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EQUITY RESEARCH
Quant sentiment
Forecasting earnings changes
Using capital flows in China
US stagflation?
Global Quantitative Research Monthly
See Appendix A-1 for analyst certification and important disclosures. Analysts employed by non-US affiliates are not registered or qualified as research analysts with FINRA in the US.
May 23, 2011
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Content
Chapter I Quant Sentiment: A view from Europe 4
■ We report on the sentiment and views of clients attending our quant conference in London and other European cities in May.
■ The overall tone was bullish. A majority of quant managers have seen positive returns in the past 12 months, a majority have won net new mandates and there was a skew to having a positive outlook for quant.
■ The strongest demand for allocation of new capital into quant was for Emerging Markets followed by global approaches. Europe and Japan were not seen as being in demand by many managers, while no one saw demand for new capital flows into US quant.
■ Discretion should have a role to play anywhere in the quant investment process was a clearly held view.
Inigo Fraser-Jenkins - European Head of Quantitative Strategy
Chapter II Projecting revisions for stocks with unchanged consensus forecasts 10
■ The 11/3 results season is under way for companies with a March fiscal year-end. While these results are likely to attract attention as a means of assessing the impact of the Great East Japan Earthquake, some companies have chosen not to announce earnings guidance for the current fiscal year. In addition, many analysts have been holding off from making major revisions to their earnings forecasts since the earthquake, and there are also substantial differences between individual analysts’ forecasts. This reflects the current difficulty in projecting earnings and may well reduce the reliability of investment strategies based on earnings forecasts.
■ Hence, we consider a method of estimating future revisions for stocks for which the consensus earnings forecast has been left unchanged. For each company, we estimate the revision factor for each of its business segments (the segment revision factor). We then add up all these segment revision factors, giving them a weighting in line with the proportion of the company’s total sales accounted for by each segment. We calculate segment revision factors from the revision factors for companies that specialize in the segment in question and have recently had their consensus earnings forecast revised.
■ The results of our analysis indicate that it might be possible to estimate future revisions using the aggregate revision factor, which is based on data for companies in the same business. We provide an investment strategy based on this factor that is effective. Most companies have refrained from issuing guidance along with their 11/3 results. We think the strategy we introduce in this report might be useful at a time such as this, when earnings are difficult to project.
Hiromichi Tamura - Japan Head of Equity Quantitative Research
Chapter III Herding in China: capital inflows 18
■ In this report, we review factor performance for the China CSI 300 universe, and observe a herd-like behaviour exhibits in the China domestic market.
■ We introduce the capital inflows ratio, which utilizes capital inflows data sourced from Wind Info. The factor distinguishes trading activities from institutional investors and individual investors. Stocks with a high capital inflows ratio are those stocks that institutional investors (or informed investors) are buying.
■ Our correlation analysis shows that stocks with a high capital inflows ratio tend to maintain high money inflows for the following days, and have a higher future return.
■ We construct a hypothesis portfolio consisting of top capital inflows ratio stocks, and back-test the effectiveness of the factor since January 2010. Up to 30 April 2011, the portfolio has delivered an absolute return of 45.8%, outperforming the CSI 300 Index by 60.5%.
Sandy Lee - Head of Equity Quantitative Strategies, Asia ex-Japan
Chapter IV Stagflation priced in the market? 27
■ In this section we revisit the question of whether the market is pricing in stagflation.
■ Inflation expectations priced in bonds have risen briskly to pre-Lehman levels, while long-term earnings growth is now priced as negligible. Inflation expectations are certainly not yet very high, but this divergence of growth and inflation pricing is not a good sign for the markets. The market currently seems to be pricing in what could be regarded as early signs of stagflation.
Joseph J. Mezrich - Head of US Quantitative Research
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
3
Foreword
This month we offer our readers four quite different topics. We recently held our series of annual quant conferences in European cities. We analyse the views of European quant fund managers, including their recent performance and asset flows.
An issue in analysing Japanese stocks at present is the absence of up-to-date earnings forecasts for many stocks in the wake of the earthquake and associated uncertainty over earnings prospects. We offer a technique for forecasting earnings changes.
Our Asian section focuses on the Chinese market. We show the propensity for herding behaviour in factor returns for that market. We also introduce a capital flow indicator and test its ability to discriminate between stocks.
In the US we test the question of whether the market is now pricing in a stagflationary environment.
Inigo Fraser-Jenkins Hiromichi Tamura
European Head of Quantitative Strategy Japan Head of Equity Quantitative Research
Sandy Lee Joseph J. Mezrich
Head of Equity Quantitative Strategy Head of US Quantitative Research
Asia ex-Japan
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
4
Quant Sentiment: A view from Europe
• We report on the sentiment and views of clients attending our quant conference in London and other European cities in May.
• The overall tone was bullish. A majority of quant managers have seen positive returns in the past 12 months, a majority have won net new mandates and there was a skew to having a positive outlook for quant.
• The strongest demand for allocation of new capital into quant was for Emerging Markets, followed by global approaches. Europe and Japan were not seen as being in demand by many managers, while no one saw demand for new capital flows into US quant.
• Discretion should have a role to play anywhere in the quant investment process was a clearly held view.
We held our annual quant conference in London and other European cities last week. As part of this we asked the attendees a series of questions on how they perceive the outlook for quant, recent performance and where they see demand for new products. The mood was upbeat. Performance has been good and new mandates are being won. This is perhaps a sign of confidence returning to quant fund managers. Many of these results mesh with the output from our models, so in this report we present the key findings of the survey and our thoughts on the issues discussed. The first part of this report covers performance and asset flow of quant managers overall. In the second part we delve into some of the specific issues that we think are of current interest to quants.
First, quants have seen good performance recently. There was a clear skew of results towards positive relative returns over the past 12 months, with 30% having beaten the market by between 2% and 5%, and another 30% having beaten the market by more than 5%.
Fig. 1: How did your quant funds perform over the last 12 months?*
Relative to the market if a run a long-only fund, or absolute return in the case of long-short). (1 = less than -5%, 2 = -5% to -2%, 3 = -2% to +2%, 4 = +2% to +5%, 5= > +5%) Source: Nomura’s Quantitative Survey
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Inigo Fraser-Jenkins
+44 20 7102 4658
A majority of quant fund managers in our survey had seen positive returns over the past 12 months
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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This accords with the finding from our quant benchmark index shown in Figure 2, which has shown good returns recently. It is still early days in terms of a recovery in performance, but the rally this year has been reasonably significant.
Fig. 2: Quant benchmark index
Figure shows the performance of equal-weighted and asset-weighted quant fund indices relative to the global market . Source: Bloomberg, Nomura Equity Strategy research
We published a positive view on quant as part of our outlook for this year.1 It turns out that 18 January marked the low-point for quants and, according to our benchmark index, they have outperformed the market by 1.2% since then. The reason for our positive view – and hence the Dante-esque labelling of Figure 2 – is that correlations between stocks are low and the dispersion of growth expectations across the market is wide. This positive view was also shared by the attendees at the conference. The modal result was neutral, but 38% were positive to varying degrees, while only 8% were negative (Figure 3).
Fig. 3: How would you rate the outlook for quant over the next 12 months?
On a scale of 1-5, where 1 was the worst ever period of quant returns and 5 the best
Source: Nomura’s Quantitative Survey
The positive returns do seem to be starting to show up in asset flows. A majority of attendees had won net new mandates over the past year. Also, when we asked about the ease of marketing quant now compared with a year ago, there was a skew towards managers finding it easier to market quant.
1 Please see Quant Outlook 2011, 16 January 2011
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Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Fig. 4: Have you won NET new mandates over the past year?
Source: Nomura’s Quantitative Survey
To present a balanced picture of flows, we have, however, yet to see this show up as an increase in the AUM of funds in our quant benchmark index (see Figure 5).
Fig. 5: AUM share of quant funds
Figure shows the quant share of AUM for active equity funds that are global or benchmarked against a US, European or Australian index. Source: Bloomberg, Nomura Equity Strategy research, EPFR
The appetite for where new quant capital is being allocated is far from uniform. Emerging markets are by far the most popular area for new funds being put to work. Only 13% of respondents thought that Europe was where their clients were most interested in allocating new capital to quant, only 8% cited Japan and not a single respondent cited the US. This fits with feedback that we have had from recent marketing trips. There have been significant outflows from emerging market mutual funds (that is all funds, not just quant) in recent months and we expect that there will be further outflows over the rest of the year. Despite this, however, there are reasons why the dynamic may be different for quant funds. Emerging markets are still perceived as an area where quants are relatively under-exploited, and there is now enough data for many of these markets for some kind of backtesting. There is also a perception that quant approaches have a lot to offer for these markets. While we agree with this, we would sound one note of caution, which is that implementing a backtest for these markets raises some challenging data questions. Not least among these is the proper treatment of differing accounting standards and changes in those standards for these markets.
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Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Fig. 6: Which region do you see the most client interest in allocating new capital to quant strategies?
Source: Nomura’s Quantitative Survey
There are other areas on ongoing debate in the quant world that we found interesting. There is the discussion on the role for discretionary inputs or overlays for quant models. The answer to our survey question was unambiguous; discretion should be allowed at any stage in the quant investment process (see Figure 7). Only 6% of respondents thought that discretion had no role to play in the quant investment process.
Fig. 7: To what extent do you think that a discretionary overlay has a place in the quant investment process?
Source: Nomura’s Quantitative Survey
We found that many attendees had seen demand for a model that could systematically adjust the risk budget of a quant model in a systematic way. This is a concept that we find appealing and our meta model2 offers one such approach for this. We find pervasive evidence that the performance of quants in aggregate is related to correlations (both between stocks and factors) and also to the dispersion of factor values across the market. We have been surprised that it has not been taken up by more quants. However, the response to the survey suggests that there is now a demand for such models.
There are signs that quants are changing data providers more than they did some years ago. The cost of changing data provider is high, so the fact that 30% of respondents had changed data sources in the past year is highly significant. We find that quant fund managers are increasingly willing to use the newer alternative sources of estimates and accounting data. These are areas where we think that real competition does now exist in a way that it did not four years ago. For our review of these data sources please see Quant Data Manual.3
2 Please see Quant Outlook 2011, 16 January 2011 3 Please see Quant Data Manual, 9 October 2009
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Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Fig. 8: For inputs that you use in your models, have you changed the data provider for any of them in the last year?
Source: Nomura’s Quantitative Survey
The topic of alternative data sources is also related to the question of exploiting new factors. Although the modal response to our question on the number of new alpha sources was that no new factors were used, respondents indicated that new factors are being used to some degree. The most popular types of new factor to use are stock-specific factors (Figure 9). These are in some cases factors that are specific to individual industries and in other cases alternative factors based on fundamental data. The second most common type of factor was macro-economic, with technical factors in third place.
Fig. 9: If you have added new factors, what types of factors are these
Source: Nomura’s Quantitative Survey
Overall, these responses show a quant investment community that is somewhat more confident than it was a year ago. Quants are continuing to innovate and experiment with new factors, new data sets and new markets. Discretion also now firmly has a place in many quant approaches.
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Despite the costs inherent in moving data provider, we are seeing increasing numbers of quant investors willing to make a switch as there is now more competition among providers of key data sources
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Projecting revisions for stocks with unchanged consensus forecasts • The 11/3 results season is underway for companies with a March fiscal year-end. While
these results are likely to attract attention as a means of assessing the impact of the Great East Japan Earthquake, some companies have chosen not to announce earnings guidance for the current fiscal year. In addition, many analysts have been holding off from making major revisions to their earnings forecasts since the earthquake, and there are also substantial differences between individual analysts’ forecasts. This reflects the current difficulty in projecting earnings and may well reduce the reliability of investment strategies based on earnings forecasts.
• Hence, we consider a method of estimating future revisions for stocks for which the consensus earnings forecast has been left unchanged. For each company, we estimate the revision factor for each of its business segments (the segment revision factor). We then add up all these segment revision factors, giving them a weighting in line with the proportion of the company’s total sales accounted for by each segment. We calculate segment revision factors from the revision factors for companies that specialize in the segment in question and have recently had their consensus earnings forecast revised.
• The results of our analysis indicate that it might be possible to estimate future revisions using the aggregate revision factor, which is based on data for companies in the same business. We provide an investment strategy based on this factor that is effective. Most companies have refrained from issuing guidance along with their 11/3 results. We think the strategy we introduce in this report might be useful at a time such as this, when earnings are difficult to project.
1. Earnings are difficult to forecast at present
The 11/3 results season is underway for companies with a March fiscal year-end. While these results are likely to attract attention as a means of assessing the impact of the Great East Japan Earthquake, some companies have chosen not to announce earnings guidance for the current fiscal year. In addition, many analysts have been holding off on making major revisions to their earnings forecasts since the earthquake, and there are also substantial differences between individual analysts’ forecasts. This reflects the current difficulty in projecting earnings and may well reduce the reliability of investment strategies based on earnings forecasts.
1.1 The current tendency is for consensus forecasts to be unchanged Let us start by looking into the number of stocks for which the latest consensus forecasts have not changed. Our analysis criteria are as follows.
The universe is TSE-1 stocks for which I/B/E/S forecasts are available, and the sample period is December 2003 through 26 April 2011. Figure 10 shows the 12-month moving average of the proportion of stocks in the universe for which the consensus earnings forecast, at the beginning of the month in question, had not changed over the preceding month. The shaded area indicates the period after the Great East Japan Earthquake.
The results of our analysis show that the percentage of stocks for which the consensus earnings forecast was unchanged had already been rising since 2010 H2, but has risen even more since the earthquake. It is possible that consensus forecasts have not yet fully factored in the impact of the earthquake.
Hiromichi Tamura – NCS
+813 3274 1079
Akihiro Murakami – NSC
+813 3274 1079
Please see full report published 12 May 2011
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Fig. 10: Percentage of stocks for which consensus forecast is unchanged
Note: Universe is TSE-1 stocks for which I/B/E/S forecasts are available; sample period is December 2003 through 26 April 2011. We count the number of stocks for which, at the beginning of each month, the consensus earnings forecast had not been revised during the preceding month, and then calculate this as a proportion of the total number of stocks in the universe. Source: Nomura
1.2 There is also substantial divergence within consensus forecasts Figure 11 shows the divergence of analysis forecasts within the consensus forecast for each stock.
Our analysis criteria are as follows. The universe and sample period are the same as in section 1.1. For each stock, we calculate the divergence of I/B/E/S recurring profit forecasts at the beginning of each month. We define the divergence of forecasts for each stock, which is based on the gap between the most bullish analyst forecast and the most bearish analyst forecast, as follows.
Figure 11 shows the 12-month moving average of the mean divergence of forecasts for all stocks in the universe. The shaded area indicates the period after the Great East Japan Earthquake.
The results of our analysis show that, as in section 1.1, the divergence of analyst forecasts has increased even more since the earthquake. When there is a substantial divergence between individual analyst forecasts within the consensus forecast, this might have a negative impact on the accuracy of a revision factor that is based on consensus forecasts.
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x 2 |most bullish analyst forecast| +| most bearish analyst forecast|
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Fig. 11: Divergence within consensus forecasts
Note: Universe is TSE-1 stocks for which I/B/E/S forecasts are available; sample period is December 2003 through 26 April 2011. For each stock, we calculated the divergence of I/B/E/S recurring profit forecasts at the beginning of each month, on the basis of the gap between the most bullish analyst forecast and the most bearish analyst forecast. Source: Nomura
2. Projecting future revisions for stocks with unchanged consensus forecasts
In this chapter, we look at ways of estimating the revision factor for stocks for which there has been no change to the consensus earnings forecast. Specifically, we break down each company’s sales into sales in each business segment, and estimate the revision factor for each segment, which we call the segment revision factor. We then add up all these segment revision factors, in line with the proportion of the company’s total sales accounted for by each segment. The results of our analysis show that it might be possible to use this estimated aggregate revision factor, which is based on the sum of the revision factors for each segment, to project the future revision factor.
2.1 Calculating the aggregate revision factor In this section, we introduce a method of estimating the revision factor for stocks for which the consensus earnings forecast did not change during the preceding month, based on the method set out in Lou and Cohen (2011). The details of the analysis method are as follows.
First, for each stock for which the consensus earnings forecast did not change during the preceding month, we calculate its sales breakdown by business segment (Figure 12, Step 1).
From all listed stocks, we pick out companies that specialize in a particular segment, and then calculate the average revision for those stocks within this group for which the consensus earnings forecast changed during the preceding month (Figure 12, Step 2). We call this the segment revision factor. We define specialist companies as companies for which one business segment accounts for 80% or more of total sales. For segments where there are no specialist companies, or where there are no specialist companies for which the consensus earnings forecast changed during the preceding month, the segment revision factor is zero.
Segment revision factor: _ = 1
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Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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We calculate Revk—defined as the revision to the consensus earnings forecast over the preceding month for the kth specialist stock in the Sth segment, where Ns is the number of specialist stocks in the same segment— as follows.
Revision over preceding month =
latest I/B/E/S consensus recurring profit forecast for current fiscal year – I/B/E/S consensus recurring profit forecast for current fiscal year one month previously
|I/B/E/S consensus recurring profit forecast for current fiscal year one month previously|
Lastly, we define the aggregate revision factor (AggRev ) as the weighted average of the segment revision factors (Seg_Rev) for each segment, as calculated in Step 2, with the weighting calculated on the basis of the proportion of the company’s total sales accounted for by each segment (Step 3).
Fig. 12: Aggregate revision factor calculation method
Source: Nomura
2.2 Segment data Next, we give a simple explanation of the segment data used in the previous section.
For each company, we calculate the proportion of its total sales accounted for by each business segment on the basis of Toyo Keizai business breakdown data and Nomura's 237 subsector categories.
Out of all listed companies, we regard a total of 2,366 as specialist companies as of the beginning of April 2011. In addition, at the same time there was a total of 1,444 companies that we do not regard as specialist companies (ie, there were 1,444 companies for which there was no business segment that accounted for 80% or more of total sales).
[Step 1] [Step 2] [Step 3]
Calculate segment revision factors Calculate aggregate revision factor
Average revision for specialist system support companies
Seg_Rev 1
Average revision for specialist pharmaceutical companies
Seg_Rev 2
Average revision for specialist fine chemicals companies
Seg_Rev 3
Average revision for specialist synthetic fiber companies
Seg_Rev 4
Estimate business portfolio for each company for which consensus earnings forecast has not h d
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Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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3. Strategy based on aggregate revision factor
In this chapter, we confirm the predictive power of the aggregate revision factor with respect to future returns on stocks for which the consensus earnings forecast has not changed. In our view, the use of data for other companies in the same business might make it possible for a revision-based strategy to be effective even in an environment in which it is difficult to forecast earnings.
3.1 The predictive power of the aggregate revision factor with respect to future return In this section, we look at the effectiveness of an investment strategy based on the aggregate revision factor. Figure 13 shows our analysis criteria.
We look at stocks in the TSE-1 for which, at each point in time, there had been no change to the consensus earnings forecast over the preceding month (ie, the historical one-month revision is zero). The sample period is June 2011 through 26 April 2011. For each month, we divide the universe into five groups on the basis of the value of the aggregate revision factor for each stock at the beginning of the month, and construct five equally weighted portfolios, one for each group. We then calculate the performance of each portfolio over one month.
Fig. 13: Grouping simulation analysis criteria
Universe Stocks in TSE-1 with no change to consensus earnings forecast over preceding month
Sample period 01/6–11/4/26
Method
Grouping simulation
Universe is divided into five groups on the basis of the historical one-month aggregate revision factor (AggREV) for the past month for each stock at the beginning of the month, and measure the monthly performance of each group.
Weighting Equal
Rebalancing Monthly
Benchmark Universe (equal weighted)
Factor Historical one-month aggregate revision factor (AggRev)
Source: Nomura
3.2 Analysis results The results of our analysis are shown in Figures 14 and 15. Figure 14 shows the performance of each group, where the universe is divided into five groups of stocks on the basis of the value of the aggregate revision factor for each stock. Figure 15 shows the difference between the return on group #5 (the group of stocks with the highest aggregate revision factors) and group #1 (the group of stocks with the lowest aggregate revision factors).
First, as Figure 14 shows, the average annual return on group #1, the group with the lowest aggregate revision factors, is -2.37%, the standard deviation is 3.92%, and the t-value is -1.91, indicating a statistically significant negative return. Meanwhile, the average annual return on group #5, the group with the highest aggregate revision factors, is 4.33%, the standard deviation is 3.80%, and the t-value is 3.59, indicating a statistically significant positive return.
The difference between group #5 and group #1 is 6.75% in terms of average annual return, 6.36% in terms of the standard deviation, and 3.33% in terms of the t-value, indicating that the aggregate revision factor is effective in predicting future return (Figure 15).
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Fig. 14: Results of grouping simulation (return on each group)
Statistical data for monthly return (annualised)
#1 #2 #3 #4 #5
Average -2.37 -1.74 -0.96 0.74 4.33
Standard deviation 3.92 3.58 3.39 3.50 3.80
t-value -1.91* -1.53 -0.89 0.67 3.59***
Average/standard deviation -0.61 -0.49 -0.28 0.21 1.14
Note: Universe is stocks in TSE-1 for which, at each point in time, there was no change to the consensus forecast over the preceding month. Sample period is June 2011 through 26 April 2011. At the beginning of each month, we divide the universe into five groups on the basis of the level of the aggregate revision factor for each stock, and then construct five equally weighted portfolios, one for each group. Figure shows statistical data for cumulative return (top) and monthly return (bottom) if each portfolio is held for one month. t-values are for the null hypothesis in which the average monthly return is zero. *** indicates significance of 1%, ** indicates significance of 5%, and * indicates significance of 10%. Source: Nomura
Fig. 15: Grouping simulation results (return on group #5– return on group #1)
Statistical data for monthly return (annualized) Period Average Standard deviation t-value Average/standard deviation
01/7–11/4 6.75 6.36 3.33*** 1.06
08/1–11/4 6.60 6.11 3.39*** 1.08
Note: Universe is stocks in TSE-1 for which, at each point in time, there was no change to the consensus earnings forecast over the preceding month. Sample period is June 2011 through 26 April 2011. At the beginning of each month, the universe is divided into five groups on the basis of the level of aggregate revision factor for each stock. Figure shows cumulative return (top) and monthly return (bottom) on strategy of going long on group of stocks with highest values of aggregate revision factor and short on group of stocks with lowest values of aggregate revision factor. t-values are for the null hypothesis in which the average monthly return is zero. *** indicates significance of 1%, ** indicates significance of 5%, and * indicates significance of 10%. Source: Nomura
Reference Lou, D. and L. Cohen, Complicated Firms, 2011, AFA 2011 Denver Meetings Paper
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Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Factor review Investors started to focus on large caps, while stocks undervalued in terms of B/P underperformed Large caps performed well on the relatively flat Japanese equity market in April, while stocks undervalued in terms of P/B underperformed. Figure 16 summarizes the performance of 14 quant factors, which come under the categories of size, reversal, valuation, growth, revision, financial indicators, and risk indicators, while Figure 17 shows the aggregate performance of each factor. Although small caps and high B/P stocks were selected soon after the earthquake, investors started to focus on the stocks shunned in March. The quant factors that underperformed after the earthquake have been recovering. Based on the past 1-month return, there seems to be a reversal tendency in prices. The return on high ROE stocks also recovered to be positive from its plunge in March.
The improvement in performance of high E/P stocks There was also a notable rise in the effectiveness of forecast E/P this month. The deteriorated performances of high E/P stocks in March appear to be the result of investors shunning stocks that had been bought on the basis of earnings growth expectations. The improved performance of forecast E/P in April looks to be basically supported by the tendency that investors started to pay attention to earnings forecasts again. More emphasis looks to have been put on earnings information given by companies recently. We will be able to gauge the effectiveness of earnings forecast- based factors in May, when data will reflect analyst revisions in the wake of the latest annual results.
Fig. 16: Factor performance summary
Note: (1) For each factor, we calculated return spreads for portfolios in quintiles based on factor size. (2) Returns include dividends and are not annualized. (3) Universe is TOPIX (excluding financials for EBITDA/EV). (4) Effectiveness evaluation based on cross-sectional (factor return ranking or positive/negative spread for month in question) or time-series (past return ranking) comparison; � and � = positive effect, � = neutral, and x and xx = negative effect. (5) For factor definitions, see note to Figure 17. Source: Nomura
Spread Effectiveness evaluation Spread Effectiveness
evaluationSpread, average
Standard deviation
% % %Size Log market cap Small–large -2.64 x 10.37 0.53 5.95
Past one-month return 4.42 1.98 0.87 5.08Past three-month return 3.8 2.42 0.51 6.23B/P -3.4 xx 10.37 1.39 4.24Forecast E/P 2.24 -2.73 x 1.36 3.06D/P -1.37 x 2.21 0.5 3.38EBITDA/EV (ex financials) 0.5 1.92 0.86 3.59Current/next FY forecast profit growth -0.76 x -4.27 xx 0.1 2.44ROE 4.97 -7.42 xx 0.11 4.27
Revision Analyst revision High–low 1.63 -4.2 xx 1.34 2.66Shareholders’ equity ratio 0.99 0.96 0.11 3.44Default probability 1.8 -2.62 x -0.21 7.37Monthly (60-month) β 1.15 -0.85 0.07 6.49Foreign sensitivity 0.34 -2.27 x 0.42 3.47
Past (1995/1–2011/3)Factor Spread calculation
2011/4 Previous month
Financial indicators High–low
Risk indicators High–low
Reversal Low–high
ValuationUndervalued –overvalued
Growth High–low
Hiromichi Tamura - NSC
+81 3 3274 1079
Yoko Ishige – NSC
+81 3 3274 1079
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
16
Fig. 17: Return spreads, monthly
Note: (1) Each graph shows cumulative return spreads for portfolios in quintiles based on each factor’s size, indexed to end-December 2004 = 100. (2) Universe is TOPIX (excluding financials for EBITDA/EV). (3) Shading indicates months in which factor returns evaluated as effective. (4) Factor definitions as follows. Size = log market cap. B/P = adjusted shareholders’ equity ÷ market cap. Forecast E/P = next-FY forecast net profits ÷ market cap. D/P = total dividends ÷ market cap. EBITDA/EV = EBITDA (recurring profits + depreciation + interest paid) ÷ EV (market cap + debt - cash & short-term securities). Current-/next-FY forecast profit growth = (next-FY forecast recurring profits ÷ current-FY forecast recurring profits - 1) x 100. Analyst revision = rate of change (from average of past 3 months) in forecast recurring profits. Shareholders’ equity to total assets = adjusted shareholders’ equity ÷ total assets. Default probability = estimated default risk based on Merton option model. Monthly (60-month) β = monthly returns (60-month rolling) calculated for TOPIX and individual stocks. Foreign sensitivity = BARRA JP3 model estimates. (5) Earnings forecasts by Nomura, supplemented by Toyo Keizai where necessary. Source: Nomura
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(CY-end)
(end-Dec 2004 = 100)
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Past 3-M return
507090
110130150170190210230250270290
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110130150170190210230250
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Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
17
Herding in China: capital inflows • In this report, we review factor performance for China’s CSI 300 universe, and observe
that a herd-like behaviour exhibits in its domestic market.
• We introduce the capital inflows ratio, which utilises capital inflows data sourced from Wind Info. The factor distinguishes trading activities from institutional investors and individual investors. Stocks with a high capital inflows ratio are those stocks that institutional investors (or informed investors) are buying.
• Our correlation analysis shows that stocks with a high capital inflows ratio tend to maintain high money inflows for the following days, and have a higher future return.
• We construct a hypothesis portfolio consisting of top capital inflows ratio stocks, and back-test the effectiveness of the factor since January 2010. Up to 30 April 2011, the portfolio has delivered an absolute return of 45.8%, outperforming the CSI 300 Index by 60.5%.
Analysing capital flow data in China’s domestic market China’s CSI 300 Index moved from the region’s best performing benchmark for 2009, to the region’s worst performing local index in 2010, as Chinese mainland accelerated monetary tightening amid rising inflationary pressures. Similarly, we have seen significant changes in style leadership in recent years. In this report, we review the performance of quant factors in the CSI 300 universe. We observe a strong herd momentum phenomenon in China’s domestic market, suggesting that capital flow signals by large institutions may have an implication for future stock performance. We conduct an initial study on capital flow data provided by a local Chinese data vendor and evaluate the potential of the factor in higher frequency trading.
China’s domestic factor performance – hearing the herd We review factor performance for China’s CSI 300 universe in Figure 18. Looking at the results for the whole period under observation, it is clear that both valuation and revision-related factors produce consistent risk-adjusted performance on a long-term basis. 2009 saw a strong risk-relief value recovery post the credit crisis. In 2010, consistency of factor performance was affected by negative market sentiment and rising risk aversion. Performance of valuation factors tends to decline when risk aversion is high and interest rates are rising. As the Chinese mainland accelerated its flight against inflation via tightening bank’s lending rules and increasing interest rates, value factors suffered, while growth and high-ROE stocks with positive earnings revisions outperformed.
Sandy Lee
+852 2252 2101
Rico Kwan, CFA
+852 2252 2102
Please see full report published 19 May 2011
We review the performance of quant factors in the CSI 300 universe, and conduct an initial study on capital flow data
In 2010, value factors suffered, while growth and high-ROE stocks with positive earnings revisions outperformed
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
18
Fig. 18: Factor performance summary – CSI 300 universe
Note: Return figures are annualised performance; long-term performance figures are from May 2005 to April 2011; R/R refers to the average return/standard deviation and rank is based on R/R. Factor returns are generated by calculating the subsequent performance of an equal-weighted portfolio that is long the highest one-third and short the one-third with the lowest scores (rebalanced monthly), except for the factors marked with *, which are reverse-based. See Figure 31 for factor definition.Source: Worldscope, I/B/E/S, StarMine, Nomura Quantitative Strategies
China’s domestic market has priced in a lot of market concern and expectation of growth disappointment in 2010. YTD in 2011, value factors seem to be recovering but on risk-adjusted performance, revision-related indicators including a change in earnings yield and StarMine predicted surprise have fared the best. Inflation is still a major concern in the near term and we believe the stock market will continue to feel the pressure from policy tightening. We believe earnings outlook and liquidity, as well as the valuations for equities, will likely be the fundamental return drivers for China’s domestic A-shares equities in 2011.
Fig. 19: Earnings momentum index – CSI 300 universe
Note: Earnings momentum index is defined as: % of companies with +ve Revi,t / %
of companies with -ve Revi,t. Source: I/B/E/S, Nomura Quantitative Strategies
Fig. 20: Proportion of total no. of accounts holding tradable market value of A-shares by capital level
Note: Data as of 31 March. Capital level is in RMB. The statistics don’t include the
market value of “non-tradable shares” (restricted shares) hold by each account. Source: Wind, Nomura Quantitative Strategies
Return R/R Rank Return R/R Rank Return R/R Rank Return R/R Rank
Market cap * 13.7 0.8 8 27.3 2.4 2 10 1.2 7 7.7 0.8 11
Price momentum (1M) -10.6 -0.7 20 -14.1 -1.4 19 -7.3 -0.7 16 -17.9 -2.5 19
Price momentum (12M -1M) -6.7 -0.5 19 -30.5 -2.5 21 11.9 1.2 6 -20 -1.8 17
Volume turnover ratio 6.4 0.4 12 22.9 1.4 5 14.3 1.2 4 -21.3 -2.3 18
Dividend yield 2 0.2 14 -1.5 -0.1 12 -6.3 -1.3 18 21.8 2.1 9
Earnings yield 11 0.9 5 15.9 1.5 4 -4.5 -0.5 15 28.3 2.9 5
B/P 9.9 0.7 9 8.2 0.9 7 -25.3 -2.6 21 38.9 2.8 8
Cashflow yield 8.3 0.9 4 10.1 1.2 6 -12.1 -1.6 19 16.6 1.7 10
EBITDA/EV 10.4 1 3 19.1 2.7 1 -14.3 -1.8 20 37.8 2.9 4
Revision index 5.1 0.8 6 -5.5 -1.1 17 6.6 1.2 5 4.1 2.9 6
Change in earnings yield 13.3 1.3 1 -3 -0.3 13 17.3 1.8 3 30.1 6.2 1
StarMine predicted surprise 7 0.8 7 -1.9 -0.3 14 3.3 0.8 8 6.4 6.1 2
Normalised E/P 9.9 1.1 2 9.9 0.9 8 0.6 0.1 12 19.7 2.8 7
Sales growth (FY2) 4.2 0.5 11 -4.1 -0.5 15 15.8 2.5 1 -5.3 -0.8 13
EPS growth (FY2) 0 0 16 -5.7 -0.9 16 3.8 0.5 10 -9.4 -3.6 21
Return on equity 1.9 0.1 15 5.3 0.5 9 20.8 2.3 2 -13.5 -2.9 20
Shareholders’ equity ratio -7.3 -1.1 21 1.5 0.2 11 -8 -1.1 17 -8 -1.5 16
Pretax profit margin -2.8 -0.3 17 -10.7 -1.1 18 -3.2 -0.4 14 -6.3 -0.8 14
Volatility 8.3 0.6 10 33.1 1.9 3 10.1 0.7 9 -11.5 -0.9 15
Estimate dispersion 3.2 0.3 13 5.4 0.4 10 0.6 0.1 11 7.1 3.3 3
Default probability * -4.7 -0.4 18 -22.5 -1.5 20 -3.4 -0.3 13 0.4 0.1 12
Long-term: CSI 300 Year 2009: CSI 300 Year 2010: CSI 300 YTD 2011: CSI 300
0.0
0.5
1.0
1.5
2.0
2.5
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r-05
Oct
-05
Ap
r-06
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-06
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% of total no. of account by capital level
We believe earnings outlook and liquidity, as well as the valuations for equities will likely be the fundamental return drivers
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
19
One interesting observation we have in China’s domestic market is a strong herd momentum phenomenon. Both on a long-term basis and since 2010, earnings-revision indicators have a consistent impact on the CSI 300 universe. Such kind of typical ‘following the flock’ and herd-like behaviour is quite common in a market dominated by retail and small investors (Figure 20). This suggests short-term liquidity and money flows by large institutions or super funds may have implications for future stock performance. In the next section, we introduce capital flow data provided by Wind Info, a China domestic data vendor. We conduct an initial study attempt to quantify the potential of the factor in higher frequency trading.
Money flow data in China domestic market Money flow in technical analysis is regarded as a measure of the strength of money going in and out of a security, and has been studied intensively by international researchers and investors. A traditional money flow indicator such as the Money Flow Index (MFI) analyses the price and volume of a stock, and is generally considered a dollar-volume momentum indicator to determine the conviction in a current trend. When the price of a stock rises, the traded value over the rising period represents the enthusiasm of buyers (positive money flow). When the price of a stock drops, the traded value over the dropping period represents the enthusiasm of sellers (negative money flow). Money flow, in this case, is a ratio between the enthusiasm of buyers and sellers over the measuring period.
However, traditional money flow calculation does not distinguish the trading activities from institutional investors and individual investors. As institutional investors are superior to individual investors in the acquisition of firm-specific and private information, institutional investors are more homogeneous and tend to trade more on particular stocks or particular groups of stocks to take advantage of information asymmetry (Campbell, Grossman, and Wang, 1993; Harris and Raviv, 1993). In contrast, less informed individual investors are likely to react to changes in volume and price as this may reflect material information. This suggests money flow from institutional investors may exhibit a herd-like behaviour, and sequentially lead to a momentum effect in individual investors.
With the launch of the Level-2 market data services by the Shanghai Stock Exchange (August 2006) and the Shenzhen Stock Exchange (January 2010), real-time tick-by-tick data is available to the public in China’s domestic market, where money flow information by investors is calculated and introduced by China’s domestic data providers.
Figure 21 shows the additional information available in the Level-2 market data when comparing with the existing Level-1 market data.
Fig. 21: Additional information available in the Level-2 market data
Source: Shanghai Stock Exchange
Value-added information Description
Transaction details Dynamic number of transactions
Tick by tick data
Order information Total instructed quantities
Weighted average bid/offer price
Quantity of each of the top 50 orders at the best bid/offer price
Quantities of orders at the 10 best prices
Order cancellation information Top 10 stocks in terms of cancelled order quantities
Ranking Real-time trading value of each sector
Real-time ranking of top 5 stocks of each sector in terms of traded value
Percentage of trading value of each sector in the total turnover value.
Yield rate on bonds Real-time yield-to-maturity of bonds
flowmoneynegativeflowmoneypositive
flowmoneypositiveIndexFlowMoney
+×= 100
‘Following the flock’ and herd-like behaviour is pronounced in China
Money flow is a measure of the strength of money going in and out of a security
Traditional money flow calculation does not distinguish the trading activities from institutional investors and individual investors
Money flow by investors is introduced after the launch of the Level-2 market data services in China’s domestic market
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
20
Introduction of capital inflows ratio In this report, we study the capital inflows data provided by Wind Info, a China domestic data vendor, and investigate the potential of the factor in predicting future stock performance. According to Wind Info, capital inflows are calculated by re-assembling the tick-by-tick trades into estimated buy and sell orders for each investor. The estimated buy and sell orders are then categorised into four classes by order size: super fund (>RMB 1mn), large-sized investor (RMB 200K-RMB1mn), medium-sized investor (RMB 40K-RMB 200K), and small-sized investor (RMB 0-RMB 40K). Net capital inflows for each investor class is the difference between the sum of the estimated buy orders and the sum of the estimated sell orders. Net capital inflows of a stock is calculated as the net capital inflows from super fund and large-sized investor, minus the net capital inflows from medium- and small-sized investors.
We then normalise the factor for stock comparison by dividing the net capital inflows of a stock by the total traded value of the stock during the same period.
Hence, stocks with a high capital inflows ratio are those stocks that institutional investors (or informed investors) are buying, and in our hypothesis, are expected to induce further capital inflows and generate positive impact to future stock performance.
Correlation analysis with stock performance We run correlation analysis to analyse the relationships between the capital inflows ratio and stock performance in China’s domestic market. We retrieve daily net capital inflows data from Wind Terminal for each stock in the CSI 300 universe, and calculate the capital inflows ratio using different formation periods (1 to 20 trading days). Our sample range starts from January 2010 to April 2011, when real-time Level-2 market data is available to the public in the Shenzhen Stock Exchange.
Figure 22 displays the correlations between the capital inflows ratio and the past price return of the stocks. We note that performance of the stocks move in line with the capital inflows ratio; that is, stocks tend to perform better when there are institutional investors buying in China’s domestic market.
Fig. 22: Correlations between capital inflows ratio and past stock performance Testing days (past stock performance)
1 2 3 4 5 6 7 8 9 10 15 20
Fo
rma
tion
da
ys (
cap
ital i
nflo
ws
ratio
)
1 0.66 0.55 0.48 0.45 0.40 0.38 0.36 0.34 0.32 0.30 0.23 0.19
2 0.47 0.67 0.62 0.57 0.52 0.49 0.46 0.44 0.41 0.39 0.30 0.24
3 0.36 0.54 0.67 0.63 0.59 0.56 0.53 0.50 0.47 0.45 0.35 0.28
4 0.31 0.46 0.58 0.67 0.64 0.61 0.58 0.55 0.52 0.50 0.39 0.32
5 0.28 0.41 0.51 0.59 0.67 0.65 0.62 0.59 0.56 0.54 0.43 0.35
6 0.25 0.37 0.46 0.54 0.61 0.67 0.65 0.62 0.60 0.57 0.46 0.38
7 0.23 0.34 0.42 0.50 0.56 0.62 0.67 0.65 0.63 0.60 0.49 0.40
8 0.22 0.32 0.40 0.46 0.52 0.57 0.62 0.66 0.65 0.63 0.51 0.43
9 0.20 0.30 0.37 0.44 0.49 0.54 0.59 0.63 0.66 0.65 0.54 0.45
10 0.19 0.28 0.36 0.41 0.47 0.51 0.56 0.59 0.63 0.66 0.56 0.47
15 0.16 0.23 0.29 0.34 0.38 0.42 0.46 0.49 0.52 0.54 0.64 0.57
20 0.13 0.19 0.24 0.29 0.33 0.36 0.40 0.42 0.45 0.47 0.56 0.63
Note: Our sample range starts from 1 January, 2010 to 30 April, 2011. Universe is based on constituents in the CSI 300 Index. Source: Wind, Datastream, Nomura Quantitative Strategies
clas sinvestorclassinvestorclassinves tor valuesorderselltotalvaluesorderbuytotalInflowsCapitalNet −=
)()(
in ves torsmallinves tormed ium
inves torlargefundsuper
InflowsCapitalNetInflowsCapitalNet
InflowsCapitalNetInflowsCapitalNetInflowsCapitalNet
+−+
=
daysofnonwherevaluetradedtotalInflowsCapitalNetRatioInflowsCapitaln
ii
n
iin .,/
11
== ==
1
6
15
0.0
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0.8
1 2 3 4 5 6 7 8 9 10 15 20
testing days (stock return)
Corr
elat
ion
coef
ficie
nt
formation days (capital inflows ratio)
Tick-by-tick trades are re-assembled into estimated buy and sell orders for each investor by Wind Info
We normalise the net capital inflows data from Wind for stock comparison
Stock return is highly correlated with the capital inflows ratio
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
21
Figure 23 shows the correlations between the current capital inflows ratio and the lagged capital inflows ratio. From the results, we note that stocks with a high capital inflows ratio tend to maintain high money inflows for the following days. However, the effect decays as we study for a longer formation and testing period. This implies the phenomenon that institutional investors take several days to complete their trades, or there is herd-like behaviour among institutional investors as they share the same information.
Fig. 23: Correlations between current capital inflows ratio and lagged capital inflows ratio
Testing days (lagged capital inflows ratio)
1 2 3 4 5 6 7 8 9 10 15 20
Fo
rma
tion
da
ys (
cap
ital i
nflo
ws
ratio
)
1 0.29 0.28 0.28 0.26 0.25 0.24 0.24 0.23 0.22 0.21 0.18 0.15
2 0.28 0.29 0.29 0.27 0.27 0.26 0.25 0.24 0.23 0.22 0.19 0.16
3 0.28 0.29 0.29 0.28 0.27 0.27 0.26 0.24 0.23 0.22 0.19 0.16
4 0.26 0.28 0.28 0.28 0.27 0.26 0.25 0.24 0.23 0.22 0.19 0.16
5 0.25 0.27 0.28 0.27 0.26 0.26 0.24 0.23 0.23 0.22 0.19 0.15
6 0.25 0.27 0.27 0.26 0.26 0.25 0.24 0.23 0.22 0.21 0.19 0.15
7 0.24 0.26 0.26 0.25 0.25 0.24 0.23 0.22 0.22 0.21 0.18 0.14
8 0.23 0.25 0.25 0.24 0.24 0.23 0.22 0.22 0.21 0.20 0.17 0.13
9 0.22 0.24 0.24 0.23 0.23 0.22 0.22 0.21 0.20 0.20 0.16 0.12
10 0.21 0.23 0.23 0.23 0.22 0.22 0.21 0.20 0.20 0.19 0.16 0.12
15 0.18 0.20 0.20 0.20 0.19 0.19 0.18 0.17 0.16 0.16 0.12 0.09
20 0.15 0.16 0.17 0.16 0.16 0.15 0.14 0.13 0.12 0.12 0.09 0.06
Note: Our sample range starts from 1 January, 2010 to 30 April, 2011. Universe is based on constituents in the CSI 300 Index. Source: Wind, Datastream, Nomura Quantitative Strategies
With the above results, we expect a positive correlation between the capital inflows ratio and future stock returns. Figure 24 shows the correlations between the capital inflows ratio and future stock returns.
Fig. 24: Correlations between capital inflows ratio and future stock returns
Testing days (future stock return)
1 2 3 4 5 6 7 8 9 10 15 20
For
ma
tion
days
(ca
pita
l inf
low
s ra
tio)
1 0.07 0.05 0.06 0.06 0.06 0.06 0.07 0.07 0.06 0.06 0.05 0.05
2 0.05 0.05 0.06 0.06 0.06 0.07 0.07 0.06 0.06 0.06 0.05 0.04
3 0.05 0.05 0.06 0.06 0.07 0.07 0.07 0.07 0.06 0.06 0.05 0.04
4 0.05 0.05 0.06 0.06 0.07 0.07 0.07 0.07 0.06 0.06 0.05 0.03
5 0.05 0.05 0.06 0.06 0.07 0.07 0.07 0.07 0.06 0.06 0.05 0.03
6 0.04 0.06 0.06 0.06 0.07 0.07 0.07 0.07 0.06 0.06 0.05 0.02
7 0.05 0.06 0.06 0.06 0.06 0.07 0.07 0.07 0.06 0.06 0.04 0.01
8 0.05 0.05 0.06 0.06 0.06 0.06 0.07 0.06 0.06 0.06 0.04 0.01
9 0.04 0.05 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.05 0.03 0.00
10 0.04 0.05 0.06 0.06 0.06 0.06 0.06 0.06 0.05 0.05 0.03 0.00
15 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.04 0.04 0.03 0.00 -0.01
20 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.01 0.01 0.01 -0.01 -0.02
Note: Our sample range starts from 1 January, 2010 to 30 April, 2011. Universe is based on constituents in the CSI 300 Index. Source: Wind, Datastream, Nomura Quantitative Strategies
As shown in Figure 24, stocks with a higher capital inflows ratio tend to have a higher future return. We note that the capital inflows factor calculated using a 5 to 8 trading-day window gives the highest correlation coefficient to future stock returns. In the next section, we will study a simple short-term trading strategy using a 5-day capital inflow ratio factor.
Back-testing portfolio performance using the capital inflows factor Based on the findings outlined in the previous section, we construct a portfolio consisting of top capital inflows ratio A shares, and back-test the performance of this portfolio since January 2010. Every day, we rank the stocks in the CSI 300 universe by the 5-day capital inflows ratio factor after market close. The top 30 stocks by the capital inflows factor are to be included in the portfolio. Those stocks in the portfolio that fall outside the top-30 rank are to be removed. We rebalance the portfolio daily and track the equal-weighted performance of the constituents.
1
70.05
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1
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(0.02)
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1 2 3 4 5 6 7 8 9 10 15 20
testing days (future stock
return)
Corr
elat
ion
coef
ficie
nt
formation days (capital inflows ratio)
Stocks with high capital inflows ratio tend to maintain the high money inflows in short term
Positive correlations exhibit between the capital inflows factor and future stock returns
Portfolio with top capital inflows ratio stocks has outperformed the CSI 300 Index since 2010
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
22
Fig. 25: Portfolio return of the top 30 A Shares by capital inflows ratio
Source: Wind, Nomura Quantitative Strategies
Fig. 26: Relative performance of the capital inflows ratio portfolios over CSI 300 Index
Source: Wind, Nomura Quantitative Strategies
Figures 25 and 26 display the absolute and relative performances of the portfolio versus the CSI 300 Index. We show in the charts the returns for which we assume we trade at the same day close prices (day T) as we rank the stocks and the returns we assume we trade at the next day close prices (day T+1). This back test is an initial analysis to check the effectiveness of the capital inflows factor, and does not consider turnover and transaction costs. Investors looking to use the result in high frequency trading should consider these issues in designing an executable strategy.
Up to 30 April, 2011, the portfolios have delivered an absolute return of 45.8 % (day T), and 15.1% (day T+1), outperforming the CSI 300 Index by 60.5% and 26.7%, respectively.
Performance comparison with price momentum factor Is the capital inflows ratio a more effective factor to predict future stock returns than a pure price momentum factor in China’s domestic market? While the capital inflows ratio is highly correlated with stock performance over the same period, it is interesting to compare the factor effectiveness in predicting future stock returns of the two factors. Figure 27 displays the correlations between past stock performance and future stock returns. Figures 28 and 29 show the comparison of time series performance of the portfolio consisting of the top 30 stocks by the 5-day capital inflows ratio and the 5-day price momentum factor, assuming we trade on the same day close (day T) as we rank the stocks. Statistically, the capital inflows ratio has a higher correlation coefficient to future stock returns than the price momentum factor does. In terms of stock performance, the portfolio of high capital inflows stocks has outshone the portfolio of high price momentum stocks, generating an outperformance of 68.0% since 2010.
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Comparing with a pure price momentum factor, the capital inflows ratio is more effective in predicting short-term return
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
23
Fig. 27: Correlations between past stock performance and future stock return
Testing days (future stock return)
1 2 3 4 5 6 7 8 9 10 15 20
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tion
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ys (
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1 0.04 0.01 0.03 0.02 0.01 0.01 0.03 0.02 0.01 0.02 0.01 0.01
2 0.01 0.02 0.02 0.01 0.00 0.02 0.02 0.01 0.02 0.01 0.01 0.00
3 0.03 0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.00
4 0.02 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.01 0.02 0.00 -0.02
5 0.01 0.00 0.02 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.00 -0.03
6 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.00 -0.04
7 0.03 0.02 0.02 0.02 0.02 0.02 0.03 0.02 0.02 0.02 0.00 -0.05
8 0.02 0.01 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.01 -0.01 -0.06
9 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.01 0.01 -0.02 -0.07
10 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 -0.03 -0.07
15 0.01 0.01 0.01 0.01 0.00 0.00 0.00 -0.01 -0.02 -0.03 -0.07 -0.09
20 0.00 -0.01 -0.01 -0.02 -0.03 -0.04 -0.05 -0.06 -0.07 -0.07 -0.08 -0.09
Note: Our sample range starts from 1 January, 2010 to 30 April, 2011. Universe is based on constituents in the CSI 300 Index.Source: Datastream, Nomura Quantitative Strategies
Fig. 28: Comparison between portfolio returns of capital inflows ratio and price momentum factors
Source: Wind, Nomura Quantitative Strategies
Fig. 29: Relative return of the capital inflows ratio portfolio over price momentum portfolio
Source: Wind, Nomura Quantitative Strategies
Conclusion To conclude, we observe herd-like behaviour in China’s domestic market. Short-term money flow by large institutions or super funds are proven to be an effective factor to predict future stock performance. We introduce the capital inflows ratio that utilises capital inflows data sourced from Wind Info, a China domestic data vendor. We show in correlation analysis that stocks with a high capital inflows ratio tend to have further money flow by institutional investors, and at the same time, have a higher future price return. Through back-testing the portfolio performance of top capital inflows ratio A-shares, we demonstrate a simple high frequency trading strategy that generates short-term alpha in China’s domestic market. Figure 30 reveals a quantitative screen on the top 30 companies in the CSI 300 universe with a high capital inflows ratio.
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We present a stock list based on high capital inflows ratio factor
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
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Fig. 30: Quantitative screen on top 30 companies in CSI 300 universe with high capital inflows ratio
Note: Data as of 17 May, 2011. Selection of stocks screened from the CSI 300 universe Source: Wind, Nomura Quantitative Strategies
MarketBloomberg code
Name SectorMarket Cap
(US$mn)Avg daily t/o
(US$mn)
5-day Capital Inflows Ratio
(%)China A 000927 CH Tianjin Faw Xiali Automobi-A Consumer Discretionary 1,922 6.85 19.93China A 600208 CH Xinhu Zhongbao Co Ltd-A Financials 4,711 22.08 14.95China A 600239 CH Yunnan Metro Real Estate-A Financials 1,573 29.13 14.89China A 600657 CH Cinda Real Estate Co Ltd -A Financials 1,727 18.64 14.60China A 600220 CH Jiangsu Sunshine-A Consumer Discretionary 1,530 27.76 13.26China A 600804 CH Chengdu Dr Peng Telecom-A Telecommunication Services 1,794 30.81 12.21China A 600143 CH Kingfa Sci.& Tech Co Ltd-A Materials 3,782 25.69 11.71China A 000031 CH Cofco Property Group Co-A Financials 1,773 11.65 10.23China A 601668 CH China State Construction -A Industrials 18,399 76.65 10.17China A 600196 CH Shanghai Fosun Pharmaceuti-A Health Care 3,492 24.52 9.67China A 002128 CH Huolinhe Opencut Coal Ind -A Energy 4,476 17.44 9.24China A 600519 CH Kweichow Moutai Co Ltd-A Consumer Staples 27,219 85.05 8.42China A 000895 CH Henan Shuanghui Investment-A Consumer Staples 5,662 35.96 8.40China A 601988 CH Bank Of China Ltd-A Financials 146,154 22.44 8.26China A 600598 CH Heilongjiang Agriculture-A Consumer Staples 3,899 35.94 7.47China A 000858 CH Wuliangye Yibin Co Ltd-A Consumer Staples 19,284 115.13 7.44China A 600062 CH Beijing Double Crane Pharm-A Health Care 2,281 13.84 7.36China A 000725 CH Boe Technology Group Co Lt-A Information Technology 5,343 52.73 7.04China A 000402 CH Financial Street Holding-A Financials 3,569 41.60 6.91China A 600600 CH Tsingtao Brewery Co Ltd-A Consumer Staples 7,461 25.01 6.42China A 600703 CH Sanan Optoelectronics Co L-A Information Technology 4,175 28.89 5.91China A 000538 CH Yunnan Baiyao Group Co Ltd-A Health Care 6,322 29.57 5.87China A 600588 CH Ufida Software Co Ltd-A Information Technology 2,570 9.78 5.56China A 600481 CH Shuangliang Eco-Energy Sys-A Materials 1,849 15.48 5.37China A 600601 CH Founder Technology Group -A Information Technology 1,420 16.55 4.93China A 600216 CH Zhejiang Medicine Co Ltd-A Health Care 2,369 43.22 4.73China A 600276 CH Jiangsu Hengrui Medicine C-A Health Care 5,633 27.05 4.61China A 601268 CH China Erzhong Group Deyang-A Industrials 2,818 24.20 4.55China A 600266 CH Beijing Urban Construction-A Financials 2,026 32.97 4.43China A 600500 CH Sinochem Intl Corp-A Industrials 2,462 28.10 4.41
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
25
Definition of factors
Fig. 31: Factor definitions
Note: The factors marked with * are reverse-based. Source: Worldscope, I/B/E/S, StarMine, MSCI, Nomura Quantitative Strategies
Factor Definition1 Market cap * Log of US$ market cap
2 Price momentum (1M) Past 1-month local currency return
3 Price momentum (3M) Past 3-month local currency return
4 Price momentum (6M -1M) Last 6-month return less the last 1-month return in local currency
5 Price momentum (12M -1M) Last 12-month return less the last 1-month return in local currency
6 Long term price momentum Past 36-month local currency return
7 Volatility Past 36-month price return volatility
8 Average daily traded value Monthly traded value in USD / number of traded days
9 Trade momentum (3M) Past 1-month trading volume - previous 3-month average trading volume
10 Volume turnover ratio Past 1-month trading volume / shares outstanding at month-end
11 Dividend yield F12-month DPS / stock price
12 Dividend Payout Actual dividends / actual net profit before extraordinary items
13 Earnings yield F12-month EPS / stock price
14 B/P Actual BPS / stock price
15 Sales/Price F12-month sales per share / stock price
16 Cashflow yield F12-month cashflow per share / stock price
17 Trailing EBITDA/EV Actual EBITDA / (market cap + interest-bearing debt - cash - short-tern marketable securities)
18 EBITDA/EV (F12-month net profit + actual interest expense + actual depreciation) / (market cap + interest-bearing debt - cash - short-tern marketable securities)
19 Revision index (Number of upward analyst revisions - number of downward analyst revisions) / total number ofanalysts’ estimate
20 Earnings revision indicator (FY2) FY2 EPS / previous 3-month average FY2 EPS
21 Change in earnings yield F12-month earnings yield - past 3-month average earnings yield
22 Normalised E/P (F12-month earnings yield - average earnings yield in past 36 months) / standard deviation of theearnings yields in the past 36 months
23 StarMine predicted surprise (SmartEstimate F12-month - consensus mean) / max(divisor, |mean|)
24 Estimate dispersion I/B/ES FY1 consensus EPS standard deviation / absolute value for FY1 consensus EPS
25 Consensus rating * I/B/E/S consensus analyst rating
26 Change in ROE (FY1) FY1 ROE - actual ROE
27 Change in ROE (FY2) FY2 ROE - FY1 ROE
28 Sales growth (1Y) Actual sales / previous year actual sales
29 Sales growth (FY1) FY1 sales / actual sales
30 Sales growth (FY2) FY2 sales / FY1 sales
31 EPS growth (FY1) FY1 EPS / actual EPS
32 EPS growth (FY2) FY2 EPS / FY1 EPS
33 Return on assets Actual net profit / actual total assets
34 Return on equity F12-month net profit / actual shareholders’ equity
35 Shareholders’ equity ratio Actual shareholders’ equity / actual total assets
36 Trailing profit margin Actual net profit / actual sales
37 Pretax profit margin F12-month pretax profit / F12-month sales
38 Asset turnover Actual sales / actual total assets
39 Capex to assets Actual capital expenditure / actual total assets
40 Capex to sales Actual capital expenditure / actual sales
41 Default probability * Default probability estimated using Merton model
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
26
Stagflation priced in the market? • In this section we revisit the question of whether the market is pricing in stagflation.
• Inflation expectations priced in bonds have risen briskly to pre-Lehman levels, while long-term earnings growth is now priced as negligible. Inflation expectations are certainly not yet very high, but this divergence of growth and inflation pricing is not a good sign for the markets. The market currently seems to be pricing in what could be regarded as early signs of stagflation.
Last November, when the Fed’s QE2 program began, we observed that stocks and bonds could be seen as beginning to price stagflation as a consequence of the Fed’s activities (see Do bonds lead stocks, or is stagflation priced? 2 November 2010). Now, as QE2 is set to wind down, we revisit our metrics. We think the evidence for stagflation being priced in stocks and bonds has become clearer.
Fig. 32: Stagflation seems to be priced in the market
Notes: Shows the implied long-term earnings growth (LTG) of S&P500 (dark blue line) and implied ten-year inflation rate (light blue line). Implied LTG (from FY1 to FY5) is derived by assuming a static risk premium, 2.8%, which is a historical average of the implied risk premium since December 1987, based on a residual income model and I/B/E/S forecasted earnings. Implied inflation rate is calculated by subtracting the real yield of the inflation linked maturity from the yield of the closest nominal Treasury maturity. Last data points are as of 18 May 2011. Source: Nomura Securities International, Inc, I/B/E/S, S&P, Compustat, IDC, Bloomberg.
Overshoot and undershoot of implied earnings growth The S&P500 has been hunting for the correct pricing of long-term earnings growth since the financial crisis erupted in the fourth quarter of 2008 (see last section of report for details of implied earnings growth estimation). Our analysis shows that market-implied earnings growth for the S&P500 dropped from a historically realistic 8% per year before the fourth quarter of 2008 to a virtual doomsday pricing of negative growth by the end of the first quarter of 2009. We think the rally in stocks that lasted for about a year after the March 2009 bottom was basically a re-pricing of earnings growth from a too-pessimistic depression-like scenario. The rally of 2009 produced an overshoot of implied earnings growth to a too-high level of over 10% growth. That overshoot, in turn, led to a ratcheting down of growth expectations, with implied earnings growth going negative again by early summer 2010. The rally that began at summer’s end in 2010 appears to have been, once again, a re-pricing of too-pessimistic earnings growth expectations. That uptick in pricing of earnings growth stalled in February 2011, heading back down again. The S&P500 is currently pricing only slightly positive earnings growth over the next five years. The implied earnings growth gyrations of the past few years are illustrated by the dark blue line in Figure 32.
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Implied 10-year inflation rate Historical average of LTGsince World War II (6.6%)
We revisit the question of whether the market is pricing stagflation
Over the past several years, the stock market has been moved by shifts in implied earnings growth, which has gyrated from overly pessimistic to overly optimistic
Joseph Mezrich
+1 212 667 9316
Yasushi Ishikawa +1 212 667 1562 [email protected] Please see full report published 10 May 2011
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
27
Inflation pricing The earnings growth priced in equities bundles inflation expectations and economic growth expectations together. Inflation expectations can be measured in several ways, courtesy of the bond market. Here we use the 10-year breakeven inflation rate, subtracting the real yield of the inflation-linked maturity from the yield of the closest nominal Treasury maturity (the so-called breakeven inflation rate as calculated by Bloomberg). In Figure 32, the light blue line displays the bond market’s 10-year breakeven inflation rate since the beginning of 2004.
As Figure 32 shows, inflation expectations priced in bonds (light blue line) and earnings growth expectations priced in stocks (dark blue line) have generally moved together, with notable exceptions. The divergent paths of inflation priced in bonds and earnings growth priced in stocks emerged with the European debt crisis in early 2010. Inflation expectations dropped, but not as severely as expectations for earnings growth. And, importantly, inflation expectations priced in bonds have risen briskly to pre-Lehman levels, while long-term earnings growth is now priced as negligible. The current situation, circled in Figure 33, is what matters now.
Fig. 33: Pricing of growth and inflation – divergent path of stocks and bonds
Notes: Shows the implied long-term earnings growth (LTG) of S&P500 (dark blue line) and implied 10-year inflation rate (light blue line). Implied LTG (from FY1 to FY5) is derived by assuming a static risk premium, 2.8%, which is a historical average of the implied risk premium since December 1987, based on a residual income model and I/B/E/S forecasted earnings. Implied inflation rate is calculated by subtracting the real yield of the inflation linked maturity from the yield of the closest nominal Treasury maturity. Last data points are as of 18 May 2011.
Source: Nomura Securities International, Inc, I/B/E/S, S&P, Compustat, IDC, Bloomberg.
Figure 33 expands the view of Figure 32 back to 1998. As we have previously reported, the implied earnings growth in stocks was far too high before the tech bubble, and settled in to a historically realistic 8% level after the 2001 recession and until the recent crisis (the historical average of five-year earnings growth has been 6.6% since World War II). Starting at the left side of Figure 33, we see that Inflation expectations oscillated between zero and about 2.5% until around 2003.
Given the longer-term view of Figure 33, the divergence of inflation pricing in bonds and earnings growth pricing in stocks that emerged in 2010, with inflation turning higher and growth turning lower, is even more striking. Inflation expectations are certainly not yet very high, but the divergence of growth and inflation pricing is not a good sign for the markets. This pattern of divergence is a new and potentially ominous signal we see in the data. To be clear, zero earnings growth with 2.3% inflation – which is what the market is pricing – is a corrosive mix. Of course, stocks could, once again, realize that almost zero earnings growth over the next five years is too pessimistic a view to be pricing. That realization could produce a rally. But the market isn’t yet as pessimistic about growth as it was in the first quarter of 2009 or the third quarter of 2010. In the meantime, the widening inflation vs. growth divergence priced in bonds and stocks raises a concern. The market currently seems to be pricing in what could be regarded as early signs of stagflation.
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Implied long-term earnings growth rate
Implied 10-year inflation rate
Historical average of LTGsince World War II (6.6%)
Earnings growth priced in equities bundles both inflation expectations and economic growth expectations
Inflation expectations are now priced higher than before the Lehman bankruptcy, while long-term earnings growth is now priced as negligible
Inflation expectations are certainly not yet very high, but the current divergence of higher inflation pricing in bonds and lower earnings growth pricing in stocks is not a good sign – the market currently seems to be pricing in what could be regarded as early signs of stagflation
After the 2001 recession, implied earnings growth in stocks settled in to a historically realistic 8% level … until the recent crisis
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
28
How we calculate implied long-term earnings growth Our estimate of the market’s implied earnings growth uses a residual income model. The classic Residual Income Model (RIM) values a company based on its current book value plus an infinite sum of discounted “residual” income. Conceptually, ‘residual’ is the return generated by a firm over and above the firm’s cost of capital. Over the years, there have been many approaches to derive and utilize RIM, e.g. Preinreich (1938), Edwards and Bell (1961), Peasnell (1982), Bernard (1994), Ohlson (1990, 1995). Our approach most closely follows Frankel and Lee (1997, 1998) as well as Lee et al (1999). A theoretical value for the S&P500 is generated based on a bottom-up analysis of discounted cash flow for S&P500 constituents. The market capitalization of each stock is aggregated to produce a capitalization for the market, which we assume to be equal to model-estimated value for the S&P500. A two-stage valuation approach is calculated from (Compustat and I/B/E/S) financial statements to generate each firm’s value V as follows:
(1)
where NIi = net income at time i,
r = cost of capital,
Bi-1 = book value of firm at time i-1,
ROEi = return on equity at time i,
V = estimated market cap based on model.
Stage 1: i = years 1 to 5 NI1 = I/B/E/S consensus estimate for FY1 net income,
NI2 = I/B/E/S consensus estimate for FY2 net income,
NI3 and NI4 are estimated by linear extrapolation from NI2 through NI5, as shown in Equations (2), (3).
(2)
(3)
NI5 was calculated using NI1 and the long-term growth rate
(4)
∞
=
−
=
−
+−
++−
+=6
15
1
10 )1(
)()1( i
i
ii
ii
ii
r
BrROE
r
rBNIBV
31)( 2523 ⋅−+= NINININI
32)( 2524 ⋅−+= NINININI
415 )1( LTGNINI +⋅=
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
29
=
−
=
−
+−
++−
+=100
6
15
1
10 )1(
)()1( i
iii
iiii
r
BrROE
r
rBNIBV
pf rrr +=
Stage 2: i = years 6 to ∞ ROEi – r is estimated using an exponential decay process with decay constant λ. The half ROEi – r is life of is assumed to be 10 years (the 10th year into the second stage):
,
(5)
A firm’s life is assumed to be 100 years so Equation (1) becomes:
(6)
where
(7)
and POR is a dividend payout ratio based on the past 5 years.
The cost of capital r consists of two parts:
(8)
where
rf = risk free rate
rp = market risk premium, assumed the same for all firms.
We use the 10-year t-bond yield for the risk free rate and calculate the risk premium rp by plugging all of our parameters into equation (6) using the current market cap for V and I/B/E/S consensus estimates for long-term earnings growth for LTG and solve for rp.
We calculate the historical average of rp, and plug this parameter back into equation (6) to solve for the market’s implied long-term growth rate (LTG). That is what we plot in Figures 32 and 33 in this report.
ti erROErROE λ−⋅−=− )( 5
( )10
21ln
=λ
121 )1( −−− −+= iii EPORBB
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
30
References • Victor L. Bernard, “Accounting-based valuation methods, determinants of book-to-
market ratios, and implications for financial statement analysis,” Working paper, University of Michigan, (1994).
• Edgar O. Edwards and Philip W. Bell, The Theory and Measurement of Business Income, University of California Press, (1961).
• Eugene F. Fama and Kenneth R. French, “Industry costs of equity.” Journal of Financial Economics, Vol. 43 (1997), pp. 153-193.
• Richard Frankel and Charles M.C. Lee, “Accounting diversity and international valuation,” Working paper, University of Michigan and Cornell University, (1997).
• Richard Frankel and Charles M.C. Lee, “Accounting valuation, market expectation, and cross-sectional stock returns,” Journal of Accounting and Economics, Vol. 25 (1998), pp. 283-319.
• Charles M.C. Lee, James Myers and Bhaskaran Swaminathan, “What is the Intrinsic Value of the Dow?” Journal of Finance, Vol. 54, No. 5 (1999), pp.1693-1741.
• Bruce N. Lehman, “Earnings, dividend policy, and present value relations: building blocks of dividend policy invariant cash flows,” Review of Quantitative Finance and Accounting, Vol. 3 (1993), pp. 263-282.
• James A. Ohlson, “A synthesis of security valuation theory and the role of dividends, cash flows, and earnings,” Contemporary Accounting Research, Vol. 6 (1990), pp. 648-676.
• James A. Ohlson, “Earnings, Book Values, and Dividends in Security Valuation,” Contemporary Accounting Research, Vol. 11 (1995), pp. 661-687.
• K.V. Peasnell, “Some formal connections between economic values and yields and accounting numbers,” Journal of Business Finance and Accounting, (1982), pp. 361-381.
• Gabriel A.D. Preinreich, “Annual survey of economic theory: the theory of depreciation,” Econometrica, Vol. 6 (1938), pp. 219-241.
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
31
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LIQUID MARKET ANALYTICS
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Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
32
Nomura | EMEA Global Quantitative Research Monthly May 23, 2011
33
Appendix A-1
Analyst Certification
We, Inigo Fraser-Jenkins, Hiromichi Tamura, Sandy Lee and Joseph Mezrich, hereby certify (1) that the views expressed in this Research report accurately reflect our personal views about any or all of the subject securities or issuers referred to in this Research report, (2) no part of our compensation was, is or will be directly or indirectly related to the specific recommendations or views expressed in this Research report and (3) no part of our compensation is tied to any specific investment banking transactions performed by Nomura Securities International, Inc., Nomura International plc or any other Nomura Group company.
Industry Specialists are senior employees within Nomura who are responsible for the sales and trading effort in the sector for which they have coverage.
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Important Disclosures Online availability of research and additional conflict-of-interest disclosures Nomura Japanese Equity Research is available electronically for clients in the US on NOMURA.COM, REUTERS, BLOOMBERG and THOMSON ONE ANALYTICS. For clients in Europe, Japan and elsewhere in Asia it is available on NOMURA.COM, REUTERS and BLOOMBERG. Important disclosures may be accessed through the left hand side of the Nomura Disclosure web page http://www.nomura.com/research or requested from Nomura Securities International, Inc., on 1-877-865-5752. If you have any difficulties with the website, please email [email protected] for technical assistance. The analysts responsible for preparing this report have received compensation based upon various factors including the firm's total revenues, a portion of which is generated by Investment Banking activities. Industry Specialists identified in some Nomura International plc research reports are employees within the Firm who are responsible for the sales and trading effort in the sector for which they have coverage. Industry Specialists do not contribute in any manner to the content of research reports in which their names appear. Marketing Analysts identified in some Nomura research reports are research analysts employed by Nomura International plc who are primarily responsible for marketing Nomura’s Equity Research product in the sector for which they have coverage. Marketing Analysts may also contribute to research reports in which their names appear and publish research on their sector. Distribution of ratings (Global) The distribution of all ratings published by Nomura Global Equity Research is as follows: 49% have been assigned a Buy rating which, for purposes of mandatory disclosures, are classified as a Buy rating; 37% of companies with this rating are investment banking clients of the Nomura Group*. 40% have been assigned a Neutral rating which, for purposes of mandatory disclosures, is classified as a Hold rating; 46% of companies with this rating are investment banking clients of the Nomura Group*. 11% have been assigned a Reduce rating which, for purposes of mandatory disclosures, are classified as a Sell rating; 16% of companies with this rating are investment banking clients of the Nomura Group*. As at 31 March 2011. *The Nomura Group as defined in the Disclaimer section at the end of this report. Explanation of Nomura's equity research rating system in Europe, Middle East and Africa, US and Latin America for ratings published from 27 October 2008 The rating system is a relative system indicating expected performance against a specific benchmark identified for each individual stock. Analysts may also indicate absolute upside to target price defined as (fair value - current price)/current price, subject to limited management discretion. In most cases, the fair value will equal the analyst's assessment of the current intrinsic fair value of the stock using an appropriate valuation methodology such as discounted cash flow or multiple analysis, etc. STOCKS A rating of 'Buy', indicates that the analyst expects the stock to outperform the Benchmark over the next 12 months. A rating of 'Neutral', indicates that the analyst expects the stock to perform in line with the Benchmark over the next 12 months. A rating of 'Reduce', indicates that the analyst expects the stock to underperform the Benchmark over the next 12 months. A rating of 'Suspended', indicates that the rating and target price have been suspended temporarily to comply with applicable regulations and/or firm policies in certain circumstances including when Nomura is acting in an advisory capacity in a merger or strategic transaction involving the company. Benchmarks are as follows: United States/Europe: Please see valuation methodologies for explanations of relevant benchmarks for stocks (accessible through the left hand side of the Nomura Disclosure web page: http://www.nomura.com/research);Global Emerging Markets (ex-Asia): MSCI Emerging Markets ex-Asia, unless otherwise stated in the valuation methodology. SECTORS A 'Bullish' stance, indicates that the analyst expects the sector to outperform the Benchmark during the next 12 months. A 'Neutral' stance, indicates that the analyst expects the sector to perform in line with the Benchmark during the next 12 months. A 'Bearish' stance, indicates that the analyst expects the sector to underperform the Benchmark during the next 12 months. Benchmarks are as follows: United States: S&P 500; Europe: Dow Jones STOXX 600; Global Emerging Markets (ex-Asia): MSCI Emerging Markets ex-Asia. Explanation of Nomura's equity research rating system for Asian companies under coverage ex Japan published from 30 October 2008 and in Japan from 6 January 2009 STOCKS Stock recommendations are based on absolute valuation upside (downside), which is defined as (Target Price - Current Price) / Current Price, subject to limited management discretion. In most cases, the Target Price will equal the analyst's 12-month intrinsic valuation of the stock, based on an appropriate valuation methodology such as discounted cash flow, multiple analysis, etc. A 'Buy' recommendation indicates that potential upside is 15% or more. A 'Neutral' recommendation indicates that potential upside is less than 15% or downside is less than 5%. A 'Reduce' recommendation indicates that potential downside is 5% or more. A rating of 'Suspended' indicates that the rating and target price have been suspended temporarily to comply with applicable regulations and/or firm policies in certain circumstances including when Nomura is acting in an advisory capacity in a merger or strategic transaction involving the subject company. Securities and/or companies that are labelled as 'Not rated' or shown as 'No rating' are not in regular research coverage of the Nomura entity identified in the top banner. Investors should not expect continuing or additional information from Nomura relating to such securities and/or companies. SECTORS A 'Bullish' rating means most stocks in the sector have (or the weighted average recommendation of the stocks under coverage is) a positive absolute recommendation.
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A 'Neutral' rating means most stocks in the sector have (or the weighted average recommendation of the stocks under coverage is) a neutral absolute recommendation. A 'Bearish' rating means most stocks in the sector have (or the weighted average recommendation of the stocks under coverage is) a negative absolute recommendation. Explanation of Nomura's equity research rating system in Japan published prior to 6 January 2009 (and ratings in Europe, Middle East and Africa, US and Latin America published prior to 27 October 2008) STOCKS A rating of '1' or 'Strong buy', indicates that the analyst expects the stock to outperform the Benchmark by 15% or more over the next six months. A rating of '2' or 'Buy', indicates that the analyst expects the stock to outperform the Benchmark by 5% or more but less than 15% over the next six months. A rating of '3' or 'Neutral', indicates that the analyst expects the stock to either outperform or underperform the Benchmark by less than 5% over the next six months. A rating of '4' or 'Reduce', indicates that the analyst expects the stock to underperform the Benchmark by 5% or more but less than 15% over the next six months. A rating of '5' or 'Sell', indicates that the analyst expects the stock to underperform the Benchmark by 15% or more over the next six months. Stocks labeled 'Not rated' or shown as 'No rating' are not in Nomura's regular research coverage. Nomura might not publish additional research reports concerning this company, and it undertakes no obligation to update the analysis, estimates, projections, conclusions or other information contained herein. SECTORS A 'Bullish' stance, indicates that the analyst expects the sector to outperform the Benchmark during the next six months. A 'Neutral' stance, indicates that the analyst expects the sector to perform in line with the Benchmark during the next six months. A 'Bearish' stance, indicates that the analyst expects the sector to underperform the Benchmark during the next six months. Benchmarks are as follows: Japan: TOPIX; United States: S&P 500, MSCI World Technology Hardware & Equipment; Europe, by sector - Hardware/Semiconductors: FTSE W Europe IT Hardware; Telecoms: FTSE W Europe Business Services; Business Services: FTSE W Europe; Auto & Components: FTSE W Europe Auto & Parts; Communications equipment: FTSE W Europe IT Hardware; Ecology Focus: Bloomberg World Energy Alternate Sources; Global Emerging Markets: MSCI Emerging Markets ex-Asia. Explanation of Nomura's equity research rating system for Asian companies under coverage ex Japan published prior to 30 October 2008 STOCKS Stock recommendations are based on absolute valuation upside (downside), which is defined as (Fair Value - Current Price)/Current Price, subject to limited management discretion. In most cases, the Fair Value will equal the analyst's assessment of the current intrinsic fair value of the stock using an appropriate valuation methodology such as Discounted Cash Flow or Multiple analysis etc. However, if the analyst doesn't think the market will revalue the stock over the specified time horizon due to a lack of events or catalysts, then the fair value may differ from the intrinsic fair value. In most cases, therefore, our recommendation is an assessment of the difference between current market price and our estimate of current intrinsic fair value. Recommendations are set with a 6-12 month horizon unless specified otherwise. Accordingly, within this horizon, price volatility may cause the actual upside or downside based on the prevailing market price to differ from the upside or downside implied by the recommendation. A 'Strong buy' recommendation indicates that upside is more than 20%. A 'Buy' recommendation indicates that upside is between 10% and 20%. A 'Neutral' recommendation indicates that upside or downside is less than 10%. A 'Reduce' recommendation indicates that downside is between 10% and 20%. A 'Sell' recommendation indicates that downside is more than 20%. SECTORS A 'Bullish' rating means most stocks in the sector have (or the weighted average recommendation of the stocks under coverage is) a positive absolute recommendation. A 'Neutral' rating means most stocks in the sector have (or the weighted average recommendation of the stocks under coverage is) a neutral absolute recommendation. A 'Bearish' rating means most stocks in the sector have (or the weighted average recommendation of the stocks under coverage is) a negative absolute recommendation. Target Price A Target Price, if discussed, reflect in part the analyst's estimates for the company's earnings. The achievement of any target price may be impeded by general market and macroeconomic trends, and by other risks related to the company or the market, and may not occur if the company's earnings differ from estimates.
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Disclaimers This publication contains material that has been prepared by the Nomura entity identified at the top or bottom of page 1 herein, if any, and/or, with the sole or joint contributions of one or more Nomura entities whose employees and their respective affiliations are specified on page 1 herein or elsewhere identified in the publication. Affiliates and subsidiaries of Nomura Holdings, Inc. (collectively, the 'Nomura Group'), include: Nomura Securities Co., Ltd. ('NSC') Tokyo, Japan; Nomura International plc ('NIplc'), United Kingdom; Nomura Securities International, Inc. ('NSI'), New York, NY; Nomura International (Hong Kong) Ltd. (‘NIHK’), Hong Kong; Nomura Financial Investment (Korea) Co., Ltd. (‘NFIK’), Korea (Information on Nomura analysts registered with the Korea Financial Investment Association ('KOFIA') can be found on the KOFIA Intranet at http://dis.kofia.or.kr ); Nomura Singapore Ltd. 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