U.S. patent application number 11/178533 was filed with the patent office on 2007-01-18 for system for analysis and prediction of financial and statistical data.
Invention is credited to Wally Tzara.
Application Number | 20070016507 11/178533 |
Document ID | / |
Family ID | 37575152 |
Filed Date | 2007-01-18 |
United States Patent
Application |
20070016507 |
Kind Code |
A1 |
Tzara; Wally |
January 18, 2007 |
System for analysis and prediction of financial and statistical
data
Abstract
System for the analysis and prediction of the evolution of
various types of data such as stock market prices, financial
indices and other statistical data, based on the presence of
characteristic figures in a dense network of curves constructed
from data.
Inventors: |
Tzara; Wally; (Paris,
FR) |
Correspondence
Address: |
WALLY TZARA
134 RUE DE GRENELLE
PARIS
75007
FR
|
Family ID: |
37575152 |
Appl. No.: |
11/178533 |
Filed: |
July 12, 2005 |
Current U.S.
Class: |
705/37 |
Current CPC
Class: |
G06Q 40/04 20130101;
G06Q 10/04 20130101 |
Class at
Publication: |
705/037 |
International
Class: |
G06Q 40/00 20060101
G06Q040/00 |
Claims
1. A system for the analysis and prediction of the evolution of
various types of data such as stock market prices, financial
indices and other statistical data, characterized by a dense
network of curves constructed mathematically from such data, in
which characteristic figures appear.
2. A system according to claim 1, wherein the curves of the network
are moving linear regressions or moving regressions other than
moving linear regressions.
3. A system according to claim 1, wherein the analysis and
prediction of the evolution of the data is achieved through
observing the way in which the representative curve of the data is
attracted or repelled by the characteristic figures.
4. A system according to claim 1, wherein for the considered data
more than one network with different scale parameter values are
displayed.
5. A system according to claim 1, wherein multiple colors are used
for the display of the network and the representative curve of the
data.
Description
FIELD OF INVENTION
[0001] The present invention relates to the analysis and prediction
of the evolution of various types of data such as stock market
prices, financial indices and other statistical data.
BACKGROUND OF THE INVENTION
[0002] Currently, the following two methods are used to analyze and
predict data in the field of finance and economics: [0003]
Technical analysis, based exclusively on the examination of a small
number of technical indicators derived from the given data; [0004]
Fundamental analysis, based on knowledge of the economic situation
with regard to the data considered. These two approaches often
result in predictions that not only differ, but are also often
invalidated afterward.
SUMMARY OF THE INVENTION
[0005] The present system allows for a superior level of analysis
and prediction of the evolution of the aforementioned data, both
qualitatively and quantitatively. It rests primarily on a dense
network of curves constructed mathematically from numerical data
(for example, a stock price) and defined by a primary parameter
(the number of data points used) and a secondary parameter (the
scale parameter). A computer is used to receive and process the
data.
[0006] The curves of this network belong to one of the following
categories: [0007] Moving regression (MR) of degree zero, known as
the moving average (MA); [0008] MR of the first degree, known as
the moving linear regression (MLR); [0009] MR of the second degree,
which we will call the moving quadratic regression (MQR); [0010] MR
of the k.sup.th degree, which we will call the moving k regression
(MKR).
[0011] The MA is a well-known indicator commonly used in technical
analysis. The MLR, a known but seldom used technical indicator, is
built upon the linear regression according to a defined method. The
MQR is built upon the quadratic regression according to the same
method. The MKR is built similarly upon a regression of the
k.sup.th degree.
[0012] The present system is fundamentally based on the utilization
of a dense network of MRs corresponding to a large set of values of
the primary parameter, chosen according to defined criteria.
[0013] When MLRs are used to construct the dense network,
characteristic figures appear strikingly on the monitor of a
computer. For this reason and others that will be discussed later,
the network described in what follows is composed of MLRs. It is on
the presence of these characteristic figures within the dense
network that rests the ability to obtain precise and reliable
information on the evolution of the data under consideration.
[0014] The system can also use adjusted data, for example, averaged
or weighted data.
[0015] The secondary parameter (the scale parameter) can be the
interval of time separating two consecutive data points, for
example, minutes, hours or days. Other types of intervals can also
be used; for a financial market, for example, the interval can be
expressed in terms of the number of exchanges.
[0016] The necessary conditions under which the characteristic
figures appear in the network are the following: [0017] 1) The
network must contain a large number of MLRs, greater than about 20.
For these characteristic figures to be better observed, ideally,
this number must be greater than 100; [0018] 2) The set of the
values of the primary parameter must extend over a sufficiently
large range; [0019] 3) The distribution of the values of the
primary parameter must be such that the corresponding network has a
uniform density on average.
[0020] In practice, criterion 3) is satisfied when the values of
the primary parameter constituting the set grow slowly and
uniformly. Furthermore, if wished, one can slightly modify the
density, for example, by making the network denser for smaller
values of the primary parameter.
[0021] The following algebraic formula is used to determine with
more than sufficient precision the values of the primary parameter,
including the possibility of modifying the density: n k = n 1 + ( k
- 1 ) .times. a + k .function. ( k - 1 ) N .function. ( N - 1 )
.function. [ n N - n 1 - ( N - 1 ) .times. a ] ##EQU1## where:
[0022] k ={1, . . . N}; [0023] N is the number of curves in the
network; [0024] n.sub.1 is the first term of the set; [0025]
n.sub.N is the N.sup.th term of the set; and [0026] a is the
interval between n.sub.1 and n.sub.2.
[0027] Taking N =100, n.sub.1=8, n.sub.N=1502, and a =8 as an
example, one obtains for the primary parameter the following set of
values: [0028] {8, 16, 24, 33, 41, 50, 59, 68, . . . , 1351, 1372,
1393, 1415, 1436, 1458, 1480, 1502} This set of values generates a
network of 100 MLRs which, as desired, has a uniform density on
average and extends over a large range.
[0029] The characteristic figures seen on the monitor of the
computer belong to one of the following three types: [0030] 1)
Cords; [0031] 2) Envelopes; [0032] 3) Boltropes.
[0033] A cord is a pronounced condensation of curves that stands
out from a less dense background of curves of the network.
[0034] An envelope outlines the boundary of a group of curves of
the network.
[0035] A boltrope is both a cord and an envelope.
[0036] A characteristic figure attracts or repels the
representative curve of the data, depending on its type, its shape
and its relative position to the representative curve of the data.
The more marked the characteristic figure, the stronger the
attraction or the repulsion.
[0037] The analysis and prediction of the evolution of the data
requires the examination of the ensemble of the cords, envelopes
and boltropes and the representative curve of the data up to a
given moment, over a sufficiently large interval of consecutive
data points. An interval is considered sufficiently large when it
contains a peripheral characteristic figure at the top of the
network exhibiting an convex upward turning point and another one
at the bottom exhibiting a convex downward turning point. The
ensemble of the cords, envelopes and boltropes and the
representative curve of the data up to a given moment observed over
a sufficiently large interval is referred to as a `spatial
configuration`.
[0038] Qualitative and quantitative indications are obtained from a
given spatial configuration by determining which characteristic
figures specifically attract and which characteristic figures
specifically repel the representative curve of the data, and this
is achieved through the examination of numerous and varied past
spatial configurations and their subsequent evolutions.
[0039] The reasons for which the MLR has been chosen, as mentioned
above, are as follows: [0040] Characteristic figures do not appear
within MAs networks; [0041] Characteristic figures appear clearly
within MLRs networks which can be implemented on last-generation
PCs; [0042] MKRs networks, starting with MQRS, are difficult to
implement on last-generation PCs, due to limited processing
capabilities.
[0043] The fact that characteristic figures appear within the
network, regardless of the value of the scale parameter, can be
exploited to broaden the spectrum of analysis and prediction.
[0044] The readability of the graphical display of the network and
the representative curve of the data can be improved by using
different colors.
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