The Predictor is designed for application in the banks, investment companies, stock markets, companies with operations in the stock markets and securities markets. Based on innovative mathematical models of multifractal and wavelet analysis, this tool is carrying out continuous scanning and processing of time series derived from the financial markets and produces signals that precede a sharp change (20%) of the securities prices or indexes exchange rate and warn about approaching of the crisis.

1. Financial market crises prediction by
multifractal and wavelet analysis.
Russian Plekhanov Academy of Economics
Romanov V.P., Bachinin Y.G., Moskovoy I.N., Badrina M.V.
Tel.: +79036169431, 8(495)9582410, Fax: (495)9982395;
Address: 36 Stremyanny lane, Moscow, 117997;
e-mail: Romanov.vp@rea.ru, victorromanov1@gmail.com

2. The aim of the research
 It is well known, that financial markets are essentially non-
linear systems and financial time series are fractals.
 That’s why prediction of crash situations at finance market
is a very difficult task. It doesn’t allow us to use effectively
such well-known methods as ARIMA or MACD in view of
their sluggishness.
 Multifractal and wavelets analysis methods are providing
more deep insight into the nature of phenomena.
Multiagent simulation makes it possible to explicate
dynamic properties of the system.
 The main aim is to find out the predictors or some kind of predicting
signals which may warn as about forthcoming crisis

3. a) Changing of ruble/dollar exchange b) American Index Dow Jones
rate at period 01.08.1997-01.11.1999 Industrial at “Black Monday” 1987
(Default in Russia) at period 17.10.1986-31.12.1987

4. с) Dow Jones Industrial d) RTSI
Index 07.10.1999 -06.10.2008
07.10.1999 -06.10.2008
e) Nasdaq 07.10.1999 -06.10.2008

5. Indexes DJI, RTS.RS, NASDAQ,
S&P 500 falling at crisis period
1 month 3 months 6 months
September 15,2008 – October 17, 2008 July 17,2008 – October 17, 2008 April 17,2008 – October 17, 2008
The collapse in the stock markets Investors fear that the attempt to When Asian stock indices
the analysts linked to the negative improve the situation by pouring collapsed to a minimum for
external background. U.S. indexes in amount of $ 700 billion, which more than three years.
have completed a week 29.09 - involves buying from banks The negative news had left the
6.10 falling, despite the fact that illiquid assets will not be able to Russian market no choice – its
the U.S. Congress approved a plan improve the situation in credit began to decline rapidly.
to rescue the economy. markets and prevent a decline in
the economy.

6.  Efficient Market Hypothesis (EMH) asserts, that financial markets are
"informationally efficient", or that prices on traded assets, e.g., stocks, bonds,
or property, already reflect all known information. The efficient-market
hypothesis states that it is impossible to consistently outperform the market by
using any information that the market already knows, except through luck.
Information or news in the EMH is defined as anything that may affect prices
that is unknowable in the present and thus appears randomly in the future.
 Capital Asset Pricing Model (CAPM) is used to determine a theoretically
appropriate required rate of return of an asset, if that asset is to be added to
an already well-diversified portfolio, given that asset's non-diversifiable risk.
The model takes into account the asset's sensitivity to non-diversifiable risk
(also known as systemic risk or market risk), often represented by the quantity
beta (β) in the financial industry, as well as the expected return of the market
and the expected return of a theoretical risk-free asset.
 Arbitrage pricing theory (APT), in finance, is a general theory of asset
pricing, that has become influential in the pricing of stocks. APT holds that the
expected return of a financial asset can be modeled as a linear function of
various macro-economic factors or theoretical market indices, where
sensitivity to changes in each factor is represented by a factor-specific beta
coefficient.

7. Efficient Market Hypothesis
versus Fractal Market Hypothesis
Efficient market hypothesys Fractral market hypothesys
(EMH) (FMH)
 Assumption of normal  Prices shows leptoexcess effect for
distribution of prices prices probability distribution(“fat
increments tails”)
 The weak form EMH from a  The prices plot looks similary for
purely random distribution of the period of time in the day, week,
prices has been criticized month (fractal pattern)
 Semi-strong form of EMH, in  Reducing the reliability of
which all available information predictions with the increase of its
is reflected in the prices used period
by professionals  Prices shows short-term and long-
 Changing prices in the long term correlation and trends (the
run does not show the effect of feedback)
presence of «memory»
 Chaotic activity of the market

8. Fractal definition
Fractals –The term fractal was coined in 1975 by Benoît
Mandelbrot, from the Latin fractus, meaning "broken" or "fractured".
(colloquial) a shape that is recursively constructed or self-
similar, that is, a shape that appears similar at all scales of
magnification.
(mathematics) a geometric object that has a Hausdorff dimension
greater than its topological dimension.
 The second feature that characterizes fractals is the fractional
dimension.
The word fractal came from fractional values – partial
values, which may take the fractal dimension of objects
Fractals may cause the application of Iterative Functions System
 The image, which is the only fixed point of IFS is called attractor

9. Chaos and dynamics of fractal market
 Market prices tend to level the
natural balance within the price
range
 These levels or ranges can be
described as «attractors»
 However, the data within those
ranges remain casual

10. Attractors types
Point attractors
• The simplest form of the attractor. In theory, compatible
with the balance of supply and demand in the economy
or the market equilibrium.
Limit cycle attractors
• Represent market volatility on balance, or "market noise"
Strange or fractal attractors
• Displays Multiple varying the amplitude fluctuation, which
are contained within the set limit cycle attractor, called
«phase space».

11. Serpinsky Triangle

12. Fractals examples

13. Another fractals examples

14. Fractal attractors and
financial markets
 Stocks and futures - classic
examples of securities. Profit
from buying and selling
comparable with fluctuations in
the pendulum
 Each bearer security or futures
contract are located in its own
phase space
 Long-term forecasting is
heavily dependent on accurate
measurement of initial
conditions of the market

15. Fractals on capital market
 Financial markets describes a
nonlinear function of active
traders
 Traditional methods of technical
analysis based on linear
equations and Euclidean
geometry are inadequate
 Market jumps growth and
recession are nonlinear
 Technical analysis methods are
poor indicators of the relationship
trend and trading decisions
 Fractals can describe the
phenomena that are not
described in Euclidean geometry

16. Multifractal
Stochastic process {x(t)} is called Multifractal, if it has fixed increments and
satisfies the condition
τ(q) 1
E(| xt+ Δt xt | ) = c(q)( Δt )
,
q
when c(q) – predictor, E- operator of mathematical
expectation, q Q , t B – intervals on the real axis.
Scaling function (q) taking into account the impact of time on points q.

17. Definiting the Fractal Dimension Index
 Fractal dimension indedx (FDI): ln( N A (1 / n))
d lim
n ln(n)
 NS(1/2n)–the number of blocks with a length of hand
1/2n, which necessary, to cover S – Serpinsky triangle.
ln(3n ) ln(3)
ds lim 1,58
n ln(2n ) ln(2)
 Где NA (1/n) – the number of blocks with length of hand, equal
1/n, which necessary to cover variety А.
Фрактальное
Множество
А

18. Fractal Dimension Index
 Defines the persistence or antipersistence of
market. Persistent market weakly fluctuated
around the market trend
 antipersistent market shows considerable volatility
on the trend
 antipersistent market is more rugged pricing
schedule and more frequently show a change
trends

19. Crisis prediction technique
 Because our goal is the prediction of crises, we
are trying to first find out the best indicator, using
methodology Multifractal and wavelet analysis.
 Then we test various types of pre-processing the
original time series to find the best indicator.

20. Hurst exponent
xt ln z t 1 ln z t , t 1,..., T
Depending on the value of Heurst
t exponent the properties of the
1 process are distinguished as follows:
x(t , ) xu x , x x
u 1 t 1 When H = 0.5, there is a process of
random walks, which confirms the
hypothesis EMH.
R( ) max x(t , ) min x(t , )
t 1 t 1
When H > 0.5, the process has long-
term memory and is persistent, that
1 2 is it has a positive correlation for
S( ) xu x different time scales.
u 1
When H < 0.5, time-series is anti-
persistent with average switching
log R ( ) from time to time.
S( )
H log

21. Time series partitioning
 Time series: {xt}; t [0, T].
 Compute: Z={zt}, zt= lnxt+1-lnxt; t [0,T];
 Divide interval [0, T] into N subintervals, 1 ≤ N ≤ Nmax.
 Each subinterval contains int (T/N)=A values Z;
 For each subinterval K; 1 ≤ K ≤ N current reading number lK;1 ≤ lK ≤ A;
t = (K-1) А+ lK
 As soon as we are looking for the best indicator of a coming default, we
will use several variants of a preliminary processing.

22. Time series preprocessing
 1. The original time series itself: Z={zt};
 2. Preprocessed time series Z1={ Z K }, K=1,2,…N,
 A
1
ZK z0l
where Al 1
K
K

Z0 K 1 A lK
ZK
 3. Preprocessed time series Z2
SK
1 A  2
where S K Z 0l K ZK
A lK 1

 4. Preprocessed time series Z3={ Z0 K 1 A l K
Z K}

23. Partition functions
For each preprocessed time series compute partition function for
different N and q values :
N
0
N ( Z , q) | Z 0 ( KA ) (T ) Z 0 ( K 1) A
|q
K 1
N  
1
N ( Z , q) | Z K (T ) Z K 1 |q
K 1
N
2
N ( Z , q) | Z 2 ( KA ) Z 2( K 1) A |q
K 1
N
3
N ( Z , q) | Z 3 ( KA ) Z 3(K 1) A |q
K 1

24. Scaling functions
0
log N ( Z , q) log A log N
0
N
(q)
log A
1
log N ( Z , q) log A log N
1
N
(q)
log A
2
log N ( Z , q) log A log N
2
N
(q)
log A
3
log N ( Z , q) log A log N
3
N
(q)
log A

25. Fractal dimension spectrum
estimation
1. Lipshitz – Hoelder exponent estimation:
:
d i   i
i ( i (q) i (q 1)) / q (q)/ q
dq
when, i = 1, 2, 3, 4.
2. Fractal dimension spectrum estimation by Legendre
transform
fI ( ) arg min [q i (q)] (arg min [q i (q) i (q)])
q q

26. Fractal dimension spectrum
width as crash indicator
 Multifractal may be composed of two or infinite
number of monofractals with continuous varying α
values. Width of α spectrum may be estimated as
difference between maximum and minimum values of α:
Δ = max - min ,
 By carrying out Legendre transform we are trying
using our program by estimating Δ to find differences in
its values before and after crash.
 Roughly speaking f( ) gives us number of time
moments, for which degree of polynomial, needed for
approximation f( ) equals (according to Lipshitz
condition).

27. Scaling functions
Changes in currency for the Non-linear scaling function
Russian default of 1998 (q) (Multifractal process)

28. Screenshots assessment of
Multifractal spectrum of singularity
Assesment of multifractal Assesment of multifractal
spectrum of singularity at spectrum of singularity at
period 09.07.96-21.07.98 period 18.11.96-30.11.98

29. Scaling functions
Dow Jones Industrial Index, pre- Non-linear scaling-function (q)
crisis situation (multifractal process)
19.12.2006-06.10.2008

30. Scaling functions
RTSI index, Non-linear scaling-function (q)
pre-crisis situation
(multifractal process)
19.12.2006-06.10.2008

31. Scaling functions
linear scaling-function (q) Assesment of multifractal
(monofractal process) spectrum of singularity RTSI at
period 16.05.2000 -30.05.2002

32. Screenshots assesment of
Multifractal spectrum of singularity
Assesment of multifractal Assesment of multifractal
spectrum of singularity DJI at spectrum of singularity RTSI at
period 19.12.2006-08.10.2008 period 16.12.2003-10.01.2006

33. "Needles" that determine the expansion of Multifractal
spectrum on an hourly schedule 5.2008-11.2008

34. Experimental results
American Dow Jones at the «Black
Monday» 1987 period 17.10.1986-
31.12.1987
Schedule assessment of the width of the Schedule assessment of the width of the
spectrum of fractal singularity (Δ(t)=αmax-αmin) spectrum of fractal singularity (Δ(t)=αmax-αmin)
for different periods of time at the «Black Monday»

35. Graph of Multifractal spectrum singularity width assessment
(Δ(t)=αmax-αmin) at russian index RTSI at period
07.10.1999-07.11.2008
interval Qmin Qma N ∆
x
1-512
07.10.1999 –18.10.2001
-2 6 47 0,964
151-662
16.05.2000 -30.05.2002
-2 6 103 0,495
301-812
15.12.2000 -31.12.2002
-2 6 129 1,62
451-962
25.07.2001 -11.08.2003
-2 5 31 0,81
601-1112
28.02.2002 -17.03.2004
-2 6 170 1,77
751-1262
03.10.2002 -19.10.2004
-2 6 129 2,17
901- 1412
15.05.2003 -02.06.2005
-2 6 129 1,927
1051-1562
16.12.2003 -10.01.2006
-2 5 43 0,952
1201-1712
26.07.2004 -15.08.2006
-2 5 21 0,868
1351-1862
04.03.2005 -26.03.2007
-2 5 22 0,89
1501-2012
06.10.2005 -25.10.2007
-2 5 23 0,848
1651-2162
19.05.2006 -07.06.2008
-2 5 40 0,927
1801-2246
19.12.2006 -06.10.2008
-2 7 145 2,133
1765-2277
25.09.2006 -07.11.2008
-2 7 161 2,177

36. Experimental results(RTSI)
Over 4 years outstanding mortgage loans in Russia rose more
than 16 times - from 3.6 billion rubles. in 2002 to 58.0 billion
rubles. in 2005. In quantitative terms - from 9,000 loans in 2002 to
78,603 in 2005.
Pre-crisis situation:
July 2008 - the beginning of september 2008
Graph of Multifractal spectrum singularity width
assessment (Δ(t)=αmax-αmin) at russian index RTSI at
period 07.10.1999-07.11.2008
Why mortgage evolving so rapidly? Many factors. This increase in real
incomes and the decline of distrust towards mortgage, as from potential
buyers, and from the sellers, and a general reduction in the average interest
rate for mortgage loans from 14 to 11% per annum, and the advent of
Moscow banks in the regions, and intensifying in the market of small and
medium-sized banks.

37. Graph of Multifractal spectrum singularity width assessment
(Δ(t)=αmax-αmin) at Russian index RTSI at period
07.10.1999-09.12.2008
2.5
2
1.5
1
0.5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

38. Graph of Multifractal spectrum singularity width assessment
(Δ(t)=αmax-αmin) at american index Dow Jones Industrial at period
07.10.1999-07.11.2008
interval Qmin Qmax N ∆
1-512
07.10.1999 –18.10.2001
-2 5 164 1,84
151-662
16.05.2000 -30.05.2002
-2 4 5 0,717
301-812
15.12.2000 -31.12.2002
-2 5 134 1,77
451-962
25.07.2001 -11.08.2003
-2 5 65 1,01
601-1112
28.02.2002 -17.03.2004
-2 5 74 1,108
751-1262
03.10.2002 -19.10.2004
-2 4 11 0,791
901- 1412
15.05.2003 -02.06.2005
-2 4 38 0,803
1051-1562
16.12.2003 -10.01.2006
-2 4 50 0,815
1201-1712
26.07.2004 -15.08.2006
-2 4 53 0,884
1351-1862
04.03.2005 -26.03.2007
-2 4 57 0,973
1501-2012
06.10.2005 -25.10.2007
-2 4 29 0,864
1651-2162
19.05.2006 -07.06.2008
-2 4 11 0,836
1801-2263
19.12.2006 -06.10.2008
-2 5 151 2,324
1765-2284
25.09.2006 -07.11.2008
-2 5 174 1,984

39. Experimental results(DJI)
Graph of Multifractal spectrum singularity
width assessment (Δ(t)=αmax-αmin) at american
index Dow Jones Industrial at period
07.10.1999-07.11.2008
3 May, 1999, the index reached a value of
11014.70. Its maximum - mark 11722.98 -
Dow-Jones index
reached at 14 January 2000.
There was a sharp drop in the index and 9 october 2002 DJIA reached an interim
minimum with a value of 7286,27.
Pre-crisis situation:
July 2008 - the beginning of september 2008
Dow Jones Industrial index of 15 september 2008, fell to 4.42 per cent to 10,917
points - is the largest of its fall in a single day since 9 october 2002, reported France
Presse. World stock markets experienced a sharp decline in major indexes in
connection with the bankruptcy Investbank Lehman Brothers.

40. Graph of Multifractal spectrum singularity width assessment
(Δ(t)=αmax-αmin) at american index Dow Jones Industrial at period
07.10.1999-09.12.2008
3
2.5
2
1.5
1
0.5
0

41. Graph of Multifractal spectrum singularity width assessment
(Δ(t)=αmax-αmin) at american index NASDAQ Composite at period
07.10.1999-07.11.2008
interval Qmi Qmax N ∆
n
1-512
07.10.1999 –18.10.2001
-2 6 47 0,91
151-662
16.05.2000 -30.05.2002
-2 6 57 0,935
301-812
15.12.2000 -31.12.2002
-2 6 86 1,092
451-962
25.07.2001 -11.08.2003
-2 5 25 0,74
601-1112
28.02.2002 -17.03.2004
-2 5 31 0,821
751-1262
03.10.2002 -19.10.2004
-2 5 129 1,385
901- 1412
15.05.2003 -02.06.2005
-2 4 9 0,726
1051-1562
16.12.2003 -10.01.2006
-2 4 13 0,765
1201-1712
26.07.2004 -15.08.2006
-2 4 19 0,78
1351-1862
04.03.2005 -26.03.2007
-2 4 19 0,792
1501-2012
06.10.2005 -25.10.2007
-2 4 15 0,778
1651-2162
19.05.2006 -07.06.2008
-2 4 5 0,772
1801-2263
19.12.2006 -06.10.2008
-2 5 77 1,185
1765-2284
25.09.2006 -07.11.2008
-2 6 207 1,067

42. Experimental results(NASDAQ)
Graph of Multifractal spectrum singularity
width assessment (Δ(t)=αmax-αmin) at american
index NASDAQ Composite at period
07.10.1999-07.11.2008
The index of technology companies
NASDAQ Composite reached its peak in
March 2000.
After that happened result in a vast dropIn 2000, he reached even five thousandth mark, but
after the general collapse of the market of computer and information technology is now in an
area of up to two thousand points.
In August 2002 the first NASDAQ closes its branch in Japan, as well as
closing branches in Europe, and now it was turn European office, where
for two years, the number of companies whose shares are traded on the
exchange fell from 60 to 38.
Pre-crisis situation:
July 2008 - the beginning of september 2008

43. Graph of Multifractal spectrum singularity width assessment
(Δ(t)=αmax-αmin) at american index NASDAQ Composite at period
07.10.1999-09.12.2008
2.5
2
1.5
1
0.5
0

44. Default’s 1998 indicator.
Part Multifractal spectrum of data
related to graph b)
Данные min max
11.08.98 2,837 3,337 0,5
a)
12.08.98 2,837 3,335 0,498
13.08.98 2,838 3,325 0,487
14.08.98 2,839 3,344 0,505
17.08.98 1,8 3,36 1,56
18.08.98 1,97 3,3 1,33
19.08.98 1,355 3,26 1,905
20.08.98 1,499 3,264 1,765
21.08.98 1,499 3,4 1,901
24.08.98 1,5 3,249 1,749
The red line shows that the width b)
multifraktalnogo spectrum begins
to grow at the same time as
changing the exchange rate, but
more clearly.

45. Wavelet-analysis
W( , ) x(t ) , (t )dt,
 где , (t)– where(t)– function with zero mean centered
,
around zero with time scale and time horizon .
 Family of wavelet vectors is created from mother function
by displacement and scaling
1 t
(t ) ( )

46. Time series f(t) representation as linear
combination of wavelet functions
f (t ) j0 j0 , k (t ) j ,k j , k (t ),
k j j0 k
j0 , k f (t ) j0 , k (t )dt
j,k f (t ) j , k (t )dt
where jo – a constant, representing the highest level of
resolution for which the most acute details are extracted
.

47. WA crisis detection
experiment - 1
 In our study we used Daubechies wavelet functions
decomposition (db-4 и db-12).
 The goal was the detection of the signal, which could
predict the sudden changes. Data on exchange rates
(USD) to the ruble were taken from the site www.rts.ru
for the period 1.09.1995 - 12.02.1999
 The total number of numbered in the order several times
in the interim for the period 1.09.1995 - 12.02.1999 was
862 value.

48. 0
5
10
15
20
25
01.09.1995
04.11.1995
22.01.1996
27.03.1996
04.06.1996
09.08.1996
14.10.1996
18.12.1996
25.02.1997
05.05.1997
10.07.1997
12.09.1997
18.11.1997
27.01.1998
1.09.1995 – 12.02.1999
02.04.1998
10.06.1998
14.08.1998
19.10.1998
24.12.1998
Graph of changing RTS indexes at period

49. The division time series on the ranges
 To achieve the goal of this time series was divided into 7
overlapping intervals located unevenly, so that the
interval 4 (242-753) immediately preceding the time of
default and subsequent intervals captured the moment of
default.
 Each interval consisted of 512 values: 1-512, 101-
612, 201-712, 242-753, 251-762, 301-812, 351-862.

50. Predicting the crisis with the help of wavelet analysis
# Interval Maximum for Difference maximum
12
all levels ratios
10
8
1 1-512 0,068796 -
6
2 101-612 0,140859 0,072062 4
2
3 201-712 0,150173 0,009314 0
13.02.1998 10.07.1998 07.09.1998 18.09.1998 30.11.1998 12.02.1999
-2
4 242-753 11,234599 11,084426 -4
-6
5 251-762 11,850877 0,616278
6 301-812 7,944381 -3,906496 The schedule changes difference ratios of maximum ratios
of decomposition of Dobeshi-12 for the period 19.09.1997-
7 351-862 9,802439 1,858058 12.02.1999 (dates are taken on the right border, ie 512
value)
1,8
# interval Average Difference 1,6
value averages 1,4
1,2
1 1-512 5,249121 - 1
0,8
2 101-612 5,518002 0,268881 0,6
0,4
3 201-712 5,759273 0,241271 0,2
4 242-753 5,926961 0,167688 0
13.02.1998 10.07.1998 07.09.1998 18.09.1998 30.11.1998 12.02.1999
5 251-762 6,077492 0,150531
The schedule change ratios of difference from the average value
6 301-812 7,124922 1,047431 of currencies this intervala to the value of the previous intervala
for the period 19.09.1997-12.02.1999 (dates are taken on the
7 351-862 8,672407 1,547484 right border, ie 512 value).

51. The difference coefficients
of Daubechies -12
№ interval Maximum for Difference 20000
all levels maximum
15000
ratios
10000
1 1-128 13083,070 --------------
2 64-192 223,834 -12859,235 5000
3 96-224 262,039 38,204 0
4 106-234 258,122 -3,916
7
7
7
7
7
7
7
7
7
98
98
98
98
98
98
98
98
98
-5000
.1
.1
.1
.1
.1
.1
.1
.1
.1
04
07
09
09
09
10
10
10
12
1.
2.
4.
1.
8.
1.
5.
9.
1.
5 111-239 262,371 3,917
-2
-2
-0
-2
-2
-0
-1
-1
-3
86
87
87
87
87
87
87
87
87
-10000
19
19
19
19
19
19
19
19
19
0.
1.
3.
3.
3.
3.
4.
4.
6.
6 114-242 14785,540 14523,169
.1
.0
.0
.0
.0
.0
.0
.0
.0
17
19
05
19
26
31
14
16
30
-15000
7 124-252 789,933 -13995,607
-20000
8 126-254 1298,050 508,117
9 177-305 475,376 -822,673
The schedule changes difference maximum
coefficients of expansion in the Dobeshi-12
(17.10.1986-31.12.1987).

52. «Black Monday» Detector
 At the previous slide we can see the positive
peak earlier 01.10.87 and negative peak before
15.10.87.
 This is more than 4 days before the «Black
Monday».
 Sharp line connects the two peaks.
Obviously, this information can serve as a
detector impending crisis.

53. 42 days prior to the default
 Of the figure shows that the start of trading, the corresponding spike in the
dollar may be adopted point 742 (21.08.1998), a peak corresponds to 754
points (07.09.1998).
As we can see from the previous slide in the event of data processing by
the Russian default by default, if we use the average of the indicator is the
intervals difference, then we can find that the sharp increase occurring
18.09.1998, ie delayed by at least 11 days. At the same time schedule for
the coefficients of wavelet functions shows us that the beginning of dramatic
changes difference wavelet coefficients of expansions is a point 712
(10.07.1998).
We can, apparently, to predict the onset of default at least 42 days
(10.07.1998 - 21.08.1998). At the same time increase the maximum value
(Fig. 4) of this indicator in the starting time was 74.5 times (initial value =
0.15; following value = 11.23)

54. WA crisis detection
experiment - 2
 In our experiment, number 2, we used Daubechies
wavelet functions decomposition (db-4).
 The goal was the detecting the signal, which could
predict the sudden changes in the index DJI (Dow Jones
Index - Dow Jones). Data on DJI were taken from the
site http://finance.yahoo.com for the period 7.10.1999 -
24.11.2008
 The total number of numbered in the order several times
in the interim for the period 7.10.1999 - 24.11.2008 at
2299 values.

55. 7000
8000
9000
10000
11000
12000
13000
14000
15000
07.10.1999
07.04.2000
07.10.2000
07.04.2001
07.10.2001
07.04.2002
07.10.2002
07.04.2003
07.10.2003
07.04.2004
07.10.2004
07.04.2005
07.10.2005
07.04.2006
07.10.2006
07.04.2007
07.10.2007
07.04.2008
07.10.2008
Graph DJI change 7.10.1999-8.11.2008

56. Change the values of Hurst exponent said that the market in anticipation
0,65
of becoming antipersistent crisis: H <0,5
0,6
0,55
0,5
0,45
0,4
0,35
0,3
0,25
0,2
Changing detailing factors wavelet decomposition of db-4 show
conversion market (antipersistent)
15000 3500
14000 3000
13000 2500
12000 2000
1500
11000
1000
10000
500
9000
0
8000
7000
07 9
07 0
07 0
07 1
07 1
07 2
07 2
07 3
07 3
07 4
07 4
07 5
07 5
07 6
07 6
07 7
07 7
07 8
08
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.
4.
0.
4.
0.
4.
0.
4.
0.
4.
0.
4.
0.
4.
0.
4.
0.
4.
0.
.1
.0
.1
.0
.1
.0
.1
.0
.1
.0
.1
.0
.1
.0
.1
.0
.1
.0
.1
07

57. Changing detailing factors wavelet decomposition of
db-4 suggest crossing a market for the period
07.07.2005 - 24.11.2008
15000 3500
14000 3000
13000
2500
12000
2000
11000
1500
10000
1000
9000
500
8000
7000 0
07.07.05
07.09.05
07.11.05
07.01.06
07.03.06
07.05.06
07.07.06
07.09.06
07.11.06
07.01.07
07.03.07
07.05.07
07.07.07
07.09.07
07.11.07
07.01.08
07.03.08
07.05.08
07.07.08
07.09.08
07.11.08

58. Fundamental analysis
 Fundamental analysis is based on an assessment of market
conditions in general and assessing the future development
of a single issuer.
 Fundamental analysis is a fairly laborious and a special
funding agencies.
 Fundamental analysis depends on the news of factors. By
random and unexpected news include political and
natural, as well as war.
 How to conduct a fundamental analysis can be divided into
four separate units, correlating with each other.

59. Fundamental analysis
technology
The first unit - is a macroeconomic analysis of the economy as a whole.
The second unit - is an industrial analysis of a particular industry.
A third unit - a financial analysis of a particular enterprise.
A fourth unit - analyzing the qualities of investment securities issuer.
Fundamental analysis technology includes an
analysis of news published in the media, and
comparing them with the securities markets.

60. Analysis Method
Keyword extraction, characterizing the market: boost
or cut, the increase / decrease.
Automatic analysis using the terminology the
ontology.
Processing time series (filtering, providing trends, the
seasonal components).
Using neural networks to classify the flow of news and
processing time series.

61. News analysis target
•Examine what news articles relevant to the company, Yahoo uses
profiling to establish consistency between articles and companies.
•For each trend formed a temporary window to explore how art
relates to the trend.
•It is believed that there is a match, if the article appeared a few
hours before the trend.

62. The intensity of the flow of news data
The joint processing of digital and text data
Time series Flow intensity:
Digital data
The movement of financial instruments
(price / volume) 5Mb/day, on the
tool
Text flows
Various types: Flow intensity:
Text data
News, financial reports, company
brochures, government documents 20Mb/day

63. Idea of system
Past articles
with news
Building
Model
model
Past data
Prediction System
pricing
results exit
securities
market New
arcticles
with news

64. Real system architecture
Reuters News
Feed
Up
SYSTEM QUIRK
Down
Time Series of 1.2
Up and Down 1
0.8
Ratio 0.6
0.4
0.2
Generate Signal
0
1 2 5 6 7 8 9 12 13 14 15 16 19 20 21 22 23 26 27 28 29 30
Financial instrument Date
(Reuters) e.g. FTSE Good words FTSE100 (Buy / Sell)
100 1.
2
INDEX 0
1
8
.
Rati o 06
.
04
.
02
.
0
1 2 5 6 7 8 9 12 13 14 15 16 19 20 21 22 23 26 27 28 29 30
Date
Good wor ds FT SE 100

65. Comparsion time and stocks by time

66. Text analysis should apply:
Recognition of the named entity. The discovery of those
(people), organizations, currencies.
Extracting key information related to organizations, persons, facts, evidence from
documents.
The establishment of relations between the patterns.
Creating a template to scripting events, organizations, regions.
The formation of coherence - to collect information on sovstrechaemosti
expressions. The result of the system is the text as a set of the following
components:
<AGENT> <CONCERN> <GOAL> <AGENT>
<CONCERN, THE IMPORTANCE> <GOAL, the value>
Between formed in such a description of news and current prices of assets in the
securities market established statistical connection to predict price changes
depending on the nature of news.

67. Fundamental analysis ontology

68. News, alter securities course

69. Fundamental analysis results with
Automatic 3-side
integration
ontology using Concentrated
content, organised
with semantic
categories
Relevant
content, not
expressed evidently
(semantic
associations)
Competetive Automatic content
researches, discover integration from sources
ed automatically and other providers

70. Price graphs and charts
Pricing models
calls figures or creatings, which appers on price
graphs
These figures, or education (chart
pattern), divided into some groups and can be
used to predict the market dynamics

71. Thank you!
Tel.: +79036169431, 8(495)9582410,
Fax: (495)9982395;
Address: 36 Stremyanny lane, Moscow, 117997;
e-mail: Romanov.vp@rea.ru, victorromanov1@gmail.com