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Classification of Open-End Investment Funds Using Artificial Neural Networks. The Case of Polish Equity Funds

Katarzyna Perez
Katarzyna Perez
 and 
Małgorzata Szczyt
Małgorzata Szczyt
   | Nov 07, 2021
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Published Online: Nov 07, 2021
Page range: 269 - 284
DOI: https://doi.org/10.2478/ceej-2021-0020
Keywords
© 2021 Katarzyna Perez et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Diagram of an artificial neural networkSource: own elaboration
Diagram of an artificial neural networkSource: own elaboration

Figure 2

Neuron diagramSource: own elaboration
Neuron diagramSource: own elaboration

Figure 3

Number of observations in the studied period (12.1995-03.2018)Source: own elaboration
Number of observations in the studied period (12.1995-03.2018)Source: own elaboration

Classification matrix based on BETA classifier

Observed class Percentage of correct classification Predicted class TOTAL
<0 <0 ; 1> > 1
< 0 14% 36 225 0 261
<0 ; 1> 99% 23 4143 3 4169
> 1 5% 0 205 10 215
TOTAL 90% 59 4573 13 4645

Descriptive statistics of quantitative independent variables

N Mean Median Minimum Maximum Std. Deviation
R 5055 0.33426 0.61367 −36.5010 23.49568 5.560999
R(-1) 5018 0.36612 0.64163 −36.5010 23.49568 5.566170
(R-3) 4944 0.37174 0.64123 −36.5010 23.49568 5.572185
(R-6) 4835 0.39409 0.66000 −36.5010 23.49568 5.613492
AGE 5055 4.10926 4.34381 0.0000 5.59099 0.975071
SIZE 5055 18.93488 19.09698 12.7528 22.53559 1.596097
CF 5054 0.05861 −0.00184 −0.3542 70.10703 1.094054

Descriptive statistics of quantitative independent variables in classes of SIGMA classifier

Variable Class of SIGMA N Mean Median Minimum Maximum Std. Dev.
R 0 2480 1,33224 1,29754 −11,4557 13,83502 3,403976
1 2575 −0,62690 −0,65290 −36,5010 23,49568 6,904846
R(-1) 0 2460 0,90702 0,81168 −16,7491 21,52702 3,267253
1 2558 −0,15406 0,22143 −36,5010 23,49568 7,069031
(R-3) 0 2432 0,82333 0,89965 −18,8818 21,52702 3,877995
1 2512 −0,06547 0,21742 −36,5010 23,49568 6,795085
(R-6) 0 2393 0,86726 0,91793 −16,7867 21,52702 4,400762
1 2442 −0,06958 0,35119 −36,5010 23,49568 6,556710
Size 0 2480 4,25810 4,46591 0,0000 5,58350 0,903739
1 2575 3,96591 4,18965 0,6931 5,59099 1,018972
WAN 0 2480 18,98222 19,03036 12,7528 22,38194 1,376150
1 2575 18,88927 19,25083 13,0466 22,53559 1,781540
CF 0 2479 0,08046 −0,00222 −0,3542 70,10703 1,534043
1 2575 0,03757 −0,00124 −0,3415 9,09067 0,288631

Classification matrix based on SIGMA classifier

Observed class Percentage of correct classification Predicted class TOTAL
< 0.75 > 0.75
< 0.75 82.9% 1921 395 2316
> 0.75 76.7% 543 1786 2329
TOTAL 79.8% 2464 2181 4645

Global analysis of network sensitivity (SIGMA classifier)

DISTRIBUTION FUND DATE AGE R(-1) SIZE R R(-6) R(-3) CF
2.90 2.75 1.51 1.35 1.20 1.19 1.12 1.03 1.02 1.01

Descriptive statistics of quantitative independent variables in classes of BETA classifier

Variable Class of BETA N Mean Median Minimum Maximum Std. Dev.
R 0 304 1.20025 1.30403 −14.628 20.05664 5.348625
1 4522 0.40023 0.67158 −36.501 23.49568 5.441381
2 229 −2.11814 −2.0185 −31.0289 20.50384 7.296892
R(-1) 0 300 1.10756 1.76688 −36.501 13.66871 5.552314
1 4491 0.40151 0.63795 −34.4015 23.49568 5.483564
2 227 −1.31399 −0.84962 −30.8209 14.91146 6.782666
(R-3) 0 292 1.82123 1.6856 −24.7961 21.52702 5.655938
1 4426 0.32228 0.65432 −36.501 23.49568 5.533347
2 226 −0.53241 −0.48742 −30.8209 12.79057 5.91571
(R-6) 0 278 0.98586 0.937 −24.6169 18.48296 5.07533
1 4336 0.38388 0.66353 −36.501 23.49568 5.621471
2 221 −0.14981 0.42433 −30.8209 12.49409 6.04421
AGE 0 304 3.50819 3.85015 0 5.12396 0.942185
1 4522 4.13225 4.37574 0.6931 5.59099 0.961346
2 229 4.45323 4.86753 0.6931 5.50533 0.979203
SIZE 0 304 17.75238 17.77637 12.7528 19.95648 1.344688
1 4522 18.96870 19.12191 12.7671 22.53559 1.57812
2 229 19.83668 19.83888 13.9245 22.51503 1.407123
CF 0 303 0.06953 0.01405 −0.3239 1.40754 0.189824
1 4522 0.06084 −0.00249 −0.3542 70.10703 1.154969
2 229 0.00008 −0.00713 −0.1938 2.24313 0.157846

Description of the network set used in classification based on SIGMA classifier

ANN No. ANN type Number of hidden neurons Activation function (hidden layer) Activation function (output layer)
1 MLP 6 Logistic sigmoid Softmax
2 MLP 9 Exponential Hyperbolic tangent
3 MPL 7 Hyperbolic tangent Softmax
4 MLP 13 Logistic sigmoid Hyperbolic tangent
5 MLP 9 Logistic sigmoid Softmax

Macro and investment fund market data for USA, European Union and Poland

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 average p.a.
GDP
GDP (in %) USA 3.8 3.5 2.9 1.9 −0.1 −2.5 2.6 1.6 2.2 1.8 2.5 2.9 1.6 2.2 2.9 2.0
EU 2.4 2.3 2.2 3.1 0.5 −4.3 2.1 1.8 −0.4 0.3 1.8 2.3 2.0 2.5 2.0 1.2
PL 5.3 3.5 5.8 6.8 4.2 2.8 3.6 5.0 1.6 1.4 3.3 3.8 3.1 4.8 5.1 4.0
Inflation
CPI (in %) USA 3.3 3.4 2.5 4.1 0.1 2.7 1.5 3.0 1.7 1.5 0.8 0.7 2.1 2.1 1.9 2.1
EU 2.4 2.3 2.2 3.2 2.2 1.5 2.7 3.0 2.3 1.0 −0.1 0.2 1.1 1.6 1.6 1.8
PL 1.7 0.7 1.4 4.0 3.3 3.5 3.1 4.6 2.4 1.0 −1.0 −0.5 0.8 2.1 1.1 1.9
Stock market capitalisation as a percentage of GDP
USA 133 130 141 138 79 105 115 101 116 144 151 138 147 166 148 130
UK* 117 121 140 125 64 116 108 110 122 142 116 106 108 117 97 116
PL 38 31 43 49 17 34 40 26 35 39 31 29 29 38 25 37
Open-end investment fund NAV growth
USA 10.2% 8.8% 16.9% 15.4% −20.0% 15.8% 6.3% −1.6% 12.2% 15.3% 5.5% −1.4% 4.4% 14.7% −5.6% 6.5%
EU 9.5% 22.8% 14.5% 7.5% −22.9% 15.2% 14.0% −1.4% 12.9% 9.5% 15.8% 19.5% 4.5% 11.6% −3.2% 8.7%
PL 36.8% 71.0% 62.3% 43.0% −52.8% 28.2% 29.1% −10.1% 38.2% 27.1% 7.5% 20.9% −0.7% 13.8% −10.6% 20.2%
Open-end investment fund structure
USA equity 49.4% 53.7% 55.0% 56.1% 53.5% 38.0% 43.9% 47.3% 44.8% 45.5% 51.6% 52.4% 52.0% 52.4% 54.9% 50.2%
money market 27.6% 23.5% 22.8% 22.5% 25.7% 39.8% 29.8% 23.7% 23.1% 20.6% 18.1% 17.2% 17.6% 16.7% 15.2% 22.6%
bond 17.0% 16.0% 15.3% 14.4% 14.0% 16.3% 19.8% 21.9% 24.4% 26.0% 21.8% 21.8% 21.8% 22.3% 21.7% 19.8%
multi-asset 6.0% 6.8% 7.0% 7.0% 6.8% 5.8% 6.5% 7.1% 7.6% 7.9% 8.5% 8.7% 8.6% 8.6% 8.2% 7.4%
EU equity 35.0% 38.0% 41.0% 39.9% 29.1% 33.9% 36.0% 33.0% 33.0% 37.0% 38.0% 38.0% 37.0% 38.0% 39.0% 36.4%
money market 21.0% 18.0% 16.0% 16.5% 25.8% 21.1% 20.0% 19.0% 16.0% 13.0% 13.0% 14.0% 13.0% 12.0% 2.0% 16.0%
bond 27.0% 25.0% 23.0% 21.7% 22.9% 23.0% 23.0% 27.0% 29.0% 28.0% 28.0% 26.0% 27.0% 27.0% 23.0% 25.4%
multi-asset 14.0% 13.0% 15.0% 15.5% 16.0% 16.4% 15.0% 16.0% 16.0% 16.0% 16.0% 17.0% 17.0% 18.0% 26.0% 16.5%
other 3.0% 6.0% 5.0% 6.4% 6.3% 5.6% 6.0% 5.0% 6.0% 6.0% 5.0% 5.0% 6.0% 4.0% 10.0% 5.7%
PL equity 12.7% 10.7% 19.5% 31.3% 23.4% 29.0% 27.7% 19.2% 16.9% 16.0% 13.8% 11.7% 10.9% 11.9% 13.4% 17.9%
money market 14.4% 13.2% 8.2% 6.5% 10.3% 9.3% 13.2% 16.1% 9.5% 11.1% 13.5% 11.9% 12.4% 14.8% 21.9% 12.4%
bond 21.2% 17.4% 7.2% 5.6% 14.6% 13.6% 14.4% 17.1% 28.3% 23.1% 20.1% 16.2% 16.8% 17.1% 17.4% 16.7%
multi-asset 16.2% 18.6% 26.3% 31.7% 26.5% 32.7% 28.8% 18.9% 13.8% 11.3% 15.1% 12.8% 9.5% 11.7% 11.4% 19.0%
other 35.5% 40.1% 38.8% 24.9% 25.2% 15.4% 15.9% 28.7% 31.5% 38.5% 37.5% 47.4% 50.4% 44.5% 35.9% 34.0%

Description of the network set used in classification based on BETA classifier

ANN No. ANN type Number of hidden neurons Activation function (hidden layer) Activation function (output layer)
1 MLP 13 Hyperbolic tangent Hyperbolic tangent
2 MLP 6 Logistic sigmoid Softmax
3 MPL 11 Exponential Hyperbolic tangent
4 MLP 13 Exponential Logistic sigmoid
5 MLP 15 Logistic sigmoid Hyperbolic tangent
eISSN:
2543-6821
Language:
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Business and Economics, Political Economics, Economic Theory, Systems and Structures, Microeconomics, Macroecomics, Economic Policy
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