Statistical Model to Predict IPO Prices for Semiconductor
fma.ny.presentation
1. Understanding rankings of financial analysts
Artur Aiguzhinov1
Carlos Soares1
Ana Paula Serra2
1
LIAAD-INESC Porto LA & Faculdade de Economia da Universidade do Porto
2
Faculdade de Economia da Universidade do Porto & CEFUP - Centro de Economia e Finan¸cas
da Universidade do Porto
October 23rd, 2010
FMA Annual Meeting, New York
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2. Motivation: the value of the recommendations
Efficient Market Hypothesis (Fama, 1970);
High information costs provide possibilities for abnormal returns
(Grossman & Stiglitz, 1980);
On average, recommendations bring value to investors (Womack, 1996);
Analysts’ accuracy in forecasts is valuable (Brown & Mohammad, 2003);
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3. Motivation: rankings of the analysts
StarMine R
issues annual analyst rankings:
Ranks the analysts based on recommendation performance and EPS
forecast accuracy;
Why not to predict stock prices directly?
Analysts’ relative performance (rankings) is more predictable than the stock
prices.
Is it possible to predict these rankings?:
If yes, can we use those predictions in profitable strategy?;
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4. The Goals of the Research
Accurately predict the rankings of financial analysts;
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5. Research contributions
Interdisciplinary approach to an interesting research topic;
Financial Economics contributions:
analysis of the financial analysts based on state variables concerning market
conditions and stock characteristics;
first methodology to predict the rankings;
verify if there is a ranking consistency over time;
identify variables that discriminate the rankings;
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6. Research design: an overview
Initial rankings of the analysts (target rankings):
Analysts evaluation models (Clement, 1999; Brown, 2001; Creamer &
Stolfo, 2009);
Predict rankings of the analysts (label ranking):
Evaluate the ranking accuracy;
Identify discriminative independent variables;
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7. Database to use
ThomsonOne I/B/E/S Detailed History:
Quarterly EPS forecasts (1989Q1-2009Q4);
ThomsonOne DataStream:
Accounting data;
Market data;
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8. Description of the data
Table: Summary of the data
Sector # analysts # stocks mean forecasts mean stocks
per stock per quarter per analysts per quarter
Energy 135 34 7.57 1.91
Industrials 208 66 3.66 1.05
Materials 147 30 5.14 1.16
IT 301 106 7.85 2.76
Total 791 236 6.06 1.72
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9. Average forecasts
Figure: Average issued forecasts per quarter
0 20 40 60 80
010203040506070
Quarters
Averagenumberofforecastsperquarter
Sectors
Energy
Industrials
IT
Materials
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10. Creating target rankings: indexing of the analysts
Based on previous research, we use EPS mean adjusted forecast error
(MAFE) as a measure of analysts predicting accuracy:
FEq,a,s = |ActEPSq,s − EPSq,a,s| (1)
FEq,s =
1
n
n
a=1
FEq,a,s (2)
MAFEq,a,s =
FEq,a,s
FEq,s
(3)
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11. Context characterization: state variables
Variables that describe market conditions and stock characteristics
(Jegadeesh, Kim, Krische, & Lee, 2004):
Analysts variables:
Lagged forecasting error;
Change in consensus;
Earnings momentum:
Standardized Unexpected Earnings;
Growth indicators:
Sales growth;
Fundamentals:
Total accruals to total assets ratio;
Valuation multiples:
Earnings-to-price ratio;
Market volatility;
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12. Label ranking algorithm
Table: Example of analysts rankings based on the observed variables x1 . . . x4
Quarters x1 x2 x3 x4 Ranks
Alex Brown Craig
1 High Low High Medium 1 2 3
2 High High High Low 2 3 1
3 Medium Medium High Low 1 2 3
4 Low Low Low High 1 3 2
5 Medium High High Medium 1 2 3
6 High Medium High Low 3 1 2
Naive Bayes algorithm for label ranking (Aiguzhinov, Soares, & Serra, 2010):
ˆπ = arg max
π∈ΠL
PLR (π)
m
a=1
PLR (xi,a|π) (4)
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13. Predicted vs. default ranking
Table: Summary of the results compared to default ranking
Sectors Outperformed Outperformed- # stocks with p-values
the default ranking to-total rate 1% 5% 10%
Energy 18 0.53 7 9 9
Industrials 31 0.47 16 18 23
Materials 12 0.40 5 7 8
IT 51 0.48 18 27 30
Total 112 0.47 46 61 70
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14. Predicted vs naive ranking
Table: Summary of the results compared to naive ranking
Sectors Outperformed Outperformed- # stocks with p-values
the naive ranking to-total rate 1% 5% 10%
Energy 11 0.32 5 7 7
Industrials 27 0.41 5 6 7
Materials 11 0.37 0 0 1
IT 36 0.34 1 1 1
Total 85 0.36 11 14 16
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19. Discriminative Value
X1 average Weights Weighted average
a1 vs. b1 0.00 1 0.00
a1 vs. c1 0.50 2 1.00
a1 vs. d1 0.25 3 0.75
b1 vs. c1 0.50 1 0.50
b1 vs. d1 0.75 2 1.50
c1 vs. d1 0.50 1 0.50
0.708
Discriminative Power : 1-0.708=0.292 The higher the discriminative power,
the more different are the rankings
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20. Discriminative power
Table: Discriminative power of independent variables
Sectors FELAG SUE consensus EP SG TA MKT
Energy 0.235 0.211 0.234 0.208 0.194 0.341 0.148
Industrials 0.248 0.230 0.279 0.233 0.263 0.282 0.089
Materials 0.180 0.197 0.278 0.232 0.156 0.378 0.051
IT 0.213 0.238 0.243 0.230 0.185 0.298 0.155
Total 0.219 0.219 0.258 0.226 0.199 0.325 0.111
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21. References (1)
Aiguzhinov, A., Soares, C., & Serra, A. P. (2010). A similarity-based
adaptation of naive bayes for label ranking: Application to the
metalearning problem of algorithm recommendation. In B. Pfahringer,
G. Holmes, & A. Hoffmann (Eds.), Discovery science (Vol. 6332, pp.
16–26). Springer.
Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial
Analysts Journal, 48(5), 28–43.
Brazdil, P., Soares, C., & Costa, J. (2003). Ranking Learning Algorithms:
Using IBL and Meta-Learning on Accuracy and Time Results.
Machine Learning, 50(3), 251–277.
Brown, L. (2001). How Important is Past Analyst Earnings Forecast
Accuracy? Financial Analysts Journal, 57(6), 44–49.
Brown, L., & Mohammad, E. (2003). The Predictive Value of Analyst
Characteristics. Journal of Accounting, Auditing and Finance, 18(4).
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22. References (2)
Clement, M. (1999). Analyst forecast accuracy: Do ability, resources, and
portfolio complexity matter? Journal of Accounting and Economics,
27(3), 285–303.
Creamer, G., & Stolfo, S. (2009). A link mining algorithm for earnings
forecast and trading. Data Mining and Knowledge Discovery, 18(3),
419–445.
Fama, E. (1970). Efficient Capital Markets: A Review of Empirical Work.
The Journal of Finance, 25, 383–417.
Grauer, R. (2008). Benchmarking measures of investment performance with
perfect-foresight and bankrupt asset allocation strategies. The Journal
of Portfolio Management, 34(4), 43–57.
Grossman, S., & Stiglitz, J. (1980). On the Impossibility of Informationally
Efficient Prices. American Economic Review, 70, 393–408.
H¨ullermeier, E., F¨urnkranz, J., Cheng, W., & Brinker, K. (2008). Label
ranking by learning pairwise preferences. Artificial Intelligence,
172(2008), 1897–1916.
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23. References (3)
Jegadeesh, N., Kim, J., Krische, S., & Lee, C. (2004). Analyzing the
Analysts: When Do Recommendations Add Value? The Journal of
Finance, 59(3), 1083–1124.
Ljungqvist, A., Malloy, C., & Marston, F. (2009). Rewriting history. The
Journal of Finance, 64(4), 1935–1960.
Vembu, S., & G¨artner, T. (2010, October). Preference learning. Springer.
Vogt, M., Godden, J., & Bajorath, J. (2007). Bayesian interpretation of a
distance function for navigating high-dimensional descriptor spaces.
Journal of chemical information and modeling, 47(1), 39-46.
Womack, K. (1996). Do Brokerage Analysts’ Recommendations Have
Investment Value? The Journal of Finance, 51, 137–168.
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24. Similarity-based Naive Bayes for Label Ranking: Prior
probability of label ranking
Table: Demonstration of the prior probability for label ranking
Quarters x1 x2 x3 x4 Ranks
Alex Brown Craig
1 High Low High Medium 1 2 3
2 High High High Low 2 3 1
3 Medium Medium High Low 1 2 3
4 Low Low Low High 1 3 2
...
...
...
...
...
...
...
...
14 Medium High High Medium 1 2 3
15 High Medium High Low 3 1 2
Maximizing the likelihood is equivalent to minimizing the distance (i.e.,
maximizing the similarity) in a Euclidean space (Vogt, Godden, & Bajorath,
2007)
25. Label ranking: formalization
Instance: X ⊆ {V1, . . . , Vm}
Labels: L = {λ1, . . . , λk }
Output: Y = ΠL
Training set: T = {xi , yi }i∈{1,...,n} ⊆ X × Y
Learn a mapping h : X → Y such that a loss function is minimized:
=
n
i=1 ρ(πi , ˆπi )
n
(5)
with ρ being a Spearman correlation coefficient:
ρ(π, ˆπ) = 1 −
6
k
j=1(πj − ˆπj )2
k3 − k
(6)
where π and ˆπ are, respectively, the target and predicted rankings for a
given instance.
26. Posterior probability of label ranking
Proir probability of label ranking:
PLR (π) =
n
i=1 ρ(π, πi )
n
(7)
Conditional probability of label ranking:
PLR (va,i |π) =
i:xi,a=va,i
ρ(π, πi )
|{i : xi,a = va,i }|
(8)
Estimated ranking:
ˆπ = arg max
π∈ΠL
PLR (π)
m
a=1
PLR (xi,a|π) (9)
