Predictive Index Quotes

Two-thirds of professionally managed funds are regularly outperformed by a broad capitalization-weighted index fund with equivalent risk, and those that do appear to produce excess returns in one period are not likely to do so in the next. The record of professionals does not suggest that sufficient predictability exists in the stock market to produce exploitable arbitrage opportunities.

We needed a man to repair the machines, to keep them going and everything. And the army was always going to send this fellow they had, but he was always delayed. Now, we always were in a hurry. Everything we did, we tried to do as quickly as possible. In this particular case, we worked out all the numerical steps that the machines were supposed to do—multiply this, and then do this, and subtract that. Then we worked out the program, but we didn’t have any machine to test it on. So we set up this room with girls in it. Each one had a Marchant: one was the multiplier, another was the adder. This one cubed—all she did was cube a number on an index card and send it to the next girl. We went through our cycle this way until we got all the bugs out. It turned out that the speed at which we were able to do it was a hell of a lot faster than the other way, where every single person did all the steps. We got speed with this system that was the predicted speed for the IBM machine. The only difference is that the IBM machines didn’t get tired and could work three shifts. But the girls got tired after a while.

An American home has not, historically speaking, been a lucrative investment. In fact, according to an index developed by Robert Shiller and his colleague Karl Case, the market price of an American home has barely increased at all over the long run. After adjusting for inflation, a $10,000 investment made in a home in 1896 would be worth just $10,600 in 1996. The rate of return had been less in a century than the stock market typically produces in a single year.

The single genetic variant identified that most powerfully predicted height explained all of 0.4 percent—four tenths of one percent—of the variation in height, and all those hundreds of variants put together explained only about 10 percent of the variation. Meanwhile, an equally acclaimed study did a GWAS regarding body mass index (BMI). Similar amazingness—almost a quarter million genomes examined, even more authors than the height study. And in this case the single most explanatory genetic variant identified accounted for only 0.3 percent of the variation in BMI.

Novice forecasters often ask why not just say 0.5, coin toss, whenever they “know nothing” about a problem. There are several reasons why not. One is the risk of being ensnared in self-contradictions. Imagine you are asked whether the Nikkei stock index will close above 20,000 by June 30, 2015. Knowing nothing, you say 0.5 chance. Now suppose you are asked whether it will close above 22,000—and you again say 0.5—or between 20,000 and 22,000, and you again say 0.5. The more possibilities the questioner unpacks, the more obvious it becomes that the casual user of 0.5 is assigning incoherent probabilities that far exceed 1.0. See Amos Tversky and Derek Koehler, “Support Theory: A Nonextensional Representation of Subjective Probability,” Psychological Review

Each business process is represented by a dimensional model that consists of a fact table containing the event's numeric measurements surrounded by a halo of dimension tables that contain the textual context that was true at the moment the event occurred. This characteristic star-like structure is often called a star join, a term dating back to the earliest days of relational databases. Figure 1.5 Fact and dimension tables in a dimensional model. The first thing to notice about the dimensional schema is its simplicity and symmetry. Obviously, business users benefit from the simplicity because the data is easier to understand and navigate. The charm of the design in Figure 1.5 is that it is highly recognizable to business users. We have observed literally hundreds of instances in which users immediately agree that the dimensional model is their business. Furthermore, the reduced number of tables and use of meaningful business descriptors make it easy to navigate and less likely that mistakes will occur. The simplicity of a dimensional model also has performance benefits. Database optimizers process these simple schemas with fewer joins more efficiently. A database engine can make strong assumptions about first constraining the heavily indexed dimension tables, and then attacking the fact table all at once with the Cartesian product of the dimension table keys satisfying the user's constraints. Amazingly, using this approach, the optimizer can evaluate arbitrary n-way joins to a fact table in a single pass through the fact table's index. Finally, dimensional models are gracefully extensible to accommodate change. The predictable framework of a dimensional model withstands unexpected changes in user behavior. Every dimension is equivalent; all dimensions are symmetrically-equal entry points into the fact table. The dimensional model has no built-in bias regarding expected query patterns. There are no preferences for the business questions asked this month versus the questions asked next month. You certainly don't want to adjust schemas if business users suggest new ways to analyze their business.

April 2 MORNING “He answered him to never a word.” — Matthew 27:14 HE had never been slow of speech when He could bless the sons of men, but He would not say a single word for Himself. “Never man spake like this Man,” and never man was silent like Him. Was this singular silence the index of His perfect self-sacrifice? Did it show that He would not utter a word to stay the slaughter of His sacred person, which He had dedicated as an offering for us? Had He so entirely surrendered Himself that He would not interfere in His own behalf, even in the minutest degree, but be bound and slain an unstruggling, uncomplaining victim? Was this silence a type of the defenselessness of sin? Nothing can be said in palliation or excuse of human guilt; and, therefore, He who bore its whole weight stood speechless before His judge. Is not patient silence the best reply to a gainsaying world? Calm endurance answers some questions infinitely more conclusively than the loftiest eloquence. The best apologists for Christianity in the early days were its martyrs. The anvil breaks a host of hammers by quietly bearing their blows. Did not the silent Lamb of God furnish us with a grand example of wisdom? Where every word was occasion for new blasphemy, it was the line of duty to afford no fuel for the flame of sin. The ambiguous and the false, the unworthy and mean, will ere long overthrow and confute themselves, and therefore the true can afford to be quiet, and finds silence to be its wisdom. Evidently our Lord, by His silence, furnished a remarkable fulfillment of prophecy. A long defence of Himself would have been contrary to Isaiah’s prediction. “He is led as a lamb to the slaughter, and as a sheep before her shearers is dumb, so He openeth not His mouth.” By His quiet He conclusively proved Himself to be the true Lamb of God. As such we salute Him this morning. Be with us, Jesus, and in the silence of our heart, let us hear the voice of Thy love.

By buying a share in a “total market” index fund, you acquire an ownership share in all the major businesses in the economy. Index funds eliminate the anxiety and expense of trying to predict which individual stocks, bonds, or mutual funds will beat the market.

In an ideal world, the intelligent investor would hold stocks only when they are cheap and sell them when they become overpriced, then duck into the bunker of bonds and cash until stocks again become cheap enough to buy. From 1966 through late 2001, one study claimed, $1 held continuously in stocks would have grown to $11.71. But if you had gotten out of stocks right before the five worst days of each year, your original $1 would have grown to $987.12.1 Like most magical market ideas, this one is based on sleight of hand. How, exactly, would you (or anyone) figure out which days will be the worst days—before they arrive? On January 7, 1973, the New York Times featured an interview with one of the nation’s top financial forecasters, who urged investors to buy stocks without hesitation: “It’s very rare that you can be as unqualifiedly bullish as you can now.” That forecaster was named Alan Greenspan, and it’s very rare that anyone has ever been so unqualifiedly wrong as the future Federal Reserve chairman was that day: 1973 and 1974 turned out to be the worst years for economic growth and the stock market since the Great Depression.2 Can professionals time the market any better than Alan Green-span? “I see no reason not to think the majority of the decline is behind us,” declared Kate Leary Lee, president of the market-timing firm of R. M. Leary & Co., on December 3, 2001. “This is when you want to be in the market,” she added, predicting that stocks “look good” for the first quarter of 2002.3 Over the next three months, stocks earned a measly 0.28% return, underperforming cash by 1.5 percentage points. Leary is not alone. A study by two finance professors at Duke University found that if you had followed the recommendations of the best 10% of all market-timing newsletters, you would have earned a 12.6% annualized return from 1991 through 1995. But if you had ignored them and kept your money in a stock index fund, you would have earned 16.4%.

Just as calories differ according to how they affect the body, so too do carbohydrates. All carbohydrates break down into sugar, but the rate at which this occurs in the digestive tract varies tremendously from food to food. This difference forms the basis for the glycemic index (GI). The GI ranks carbohydrate-containing foods according to how they affect blood glucose, from 0 (no affect at all) to 100 (equal to glucose). Gram for gram, most starchy foods raise blood glucose to very high levels and therefore have high GI values. In fact, highly processed grain products – like white bread, white rice, and prepared breakfast cereals – and the modern white potato digest so quickly that their GI ratings are even greater than table sugar (sucrose). So for breakfast, you could have a bowl of cornflakes with no added sugar, or a bowl of sugar with no added cornflakes. They would taste different but, below the neck, act more or less the same. A related concept is the glycemic load (GL), which accounts for the different carbohydrate content of foods typically consumed. Watermelon has a high GI, but relatively little carbohydrate in a standard serving, producing a moderate GL. In contrast, white potato has a high GI and lots of carbohydrate in a serving, producing a high GL. If this sounds a bit complicated, think of GI as describing how foods rank in a laboratory setting, whereas GL as applying more directly to a real-life setting. Research has shown that the GL reliably predicts, to within about 90 percent, how blood glucose will change after an actual meal – much better than simply counting carbohydrates as people with diabetes have been taught to do.

If I'm being totally honest, I didn't pick the lock." She spun the lock around her index finger. "I guessed the combination." Jackson swallowed what felt like sand. The combination was her birthday. "That's what I get for being predictable.

When playing a bear market, the same rules hold: You want to diversify your risks, especially knowing that collapses move even faster than rallies. You need to decide how much safe cash or near cash you want to hold to sleep at night and to handle financial emergencies, like the loss of your job or your house. Then decide how much to put into longer-term high-quality bonds, like those 30-year Treasuries and AAA corporates, but I think it’s still premature to make this move at the time of this writing, in August 2017. Then decide how much you want to put into a dollar bull fund or the ETF UUP, which tracks the U.S. dollar versus its six major trading partners. If you’re willing to risk part of your wealth, you can also bet on financial assets going down—from stocks to gold. Stocks are the one type of financial asset that goes down in either a deflationary crisis, like the 1930s, or an inflationary one, like the 1970s. So shorting stocks is the best way to prosper in the downturn, either way. But don’t leverage this bet. The markets are simply too volatile. You can short the stock market with no leverage by simply buying an ETF (exchange-traded fund) like the ProShares Short S&P 500 (NYSEArca: SH). It’s an inverse fund on the S&P 500, so if the index goes down 50 percent, you make 50 percent. The ProShares Ultrashort (NYSEArca: QID) is double short the NASDAQ 100, which is likely to get hit the worst. If you make this play, just do a half share, to avoid that two-times leverage (hold the other half in cash or short-term bonds). Direxion Daily Small Cap Bear 3X ETF (NYSEArca: TZA) is triple short the Russell 2000, which is also likely to lead on the way down. So buy only a one-third share of this one, to remain without leverage. (That means the money you allocate here should be one-third in TZA and two-thirds in cash, to offset the leverage.) And unlike the gold bugs, I see gold collapsing. It’s an inflation hedge, not a deflation hedge. If gold rallies back as high as $1,425—on my predicted bear-market rally—then it could easily drop to around $700 within a year. Your last decision is whether to risk some of your funds betting on gold’s downside, for the greatest potential returns. You can buy DB Gold Double Short ETN (NYSEArca: DZZ)—double short gold—at a half share, to offset the leverage, or just simply short GLD, the ETF that follows gold. There you have it. How to handle the coming crash.

Moderate P/E ratio. Graham recommends limiting yourself to stocks whose current price is no more than 15 times average earnings over the past three years. Incredibly, the prevailing practice on Wall Street today is to value stocks by dividing their current price by something called “next year’s earnings.” That gives what is sometimes called “the forward P/E ratio.” But it’s nonsensical to derive a price/earnings ratio by dividing the known current price by unknown future earnings. Over the long run, money manager David Dreman has shown, 59% of Wall Street’s “consensus” earnings forecasts miss the mark by a mortifyingly wide margin—either underestimating or overestimating the actual reported earnings by at least 15%.2 Investing your money on the basis of what these myopic soothsayers predict for the coming year is as risky as volunteering to hold up the bulls-eye at an archery tournament for the legally blind. Instead, calculate a stock’s price/earnings ratio yourself, using Graham’s formula of current price divided by average earnings over the past three years.3 As of early 2003, how many stocks in the Standard & Poor’s 500 index were valued at no more than 15 times their average earnings of 2000 through 2002? According to Morgan Stanley, a generous total of 185 companies passed Graham’s test.

A study indicated that body mass index (BMI) and traumatic cause independently predict coccyx injury and pain treatment outcomes.

To maximize pleasure and to minimize pain - in that order - were characteristic Enlightenment concerns. This generally more receptive attitude toward good feeling and pleasure would have significant long-term consequences. It is a critical difference separating Enlightenment views on happiness from those of the ancients. There is another, however, of equal importance: that of ambition and scale. Although the philosophers of the principal classical schools sought valiantly to minimize the role of chance as a determinant of human happiness, they were never in a position to abolish it entirely. Neither, for that matter, were the philosophers of the eighteenth century, who, like men and women at all times, were forced to grapple with apparently random upheavals and terrible reversals of forture. The Lisbon earthquake of 1755 is an awful case in point. Striking on All Saints' Day while the majority of Lisbon's inhabitants were attending mass, the earthquake was followed by a tidal wave and terrible fires that destroyed much of the city and took the lives of tens of thousands of men and women. 'Quel triste jeu de hasard que le jeu de la vie humaine,' Voltaire was moved to reflect shortly thereafter: 'What a sad game of chance is this game of human life.' He was not alone in reexamining his more sanguine assumptions of earlier in the century, doubting the natural harmony of the universe and the possibilities of 'paradise on earth'; the catastrophe provoked widespread reflection on the apparent 'fatality of evil' and the random occurrence of senseless suffering. It was shortly thereafter that Voltaire produced his dark masterpiece, Candide, which mocks the pretension that this is the best of all possible worlds. And yet, in many ways, the incredulity expressed by educated Europeans in the earthquake's aftermath is a more interesting index of received assumptions, for it demonstrates the degree to which such random disasters were becoming, if not less common, at least less expected. Their power to shock was magnified accordingly, but only because the predictability and security of daily existence were increasing, along with the ability to control the consequences of unforeseen disaster. When the Enlightened Marquis of Pombal, the First Minister of Portugal, set about rebuilding Lisbon after the earthquake, he paid great attention to modern principles of architecture and central planning to help ensure that if such a calamity were to strike again, the effects would be less severe. To this day, the rebuilt Lisbon of Pombal stands as an embodiment of Enlightened ideas. Thus, although eighteenth-century minds did not - and could not - succeed in mastering the random occurrences of the universe, they could - and did - conceive of exerting much greater control over nature and human affairs. Encouraged by the examples of Newtonian physics, they dreamed of understanding not only the laws of the physical universe but the moral and human laws as well, hoping one day to lay out with precision what the Italian scholar Giambattista Vico described as a 'new science' of society and man. It was in the eighteenth century, accordingly, that the human and social sciences were born, and so it is hardly surprising that observers turned their attention to studying happiness in similar terms. Whereas classical sages had aimed to cultivate a rarified ethical elite - attempting to bring happiness to a select circle of disciples, or at most to the active citizens of the polis - Enlightenment visionaries dreamed of bringing happiness to entire societies and even to humanity as a whole.

Security analysis does not assume that a past average will be repeated, but only that it supplies a rough index to what may be expected of the future. A trend, however, cannot be used as a rough index; it represents a definite prediction of either better or poorer results, and it must be either right or wrong.

After a seminal paper in 2010 (see Bollen et al., 2011), the topic of alternative data started getting traction both in academia and in the hedge fund industry. The paper showed an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the Dow Jones index when using Twitter mood data. This

The Political Instability Task Force (the one I later joined) came up with dozens of social, economic, and political variables—thirty-eight, to be precise, including poverty, ethnic diversity, population size, inequality, and corruption—and put them into a predictive model. To everyone’s surprise, they found that the best predictor of instability was not, as they might have guessed, income inequality or poverty. It was a nation’s polity index score, with the anocracy zone being the place of greatest danger. Anocracies, particularly those with more democratic than autocratic features—what the task force called “partial democracies”—were twice as likely as autocracies to experience political instability or civil war, and three times as likely as democracies.25 All the things that experts thought should matter in the outbreak of civil war somehow didn’t. It wasn’t the poorest countries that were at the highest risk of conflict, or the most unequal, or the most ethnically or religiously heterogeneous, or even the most repressive. It was living in a partial democracy that made citizens more likely to pick up a gun and begin to fight. Saddam Hussein never

The Nydia you left behind. She’s gone. This person in front of you likes predictable things, she doesn’t like to travel, and she prefers to have dinner in a quiet place. Not some fancy restaurant where people can see you.” He leans closer and curls his index finger as if telling me to come-closer-I-have-a-secret-for-you. I do, and he whispers, “Oh, she’s there. I’ve seen her. We just need to peel back a couple of layers.” He shrugs. “Maybe more. I’m willing to do the work.

When we compared the two indexes using diet data from the Nurses’ Health Study and the Health Professionals Follow-Up Study, the Alternative Healthy Eating Index was far better at predicting the development of cardiovascular disease and other chronic conditions

Technical analysis is more concerned with the price movements of a stock or an index by examining historical records of trading activity. A technical analyst looks at past data to predict future price movements. They believe that history tends to repeat itself in the stock market and that past performance is the best indicator of what will happen in the future.

Myers-Briggs, DISC, StrengthsFinder, Caliper, Johnson-O’Connor, AIMS, Strong-Campbell, Birkman, Predictive Index, Wechsler Adult Intelligence Scale, MMPI, the Enneagram, Lion/Otter/Beaver/Retriever, True Colors. Many NFL teams use the Wonderlic test to assess the smarts of aspiring quarterbacks. Other

Etiology l Genetic studies provide evidence that bipolar disorder is strongly heritable and that depression is somewhat heritable. l Neurobiological research has focused on the sensitivity of receptors rather than on the amount of various transmitters, with the strongest evidence for diminished sensitivity of the serotonin receptors in depression and mania. There is some evidence that mania is related to heightened sensitivity of the dopamine receptors and that depression is related to diminished sensitivity of dopamine receptors. l Bipolar and unipolar disorders seem tied to elevated activity of the amygdala and the subgenual anterior cingulate and to diminished activity in the dorsolateral prefrontal cortex and hippocampus during tasks that involve emotion and emotion regulation. During mania, greater levels of activation of the striatum have been observed. Mania also may involve elevations in protein kinase C. l Overactivity of the hypothalamic–pituitary–adrenal axis (HPA), as indexed by poor suppression of cortisol by dexamethasone, is related to severe forms of depression and to bipolar disorder. l Socioenvironmental models focus on the role of negative life events, lack of social support, and family criticism as triggers for episodes but also consider ways in which a person with depression may elicit negative responses from others. People with less social skill and those who tend to seek excessive reassurance are at elevated risk for the development of depression. l The personality trait that appears most related to depression is neuroticism. Neuroticism predicts the onset of depression. l Influential cognitive theories include Beck’s cognitive theory, hopelessness theory, and rumination theory. All argue that depression can be caused by cognitive factors, but the nature of the cognitive factors differs across

Table 14.1 also shows R-square (R2), which is called the coefficient of determination. R-square is of great interest: its value is interpreted as the percentage of variation in the dependent variable that is explained by the independent variable. R-square varies from zero to one, and is called a goodness-of-fit measure.5 In our example, teamwork explains only 7.4 percent of the variation in productivity. Although teamwork is significantly associated with productivity, it is quite likely that other factors also affect it. It is conceivable that other factors might be more strongly associated with productivity and that, when controlled for other factors, teamwork is no longer significant. Typically, values of R2 below 0.20 are considered to indicate weak relationships, those between 0.20 and 0.40 indicate moderate relationships, and those above 0.40 indicate strong relationships. Values of R2 above 0.65 are considered to indicate very strong relationships. R is called the multiple correlation coefficient and is always 0 ≤ R ≤ 1. To summarize up to this point, simple regression provides three critically important pieces of information about bivariate relationships involving two continuous variables: (1) the level of significance at which two variables are associated, if at all (t-statistic), (2) whether the relationship between the two variables is positive or negative (b), and (3) the strength of the relationship (R2). Key Point R-square is a measure of the strength of the relationship. Its value goes from 0 to 1. The primary purpose of regression analysis is hypothesis testing, not prediction. In our example, the regression model is used to test the hypothesis that teamwork is related to productivity. However, if the analyst wants to predict the variable “productivity,” the regression output also shows the SEE, or the standard error of the estimate (see Table 14.1). This is a measure of the spread of y values around the regression line as calculated for the mean value of the independent variable, only, and assuming a large sample. The standard error of the estimate has an interpretation in terms of the normal curve, that is, 68 percent of y values lie within one standard error from the calculated value of y, as calculated for the mean value of x using the preceding regression model. Thus, if the mean index value of the variable “teamwork” is 5.0, then the calculated (or predicted) value of “productivity” is [4.026 + 0.223*5 =] 5.141. Because SEE = 0.825, it follows that 68 percent of productivity values will lie 60.825 from 5.141 when “teamwork” = 5. Predictions of y for other values of x have larger standard errors.6 Assumptions and Notation There are three simple regression assumptions. First, simple regression assumes that the relationship between two variables is linear. The linearity of bivariate relationships is easily determined through visual inspection, as shown in Figure 14.2. In fact, all analysis of relationships involving continuous variables should begin with a scatterplot. When variable

In these uncertain days, bond funds are an especially important option for investors. Unlike stock funds, they have high predictability in at least these five ways: (1) The current yields (on longer-term issues) are an excellent—if imperfect—predictor of future returns. (2) The range of gross returns earned by bond managers clusters in an inevitably narrow range that is established by the current level of interest rates in each sector of the market. (3) The choices are wide. As the maturity date lengthens, volatility of principal increases, but volatility of income declines. (4) Whether taxable or municipal, bond fund returns are highly correlated with one another. Municipal bond funds are fine choices for investors in high tax brackets, and inflation-protected bond funds are a sound option for those who believe that much higher living costs will result from the huge federal government deficits of this era. (5) The greatest constant of all is that—given equivalent portfolio quality and maturity—lower costs mean higher returns. (Don’t forget that index bond funds—or their equivalent—carry the lowest costs of all.)

Ownership is dead. Access is the new imperative. International Data Corporation (IDC) predicts that by 2020, 50 percent of the world’s largest enterprises will see the majority of their business depend on their ability to create digitally enhanced products, services, and experiences. Focusing on services over products is also a sound business strategy. Zuora’s Subscription Economy Index, which you’ll find at the end of this book, shows that subscription-based companies are growing eight times faster than the S&P 500 and five times faster than US retail sales.

Leigh Freeman moved his printing press to Laramie and set about publishing the Frontier Index there. In its first issue, May 5 [1868], the paper predicted that Laramie would soon rival Chicago. When it was only two weeks old, the Index boasted, “Laramie already contains a population of two thousand inhabitants.