term for index arbitrage.
28 The studies were flawed because the time periods were too short and did not cover characteristics of behavior in different market cycles.
29 According to EMH, futures cannot fall below the cash price for the same expiration date. For details, see page 114.