US20070016507A1 - System for analysis and prediction of financial and statistical data - Google Patents
System for analysis and prediction of financial and statistical data Download PDFInfo
- Publication number
- US20070016507A1 US20070016507A1 US11/178,533 US17853305A US2007016507A1 US 20070016507 A1 US20070016507 A1 US 20070016507A1 US 17853305 A US17853305 A US 17853305A US 2007016507 A1 US2007016507 A1 US 2007016507A1
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- US
- United States
- Prior art keywords
- data
- network
- analysis
- prediction
- evolution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
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Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Definitions
- 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.
- 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.
- the curves of this network belong to one of the following categories:
- 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 th degree.
- 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.
- the system can also use adjusted data, for example, averaged or weighted data.
- the secondary 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.
- 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.
- n k n 1 + ( k - 1 ) ⁇ a + k ⁇ ( k - 1 ) N ⁇ ( N - 1 ) ⁇ [ n N - n 1 - ( N - 1 ) ⁇ a ]
- a cord is a pronounced condensation of curves that stands out from a less dense background of curves of the network.
- An envelope outlines the boundary of a group of curves of the network.
- a boltrope is both a cord and an envelope.
- 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.
- 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’.
- the readability of the graphical display of the network and the representative curve of the data can be improved by using different colors.
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.
Description
- 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.
- Currently, the following two methods are used to analyze and predict data in the field of finance and economics:
-
- Technical analysis, based exclusively on the examination of a small number of technical indicators derived from the given data;
- 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.
- 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.
- The curves of this network belong to one of the following categories:
- Moving regression (MR) of degree zero, known as the moving average (MA);
- MR of the first degree, known as the moving linear regression (MLR);
- MR of the second degree, which we will call the moving quadratic regression (MQR);
- MR of the kth degree, which we will call the moving k regression (MKR).
- 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 kth degree.
- 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.
- 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.
- The system can also use adjusted data, for example, averaged or weighted data.
- 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.
- The necessary conditions under which the characteristic figures appear in the network are the following:
- 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;
- 2) The set of the values of the primary parameter must extend over a sufficiently large range;
- 3) The distribution of the values of the primary parameter must be such that the corresponding network has a uniform density on average.
- 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.
- 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:
where: - k ={1, . . . N};
- N is the number of curves in the network;
- n1 is the first term of the set;
- nN is the Nth term of the set; and
- a is the interval between n1 and n2.
- Taking N =100, n1=8, nN=1502, and a =8 as an example, one obtains for the primary parameter the following set of values:
- {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.
- The characteristic figures seen on the monitor of the computer belong to one of the following three types:
- 1) Cords;
- 2) Envelopes;
- 3) Boltropes.
- A cord is a pronounced condensation of curves that stands out from a less dense background of curves of the network.
- An envelope outlines the boundary of a group of curves of the network.
- A boltrope is both a cord and an envelope.
- 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.
- 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’.
- 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.
- The reasons for which the MLR has been chosen, as mentioned above, are as follows:
- Characteristic figures do not appear within MAs networks;
- Characteristic figures appear clearly within MLRs networks which can be implemented on last-generation PCs;
- MKRs networks, starting with MQRS, are difficult to implement on last-generation PCs, due to limited processing capabilities.
- 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.
- The readability of the graphical display of the network and the representative curve of the data can be improved by using different colors.
Claims (5)
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.
Priority Applications (6)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/178,533 US20070016507A1 (en) | 2005-07-12 | 2005-07-12 | System for analysis and prediction of financial and statistical data |
JP2008520913A JP5101500B2 (en) | 2005-07-12 | 2006-07-07 | A computer system that predicts the evolution of numerical chronological sets. |
US11/988,624 US8301675B2 (en) | 2005-07-12 | 2006-07-07 | Computer system for predicting the evolution of a chronological set of numerical values |
CN200680031109.5A CN101248448B (en) | 2005-07-12 | 2006-07-07 | The information system of the differentiation of prediction numerical value group sequentially in time |
PCT/FR2006/001639 WO2007006943A2 (en) | 2005-07-12 | 2006-07-07 | Computer system which can be used to predict the future of a chronological set of numerical values |
EP06778814A EP1902412A2 (en) | 2005-07-12 | 2006-07-07 | Computer system which can be used to predict the future of a chronological set of numerical values |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/178,533 US20070016507A1 (en) | 2005-07-12 | 2005-07-12 | System for analysis and prediction of financial and statistical data |
Related Child Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/988,624 Continuation-In-Part US8301675B2 (en) | 2005-07-12 | 2006-07-07 | Computer system for predicting the evolution of a chronological set of numerical values |
US11/988,624 Continuation US8301675B2 (en) | 2005-07-12 | 2006-07-07 | Computer system for predicting the evolution of a chronological set of numerical values |
Publications (1)
Publication Number | Publication Date |
---|---|
US20070016507A1 true US20070016507A1 (en) | 2007-01-18 |
Family
ID=37575152
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/178,533 Abandoned US20070016507A1 (en) | 2005-07-12 | 2005-07-12 | System for analysis and prediction of financial and statistical data |
US11/988,624 Active 2029-03-02 US8301675B2 (en) | 2005-07-12 | 2006-07-07 | Computer system for predicting the evolution of a chronological set of numerical values |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/988,624 Active 2029-03-02 US8301675B2 (en) | 2005-07-12 | 2006-07-07 | Computer system for predicting the evolution of a chronological set of numerical values |
Country Status (5)
Country | Link |
---|---|
US (2) | US20070016507A1 (en) |
EP (1) | EP1902412A2 (en) |
JP (1) | JP5101500B2 (en) |
CN (1) | CN101248448B (en) |
WO (1) | WO2007006943A2 (en) |
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US20060184558A1 (en) * | 2005-02-03 | 2006-08-17 | Musicstrands, Inc. | Recommender system for identifying a new set of media items responsive to an input set of media items and knowledge base metrics |
US20070244768A1 (en) * | 2006-03-06 | 2007-10-18 | La La Media, Inc. | Article trading process |
US20080270242A1 (en) * | 2007-04-24 | 2008-10-30 | Cvon Innovations Ltd. | Method and arrangement for providing content to multimedia devices |
US20100030682A1 (en) * | 2007-03-21 | 2010-02-04 | Wally Tzara | Processing device for normalizing bars representative of weighted variable quantities |
US20100070917A1 (en) * | 2008-09-08 | 2010-03-18 | Apple Inc. | System and method for playlist generation based on similarity data |
US7693887B2 (en) | 2005-02-01 | 2010-04-06 | Strands, Inc. | Dynamic identification of a new set of media items responsive to an input mediaset |
US7743009B2 (en) | 2006-02-10 | 2010-06-22 | Strands, Inc. | System and methods for prioritizing mobile media player files |
US7797321B2 (en) | 2005-02-04 | 2010-09-14 | Strands, Inc. | System for browsing through a music catalog using correlation metrics of a knowledge base of mediasets |
US7840570B2 (en) | 2005-04-22 | 2010-11-23 | Strands, Inc. | System and method for acquiring and adding data on the playing of elements or multimedia files |
US7877387B2 (en) | 2005-09-30 | 2011-01-25 | Strands, Inc. | Systems and methods for promotional media item selection and promotional program unit generation |
US20110060738A1 (en) * | 2009-09-08 | 2011-03-10 | Apple Inc. | Media item clustering based on similarity data |
US7962505B2 (en) | 2005-12-19 | 2011-06-14 | Strands, Inc. | User to user recommender |
US20110173133A1 (en) * | 2010-01-11 | 2011-07-14 | Augustine Adebayo Ojo | Securities data analysis and prediction tool |
US8332406B2 (en) | 2008-10-02 | 2012-12-11 | Apple Inc. | Real-time visualization of user consumption of media items |
US8477786B2 (en) | 2003-05-06 | 2013-07-02 | Apple Inc. | Messaging system and service |
US8583671B2 (en) | 2006-02-03 | 2013-11-12 | Apple Inc. | Mediaset generation system |
US8892495B2 (en) | 1991-12-23 | 2014-11-18 | Blanding Hovenweep, Llc | Adaptive pattern recognition based controller apparatus and method and human-interface therefore |
US8983905B2 (en) | 2011-10-03 | 2015-03-17 | Apple Inc. | Merging playlists from multiple sources |
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- 2006-07-07 US US11/988,624 patent/US8301675B2/en active Active
- 2006-07-07 WO PCT/FR2006/001639 patent/WO2007006943A2/en active Application Filing
- 2006-07-07 EP EP06778814A patent/EP1902412A2/en not_active Ceased
- 2006-07-07 CN CN200680031109.5A patent/CN101248448B/en active Active
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Also Published As
Publication number | Publication date |
---|---|
JP2009501377A (en) | 2009-01-15 |
CN101248448B (en) | 2016-08-03 |
EP1902412A2 (en) | 2008-03-26 |
CN101248448A (en) | 2008-08-20 |
WO2007006943A8 (en) | 2007-03-22 |
WO2007006943A2 (en) | 2007-01-18 |
JP5101500B2 (en) | 2012-12-19 |
US8301675B2 (en) | 2012-10-30 |
US20090172057A1 (en) | 2009-07-02 |
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