KR102441682B1 - System, sever and method for analyzing investment of securities - Google Patents

Abstract

The present invention relates to a system, server and method for analyzing a securities investment, a database in which stock items registered in the securities market are classified and stored in a classification system according to industries and themes within the industry, and stock items stored in the database , primary filtering is performed according to a predefined first filtering algorithm that serves as a premise for analyzing and selecting individual items, and secondary filtering is performed according to a predefined second filtering algorithm based on the basic analysis of individual items. Performs tertiary filtering according to a predefined third filtering algorithm based on the technical analysis of individual stocks and corresponding themes, and a fourth predefined filtering algorithm based on the index indicating the stock price trend and stock price sensitivity of individual stocks It is characterized in that it includes a stock selection module that provides ranking information of individual stocks according to the degree of prediction of stock price rise in a manner of performing quaternary filtering according to

Description

SYSTEM, SEVER AND METHOD FOR ANALYZING INVESTMENT OF SECURITIES

The present invention relates to a securities investment analysis system, server and method, and more particularly, to a securities investment analysis system for presenting criteria for selecting a stock to be invested by a user from among stock stocks registered in the securities market; Servers and methods.

Investing in stocks is divided into a method of directly selecting and investing in stocks and a method of investing in a stock-type fund. There is an increasing number of cases of buying and selling these direct stocks. Individuals invest in stocks by searching for desired investment stocks on HTS or MTS based on stock information provided by securities companies, etc. are doing

In the case of an individual's stock investment as above, there is the inconvenience of having to search the stock market information that the user wants and extract the desired stock stock. There is a problem in that it is difficult to select investment stocks in consideration of multi-faceted criteria such as stock prospects and risk versus profitability. When an individual decides an investment item by analyzing information obtained through the Internet, etc. is uncommon

Background art of the present invention is disclosed in Korean Patent Application Laid-Open No. 10-2001-0009718 (published on Feb. 5, 2001).

SUMMARY OF THE INVENTION An object of the present invention is to provide a securities investment analysis system, server and method for presenting criteria for selecting stocks that are expected to rise in the short to medium term among stocks registered in the securities market.

Securities investment analysis system according to an aspect of the present invention includes: a database in which stock items registered in the securities market are classified and stored in a classification system according to industries and themes within the industry; And with respect to the stock items stored in the database, perform primary filtering according to a predefined first filtering algorithm serving as a premise for analyzing and selecting individual stocks, and based on the basic analysis of the individual stocks, 2 Secondary filtering is performed according to the filtering algorithm, and tertiary filtering is performed according to a predefined third filtering algorithm based on the technical analysis of individual stocks and corresponding themes, and an index indicating stock price trend and stock price sensitivity of individual stocks and a stock selection module that provides ranking information of individual stocks according to the degree of prediction of stock price rise in a manner of performing quaternary filtering according to a predefined fourth filtering algorithm based on .

In the present invention, when performing the first to fourth filtering, the stock selection module selects stock stocks selected through N-th filtering according to the N-th filtering algorithm (where N is a natural number of 1 or more and 3 or less) , N+1-th filtering is performed as an input to the N+1-th filtering algorithm.

In the present invention, the first filtering algorithm is predefined according to a first exclusion condition predefined according to investment risk and suitability of analysis of individual stocks, and one or more of market capitalization, closing price, trading volume, fluctuation rate, and listing date of individual stocks. and a first selection condition, wherein the stock selection module performs the primary filtering on the stock stocks stored in the database according to the first filtering algorithm.

In the present invention, the second filtering algorithm includes a second selection condition predefined according to sales, operating profit ratio, and earnings per share (EPS) as parameters representing the momentum of individual stocks, and the stock selection module is characterized by performing the secondary filtering according to the second filtering algorithm with respect to the stock items selected through the primary filtering according to the first filtering algorithm.

In the present invention, the third filtering algorithm includes a third selection condition predefined according to the moving average line and separation degree of the theme to which the individual item belongs, the moving average line and the separation degree of the individual item, and the supply and demand status of the individual item, The item selection module is characterized in that the tertiary filtering is performed according to the third filtering algorithm on the stock item selected through the secondary filtering according to the second filtering algorithm.

In the present invention, the fourth filtering algorithm is based on RSI (Relative Strength Index) and MACD (Moving Average Convergence and Divergence) as indices indicating the stock price trend of individual stocks, and the beta index as an index indicating the stock price sensitivity of individual stocks. including a predefined fourth selection condition, wherein the stock selection module performs the quaternary filtering according to the fourth filtering algorithm with respect to the stock stock selected through the tertiary filtering according to the third filtering algorithm characterized in that

In the present invention, the stock selection module is characterized in that the ranking information of individual stocks according to the prediction degree of stock price rise is finally determined and provided based on the beta index.

The present invention may further include a filter setting module in which detailed conditions of each parameter for defining the first to fourth filtering algorithms are set.

The securities investment analysis server according to an aspect of the present invention includes a database in which stock items registered in the securities market are classified and stored in a classification system according to industries and themes within the industry, and with respect to the stock items stored in the database , primary filtering is performed according to a predefined first filtering algorithm that serves as a premise for analyzing and selecting individual items, and secondary filtering is performed according to a predefined second filtering algorithm based on the basic analysis of individual items. Performs tertiary filtering according to a predefined third filtering algorithm based on the technical analysis of individual stocks and corresponding themes, and a fourth predefined filtering algorithm based on the index indicating the stock price trend and stock price sensitivity of individual stocks It is characterized by providing ranking information of individual stocks according to the degree of prediction of stock price rise in a method of performing quaternary filtering according to

In the securities investment analysis method according to an aspect of the present invention, the server performs primary filtering according to a predefined first filtering algorithm that functions as a premise for analyzing and selecting individual stocks with respect to stock stocks stored in a database In the database, the stock items registered in the securities market are classified and stored in a classification system according to the industry type and the theme within the industry type; performing, by the server, secondary filtering according to a predefined second filtering algorithm based on a basic analysis of individual items; performing, by the server, tertiary filtering according to a predefined third filtering algorithm based on technical analysis of individual items and corresponding themes; performing, by the server, quaternary filtering according to a predefined fourth filtering algorithm based on an index indicating a stock price trend and stock price sensitivity of individual stocks; and providing, by the server, ranking information of individual stocks according to the degree of prediction of a stock price increase after performing each of the filtering.

According to one aspect of the present invention, the present invention sequentially performs a plurality of predefined filtering algorithms according to analysis and selection of individual stocks, basic analysis, technical analysis, stock price trend, and stock price sensitivity for stocks registered in the securities market. By adopting a configuration that provides ranking information of individual stocks according to the degree of prediction of stock price rise through a filtering process applied to can make it

1 is a block diagram illustrating a security investment analysis system and server according to an embodiment of the present invention.
2 is a flowchart illustrating a method for analyzing investment in securities according to an embodiment of the present invention.

Hereinafter, an embodiment of a security investment analysis system, server and method according to the present invention will be described with reference to the accompanying drawings. In this process, the thickness of the lines or the size of the components shown in the drawings may be exaggerated for clarity and convenience of explanation. In addition, the terms to be described later are terms defined in consideration of functions in the present invention, which may vary according to intentions or customs of users and operators. Therefore, definitions of these terms should be made based on the content throughout this specification.

1 is a block diagram illustrating a security investment analysis system and server according to an embodiment of the present invention.

As shown in Figure 1, the securities investment analysis system according to this embodiment is implemented based on the interworking operation between the server 100, the manager terminal 200, and the user terminal 300 that are linked through mutual wired and wireless communication. , it operates to provide ranking information of individual stocks according to the degree of prediction of stock price rise through a plurality of filtering processes for stocks registered in the securities market. As shown in FIG. 1 , the server 100 may include a database 110 , an event selection module 120 , and a filter setting module 130 , and the server 100 having the above sub-configuration can be viewed independently. The securities investment analysis system of the embodiment may be configured. In FIG. 1, the server 100 is shown as an example having a functionally separated database 110, an item selection module 120, and a filter setting module 130 as its sub-configurations, but depending on the embodiment, each sub-configuration is It may be implemented as an integrated configuration within the server 100 . The 'server' and 'security investment analysis server' indicated below are defined as meaning the same configuration.

Hereinafter, the operation of the present embodiment will be described in detail focusing on the server 100 .

As described above, the server 100 of this embodiment may include a database 110 , an item selection module 120 , and a filter setting module 130 .

In the database 110 , stock items registered in the securities market may be classified and stored in a classification system according to industries and themes within the industry. For example, the server 100 obtains information about stocks listed on the stock market (eg, KOSPI and KOSDAQ) from an exchange server (eg, Korea Exchange Server), and sets the acquired stocks to industries and themes. It can be classified based on the classification and stored in the database 110 .

In the above database 110, all stock items registered in the securities market are first classified by industry, and are secondarily classified by theme within the first classified industry, and 3 by market capitalization size within the second classified theme. The car may be classified and stored. For example, stocks corresponding to the industry 'biopharmaceutical' are classified first, and stocks corresponding to the themes 'gene therapy' and 'stem cells' within the 'biopharmaceutical' industry are classified secondarily, Stocks may be tertiarily classified by market capitalization and stored in the database 110 . This classification system is a premise for preventing duplicate investment in stocks.

The item selection module 120 performs primary filtering on the stock items stored in the database 110 according to a predefined first filtering algorithm serving as a premise for analyzing and selecting individual items, and Based on the analysis, secondary filtering is performed according to a predefined second filtering algorithm, and tertiary filtering is performed according to a predefined third filtering algorithm based on the technical analysis of individual items and corresponding themes, and In a way that quaternary filtering is performed according to a predefined fourth filtering algorithm based on an index indicating a stock price trend and stock price sensitivity, ranking information of individual stocks according to the prediction degree of stock price rise can be provided through the user terminal 300 have.

In this case, when the stock selection module 120 performs the first to fourth filtering, the stock stock selected through the N-th filtering according to the N-th filtering algorithm (where N is a natural number of 1 or more and 3 or less), As an input to the N+1th filtering algorithm, the N+1th order filtering may be performed. That is, secondary filtering is performed on the item selected according to the primary filtering, tertiary filtering is performed on the item selected according to the secondary filtering, and quaternary filtering is performed on the item selected according to the tertiary filtering. As a result, individual stocks according to the degree of prediction of a stock price increase may be finally selected and provided through the user terminal 300 together with the ranking information.

As will be described later, the primary filtering process is based on the investment risk for individual stocks, the suitability of the analysis, and the basic trading size (market cap, closing price, trading volume, fluctuation rate, etc.) of individual stocks. This is the filtering process. In addition, the secondary filtering process corresponds to a filtering process from the perspective of basic analysis such as growth potential and sales capability of individual stocks, and the third filtering process is a technical analysis of individual stocks and the theme (moving average, separation and supply/demand status) It corresponds to the filtering process from the point of view. And, the fourth filtering process corresponds to a filtering process from a microscopic point of view of individual stocks and themes, which is filtered based on indices (RSI, MACD, beta index) indicating the stock price trend and stock price sensitivity of individual stocks. That is, in the process of sequential filtering according to the 1st to 4th filtering, since the stock selection criteria are shifted from a macro perspective to a micro perspective, we break away from the conventional stock selection criteria that select investment stocks only with local indicators. The reliability of the stock selection can be secured, and the objectivity of the stock selection can also be secured because the basic analysis and technical analysis of the stocks are involved in the filtering process.

Hereinafter, each step of the 1st to 4th filtering process will be described in detail.

First, the primary filtering process will be described. As described above, the first filtering algorithm applied to the primary filtering may function as a premise for analyzing and selecting individual items, and the first exclusion condition and the first selection condition may include In this case, the stock selection module 120 may first perform primary filtering on the stock stocks stored in the database 110 according to the first filtering algorithm.

The first exclusion condition may be predefined according to investment risk and suitability of individual stock analysis. Here, investment risk may mean whether the relevant stock is in a trading suspension state or whether it is a management stock, and the suitability of individual stock analysis is whether the relevant stock is a preferred stock or a holding company stock (eg, holdings, etc.) can mean Accordingly, the stock selection module 120 may exclude the stock by determining that the investment risk is high when the stock is in a trading suspension state or falls under a management stock, and when the stock is a preferred stock or a holding company stock As it is judged that it is not suitable for analyzing individual stocks, the relevant stock may be excluded.

The first selection condition may be predefined according to at least one of a market capitalization, closing price, trading volume, fluctuation rate, and listing date of individual stocks. In this case, the stock selection module 120 satisfies the predetermined range (set in the filter setting module 130 to be described later) set for each of the stock market capitalization, previous-day closing price, trading volume, fluctuation rate, and number of days elapsed from the listing date. In this case, the relevant item can be selected as the item selected according to the primary filtering result. That is, it can be determined whether the market capitalization meets a predetermined range to exclude mega-cap stocks and micro-cap stocks. It can be determined whether the trading volume meets a predetermined range to ensure that the trading is concluded at the time when the trading is intended to be concluded, and the fluctuation rate can be determined to exclude stocks that have risen sufficiently at the current time. It can be determined whether a predetermined range is satisfied, and it can be determined whether or not the listing date of the relevant stock has elapsed by more than a predetermined day in order to secure the reliability of the basic analysis in the secondary filtering process to be described later.

Table 1 below shows an example of the first filtering algorithm. x, y, and z shown in Table 1 are variables, and may be set in the filter setting module 130 to be described later in a manner that an administrator sets through the administrator terminal 200 .

No. Condition parameter Parameter Details One

First exclusion condition Transaction Suspension exclude or include 2 Management items exclude or include 3 preferred stock exclude or include 4 Holding company stock (Holdings) exclude or include 5

first selection condition market cap More than x won and less than y won 6 previous day's closing price More than x won and less than y won 7 trading volume The average of x days is y or more and z or less 8 Day-to-day fluctuations compared to the previous day's closing price x% or more and y% or less 9 Elapsed date from the listing date more than x days 10 Other conditions Conditions for repurchase after the stocks held are completely sold after x days

Next, describing the secondary filtering process, as described above, the second filtering algorithm applied to the secondary filtering may be predefined based on the basic analysis of individual items, and may include a second selection condition. have. In this case, the item selection module 120 may perform secondary filtering according to the second filtering algorithm on the stock item selected through the primary filtering according to the first filtering algorithm.

The second selection condition may be predefined according to sales, operating profit ratio, and earnings per share (EPS) as parameters representing momentum of individual stocks for basic analysis of individual stocks. In this case, the item selection module 120 may select the item as the item selected according to the secondary filtering result when the sales growth rate, operating profit increase rate, and EPS increase rate of the item satisfy predetermined ranges set for each. . In order to secure the reliability of the basic analysis, the sales growth rate, operating profit growth rate, and EPS growth rate are each divided into year-on-year and QoQ (that is, year-on-year sales growth rate, operating profit growth rate and EPS growth rate / QoQ sales growth rate, operating profit growth rate) and EPS increase rate), a method in which it is determined whether or not a predetermined range set for each is satisfied may be employed.

Table 2 below shows an example of the second filtering algorithm. x and y shown in Table 2 are variables, and may be set in the filter setting module 130 to be described later in a manner that the administrator sets through the administrator terminal 200 .

No. Condition parameter Parameter Details 11


2nd selection condition Sales growth rate (compared to the previous year) x% or more and y% or less 12 Operating profit growth rate (compared to the previous year) x% or more and y% or less 13 EPS growth rate (compared to the previous year) x% or more and y% or less 14 Sales growth rate (QoQ) x% or more and y% or less 15 Operating Profit Growth Rate (QoQ) x% or more and y% or less 16 EPS growth rate (QoQ) x% or more and y% or less

Next, describing the tertiary filtering process, as described above, the third filtering algorithm applied to the tertiary filtering may be predefined based on the technical analysis of individual items and corresponding themes, and the third selection condition may include In this case, the item selection module 120 may perform tertiary filtering according to the third filtering algorithm on the stock items selected through the secondary filtering according to the second filtering algorithm.

The third selection condition may be predefined according to the moving average line and separation degree of the theme to which the individual item belongs, the moving average line and the separation degree of the individual item, and the supply and demand status of the individual item. In this case, the item selection module 120 selects a theme by determining whether the moving average line and the separation degree of the theme to which the stock item selected through secondary filtering belongs meet a predetermined range set for each, and selects a theme in the selected theme. Whether the moving average and separation of individual stocks to which it belongs (in the case of a moving average, it is subdivided into a moving average of a stock price and a moving average of trading volume, and in the case of a separation, it may be subdivided into a degree of separation of a stock price and a separation of trading volume) satisfies the predetermined range set for each A method of determining whether individual stocks are selected by judging whether the supply/demand status of the selected individual stocks, such as the growth rate of net buying by foreign investors, increase in net buying by institutional investors, and increase in net buying by individual investors, meets a predetermined range set for each 3rd-order filtering can be performed.

Table 3 below shows an example of the third filtering algorithm. x, y, z, and u shown in Table 3 are variables, and may be set in the filter setting module 130 to be described later in a manner that an administrator sets through the administrator terminal 200 .

No. Condition parameter Parameter Details 17









3rd selection condition theme moving average The number of days elapsed since the golden cross occurs when the x-day average value is the y-day average value is more than z days and less than u days. 18 separation of themes The increase rate of the x-day average value compared to the y-day average value is z% or more and u% or less 19 Moving average of individual stocks (stock price) The number of days elapsed since the golden cross occurs when the x-day average value is the y-day average value is more than z days and less than u days. 20 Separation of individual stocks (stock price) The increase rate of the x-day average value compared to the y-day average value is z% or more and u% or less 21 Moving average of individual stocks (trading volume) The number of days elapsed since the golden cross occurs when the x-day average value is the y-day average value is more than z days and less than u days. 22 Disparity of individual stocks (trading volume) The increase rate of the x-day average value compared to the y-day average value is z% or more and u% or less 23 Foreign Investor Net Buying Growth Rate The x-day average value is z% or more and u% or less compared to the y-day average value. 24 Institutional Investor Net Buying Growth Rate The x-day average value is z% or more and u% or less compared to the y-day average value. 25 Individual Investor Net Buying Growth Rate The x-day average value is z% or more and u% or less compared to the y-day average value.

Next, the fourth filtering process will be described. As described above, the fourth filtering algorithm applied to the fourth filtering may be predefined based on the index indicating the stock price trend and stock price sensitivity of individual stocks, and the fourth selection Conditions may be included. In this case, the stock item selection module 120 may perform quaternary filtering according to the fourth filtering algorithm on the stock item selected through the third filtering according to the third filtering algorithm.

The fourth selection condition may be predefined based on the Relative Strength Index (RSI) and Moving Average Convergence and Divergence (MACD) as indices indicating the stock price trend of individual stocks, and the beta index as an index indicating the stock price sensitivity of individual stocks. . In this case, the stock selection module 120 determines whether the RSI and MACD of the corresponding stock meet a predetermined range set for each, and determines the ranking information for the stock stock selected through the above process, that is, the prediction degree of the stock price rise. The ranking information of individual stocks can be finally determined based on the beta index.

Table 4 below shows an example of the fourth filtering algorithm. x, y, z, u, and w shown in Table 4 are variables, and may be set in the filter setting module 130 to be described later in a manner that the administrator sets through the administrator terminal 200 .

No. Condition parameter Parameter Details 26

4th selection condition RSI The sum of the rises for x days is y% or more and z% or less 27 MACD After the x-day short-term moving average crosses the y-day long-term moving average and the z-day signal rises, more than u days and less than w days 28 theme or individual
beta index Select up to xth place based on the largest value

On the other hand, an additional filtering algorithm for finally selecting individual items to be provided to the user may be predefined in the item selection module 120 (the additional filtering algorithm may be named as a fifth filtering algorithm, and The filtering process according to this may be referred to as fifth-order filtering). Table 5 below shows an example of the fifth filtering algorithm. x shown in Table 5 is a variable and may be set in the filter setting module 130 to be described later in a manner that the administrator sets through the manager terminal 200 .

No. Condition parameter Parameter Details 29


Fifth selection condition Maximum number of stocks per theme Limited to no more than x stocks 30 When selecting items according to the ranking of themes Recommend ranking up to x 31 When selecting a stock according to the ranking of the stock Recommend ranking up to x 32 The final maximum number of stocks is the top No more than x stocks (at the time of initial incorporation, after that, only the necessary number of stocks)

The process of finally selecting individual items to be provided to users through Tables 4 and 5 will be described as a specific example as follows.

① (No. 26, 27 of the fourth filtering algorithm) The items in each theme to which each individual item selected through tertiary filtering belongs are filtered through the RSI and MACD of the fourth filtering algorithm. For example, assuming that themes A to D are selected through tertiary filtering, and the number of individual items in each theme is 7, 10, 8, and 7, respectively, through RSI and MACD Through filtering, 5 items of theme A, 3 items of theme B, 6 items of theme C, and 7 items of theme D are selected.

② (No. 28 of the fourth filtering algorithm) After that, the ranking of the themes is determined through the beta index of each theme. For example, when the beta index of a theme is large in the order of theme A, theme D, theme B, and theme C, the ranking of themes is determined as theme A, theme D, theme B, and theme C.

③ (No. 28 of the fourth filtering algorithm) In addition, the ranking of individual items in the theme is determined through the beta index of the individual items in the ranked theme.

④ (No. 29 of the fifth filtering algorithm) No. of the fifth filtering algorithm. If it is assumed that the detailed condition of the parameter of 29 is less than '3' items, individual items corresponding to the 1st to 3rd positions in each theme are selected. For example, the ranking of individual items within the theme as theme A(c,b,a), theme D(e,g,j), theme B(r,v,n), theme C(m,p,q). is selected (here, the alphabet indicates individual items, and the order of their description indicates the ranking).

⑤ (No. 30 or No. 31 of the fifth filtering algorithm) No. of the fifth filtering algorithm. 30 Conditions and No. 31 The condition may be set so that only one of them is selected.

-i) No. of the fifth filtering algorithm. 30 When conditions (selection of items according to the ranking of themes) are selected, the final selected individual items are determined by c, b, a, e, g, j, r, v, n, m, p, q, The order in which the individual items are listed above indicates the ranking information of the individual items.

-ii) No. of the fifth filtering algorithm. 30 When the condition (selection of items according to the ranking of items) is selected, each item belonging to each theme, i.e., each of c, b, a, e, g, j, r, v, n, m, p, q Each individual stock is ranked according to the high value of the beta index. For example, according to the size of the beta index, the ranking of each individual stock may be determined as c, e, r, m, b, g, v, p, a, j, n, q, unlike the case of i) above.

The detailed conditions of each parameter for defining the above-described first to fifth filtering algorithms (that is, variables in Tables 1 to 5) are input by the administrator through the administrator terminal 200 through the filter setting module 130 . may be set in, and the item selection module 120 performs the first to fifth filtering based on the first to fifth filtering algorithms defined according to the detailed conditions of each parameter set in the filter setting module 130, respectively. can

2 is a flowchart illustrating a method for analyzing investment in securities according to an embodiment of the present invention.

Referring to FIG. 2 , the method for analyzing investment in securities according to an embodiment of the present invention will be described. First, the server 100 functions as a premise for analyzing and selecting individual stocks for stock stocks stored in the database 110 . Primary filtering is performed according to a predefined first filtering algorithm (S100). As described above, in the database 110 , stock items registered in the securities market may be classified and stored in a classification system according to industries and themes within the industry. The first filtering algorithm applied in step S100 is pre-defined according to the first exclusion condition predefined according to investment risk and suitability of individual stock analysis, and one or more of the market capitalization, closing price, trading volume, fluctuation rate, and listing date of individual stocks. The first selection condition may be included.

Next, the server 100 performs secondary filtering according to a predefined second filtering algorithm based on the basic analysis of individual items (S200). The second filtering algorithm applied in step S200 may include a second selection condition predefined according to sales, operating profit ratio, and earnings per share (EPS) as parameters representing momentum of individual stocks.

Next, the server 100 performs tertiary filtering according to a predefined third filtering algorithm based on the technical analysis of individual items and corresponding themes ( S300 ). The third filtering algorithm applied in step S300 may include a moving average and separation degree of the theme to which an individual item belongs, a moving average line and a separation degree of each item, and a third selection condition predefined according to the supply and demand status of each item. .

Next, the server 100 performs quaternary filtering according to a predefined fourth filtering algorithm based on the index indicating the stock price trend and stock price sensitivity of individual stocks (S400). The fourth filtering algorithm applied in step S400 may include a fourth selection condition predefined based on the RSI and MACD as indices indicating the stock price trend of individual stocks and the beta index as an index indicating the stock price sensitivity of individual stocks.

Subsequently, the server 100 performs each filtering of steps S100 to S400 and provides ranking information of individual stocks according to the degree of prediction of a stock price increase through the user terminal 300 ( S500 ).

As described above, this embodiment is a filtering process of sequentially applying a plurality of predefined filtering algorithms according to analysis and selection of individual stocks, basic analysis, technical analysis, stock price trend, and stock price sensitivity with respect to stock stocks registered in the securities market. By adopting a configuration that provides ranking information of individual stocks according to the degree of prediction of stock price rise through the process, it provides objective criteria for selecting stocks for investors to invest in, thereby enabling investors to secure stable returns.

Implementations described herein may be implemented in, for example, a method or process, an apparatus, a software program, a data stream, or a signal. Although discussed only in the context of a single form of implementation (eg, discussed only as a method), implementations of the discussed features may also be implemented in other forms (eg, as an apparatus or program). The apparatus may be implemented in suitable hardware, software and firmware, and the like. A method may be implemented in an apparatus such as, for example, a processor, which generally refers to a computer, a microprocessor, a processing device, including an integrated circuit or programmable logic device, or the like. Processors also include communication devices such as computers, cell phones, portable/personal digital assistants ("PDA") and other devices that facilitate communication of information between end-users.

As described above, the present invention has been described with reference to the embodiment shown in the drawings, but this is merely an example, and various modifications and equivalent other embodiments are possible therefrom by those skilled in the art. will understand the point. Therefore, the technical protection scope of the present invention should be defined by the following claims.

100: Securities investment analysis server
110: database
120: event selection module
130: filter setting module
200: administrator terminal
300: user terminal

Claims (10)

a database in which stocks registered in the securities market are classified and stored in a classification system according to industries and themes within the industry; and
For stock items stored in the database, primary filtering is performed according to a predefined first filtering algorithm serving as a premise for analyzing and selecting individual items, and a second predefined filtering algorithm is performed based on the basic analysis of individual items. Secondary filtering is performed according to the filtering algorithm, and tertiary filtering is performed according to a predefined third filtering algorithm based on the technical analysis of individual stocks and corresponding themes, and indexes representing stock price trends and stock price sensitivity of individual stocks are calculated. a stock selection module for providing ranking information of individual stocks according to a degree of prediction of stock price rise in a manner of performing quaternary filtering according to a fourth filtering algorithm predefined based on the fourth filtering algorithm;
including,
The first to fourth-order filtering is performed sequentially,
The first filtering algorithm includes a first selection condition predefined according to market capitalization, closing price, trading volume, fluctuation rate, and listing date of individual stocks, and whether or not a predetermined date or more has elapsed,
The second filtering algorithm includes a second selection condition predefined according to sales, operating profit ratio, and earnings per share (EPS) as parameters representing the momentum of individual stocks,
The third filtering algorithm includes a first analysis process for the moving average line and separation degree of the theme to which individual items belong, and a second analysis process for the moving average line and separation degree of individual items belonging to the theme selected through the first analysis process. and a third selection condition predefined according to the supply and demand status of individual items selected through the second analysis process,
The fourth filtering algorithm, RSI (Relative Strength Index) and MACD (Moving Average Convergence and Divergence) as indices indicating the stock price trend of individual stocks, and a beta index as an index indicating stock price sensitivity of individual stocks 4 including the selection conditions;
wherein the stock selection module provides ranking information of individual stocks according to the degree of prediction of stock price rise selected through the RSI and the MACD, based on the beta index, is finally determined and provided. analysis system.
According to claim 1,
The item selection module, when performing the 1st to 4th filtering, selects the stock item selected through the Nth filtering according to the Nth filtering algorithm (where N is a natural number of 1 or more and 3 or less), N+ 1 A security investment analysis system, characterized in that N+1 filtering is performed as an input of the filtering algorithm.
3. The method of claim 2,
The first filtering algorithm further includes a first exclusion condition predefined according to investment risk and suitability of individual stock analysis,
The stock investment analysis system, wherein the stock selection module performs the primary filtering on the stock stocks stored in the database according to the first filtering algorithm.
4. The method of claim 3,
The stock investment analysis system, wherein the stock selection module performs the secondary filtering according to the second filtering algorithm with respect to the stock stock selected through the first filtering according to the first filtering algorithm.
5. The method of claim 4,
The stock investment analysis system, wherein the stock selection module performs the tertiary filtering according to the third filtering algorithm on the stock stocks selected through the secondary filtering according to the second filtering algorithm.
6. The method of claim 5,
The stock investment analysis system, wherein the stock selection module performs the quaternary filtering according to the fourth filtering algorithm on the stock stocks selected through the tertiary filtering according to the third filtering algorithm.
delete According to claim 1,
The securities investment analysis system further comprising a; a filter setting module in which detailed conditions of each parameter for defining the first to fourth filtering algorithms are set.
Equipped with a database in which stock items registered in the securities market are classified and stored in a classification system according to industries and themes within the industry, and with respect to the stock items stored in the database, it functions as a premise for analyzing and selecting individual stocks Primary filtering is performed according to a predefined first filtering algorithm, secondary filtering is performed according to a second predefined filtering algorithm based on a basic analysis of individual items, and based on technical analysis of individual items and corresponding themes In a method of performing tertiary filtering according to a third filtering algorithm predefined as As a securities investment analysis server that provides ranking information of individual stocks according to the degree of prediction,
The first to fourth-order filtering is performed sequentially,
The first filtering algorithm includes a first selection condition predefined according to market capitalization, closing price, trading volume, fluctuation rate, and listing date of individual stocks, and whether or not a predetermined date or more has elapsed,
The second filtering algorithm includes a second selection condition predefined according to sales, operating profit ratio, and earnings per share (EPS) as parameters representing the momentum of individual stocks,
The third filtering algorithm includes a first analysis process for the moving average line and separation degree of the theme to which individual items belong, and a second analysis process for the moving average line and separation degree of individual items belonging to the theme selected through the first analysis process. and a third selection condition predefined according to the supply and demand status of individual items selected through the second analysis process,
The fourth filtering algorithm, RSI (Relative Strength Index) and MACD (Moving Average Convergence and Divergence) as indices indicating the stock price trend of individual stocks, and a beta index as an index indicating stock price sensitivity of individual stocks 4 including the selection conditions;
The server, characterized in that the ranking information of individual stocks according to the degree of prediction of the stock price increase selected through the RSI and the MACD is finally determined and provided based on the beta index, characterized in that it is provided. server.
The server performs primary filtering on stock items stored in the database according to a predefined first filtering algorithm serving as a premise for analyzing and selecting individual stocks, wherein the database contains The stock items are classified and stored in a classification system according to the themes within the industry and industry, step;
performing, by the server, secondary filtering according to a predefined second filtering algorithm based on a basic analysis of individual items;
performing, by the server, tertiary filtering according to a predefined third filtering algorithm based on technical analysis of individual items and corresponding themes;
performing, by the server, quaternary filtering according to a predefined fourth filtering algorithm based on an index indicating a stock price trend and stock price sensitivity of individual stocks; and
providing, by the server, ranking information of individual stocks according to the prediction degree of a stock price increase after each filtering;
including,
The first to fourth-order filtering is performed sequentially,
The first filtering algorithm includes a first selection condition predefined according to market capitalization, closing price, trading volume, fluctuation rate, and listing date of individual stocks, and whether or not a predetermined date or more has elapsed,
The second filtering algorithm includes a second selection condition predefined according to sales, operating profit ratio, and earnings per share (EPS) as parameters representing the momentum of individual stocks,
The third filtering algorithm includes a first analysis process for the moving average line and the separation degree of the theme to which the individual item belongs, and a second analysis process for the moving average line and the separation degree of the individual item belonging to the theme selected through the first analysis process. and a third selection condition predefined according to the supply and demand status of individual items selected through the second analysis process,
The fourth filtering algorithm, RSI (Relative Strength Index) and MACD (Moving Average Convergence and Divergence) as indices indicating the stock price trend of individual stocks, and a beta index as an index indicating the stock price sensitivity of individual stocks. 4 including the selection conditions;
In the step of providing the ranking information of the individual event, the server,
The method for analyzing investment in securities, characterized in that the ranking information of individual stocks according to the degree of prediction of stock price increase selected through the RSI and the MACD is finally determined and provided based on the beta index.

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