star mine arm_white_paper

StarMine white paper 20 August 2007

StarMine Analyst Revisions Model (ARM) Global Performance

George Bonne, Ph.D. +1 (415) 874 – 8158 [email protected] Ali Jahansouz David Lichtblau Stephen Malinak, Ph.D.

Abstract The StarMine Analyst Revisions Model (StarMine ARM) is an analyst revision-based stock ranking system that improves on existing revisions models by incorporating more accurate earnings estimates through StarMine’s proprietary SmartEstimate® earnings prediction service; by including estimates on multiple fiscal periods; by utilizing other financial measures in addition to earnings; and by considering changes in analyst recommendations. StarMine ARM is a robust predictor of future earnings revisions and surprises, which are important drivers of future returns. Globally, the top decile of StarMine ARM has outperformed the bottom decile by 29% per year in the February 1995 – June 2007 period. The model is robust across regions, time periods, holding periods, sectors, and capitalization ranges. The results are also superior to those of a conventional consensus change model. The model is available as a referential tool via the StarMine Professional web service, via daily data feed, and through historical testing files.

Contents

1. Introduction ____________________________________________ 2

2. Understanding the earnings revisions anomaly ________________ 3

3. Measuring analyst accuracy & creating more accurate estimates __ 7

4. Improving predictions of future revisions with SmartEstimates___ 10

5. Constructing the Analyst Revisions Model ___________________ 12

6. Data density ___________________________________________ 13

7. StarMine’s outlook for the Analyst Revisions Model ___________ 16

8. Conclusions____________________________________________ 16

9. Data and StarMine’s model construction techniques ___________ 17

10. Historical Performance of the Analyst Revisions Model ________ 18

11. References_____________________________________________ 26

StarMine and SmartEstimate are registered trademarks of StarMine Corporation.

StarMine white paper: StarMine Analyst Revisions Model 8/20/2007

StarMine confidential. Do not copy or distribute. 2

1. Introduction This paper presents the StarMine Analyst Revisions Model (ARM), our enhanced revisions-based stock-ranking system. StarMine ARM is the successor to our existing revisions models, the StarMine Indicator (SMI) and the Supplemental Revisions Model (SRM). Essentially, StarMine ARM intelligently incorporates the information utilized in both SMI and SRM into a single measure of the overall change in analyst sentiment. Before we describe model formulation or historical performance details, we will first investigate the fundamentals of the earnings revisions anomaly on which the model is based in order to understand why it works at all and to provide insight into how one might build a better earnings revisions model. The “earnings revisions anomaly” was first documented in the 1970’s [see for example Elton and Gruber, 1972; Givoly and Lakonishok, 1979] and continues to be among the most successful quantitative stock selection strategies [see for example Chan et al., 1996]. The observation is that stocks experiencing upward analyst earnings estimate revisions tend to outperform stocks experiencing downward earnings estimate revisions. That is, trailing analyst revisions are positively correlated to future returns. The phenomenon has persisted since its discovery and has been documented globally, providing convincing evidence of a fundamental market inefficiency. In today’s increasingly efficient markets, however, exploiting the anomaly has become more difficult and now requires more advanced strategies. The reason the anomaly exists is not because investors are slow to react, but rather because earnings revisions have a tendency to trend. Past revisions are correlated with future revisions and future changes in earnings expectations are a major driver of future stock price changes. Because past analyst revisions are correlated to future analyst revisions, and future analyst revisions drive stock price changes, past analyst revisions are also (albeit to a much smaller extent) correlated to future stock price changes. Thus, improving the efficacy of earnings revisions models requires creating better forecasts of future analyst revisions. The naïve extrapolation of past revisions serves as a simple starting point for this goal. We note that past earnings surprises are not inputs into StarMine ARM. A strategy based on analyst revisions (such as StarMine ARM) is markedly different than one based on past earnings surprises. This paper documents how StarMine’s Analyst Revisions Model enhances basic earnings revisions models to better predict future changes in analyst sentiment. StarMine improves on prior strategies by:

1) Analyzing revisions at the individual analyst level, not just the consensus level. For each analyst on each stock, StarMine objectively measures the analyst’s historical accuracy in an effort to better predict the direction of future estimate revisions and actual reported earnings.

2) Creating a composite forecast more accurate than the consensus. The “consensus” estimate that drives most earnings revisions models (and forms the benchmark in earnings surprise strategies) is literally a simple average of all analyst published forecasts, regardless of the quality of those analysts or the age of their estimates. StarMine creates a more accurate estimate by putting more weight on the most accurate analysts and most recent estimates. This “SmartEstimate” serves as a predictor of future analyst revisions and future earnings surprises.

3) Considering multiple forecast horizons. Stock prices are driven by a blend of all future analyst expectations, as reflected in near-term (current quarter) estimates as well as



longer-term (this year and next year) estimates. StarMine blends multiple time horizons into a composite revisions score.

4) Incorporating data from other financial measures in addition to earnings. While revisions and SmartEstimates on earnings are the most powerful factors in predicting future price changes, information from other financial measures, such as Revenue and EBITDA, can add incremental value to a pure earnings factor. StarMine intelligently incorporates estimates on these other measures to create a factor that is more powerful than a strictly earnings-based factor.

5) Using changes in buy/sell recommendations as corroborating signals. While the levels of analyst buy/sell recommendations have little value, the changes in recommendation levels do provide a more meaningful signal when they corroborate changes in earnings estimates [Jha et al., 2003; Jegadeesh et al., 2004].

In short, the StarMine advantage is derived by generating a more predictive signal of future changes in analyst sentiment. Indirectly, such predictions serve investors trying to anticipate future stock price changes. StarMine ARM can be used in a stock ranking or screening system, as an input into a quantitative multi-factor investment model, or for reference in a non-quantitative strategy. The structure of the remainder of this paper is as follows: Section 2 discusses the driving forces behind the earnings revision anomaly. Section 3 details StarMine’s methodology for creating SmartEstimates, our proprietary financial estimates that are important inputs of StarMine ARM. Section 4 describes how we utilize SmartEstimates to create better predictions of future revisions. Section 5 describes the construction of StarMine ARM. Section 6 presents information on the historical data density of the three most frequently used financial measures utilized in StarMine ARM. Section 7 outlines our future outlook for StarMine ARM. Section 8 concludes and Section 9 describes the data sources we used and our general model construction techniques. Section 10 presents historical performance results.

2. Understanding the earnings revisions anomaly Savvy investors understand the risks in making statistical inferences from historic stock market data. It is all too easy to identify quantitative “signals” that are correlated with future returns. With thousands of investors combing over the same (limited) sets of historical data, it is no surprise that many “anomalies” get discovered, published, and reworked. Believers in the efficient market hypothesis could attribute many of these findings (rightfully) to identifying false patterns in the noise, as the human brain is naturally wired to do. Others attribute many of these findings to being proxies for some other phenomenon, such as risk. Nonetheless, the evidence that earnings revisions are a true market anomaly is unusually compelling. The anomaly was originally analyzed on U.S. data more than thirty years ago. Since then, it has behaved similarly out-of-sample and has shown similar results in all regions of the world. The outperformance has been demonstrated and exploited based on information then available and has offered excess returns, alpha, higher information coefficients, and a variety of other positive performance characteristics not explained by the traditional “risk factors” such as beta, book to market, or market cap. To illustrate the magnitude of the phenomenon, Figure 1 and Table 1 below display the performance as measured by the information coefficient of a basic earnings revisions factor over the February 1995 – April 2007 period. We define our Basic Revisions model as the percentage



change in the current fiscal year (FY1) consensus estimate over the last 30 days based on data from Thomson/IBES. We define the Information Coefficient as the Spearman rank correlation between a factor and the subsequent stock returns, where the default return horizon is one month. As can be seen, even a simple earnings revision factor has produced a consistently positive information coefficient over the last 12 years in all global markets.

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Figure 1. Trailing twelve month average of monthly Information Coefficient of a basic revisions factor for all stocks by region, Feb 1995 - April 2007. The basic revisions factor has produced a consistently positive information coefficient over the last 12 years in all global markets. Table 1. Average monthly Information Coefficient and t-statistic of a basic earnings revisions factor for all stocks by region, Feb 1995 – April 2007.

Region IC t(IC)

Developed Asia 0.031 8.77

Developed Europe 0.039 11.24

North America 0.028 7.39

Emerging Markets 0.030 9.94 The obvious question is why does this anomaly exist and how could it have survived? Is there an underlying fundamental phenomenon that we can believe in? The traditional explanation centers on the idea of trending behavior by analysts. There is a gradual flow of information from companies to analysts, and analysts vary in their speed of incorporating that information. In turn, there is a delay in the information flowing from the analysts into equilibrium market prices. The trending stems from revisions by “leader” analysts, who are potentially also more insightful, being followed by later revisions from laggard analysts. It has also been suggested that analysts prefer to make incremental changes to their estimates rather than large single changes for fear of



being wrong. In other words, it’s safer to move slowly and with the herd rather than stick your neck out. This conceptual model suggests two potential explanations (not necessarily exclusive) of how the earnings revisions anomaly drives stock prices. One explanation, what might be called the “price drift” theory, is that the market initially underreacts to earnings revisions or the market is slow to reach the new equilibrium price implied by the new information. If this theory were valid, the idea would then be to ride the gradual price response to revisions as the market slowly incorporates the new information. While there is some evidence of post-revision price drift historically, our research suggests it is not the primary driver of the anomaly. The second explanation, and the one that is most supported by our research, is that the primary driver of the earnings revisions anomaly is the contemporaneous response of future stock price movements to future earnings revisions. The primary reason that past revisions are predictive of future stock prices is because past revisions are serially correlated to future revisions. If a slow reaction to past revisions explained the anomaly, one would expect to profit from trading on past revisions regardless of whether they were followed by future revisions. Our research indicates that is simply not the case. Trading on past revisions is profitable only when those revisions are followed by more revisions in the same direction. Table 2 below displays the average information coefficient conditional on the direction of follow-on revisions in FY1 for the 1000 largest North American stocks over the February 1995 – April 2007 period. Results in other regions are similar. Table 2. Average monthly Information Coefficient of a basic earnings revisions factor conditional on the direction of follow-on revisions in the 1000 largest North American stocks, Feb 1995 – April 2007. The t-statistic and the average percentage of stocks satisfying each condition are also shown. The data show that the driver behind the earnings revisions anomaly is that past revisions are predictive of future revisions, rather than that prices slowly drift in response to earnings revisions. Subsequent change in FY1 mean

estimate

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Non-zero and direction of follow-on revisions is:

The same as the previous month's 0.100 9.17 45.5%

Opposite of the previous month's -0.167 -26.68 32.4% In general, when future revisions are non-zero, past revisions are predictive in the aggregate of future price movements, as indicated in the information coefficient of 0.017. Note, however, that the performance is driven by the cases where future revisions line up in the same direction as the past revisions. In those cases, the IC shows a stunning 0.100. On the other hand, when future revisions reverse direction, the whole factor collapses spectacularly with an IC of -0.167. In short, this rules out the “slow response” theory. The key is to predict when past revisions will persist into the future, or better yet, when they will reverse. The upper limit of an earnings revisions strategy is a perfect prediction of future revisions. As an exercise, we simulated a perfect foresight model that reveals the changes in the FY1 consensus estimate one month in advance and these results are shown in Figure 2. Of course, such a strategy can not be implemented in practice, but serves as a useful measure of the maximum potential



value in predicting future revisions. As it turns out, knowledge of next month’s revisions would be incredibly profitable.

Perfect foresight revisions ICs by region

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Figure 2. Trailing twelve month moving average of monthly Information Coefficient of a perfect foresight earnings revisions factor over all stocks by region, Feb 1995 – April 2007. Predicting future revisions is valuable and that value has been generally increasing over the last five years in all global markets. Information coefficients of such a perfect foresight model have been in the 0.1 to 0.2 range over the past several years. It is also interesting to note that in all regions, the value of perfect foresight has generally been increasing over about the last five years and is currently at or near its highest levels of the last twelve years. Thus, future revisions are definitely worth predicting. Our goal is to capture as much of this potential as possible. The basic revisions strategy, which looks purely at past revisions, captures some of this potential. The reason it captures any of it is due to the serial correlation of one period’s revisions to the next. Over the long run, the serial correlation of revisions from month to month has been about 0.2 in most regions, as can be seen in Figure 3. This number has dropped somewhat over the past few years in North America. One possible contributor is the dramatically increased amount of guidance provided in the U.S. since the implementation of Regulation FD (“Fair Disclosure”). One effect of increased corporate guidance is a reduction in the number of incremental revisions and an increase in the number of clusters of significant revisions where many analysts revise in the same direction over a short amount of time.



Serial correlation of earnings revisions

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Figure 3. Trailing six month moving average of the serial correlation of earnings revisions over all stocks by region, Feb 1995 – April 2007. Earnings revisions have shown significantly and consistently positive serial correlation in all global markets. The enactment of Regulation FD in North America has likely contributed to a drop in that region’s serial correlation and highlights the need to generate predictions of future revisions that are better than those based on a simple extrapolation of past revisions. An important consequence of the drop in serial correlation in North America and the increased responsiveness of markets to increased corporate guidance has been the erosion of the efficacy of basic revisions strategies in North America. This begs the question of what can be done to improve our ability to forecast the underlying fundamental metric of interest: future analyst sentiment.

3. Measuring analyst accuracy & creating more accurate estimates

Intuitively, one might expect that some analysts are likely to be consistently more accurate than others. Our research demonstrates that this is true, although we have also found that how one defines analyst accuracy is important. StarMine objectively measures the accuracy of analysts at forecasting earnings with our proprietary metric, the Single-stock Estimate Score (SES). The core inputs into SES includes the forecast error of the analyst relative to all other analysts, the absolute forecast error relative to the actual earnings, and a measure of the dispersion of analyst estimates to control for varying difficulty of forecasting different stocks. To get a high rating, an analyst needs to be different from the mean, early with his revisions, and generally more accurate throughout the entire fiscal period. We construct a time series for every estimate made by each analyst and each of his competitors. We score analysts for each day of a measurement period leading up to the actual report date, as opposed to other studies that have relied only on the last



estimate made in the period. We examine a rolling track record over the most recent four reported quarters (where available) and two reported fiscal years. As an illustration, the analyst in Figure 4 below scored highly during the fiscal period shown due to his estimate (blue line) leading the consensus (thick gold line) throughout the quarter and being closer to the reported earnings (black bar) throughout the quarter.

Figure 4. An example of earnings estimates over one quarter showing the consensus (thick gold line), the estimates of one particular analyst who was scored highly by StarMine (thick blue line,) and the high and low estimates (thin gold lines). Since their release in 1999, we’ve found our analyst ratings to be highly predictive of future analyst accuracy in pure out-of-sample studies both in the U.S. and globally. In fact, analysts who earn our top decile five-star rating in one year are three times as likely to repeat the performance than to drop to our bottom decile one-star rating. Identifying individual analysts that are more likely to be accurate in the future is a key factor in the calculation of StarMine’s SmartEstimate. The “consensus” earnings estimate is clearly a misnomer. Analysts don’t meet and agree on a number. Rather, the consensus is a simple average (mean) of all published forecasts. StarMine’s goal is to generate a more accurate forecast; these forecasts become predictive of future changes in analyst expectations and earnings surprises. StarMine algorithms create SmartEstimates in two steps:

1) Excluding stale estimates and suspected data errors. The first step involves analyzing the range of estimates and discarding severe outliers and obviously stale data (estimates made more than four months prior). More importantly, StarMine employs its proprietary RevisionCluster(sm) analysis service to analyze patterns of analyst revisions and dynamically determine which estimates should be considered “fresh” or “stale”. When a series of analysts revise their estimates in the same direction over a short period of time, StarMine identifies them as a cluster of revisions. Any estimates made before the start of the RevisionCluster are deemed stale and excluded from the calculation of the SmartEstimate. Clusters of revisions typically follow news announcements or the release of other material information on the company or its competitors. In some cases, estimates need to be made within the last week to be considered “fresh”; in others, three-month old

RevisionCluster is a worldwide service mark of StarMine Corporation.



estimates are still perfectly relevant. The key is that the RevisionCluster algorithm dynamically determines which estimates are stale and which are fresh.

2) Weighting the remaining estimates based on the analyst track record and the dates the estimates were made.

StarMine defines the term “Predicted Surprise” as the percent difference between the SmartEstimate and the consensus. Figure 5 below shows a screenshot of the SmartEstimate calculation from the StarMine Professional service. In this cherry-picked example, the blue line (SmartEstimate) led the gold line (consensus estimate) early throughout the quarter, successfully anticipating future revisions and ultimately a negative earnings surprise.

Figure 5. An example from the StarMine Professional service displaying the behavior of the SmartEstimate relative to the consensus and the individual estimates going into the calculation of the SmartEstimate. StarMine finalized the SmartEstimate algorithm in 1999 based on historical studies of U.S. data. The goal was to explicitly improve the accuracy of the earnings forecast; no consideration was made to predicting stock prices. Since then, in pure out-of-sample testing in the U.S. and globally, SmartEstimates have proven to be consistently more accurate than consensus estimates in all markets. When the SmartEstimate diverges from the consensus by two percent or more, the SmartEstimate correctly calls the direction of the ultimate earnings report with a success rate of approximately 70%. For more information on SmartEstimates, please see our white paper, Creating and profiting from more accurate earnings estimates with StarMine Professional.

Estimates prior to the most

recent RevisionCluster were excluded.

Estimates prior to the most

recent RevisionCluster were excluded.

On May 12, 2005:

SmartEstimate: $2.08

Mean: $2.13

Predicted Surprise: - 2.5%

On May 12, 2005:

SmartEstimate: $2.08

Mean: $2.13

Predicted Surprise: - 2.5%

Our Predicted Surprise grew

throughout the quarter, reaching -7.0% at one point.

- results on June 16: $1.71!

Our Predicted Surprise grew throughout the quarter, reaching -7.0% at one point.

The company reported their FQ results on June 16: $1.71!



4. Improving predictions of future revisions with SmartEstimates Basic revisions strategies work due to the serial correlation of revisions. Essentially, a basic revisions strategy is a direct extrapolation of past revisions into the future. We find that we can better anticipate future analyst revisions by combining an extrapolation of past revisions with StarMine’s Predicted Surprise. Consider a stylized example with eight estimates on a stock. In Figure 6 below all eight analysts revised their estimates to the same 75 cents. We might see a step jump like this in response to explicit company guidance. In this case, since all eight analysts have the same estimate of 75 cents, the consensus increased 18 cents (32%) from 57 to 75 cents. Since all analysts have already revised their estimates on this stock, the SmartEstimate equals the consensus (thus Predicted Surprise equals 0.0%) and we are less likely to see subsequent (serial) revisions relative to a case where only several of the analysts revised their estimates up, as in the next example in Figure 7.

Figure 6. Depiction of a hypothetical scenario in which the consensus estimate (gold) and the SmartEstimate (blue) are equal and both move by the same large amount at the same time, but follow-on revisions did not materialize in the subsequent month.

Figure 7. Depiction of a hypothetical scenario in which the consensus estimate (gold) and the SmartEstimate (blue) are initially equal and both move on the same day, but the SmartEstimate moves by more than the consensus, creating a Predicted Surprise. In this scenario, we find that follow-on revisions (and hence future price reaction) are more likely to occur.



In the example in Figure 7, only five of the eight analysts increased their estimates (but to new values near 79 cents). The consensus increased only 13 cents (23%) to 70 cents, but the SmartEstimate jumped to 79 cents. In this case, the Predicted Surprise (SmartEstimate minus Consensus) is nine cents (13%). This scenario occurs when leader analysts, identified previously by their superior track record, anticipate a major change in the earnings outlook. Laggard analysts catch up, but slowly. While we had a smaller consensus change in this example, we can more confidently predict that there will be future analyst revisions in this second case. We expect the laggard or less accurate analysts to move their estimates closer to the leading and more accurate analysts; that is, we expect the consensus estimate to move toward the SmartEstimate. If they do not converge by the time of the earnings report, it is likely that the company will report an earnings surprise. Either way, we expect positive revisions or a positive earnings surprise for this stock. A typical revision model would rank the first example (Figure 6) more highly than the second (Figure 7). ARM ranks the second example higher, because there is a greater chance of future revisions in the second example with the given combination of positive revisions and a positive Predicted Surprise. StarMine creates more accurate predictions of future changes in analyst sentiment (i.e., revisions) by incorporating historical revisions and Predicted Surprises on multiple financial measures into a single revisions score in StarMine ARM. The Preferred-Earnings Revisions Component is the component of StarMine ARM that incorporates revisions and Predicted Surprises on earnings to explicitly predict future earnings revisions. Figure 8 below compares how well a basic revisions factor and the StarMine Preferred-Earnings Revisions Component predict future earnings revisions for large cap stocks. By combining Predicted Surprises with revisions, StarMine is able to create a superior prediction of future revisions.

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Figure 8. Rank correlations to future revisions of a basic revisions factor and the StarMine Preferred-Earnings Revisions Component, average over the Feb 1995 – April 2007 period. By blending Predicted Surprises with revisions, StarMine creates a superior prediction of future revisions.



5. Constructing the Analyst Revisions Model The key inputs to StarMine ARM are:

1) The Predicted Surprise (the percentage difference between StarMine’s SmartEstimate and the consensus estimate) for the current fiscal quarter (FQ1), the current fiscal year (FY1), and next fiscal year (FY2) on preferred earnings (which is most often EPS), secondary earnings (which is most often EBITDA), and revenue.

2) Recent changes in the consensus estimates for the preferred-earnings measure, secondary-earnings measure, and revenue for the FQ1, FY1, and FY2 fiscal periods. We look at the change in the consensus estimate over the last 7, 14, 30, 60 and 90 days.

3) Changes in the average analyst buy/sell/hold recommendation over the last 30, 60 and 90 days.

Figure 9 below displays a pictorial representation of the construction of StarMine ARM. Figure 9. Construction of StarMine ARM. We combine Predicted Surprises and changes in the consensus estimate for the FQ1, FY1, and FY2 fiscal periods on preferred earnings, secondary earnings, and revenue. We also incorporate recommendation changes in the ARM score. We combine Predicted Surprises and consensus changes on three separate financial measures—preferred earnings, secondary earnings, and revenue—to create an overall revisions component score for each measure. We blend Predicted Surprises with consensus changes in a non-linear fashion that emphasizes agreement between the two elements. We then combine the revisions component scores for each measure with the Recommendation Revisions Component to form the final StarMine ARM score. We provide additional details of the model construction below. The weighting of the three estimate revisions components (preferred earnings, secondary earnings, and revenue) in the final StarMine ARM score is a function of the number of analysts providing estimates on each measure and the profitability of the company. We start with a set of “equal-coverage weights” that are a function of profitability and assume the same number of

ARMScore

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analysts providing estimates on all three measures. These equal-coverage weights are consistent with the way companies are often valued, namely that the most weight is given to the item farthest down on the income statement that is still positive. Thus, for companies with positive preferred earnings, the Preferred-Earnings Revisions Component will have the most weight. For companies that have negative preferred earnings (usually EPS) but positive secondary earnings (usually EBITDA), the most weight is given to the Secondary-Earnings Revisions Component. Similarly, when both preferred earnings and secondary earnings are negative, the Revenue Revisions Component receives the most weight. We then adjust the equal-coverage weights to account for different analyst coverage among the three measures, boosting the weight of the measure with the most coverage and decreasing the weight of the measure with the least coverage. Finally, the information from changes in recommendations is combined with the estimate revisions components to form the final StarMine ARM score. Unlike many stock selection factors, StarMine’s Preferred-Earnings Revisions Component was designed to predict future changes in analyst expectations (earnings estimates), not stock price performance directly. The methodology behind the Preferred-Earnings Revisions Component was then applied to the other estimate revisions components and the three estimate revisions components are blended together in the scheme described above. The Recommendation Revisions Component is the fourth component of the model. It is a percentile rank of a weighted blend of 30, 60, and 90-day changes to the mean recommendation on a stock, and is used in conjunction with earnings revisions as a corroborating signal for the direction of analyst sentiment. The three estimate revisions components, Recommendation Revisions Component, and final StarMine ARM scores are all relative percentile rankings. That is, stocks are ranked from 1 to 100, with the most positive stocks scoring 100 and the most negative stocks scoring 1. While both the estimate revisions components and the Recommendation Revisions Component are powerful signals on a stand-alone basis, their explanatory power is significantly increased when they are used in combination. The final StarMine ARM score is provided as a 1 to 100 percentile region-relative ranking. Both country-relative and global ranking scores are also available. Tables 3-9 in section 10 display historical performance of StarMine ARM in various geographic regions.

6. Data density In this section we show the coverage level of the three most frequently used financial measures utilized in StarMine ARM by region. In each region, coverage has been quite good on all measures in recent years. Coverage in earlier years, particularly in North America and Emerging Markets, has been thinner on some measures.



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Figure 12. Number of stocks with FY1 estimates on the given measure over time in North America. For the past four years coverage has been quite good on all three measures.


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EPS EBITDA Revenue

Figure 13. Number of stocks with FY1 estimates on the given measure over time in Emerging Markets. For the past three years coverage has been quite good on all three measures.



7. StarMine’s outlook for the Analyst Revisions Model With any quantitative stock ranking factor, it is critical to first understand the underlying fundamental relationship or anomaly that the factor is attempting to exploit. History is littered with the failures of “anomalies,” findings in the “technical analysis” literature, and other quantitative, statistical, and anecdotal discoveries in the markets that have turned out to have no lasting predictive power beyond random chance. Nevertheless, StarMine ARM captures an anomaly that is truly fundamental. Stock prices are ultimately tied to market expectations, and StarMine ARM is a superior forecasting tool for changes in future analyst expectations. The historical effectiveness of StarMine ARM, when judged on the price performance measures frequently used within quantitative research teams, such as fractile spreads, information coefficients, Sharpe ratios, and the like, is impressive (see the extensive performance statistics in Section 10). By basing StarMine ARM on an anomaly with solid fundamental grounding and using rigorous model construction techniques to avoid the pitfalls of data mining (see Section 9), we expect the strong performance of the model to extend well into the future. Specifically, our expectations are:

1) Earnings forecasting accuracy will continue to be a persistent skill among analysts. That is, StarMine will be able to identify the analysts that have been most accurate, and these analysts will have a statistical edge over the consensus in their ability to predict earnings.

2) SmartEstimates will continue to successfully predict earnings revisions and surprises. We expect our earnings estimates to be more accurate than the consensus in aggregate, in nearly all future years.

3) The profitability of “perfect foresight” of revisions will continue to be significant. Even as markets get increasingly efficient and anomalies get arbitraged away, there will always be significant investment value in correctly anticipating changes in expectations. The value of perfect foresight of analyst revisions has fluctuated but has not trended down over the past 10 years—it has actually generally been increasing (see Figure 2 on page 6).

4) StarMine will anticipate future changes in analyst expectations better than simple extrapolations of past revisions and this will continue to lead to improved performance as a stock selection factor. Note that the “StarMine Value Added” column in the tables in the Appendix has not materially degraded over time in any geographic region.

5) In market conditions when earnings and analyst expectations matter, StarMine ARM will add value. No single factor adds portfolio value in every year. Similarly, neither earnings, earnings revisions, nor analyst expectations more broadly always drive markets. But in the long term, we believe they are similar, key fundamental drivers of stock prices.

8. Conclusions Changes in analyst expectations are direct drivers of stock prices. Generally, the value of revisions strategies based on trailing revisions varies with the degree of serial correlation in revisions and the value of perfect foresight. The serial correlation of revisions has remained fairly constant over the last 12 years except in North America, where the enactment of Regulation FD has likely contributed to a decrease in correlation. Also, the value of perfect foresight of revisions



has generally been increasing in all regions over the last several years and is now at or near the highest levels of its historical range. Thus, future revisions are definitely worth predicting and those who can create a good prediction of future revisions will do well. By intelligently combining revisions and Predicted Surprises of earnings with those of other financial measures as well as recommendations, StarMine has done exactly that—created a better prediction of future changes in analyst sentiment. For investors who decide to include a revisions factor in their models, StarMine ARM consistently outperforms a basic revisions strategy. There has been no convincing ex ante model that anticipates under what market conditions perfect foresight of revisions will work best (and hence when revisions strategies in general will work best). Therefore, we recommend that investors keep their revisions strategies “on” all the time, rather than attempt to “market time” the use of this fundamental factor. Current StarMine ARM scores are available via the StarMine Professional web service and via daily data feed. Scores are also available in historical files for those quantitative managers who wish to backtest the model.

9. Data and StarMine’s model construction techniques We sample month-end data from February 1995 through April 2007 from the I/B/E/S detailed analyst-by-analyst (as opposed to consensus) estimates. We use the data on initiations, revisions, stopped and excluded estimates to assemble for each stock a daily time series of estimates for each measure and fiscal period. From that we construct daily time series of consensus estimates, SmartEstimates, and StarMine ARM scores. For purposes of research and performance reporting, we generally use month-end samples over the period described. For the purposes of analyzing signal volatility, we utilize several years of daily scores. Unless specified otherwise, large cap stocks are defined by US Dollar market capitalization on each date, where we take the top 1000 in North America, top 500 in Developed Europe, top 500 in Developed Asia (including Japan), and top 300 in Emerging Markets. Data mining is one of the key risks involved in creating any model that is designed to forecast future outcomes. It is particularly risky on models designed to predict stock price movements directly, particularly since typically 99%+ of the statistical “explanation” of stock price movements are not captured by typical quantitative models due to the highly noisy and efficient nature of global markets. StarMine reduced this risk tremendously with StarMine ARM by explicitly forecasting analyst revisions rather than stock prices. As such, our results are more significant, robust, and consistent in both in- and out-of-sample tests. In addition, we have used several approaches to control for typical data mining errors including survivorship bias, look-ahead bias, and over fitting. In our historical results and testing files we incorporate estimate and recommendation data from all stocks that were in the I/B/E/S universe at a given point in time, regardless of whether those stocks are currently active. When creating historical scores, we use only prior, then-available data. To avoid spurious relationships and general over-fitting of historical data, the StarMine research team employed a variety of techniques, starting with dividing the data into a calibration sample and a holdout sample. Our model estimation and selection of variables was performed on a



randomly selected half of the I/B/E/S universe, holding out some stocks for the entire period and holding out all stocks for some years. Each factor in the model was included only if it exhibited consistent contribution to the model’s performance. We looked for consistency over the following dimensions: time (year by year), geographic region, market sector, capitalization range, factor contribution to Information Coefficients and fractile returns, and factor contribution to predicting forward consensus changes. We limited our testing to factors that had a clear, intuitive justification prior to any testing, where the simplicity of formulation was a goal. We also constructed our model using the smallest plausible number of inputs, to control for statistical over-fitting and to avoid excessive multi-colinearity in our inputs. We apply the same formulation to all stocks globally in all time periods, and we do not change or re-calibrate parameters or weights after the model is released.

10. Historical Performance of the Analyst Revisions Model Tables 3-9 below display detailed the performance of the StarMine ARM in various global markets over the February 1995 – June 2007 period across a variety of dimensions in each market region: by year, capitalization, sector, and holding period. The performance by sector is calculated by dividing the given universe along the lines of S&P GICS codes. We also compare StarMine ARM performance to that of the basic revisions model—the StarMine Value Added represents the difference between the StarMine ARM performance and the basic revisions model performance. In addition to evaluating performance by year and for the entire period, we also show aggregate performance over the earlier 1995-2000 period and the more recent 2001-2007 period to evaluate the impact of the greater data density of non-earnings measures in the later period. In every market, the StarMine ARM value added over the basic revisions model is greater in the more recent period, indicating the availability of the additional measures is producing incremental value over the purely earnings-based basic revisions model. We use two measures of stock selection performance. The most straightforward is decile spread. We examine the StarMine ARM scores on the last trading day of the month and create two equally weighted portfolios based on these scores assuming investments are made at the closing prices of the next trading day (nominally the first day of the next month). One portfolio consists of stocks in the top decile (ARM score > 90) and the other consists of stocks with bottom decile scores (ARM score ≤ 10). We then hold these stocks until the subsequent month end at which point we rebalance, ignoring transaction costs. The decile spread is calculated as the annualized return of the top decile portfolio minus the annualized return of the bottom decile portfolio. All returns are annualized. Because decile spreads ignore the performance of the middle 80% of stocks and may not be representative of the model’s contribution to a multifactor quantitative stock selection methodology, we also consider the Information Coefficient, or IC. The ICs are calculated on each month end date as the Spearman rank correlation between the StarMine ARM score and subsequent 1-month returns.



Table 3. Historical performance of the StarMine ARM in the United States.

United States

Bottom Top Decile Bottom Top Decile Decile

Decile Decile Spread IC Decile Decile Spread IC Spread IC

Overall

5.2% 32.4% 27.3% 0.051 10.4% 24.9% 14.5% 0.026 12.8% 0.025

By Year

1995 15.5% 67.1% 51.6% 0.084 22.0% 57.9% 35.8% 0.062 15.8% 0.023

1996 -1.8% 34.7% 36.5% 0.072 4.0% 31.5% 27.5% 0.049 9.0% 0.023

1997 4.3% 37.5% 33.2% 0.069 5.4% 28.9% 23.4% 0.045 9.8% 0.024

1998 -14.4% 21.2% 35.7% 0.073 -8.8% 8.2% 17.0% 0.046 18.7% 0.027

1999 42.5% 61.5% 19.1% 0.038 38.8% 51.8% 13.0% 0.025 6.0% 0.013

2000 -17.8% 5.4% 23.2% 0.030 -15.5% -9.1% 6.3% 0.020 16.9% 0.010

2001 20.8% 28.5% 7.6% 0.011 19.4% 16.6% -2.8% -0.005 10.5% 0.016

2002 -29.1% -4.0% 25.0% 0.060 -19.0% -4.8% 14.2% 0.025 10.8% 0.036

2003 64.6% 94.0% 29.4% 0.033 74.7% 88.9% 14.2% 0.005 15.2% 0.027

2004 15.4% 23.0% 7.6% 0.030 12.7% 25.4% 12.7% 0.017 -5.1% 0.013

2005 -6.8% 23.9% 30.7% 0.066 5.4% 17.9% 12.4% 0.015 18.2% 0.051

2006 7.7% 31.3% 23.6% 0.037 21.8% 21.0% -0.8% -0.001 24.3% 0.038

2007 (Jan-Jun) -4.4% 24.8% 29.2% 0.065 -2.1% 19.7% 21.7% 0.039 7.5% 0.026

1995-2000 2.7% 35.8% 33.1% 0.061 6.0% 25.6% 19.6% 0.041 13.5% 0.020

2001-2007 7.5% 29.4% 21.9% 0.042 10.5% 20.8% 10.3% 0.012 11.6% 0.030

By Sector

Energy 7.8% 27.7% 19.9% 0.034 23.5% 25.5% 2.0% -0.002 17.9% 0.036

Materials 1.4% 23.4% 22.0% 0.034 7.0% 23.5% 16.6% 0.013 5.4% 0.020

Industrials -0.3% 36.3% 36.6% 0.051 7.2% 34.6% 27.4% 0.021 9.2% 0.030

Consumer Discretionary -2.0% 34.2% 36.2% 0.040 4.0% 23.8% 19.8% 0.019 16.4% 0.021

Consumer Staples 0.6% 25.1% 24.5% 0.049 3.6% 17.3% 13.7% 0.017 10.8% 0.032

Health Care 16.3% 32.7% 16.4% 0.039 14.0% 21.9% 7.9% 0.024 8.4% 0.015

Financials -0.3% 31.1% 31.3% 0.057 4.7% 26.5% 21.8% 0.028 9.6% 0.029

Information Technology 3.8% 33.8% 30.1% 0.044 8.8% 28.4% 19.6% 0.030 10.5% 0.014

Telecommunication Services -5.3% 24.3% 29.6% 0.058 8.3% 18.5% 10.2% 0.037 19.3% 0.022

Utilities -0.2% 25.1% 25.3% 0.038 7.3% 22.8% 15.5% 0.013 9.8% 0.025

By Capitalization

Large (top 500) 1.8% 15.3% 13.5% 0.031 6.9% 12.8% 5.9% 0.011 7.6% 0.020

Mid (next 500) 10.8% 20.8% 10.0% 0.031 15.7% 13.9% -1.8% 0.008 11.8% 0.023

Small (next 2000) 6.6% 31.8% 25.2% 0.046 10.9% 25.2% 14.3% 0.024 10.9% 0.021

Micro (all else) 3.3% 48.6% 45.3% 0.062 9.7% 33.9% 24.1% 0.035 21.1% 0.027

By Holding Period

30 days 5.2% 32.4% 27.3% 0.051 10.4% 24.9% 14.5% 0.026 12.8% 0.025

60 days 5.2% 27.6% 22.4% 0.050 7.9% 22.8% 14.9% 0.032 7.5% 0.018

90 days 5.5% 25.0% 19.5% 0.051 8.2% 22.1% 14.0% 0.033 5.5% 0.018

180 days 6.5% 20.7% 14.2% 0.056 9.1% 18.8% 9.8% 0.034 4.4% 0.022

StarMine ARM Basic Revisions Value Added

Starmine



Table 4. Historical performance of the StarMine ARM in Canada.

Canada



Overall

-10.9% 37.5% 48.3% 0.098 -6.9% 27.2% 34.2% 0.061 14.2% 0.037

By Year

1995 5.9% 29.9% 24.0% 0.083 -6.7% 23.0% 29.7% 0.076 -5.6% 0.007

1996 -11.6% 37.1% 48.7% 0.126 0.4% 24.6% 24.2% 0.065 24.5% 0.062

1997 -26.2% 19.2% 45.4% 0.132 -28.8% 18.7% 47.5% 0.095 -2.1% 0.037

1998 -38.2% -3.5% 34.7% 0.088 -39.5% -14.4% 25.1% 0.061 9.6% 0.027

1999 4.3% 59.0% 54.8% 0.094 29.8% 47.9% 18.1% 0.054 36.7% 0.040

2000 -0.9% 77.7% 78.7% 0.114 -22.7% 35.9% 58.6% 0.104 20.0% 0.010

2001 -4.8% 33.6% 38.3% 0.046 -9.5% 46.3% 55.8% 0.019 -17.4% 0.027

2002 -22.6% 6.0% 28.7% 0.064 -15.8% 8.0% 23.9% 0.030 4.8% 0.034

2003 8.2% 73.2% 64.9% 0.087 30.2% 58.1% 27.9% 0.055 37.0% 0.032

2004 -9.4% 47.5% 56.9% 0.126 6.7% 34.1% 27.4% 0.062 29.5% 0.064

2005 -6.8% 59.1% 66.0% 0.127 -2.3% 35.8% 38.0% 0.060 27.9% 0.066

2006 -8.0% 36.1% 44.1% 0.081 -1.9% 29.3% 31.2% 0.058 12.9% 0.023

2007 (Jan-Jun) -22.0% 37.6% 59.6% 0.103 -1.9% 20.7% 22.6% 0.044 37.0% 0.059

1995-2000 -13.0% 34.1% 47.1% 0.107 -14.1% 20.9% 35.0% 0.076 12.1% 0.031

2001-2007 -8.9% 40.7% 49.5% 0.090 0.1% 33.2% 33.2% 0.047 16.4% 0.042

By Sector

Energy -20.6% 31.6% 52.1% 0.099 -13.9% 41.3% 55.2% 0.056 -3.1% 0.043

Materials -20.2% 23.6% 43.8% 0.074 -11.0% 7.3% 18.3% 0.045 25.4% 0.029

Industrials -6.9% 43.1% 49.9% 0.100 9.6% 9.4% -0.3% 0.051 50.2% 0.049

Consumer Discretionary -21.1% 61.9% 83.0% 0.119 -17.5% 33.7% 51.2% 0.077 31.9% 0.042

Consumer Staples -16.1% 5.6% 21.7% 0.091 -29.8% 2.8% 32.6% 0.092 -10.9% -0.001

Health Care -12.4% 39.3% 51.7% 0.051 -30.6% 0.1% 30.7% 0.040 20.9% 0.011

Financials 0.3% 31.2% 30.9% 0.069 21.8% 26.5% 4.6% 0.027 26.2% 0.042

Information Technology -20.0% 13.3% 33.4% 0.057 -10.9% 1.8% 12.7% 0.052 20.7% 0.004

Telecommunication Services 10.5% 131.9% 121.4% 0.124 -11.3% 17.4% 28.7% 0.086 92.7% 0.038

Utilities -4.6% 18.0% 22.6% 0.063 -6.1% 13.2% 19.3% 0.063 3.3% 0.000

By Capitalization

Large (top 100) -3.1% 24.3% 27.4% 0.084 -2.2% 17.2% 19.4% 0.052 8.1% 0.032

Small (all else) -14.6% 45.5% 60.1% 0.100 -9.9% 31.4% 41.3% 0.058 18.8% 0.043

By Holding Period

30 days -10.9% 37.5% 48.3% 0.098 -6.9% 27.2% 34.2% 0.061 14.2% 0.037

60 days -10.3% 35.1% 45.4% 0.108 -7.3% 24.8% 32.1% 0.066 13.3% 0.042

90 days -9.1% 31.7% 40.8% 0.115 -7.7% 23.0% 30.7% 0.074 10.1% 0.041

180 days -5.3% 26.6% 31.9% 0.123 -4.2% 20.3% 24.4% 0.084 7.4% 0.039


Starmine



Table 5. Historical performance of the StarMine ARM in Developed Europe.

Developed Europe



Overall

-4.0% 26.8% 30.8% 0.069 -0.7% 19.7% 20.4% 0.040 10.3% 0.029

By Year

1995 -9.4% 11.6% 21.0% 0.073 -10.5% 8.0% 18.4% 0.046 2.6% 0.026

1996 1.5% 27.1% 25.6% 0.068 6.9% 24.5% 17.5% 0.041 8.0% 0.027

1997 13.9% 38.2% 24.3% 0.058 8.7% 35.0% 26.3% 0.045 -2.0% 0.012

1998 -0.4% 34.2% 34.6% 0.069 -2.2% 25.2% 27.4% 0.045 7.2% 0.024

1999 33.6% 52.7% 19.2% 0.043 41.5% 50.9% 9.4% 0.011 9.8% 0.032

2000 -27.5% 14.1% 41.6% 0.077 -27.9% -0.4% 27.4% 0.056 14.2% 0.020

2001 -30.3% -9.4% 20.9% 0.059 -26.1% -19.7% 6.4% 0.026 14.5% 0.033

2002 -43.8% -13.9% 29.9% 0.090 -45.8% -21.2% 24.7% 0.058 5.3% 0.032

2003 32.9% 54.5% 21.7% 0.040 44.1% 53.4% 9.4% 0.018 12.3% 0.022

2004 1.4% 35.0% 33.6% 0.069 4.9% 30.0% 25.0% 0.044 8.6% 0.025

2005 6.9% 54.5% 47.6% 0.096 21.2% 41.0% 19.9% 0.047 27.7% 0.049

2006 6.1% 42.2% 36.1% 0.082 15.2% 37.0% 21.8% 0.041 14.3% 0.042

2007 (Jan-Jun) -2.3% 37.1% 39.4% 0.087 6.0% 27.0% 21.0% 0.046 18.4% 0.041

1995-2000 0.3% 29.2% 28.8% 0.064 0.8% 22.9% 22.1% 0.041 6.7% 0.024

2001-2007 -7.8% 24.7% 32.4% 0.074 -2.1% 16.8% 19.0% 0.040 13.5% 0.034

By Sector

Energy 17.1% 29.9% 12.8% 0.047 11.7% 20.6% 8.8% 0.026 4.0% 0.021

Materials 0.0% 22.9% 22.9% 0.043 3.0% 21.0% 18.0% 0.014 4.8% 0.029

Industrials -2.0% 24.8% 26.8% 0.067 1.8% 21.0% 19.1% 0.032 7.7% 0.035



Health Care -2.0% 27.8% 29.8% 0.065 -2.4% 20.7% 23.1% 0.025 6.7% 0.040

Financials 8.3% 23.2% 14.9% 0.046 10.4% 17.4% 7.0% 0.023 7.9% 0.022


Telecommunication Services -5.2% 25.0% 30.3% 0.070 -4.0% 17.3% 21.3% 0.038 8.9% 0.032

Utilities -3.9% 23.7% 27.6% 0.041 12.2% 16.0% 3.8% 0.025 23.7% 0.016

By Capitalization

Large (top 500) -1.8% 19.0% 20.8% 0.051 0.2% 14.8% 14.6% 0.037 6.2% 0.014

Mid (next 500) -6.8% 28.6% 35.3% 0.076 -0.8% 17.7% 18.4% 0.045 16.9% 0.031

Small (all else) -3.6% 28.0% 31.7% 0.069 -1.0% 21.3% 22.3% 0.038 9.4% 0.031

By Holding Period

30 days -4.0% 26.8% 30.8% 0.069 -0.7% 19.7% 20.4% 0.040 10.3% 0.029

60 days -3.8% 23.6% 27.4% 0.072 -1.1% 18.3% 19.5% 0.043 8.0% 0.029

90 days -3.2% 22.7% 25.9% 0.078 -0.5% 17.7% 18.2% 0.048 7.7% 0.030

180 days -0.5% 20.9% 21.4% 0.090 1.7% 17.6% 16.0% 0.055 5.4% 0.034


Starmine



Table 6. Historical performance of the StarMine ARM in Developed Asia ex-Japan.

Developed Asia ex-Japan



Overall

-5.2% 30.0% 35.3% 0.078 0.9% 24.3% 23.4% 0.045 11.9% 0.033

By Year

1995 3.8% 29.6% 25.8% 0.065 6.3% 29.2% 22.9% 0.030 3.0% 0.035

1996 8.6% 40.4% 31.8% 0.074 13.8% 27.2% 13.4% 0.029 18.4% 0.045

1997 -39.4% -3.1% 36.2% 0.101 -35.4% -10.2% 25.2% 0.066 11.1% 0.035

1998 -5.2% 12.4% 17.6% 0.082 -0.2% 9.4% 9.6% 0.033 8.0% 0.049

1999 33.5% 61.8% 28.3% 0.050 42.7% 73.8% 31.1% 0.044 -2.8% 0.006

2000 -33.9% -14.5% 19.4% 0.058 -35.6% -12.5% 23.2% 0.048 -3.8% 0.011

2001 -16.7% 29.2% 46.0% 0.070 -14.8% 11.2% 26.0% 0.043 20.0% 0.027

2002 -14.6% 8.4% 23.0% 0.069 -15.6% 1.9% 17.5% 0.043 5.5% 0.026

2003 35.4% 84.3% 48.9% 0.063 46.6% 76.8% 30.1% 0.033 18.8% 0.031

2004 -9.0% 29.0% 37.9% 0.099 6.3% 27.9% 21.6% 0.057 16.4% 0.042

2005 -8.9% 35.2% 44.1% 0.098 -0.1% 27.8% 27.8% 0.054 16.2% 0.045

2006 2.3% 62.6% 60.3% 0.089 21.7% 47.3% 25.6% 0.058 34.7% 0.031

2007 (Jan-Jun) 21.9% 76.7% 54.8% 0.099 33.7% 66.9% 33.3% 0.045 21.6% 0.053

1995-2000 -9.1% 18.1% 27.2% 0.072 -5.5% 16.0% 21.5% 0.042 5.7% 0.030

2001-2007 -1.6% 41.9% 43.5% 0.083 7.2% 32.5% 25.3% 0.048 18.2% 0.035

By Sector

Energy -9.4% 27.7% 37.0% 0.070 -0.6% 18.7% 19.3% 0.027 17.8% 0.043

Materials -4.3% 37.9% 42.2% 0.064 2.7% 23.1% 20.4% 0.041 21.8% 0.023

Industrials -3.9% 32.1% 35.9% 0.085 0.0% 19.5% 19.5% 0.046 16.5% 0.039


Consumer Staples -4.9% 38.7% 43.6% 0.101 5.0% 40.0% 35.0% 0.070 8.6% 0.031

Health Care -18.9% 34.1% 53.0% 0.081 -12.6% 6.4% 19.0% 0.012 34.0% 0.069

Financials 4.1% 28.2% 24.0% 0.044 8.9% 24.8% 15.9% 0.017 8.2% 0.026


Telecommunication Services -21.1% 6.2% 27.3% 0.083 -6.7% -13.0% -6.3% 0.041 33.6% 0.042

Utilities -6.0% 17.7% 23.7% 0.052 -15.2% 36.5% 51.7% 0.054 -28.0% -0.001

By Capitalization

Large (top 500) -1.5% 28.2% 29.6% 0.075 3.3% 26.5% 23.3% 0.045 6.4% 0.029

Small (all else) -8.6% 33.3% 41.9% 0.073 -1.1% 19.8% 20.9% 0.045 21.0% 0.029

By Holding Period

30 days -5.2% 30.0% 35.3% 0.078 0.9% 24.3% 23.4% 0.045 11.9% 0.033

60 days -5.8% 25.5% 31.3% 0.085 -0.1% 21.6% 21.8% 0.054 9.6% 0.030

90 days -4.7% 23.2% 27.8% 0.090 0.0% 20.1% 20.1% 0.059 7.8% 0.032

180 days -1.1% 21.2% 22.4% 0.099 2.6% 18.8% 16.3% 0.065 6.1% 0.034


Starmine



Table 7. Historical performance of the StarMine ARM in Japan.

Japan



Overall

-7.2% 16.4% 23.6% 0.045 -2.1% 15.5% 17.6% 0.037 6.0% 0.008

By Year

1995 16.8% 24.1% 7.3% 0.014 13.5% 33.3% 19.8% 0.027 -12.5% -0.013

1996 -16.4% -9.4% 7.0% 0.024 -18.1% -8.7% 9.4% 0.026 -2.4% -0.002

1997 -52.9% -30.9% 22.0% 0.075 -54.0% -35.4% 18.6% 0.075 3.4% 0.001

1998 3.3% 20.5% 17.2% 0.033 11.6% 18.3% 6.7% 0.035 10.5% -0.002

1999 22.5% 71.0% 48.5% 0.064 29.0% 51.5% 22.5% 0.045 26.0% 0.020

2000 -18.5% -7.7% 10.8% 0.015 -4.1% -2.6% 1.5% 0.003 9.3% 0.012

2001 -17.2% 4.7% 21.9% 0.046 -9.6% 12.5% 22.2% 0.026 -0.3% 0.020

2002 -14.2% 2.0% 16.2% 0.037 -7.6% 3.0% 10.6% 0.016 5.6% 0.021

2003 39.8% 81.3% 41.5% 0.043 54.7% 71.9% 17.3% 0.028 24.2% 0.015

2004 5.3% 39.6% 34.3% 0.045 12.6% 38.3% 25.7% 0.039 8.6% 0.007

2005 24.8% 76.9% 52.1% 0.095 27.4% 78.6% 51.3% 0.076 0.8% 0.019

2006 -29.2% -8.5% 20.7% 0.046 -26.0% -8.9% 17.1% 0.054 3.6% -0.007

2007 (Jan-Jun) -16.1% 7.5% 23.6% 0.046 -8.6% -1.7% 6.9% 0.019 16.7% 0.027

1995-2000 -12.0% 6.5% 18.5% 0.038 -8.7% 5.1% 13.7% 0.035 4.8% 0.003

2001-2007 -2.5% 26.2% 28.7% 0.052 4.2% 25.8% 21.6% 0.038 7.2% 0.014

By Sector

Energy -10.1% 15.9% 26.0% 0.060 12.9% 3.8% -9.1% 0.021 35.0% 0.038

Materials -9.8% 15.6% 25.4% 0.033 0.8% 16.7% 16.0% 0.028 9.4% 0.005

Industrials -4.4% 12.3% 16.7% 0.034 -1.7% 10.6% 12.3% 0.026 4.4% 0.008


Consumer Staples -5.6% 19.3% 24.8% 0.033 -4.8% 3.1% 7.9% 0.008 16.9% 0.024

Health Care -13.4% 24.6% 38.0% 0.077 -6.5% 21.6% 28.2% 0.063 9.9% 0.013

Financials -2.6% 22.4% 25.0% 0.044 -2.1% 14.4% 16.5% 0.021 8.5% 0.023


Telecommunication Services -32.7% 3.9% 36.6% 0.055 -21.4% 4.3% 25.7% 0.030 11.0% 0.024

Utilities 9.9% 11.0% 1.0% 0.045 6.6% 8.7% 2.1% 0.009 -1.1% 0.036

By Capitalization

Large (top 500) -3.0% 13.0% 16.0% 0.041 -0.6% 12.4% 13.0% 0.032 3.0% 0.009

Small (all else) -9.4% 19.8% 29.2% 0.045 -4.0% 16.2% 20.2% 0.038 9.0% 0.007

By Holding Period

30 days -7.2% 16.4% 23.6% 0.045 -2.1% 15.5% 17.6% 0.037 6.0% 0.008

60 days -5.2% 14.0% 19.2% 0.042 -1.5% 12.8% 14.2% 0.026 5.0% 0.016

90 days -3.9% 12.7% 16.6% 0.043 0.0% 11.8% 11.7% 0.024 4.8% 0.018

180 days -0.8% 11.7% 12.5% 0.045 2.2% 10.4% 8.2% 0.020 4.3% 0.025


Starmine



Table 8. Historical performance of the StarMine ARM in Emerging Markets.

Emerging Markets



Overall

4.1% 32.3% 28.2% 0.051 7.9% 24.3% 16.4% 0.032 11.7% 0.018

By Year

1995 -8.3% -3.7% 4.5% 0.029 -10.7% -1.4% 9.3% 0.022 -4.7% 0.007

1996 -2.4% 27.3% 29.7% 0.062 -4.9% 15.0% 19.8% 0.042 9.8% 0.020

1997 -25.2% 5.7% 30.9% 0.046 -35.9% -13.8% 22.2% 0.042 8.8% 0.003

1998 13.5% 17.1% 3.6% 0.039 28.7% 34.6% 5.9% 0.026 -2.3% 0.012

1999 65.5% 107.4% 41.9% 0.037 49.0% 63.7% 14.7% 0.012 27.2% 0.025

2000 -33.8% -18.8% 15.0% 0.030 -29.6% -22.8% 6.8% 0.021 8.2% 0.009

2001 35.6% 38.6% 3.0% 0.022 30.9% 38.7% 7.8% 0.012 -4.8% 0.011

2002 -9.3% 17.1% 26.4% 0.046 11.7% 14.7% 3.0% 0.016 23.4% 0.030

2003 43.6% 95.4% 51.8% 0.051 51.6% 78.7% 27.0% 0.032 24.8% 0.019

2004 -10.3% 22.7% 33.0% 0.070 -2.9% 16.3% 19.2% 0.056 13.8% 0.014

2005 1.4% 48.0% 46.6% 0.079 17.4% 36.4% 19.0% 0.043 27.7% 0.036

2006 2.5% 52.9% 50.4% 0.085 12.3% 42.8% 30.5% 0.056 19.9% 0.029

2007 (Jan-Jun) 41.8% 103.2% 61.4% 0.066 48.7% 93.2% 44.5% 0.039 16.9% 0.027

1995-2000 -2.9% 17.2% 20.2% 0.041 -4.9% 9.0% 13.9% 0.028 6.2% 0.013

2001-2007 10.9% 47.6% 36.7% 0.060 16.7% 37.1% 20.5% 0.036 16.2% 0.024

By Sector

Energy 3.8% 41.2% 37.4% 0.057 13.5% 50.2% 36.7% 0.037 0.6% 0.020

Materials 3.1% 41.6% 38.5% 0.051 14.1% 26.3% 12.2% 0.020 26.4% 0.032

Industrials 8.2% 33.2% 25.0% 0.060 5.5% 29.5% 24.0% 0.023 0.9% 0.037

Consumer Discretionary 3.5% 31.7% 28.2% 0.056 5.4% 20.0% 14.6% 0.025 13.6% 0.031


Health Care 3.1% 46.9% 43.7% 0.042 -3.3% 33.0% 36.4% 0.018 7.4% 0.024

Financials 1.0% 33.3% 32.3% 0.046 5.8% 24.3% 18.5% 0.021 13.8% 0.025


Telecommunication Services -18.8% 33.3% 52.2% 0.082 0.1% 20.5% 20.4% 0.045 31.8% 0.037

Utilities 37.5% 26.6% -11.0% 0.029 18.9% 33.9% 15.0% 0.022 -26.0% 0.007

By Capitalization

Large (top 300) -3.3% 28.4% 31.7% 0.059 -2.3% 21.8% 24.1% 0.042 7.6% 0.018

Micro (all else) 4.9% 33.1% 28.1% 0.048 9.3% 25.2% 15.8% 0.030 12.3% 0.018

By Holding Period

30 days 4.1% 32.3% 28.2% 0.051 7.9% 24.3% 16.4% 0.032 11.7% 0.018

60 days 3.4% 28.4% 24.9% 0.052 5.8% 21.0% 15.3% 0.028 9.7% 0.025

90 days 5.0% 26.6% 21.7% 0.056 5.0% 19.4% 14.4% 0.034 7.3% 0.022

180 days 7.3% 25.0% 17.6% 0.060 7.2% 18.7% 11.6% 0.034 6.1% 0.026


Starmine



Table 9. Historical performance of the StarMine ARM by country.



Developed Asia -5.0% 22.0% 26.9% 0.055 1.6% 19.3% 17.7% 0.036 9.2% 0.019

Australia -11.3% 35.7% 47.0% 0.085 -3.8% 28.2% 32.0% 0.050 15.0% 0.034

Hong Kong -2.8% 23.9% 26.7% 0.059 -1.6% 20.3% 21.9% 0.031 4.8% 0.028

Japan -7.2% 16.4% 23.6% 0.045 -2.1% 15.5% 17.6% 0.037 6.0% 0.008

New Zealand -5.7% 21.4% 27.1% 0.064 0.7% 16.9% 16.2% 0.036 10.8% 0.028

Singapore -9.5% 22.4% 31.9% 0.074 -1.0% 22.3% 23.3% 0.046 8.6% 0.028

Developed Europe -4.0% 26.8% 30.8% 0.069 -0.7% 19.7% 20.4% 0.040 10.3% 0.029

Austria -17.9% 22.7% 40.6% 0.091 -15.3% 24.7% 40.0% 0.051 0.6% 0.040

Belgium -1.8% 33.6% 35.4% 0.077 4.9% 31.1% 26.2% 0.034 9.2% 0.043

Denmark -2.1% 30.1% 32.2% 0.079 -5.2% 28.0% 33.2% 0.051 -1.0% 0.028

Finland -2.9% 34.5% 37.4% 0.101 6.3% 28.4% 22.0% 0.066 15.3% 0.035

France -4.9% 26.1% 31.0% 0.054 -1.7% 22.1% 23.7% 0.027 7.3% 0.026

Germany -16.3% 20.6% 36.9% 0.083 -12.5% 9.9% 22.4% 0.036 14.5% 0.046

Greece 7.6% 24.0% 16.4% 0.048 5.2% 17.7% 12.5% 0.018 3.9% 0.030

Iceland -23.7% 37.6% 61.4% 0.210 -17.2% 17.0% 34.2% 0.077 27.2% 0.133

Ireland -18.8% 19.0% 37.8% 0.060 -9.4% 14.5% 23.9% 0.038 13.9% 0.023

Italy -0.5% 19.1% 19.7% 0.041 0.8% 17.4% 16.6% 0.022 3.1% 0.019

Netherlands -2.1% 25.8% 27.9% 0.071 -7.1% 15.5% 22.6% 0.041 5.3% 0.030

Norway 6.3% 34.9% 28.6% 0.072 11.6% 27.1% 15.5% 0.041 13.1% 0.031

Portugal -6.5% 24.3% 30.8% 0.064 13.4% 19.7% 6.3% 0.035 24.5% 0.029

Spain 3.0% 33.9% 30.9% 0.059 -2.5% 12.1% 14.6% 0.029 16.3% 0.030

Sweden -3.6% 23.4% 27.0% 0.045 2.2% 19.3% 17.1% 0.024 9.8% 0.020

Switzerland -2.0% 32.5% 34.5% 0.069 -0.7% 18.5% 19.2% 0.039 15.3% 0.030

United Kingdom -5.5% 22.3% 27.8% 0.057 -4.7% 18.6% 23.3% 0.035 4.5% 0.022

North America 3.9% 33.1% 29.2% 0.055 8.8% 25.2% 16.5% 0.029 12.8% 0.026

Canada -10.9% 37.5% 48.3% 0.098 -6.9% 27.2% 34.2% 0.061 14.2% 0.037

United States 5.2% 32.4% 27.3% 0.051 10.4% 24.9% 14.5% 0.026 12.8% 0.025

Emerging Markets 4.1% 32.3% 28.2% 0.051 7.9% 24.3% 16.4% 0.032 11.7% 0.018

Argentina 55.7% 45.9% -9.8% -0.053 34.4% 42.8% 8.4% -0.012 -18.2% -0.041

Brazil 18.8% 34.2% 15.4% 0.055 13.6% 26.2% 12.6% 0.029 2.8% 0.025

Chile 40.0% 39.5% -0.5% 0.009 25.3% 9.1% -16.3% -0.045 15.8% 0.054

China -5.3% 38.3% 43.5% 0.058 15.0% 27.3% 12.3% 0.034 31.3% 0.024

Czech Republic -22.6% 17.9% 40.5% 0.057 -15.9% 23.3% 39.2% 0.007 1.3% 0.050

Egypt 20.0% 115.5% 95.5% 0.058 21.5% 37.4% 15.9% -0.002 79.7% 0.060

Hungary -3.7% 22.7% 26.4% 0.055 -1.0% 17.6% 18.6% 0.031 7.7% 0.024

India 3.2% 32.2% 29.0% 0.052 1.4% 19.2% 17.8% 0.021 11.2% 0.031

Indonesia 1.2% 50.0% 48.8% 0.076 14.1% 33.9% 19.8% 0.021 29.0% 0.055

Israel 0.2% 23.3% 23.0% 0.054 1.8% 1.7% -0.1% 0.013 23.1% 0.041

Korea -5.4% 15.6% 21.0% 0.027 -0.2% 7.0% 7.1% 0.002 13.8% 0.025

Malaysia -7.8% 11.3% 19.1% 0.065 -5.7% 11.2% 16.9% 0.027 2.1% 0.038

Mexico 3.7% 45.4% 41.7% 0.052 5.2% 28.4% 23.2% 0.035 18.5% 0.017

Morocco -5.8% 40.9% 46.6% 0.017 25.1% 39.0% 13.9% 0.072 32.7% -0.055

Pakistan -29.6% 32.2% 61.8% 0.002 -7.0% -1.2% 5.8% -0.054 56.0% 0.056

Peru -19.0% 25.3% 44.4% 0.050 6.1% -7.9% -14.0% 0.007 58.4% 0.044

Philippines -19.6% 19.6% 39.2% 0.090 -14.9% 9.8% 24.7% 0.061 14.5% 0.029

Poland -1.0% 53.9% 54.9% 0.048 6.8% 35.9% 29.1% 0.063 25.8% -0.015

Russia 2.9% 54.5% 51.6% 0.051 25.5% 68.6% 43.0% 0.028 8.6% 0.023

South Africa -1.5% 44.1% 45.6% 0.070 -2.1% 33.4% 35.5% 0.043 10.0% 0.027

Sri Lanka 10.1% 2.9% -7.3% 0.023 -11.2% -3.7% 7.5% -0.015 -14.8% 0.038

Taiwan 0.3% 9.5% 9.2% 0.014 -1.7% 9.3% 11.0% -0.004 -1.8% 0.018

Thailand -15.8% 15.2% 31.0% 0.068 -13.4% 15.2% 28.5% 0.033 2.5% 0.036

Turkey 49.2% 78.5% 29.3% 0.026 20.8% 67.4% 46.6% 0.030 -17.3% -0.004

Venezuela 45.6% 28.6% -17.0% -0.010 30.1% 11.8% -18.3% -0.018 1.4% 0.008


Starmine



11. References Chan, L., N. Jegadeesh, and J. Lakonishok, 1996, Momentum Strategies, Journal of Finance 51,

1681-1713. Elton, E., and M. Gruber, 1972, Earnings Estimates and the Accuracy of Expectational Data,

Management Science 18, B409-B424. Givoly, D., and J. Lakonishok, 1979, The information content of financial analysts' forecasts of earnings: Some evidence on semi-strong inefficiency, Journal of Accounting and Economics 1, 165-185. Jegadeesh, N., J. Kim, S. Krische, and C. Lee, 2004, Analyzing the analysts: When do recommendations add value?, Journal of Finance 59, 1083-1124. Jha, V., and H. Mozes, 2001, Creating and profiting from more accurate earnings estimates with StarMine Professional, StarMine white paper. Jha, V., D. Lichtblau, and H. Mozes, 2003, The Usefulness of Analysts’ Recommendations,

Journal of Investing, 7-18.

star mine arm_white_paper

Education

past analyst revisions

trailing analyst revisions

earnings revisions models

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upward analyst earnings

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