KR101543643B1 - System for ranking stock and method for selecting stock using the same system - Google Patents

Abstract

The present invention provides a method for selecting a stock item in a stock asset management system in which a plurality of user terminals and a server are connected through a network, wherein the server calculates a score and rank (rank) Calculating a score and ranking information through sentiment analysis, and calculating a total score and ranking information that combines the net profit score and the sentiment score; Providing the calculated score and rank information to the user terminal when the stock price analysis request is received from the user terminal connected to the server through the network; Updating the score and rank information according to a set period and providing the updated score and rank information to the user terminal in real time; And the server provides the user terminal with a transition of the score and ranking information according to the set period.

Description

[0001] The present invention relates to a system and method for selecting stocks,

The present invention relates to a stock selection method in a stock asset management system, and more particularly, to a method for selecting a stock item in a stock asset management system, And selecting a stock item using the ranked stocks to realize the best return rate.

Currently, domestic securities firms provide HomeTrading System (HTS) where investors can easily conduct cyber securities transactions using the terminals where wired and wireless networks are established.

1 is a block diagram of a general stock trading system and its peripheral devices.

1, the stock trading system 100 includes an exchange server 110 for enabling securities transactions on-line, and an exchange server 110 connected to the exchange server 110 via wired / wireless Internet to perform a cyber securities transaction And a customer terminal 130 that connects with the HTS 120 through the Internet and enables a customer to perform a cyber securities transaction in the exchange server 110. [

The individual obtains investment information using the service provided by the HTS 120 of the securities company through the customer terminal 130, places an order with the exchange server 110, and performs the securities investment by confirming the result. Particularly, since the smart phone, tablet PC, and the like using the wireless Internet environment are widely used in the current customer terminal 130, it is possible to use the cyber securities transaction more easily without space.

However, when a customer invests in a stock using the HTS 120 through a terminal, the HTS 120 processes and provides various information necessary for stock investment. Further, the stock price index is used to select stock items We provide programs to build a portfolio of stocks, but they have limitations in finding stocks with high return on investment, which are the main concern of stock investors, and in improving profitability.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to provide a method and apparatus for estimating a ranking based on a score and a score using a sentence factor and a net rate of return factor, The present invention provides a system for analyzing the ranking of stocks and a method of selecting stocks using the same.

According to an aspect of the present invention, there is provided a method of selecting a stock item registered in a KOSPI and a KOSDAQ in a stock asset management system in which one or more user terminals and a server are connected through a network, Classifying the total stocks registered on the KOSPI listed and KOSDAQ into a plurality of universes based on the use of the estimated data and the market capitalization; The server calculates the score and rank (rank) information of the stocks in each universe through the net profit analysis based on the EPS of each stock, and calculates the momentum of the share price of each stock, Calculating the score and ranking information based on the supply and demand status of the institutional investors based on the sentiment analysis, and calculating the total score and rank information that combine the net profit score and the sentiment score; When the stock price analysis request is received from a user terminal connected to the server through a network, the server provides the calculated score and ranking information to each user terminal for each stock item; Updating the score and rank information according to a set cycle for each stock tick, and providing updated score and rank information to the user terminal in real time; The server provides the user terminal with a transition of the net profit score, the sentiment score or the total score and the transition of ranking information corresponding to the score, according to the set cycle for each stock item, And providing ranking information on a rate of increase of the score or ranking information to the user terminal by obtaining a rate of increase according to a change in information.

Also, the above-mentioned net profit analysis can calculate the score using the five factors of the short-term profit growth rate, the long-term profit growth rate, the profit retention rate, the return on equity capital, and the reverse PEG value.

The sentiment analysis was performed using ten factors of the stock price rising strength, rising inversion, 120MA spacing, 20MA spacing, foreign net buying, institutional net buying, efficiency ratio, industrial combo model, foreign cumulative net buying, Can be calculated.

Also, the total score is a relative score obtained by combining 50 points of the net profit score and 50 points of the sentiment score. The highest score is 100 points and the lowest score is 0 point, and the ranking can be indicated by the standardized score.

In addition, the transaction suspension items, the management items, the companies with no branch accounting data, the ETF items, the ELW items, the new listing and the registered items may be excluded from the plurality of universes.

In the case of the item in which the estimation data is not provided, the weight is set for each of the four quarters immediately before the final financial data to generate the trailing data, and the rank analysis is performed using the generated trailing data Can be performed.

If the universe, the usage factor, and the weighting factor of the usage factor for calculating the score and ranking information are set from the user terminal in the stock price analysis request step, the server calculates the universe, And providing the calculated score and ranking information to the user terminal.

According to another aspect of the present invention, there is provided a stock asset management system in which a database storing data such as stock price DB, financial DB, IBES, etc. of one or more user terminals and an exchange is connected to a server via a network, The universe management unit classifies the stocks listed on the KOSPI or all KOSDAQ stocks into a plurality of universes based on the use of estimated data and the market capitalization. EPS, and net income analysis, which are used to measure and rank scores and rankings. The analysis is based on sentiment analysis based on stock momentum, volume, and supply and demand of foreign investors and institutional investors. A sentiment analyzing section for calculating a sentiment score and a net profit point The server updates the score and rank information according to a set cycle for each stock tick, and updates the score and rank information updated in real time to the user terminal And the server provides the user terminal with the transition of the net profit score, the sentiment score or the total score and the transition of the rank information corresponding to the score according to the set cycle for each stock item, And provides ranking information on the rate of increase of the score or ranking information to the user terminal by obtaining a rate of increase of the user terminal based on the trend of the ranking information and the trend of the ranking information.

According to the stock ticker ranking system of the present invention and the method of selecting stock tick ticks using the stock ticker analysis system, the stock ticker analysis uses complex and various types of advanced financial engineering techniques, but the display method is a score quantified by an intuitive screen design and a simplified expression method And ranking information so that individual investors can more easily understand the company and the market than any other method. At this time, the universe, the usage factor, and the specific gravity value of each factor for calculating the score and the ranking can be selected or combined through the user setting as well as the automatic score and rank calculation, so that the user can actively use the score. This enables investors to easily understand the financial position of the company and to find items that continuously create high shareholder value.

Therefore, it is possible to increase the profit rate by short selling long stocks with high scores and ranking, and short selling short stocks with low stocks.

In addition, we provide stocks that are improving their score and ranking in comparison with previous day, week, and 4 weeks ago, so that they can select the stocks by selecting the data by quantitative data on how the stocks are responding to short- It is possible to get a profit with probability.

Thus, even if the stock market moves in an unexpected direction in a non-linear stock market, if the financial value and the supply and demand status of the enterprise are better understood, it is possible to respond to the market situation. .

1 is a block diagram showing a general stock trading system,
FIGS. 2A and 2B are diagrams illustrating an internal function of a stock ticker ranking analysis system according to a preferred embodiment of the present invention;
3 is a diagram illustrating a universe management function of a stock event ranking analysis system according to a preferred embodiment of the present invention;
4 is a diagram illustrating a net profit analysis function of a stock ticker ranking analysis system according to a preferred embodiment of the present invention,
5A and 5B are diagrams illustrating a sentiment analysis function of a stock item ranking analysis system according to a preferred embodiment of the present invention;
FIG. 6 is a diagram illustrating a comprehensive score analysis function of a stock item ranking analyzing system according to a preferred embodiment of the present invention;
FIG. 7 is a flow chart illustrating a stock item selection process according to an exemplary stock item ranking analysis according to a preferred embodiment of the present invention;
FIGS. 8 to 20 are views showing a screen of a stock price ranking program according to a preferred embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

FIGS. 2A and 2B are diagrams illustrating an internal function of the stock inventory server 200 according to an exemplary embodiment of the present invention.

2A, a stock ticker rank analysis server 200 according to the present invention receives stock tick data from a ranking analysis database 250 that stores data for individual tick ticks, And provides ratings and rank evaluation information for individual stock items at the request of the individual customer terminal 104 and the customer terminal 108 of the institution such as a securities company.

In addition, the plurality of customer terminals 104 and 108 may connect with the HTS through a wired / wireless network and perform cyber securities transaction on the exchange server based on the provided score and ranking evaluation information.

The stock event ranking analysis server 200 according to the present invention includes a rank analysis unit 230, a data processing unit 245, and a universe management unit 240.

The ranking analysis database 250 stores personal information such as personal data for storing data such as stock price DB 251, financial DB 252, and IBES 253 of a stock exchange or user login, A sentiment score, a net profit score, an overall score, and the like, which are generated through the Internet 230. Here, IBES (Institutional Brokers Estimate System) is a subordinate to Thomson Reuters, a global information provider, which compiles performance estimates for companies around the world and provides data on analyst forecasts.

The data processing unit 245 generates the trailing data by processing the determined financial data provided by the domestic credit evaluation, in the case of the data having no IBES estimate. At this time, estimated financial data can be generated by assigning a predetermined weight to each quarter of the finalized financial data of the last four quarters. For example, in February 2012, the trailing data processing division will have a 40% portion of data for the third quarter of 2012, a 30% portion of data for the second quarter, a 20% The weighting ratio is set to 10% of the data in the fourth quarter, and the trailing data for the corresponding item is generated.

The universe management unit 240 classifies a total of seven universes for all the KOSPI and KOSDAQ candidates to be subjected to the rank analysis and calculates a sum of the sentiment factor score and the net profit factor Allows you to manage scores and aggregate scores. In this case, universe-exempt stocks can be set up. For example, in the case of excluding stocks, transaction suspension stocks (management stocks), companies without quarterly accounting data, ETFs (Equity Linked Warrant) A listed item or the like may be set.

The rank analysis unit 230 performs rank analysis on the stock items classified by the data processing unit 245 and the universe management unit 240. That is, the stocks of the stocks are ranked according to their scoring by ranking the stock value of the stocks through the net profit factor and the sentiment factor. To this end, the ranking analyzer 230 analyzes the sentiment factors of individual stocks and quantifies them and scales them based on the sentiment analysis unit 232, and analyzes the net profit factors of individual stocks and quantifies them, And a comprehensive analyzer 233 for calculating an overall score by combining the scores of the sentiment factor and the net profit factor. Here, the Balanced Score is a combination of the sentiment factor score and the net profit factor score. The highest score is 100, the lowest score is 0, and the ranking can be expressed as a standardized score. For example, it is recommended not to buy stocks with a final score of 90 or more and not buy stocks with a score below 60.

In addition, the point of time analysis and score is calculated from the beginning of the system configuration, the score and ranking are calculated, and the past history is constructed for a certain period at the time of composition. In addition to the abovementioned sentiment, net profit, , Value points, etc. can also be calculated.

The ranking process is described below using the stock ticker ranking analysis system 200 according to the preferred embodiment of the present invention shown in FIG. 2, followed by the universe management of FIG. 3, the net profit factor analysis of FIG. 4, The analysis of the factors, and the comprehensive score analysis process of FIG. 6 will be described.

<Universe management ( Universe Management )>

The stock ranking analysis system according to the present invention classifies the stock items (hereinafter, "universes"), which are candidates for rank analysis through the universe management section, into seven universes, which are all KOSPI and KOSDAQ. However, as the universe exclusion criterion, transaction suspension items (management items), companies without quarterly accounting data, exchange traded funds (ETF), equity linked warrants (ELW), and new listed items may be set.

Referring to FIG. 3, the classification criteria of the seven universes can be based on the use of the estimated value of the IBES (hereafter, estimated data) and the market capitalization.

First, using the estimated data for the seven universe classification criteria is intended to improve the accuracy of the analysis.

In the present invention, seven universes can be classified according to the items using the estimation data and the items using the estimation data and using the trailing data. Here, the estimated data includes data on the company's sales and net profit over the next two years. In Korea, estimated data of 700 companies are published annually. On the other hand, stocks for which no forecast data are published use fixed financial data provided by domestic credit rating agencies, which usually takes about three months. For example, since the data for the end-December 2011 quarter is announced in March 2012, the most recent quarterly data that investors can see at the end of February 2012 is the data for the end of September 2011 do. Thus, using the Earnig value of the past five months' time difference data can not reflect the highly volatile market environment and can not immediately reflect the volatility of the company's earnings. In the case of a company, it may be difficult to pay a lot of profits last year, and the performance of this year will be significantly improved compared to last year. Therefore, the price earning ratio (PER) There is a problem that it is not possible to accurately evaluate the appropriate value of.

Therefore, in the present invention, the estimation data is used for quick analysis, but in order to increase the accuracy of the items in which the estimation data is not provided, the data processing section generates the trailing data by processing the determined financial data provided in the domestic credit evaluation. In general, trailing data can be the final quarterly financial data of the last quarter. For example, in February 2012, the Data Processing Department estimates that 40% of the data for the third quarter of 2012, 30% for the second quarter, 20% for the first quarter, The weighting ratio is set to 10% of the data in the fourth quarter to generate trailing data for the corresponding item.

Second, dividing the universe into market capitalization sizes is intended to increase the efficiency of analysis.

In the case of the KOSPI market, KRX100 stocks including large stocks are classified into one universe, and the remaining stocks are classified into two universes, KF-ALL and KT-ALL, depending on whether the estimated data is provided.

In the case of KOSDAQ stocks, stocks using the estimated data are classified into large stocks (QFL) and middle stocks (QFM), and stocks using trailing data are classified into middle stocks (QTM) and small stock groups (QTS) .

First, KRX100 is a group of large-cap stocks based on KOSPI's market cap of 100th place, which is calculated using forward data.

KF-ALL is a group in which the score is calculated using the estimated data among the KOSPI stocks. The number of stocks can be changed according to the provision of the estimated data, and it can be composed of about 250 middle-sized stocks. Also, KT-ALL is a group in which there is no estimation data among the KOSPI items and scores are calculated using trailing data. The number of items can be adjusted according to the setting, and it can be composed of about 450 small to mid-size items .

In the KOSDAQ stock, it is classified into four universes QFL, QFM, QTM and QTS according to the use of the estimation data and the trailing data.

The QFL is a group of estimates using KOSDAQ stocks, and can be composed of the top 50 large-cap stocks with the highest market cap.

The QFM is a group in which the score is calculated using the estimation data among the KOSDAQ stocks. The QFM may be composed of about 150 medium-sized stocks excluding the 50 stocks of the QFL. The number of stocks is adjusted according to the provision of the estimated data .

The QTM is a group of KOSDC stocks that uses the trailing data because there is no such data and can be composed of about 300 medium-sized stocks. The number of stocks can be adjusted according to the provision of the firm's financial data. have.

The QTS is a group of scores calculated by using trailing data due to lack of the above-mentioned estimated data among KOSDAQ stocks, and is composed of about 450 small-sized stocks with remaining stocks not belonging to the 300-item group of the QTM, The number of items can be adjusted in accordance with the provision of firm financial data.

< Earning Analysis Analysis )>

Stock prices are determined by how much money the company is making now and how much money it can make in the future. There is a lot of noise in the flood of information, which makes it difficult for investors to make the right decision, but one of the most reliable sources of information is EPS. For example, the present invention can accurately calculate the net profit score using the company's financial data for the last four years and the financial data for the next two years.

4, net profit analysis is based on five determinants: 1) short-term earnings growth, 2) long-term earnings growth, 3) earnings consistency, and 4) ROE: Return On Equity, and 5) Reverse PEG (Reverse-PEG) values. At this time, the net profit score represents the relative score in the universe. For example, if the ROE score is 90 points, it means that the ROE value of the universe stocks is in the top 10% (it can be shown that the top 10% is in the range of 90-100).

Here, the weight of each factor can be readjusted using the difference of each quarterly return rate, and each factor will be described in detail below.

(1) The short-term profit growth rate is calculated using the estimated data and trailing data according to seven universes. The relative position of the growth rate of the net profit is expressed as a score. KF-ALL, QFL, and QFM indicate the relative position of the estimated for-profit growth rate.

(i) "Short-term profit growth rate (estimated data) = [(net profit _t0 - net profit _t-1 ) / net profit _t-1 ] × 100".

(ii) "Short-term profit growth rate (trailing data) = [(recent quarter trailing EPS / quarterly trailing EPS-1) × 100]" is calculated and calculated for each item, to be.

(2) Long-term profit growth rate refers to the company's long-term growth trend.

(i) "Growth trend line (Estimated data) = [Change in net profit for the past 4 years (%) + Rate change rate for the next two years]" and the calculation period is Weekly.

(ii) "Growth trend line (trailing data) = change rate of net profits in the past twelve months" (%).

For example, the relative rate of increase in net profit for the previous 12 quarters is shown as a score. KT-ALL, QFL, and QFM calculate the growth rate including the estimated values for the future two years.

(3) The rate of earnings retention is relatively high for firms with steady net profit growth, with less volatility in net earnings over the last three quarters. Here, the volatility of the 12th quarter net profit is calculated as the standard deviation, the lower the standard deviation, the more points are given, and the calculation period is the quarter unit.

(4) The return on equity (ROE) is calculated on a quarterly basis. The estimated ROE is used when there is estimated data, and the entire universe trailing ROE is used when trailing data is used. The relative score is given in descending order of ROE.

(5) The reverse PEG represents the value of the company with the growth rate to P / E ratio, which is the growth rate of the company. The reverse score (reverse PEG) of the PEG is given a higher score in the order of the higher relative. Here, PER represents stock price earnings ratio and EPS represents net profit per share.

Based on this, we can calculate the net profit growth rate, the expected P / E, and the growth rate as follows.

(i) Net profit growth rate = [(Estimated EPS - Previous EPS) / Previous EPS × 100]

(ii) Estimated PER = F1PER (using the estimated PER provided by the estimation data),

(iii) Growth rate of return (estimated data) = [((Estimated EPS - immediately preceding EPS) / immediately preceding EPS × 100] F1PER]

(iv) Growth rate (Trailing data) = [((Current quarter EPS - Previous quarter EPS / Previous quarter EPS × 100) / Trailing PER]

In this way, we can calculate five determinants for net profit analysis: 1) short-term profit increase, 2) long-term profit increase, 3) profit retention rate, 4) return on equity, and 5) growth rate.

In addition, companies can use the latest 4Q financial data and future financial data to calculate their net profit scores, and analyze their net profits to determine their fundamentals.

<Sentiment analysis (Sentiment Analysis )>

5A and 5B, the sentiment analysis is performed by analyzing stock price volatility, momentum, supply / demand status of foreign investors and institutional investors, noise analysis of stock price, and quantifying the movement of stocks in the market through eight determinants It is expressed as a score. 1) The relative strength, relative strength, 2) Retrun Reversal, 3) 120MA spacing (Price / 120MA), 4) 20MA spacing (8) Sector Combo Model (8) Factors Affecting Net Purchasing by Foreigners, 6) Net Purchasing by Institutional Investors, 7) Efficiency Ratio, &Lt; / RTI &gt;

In addition, as shown in FIG. 5B, the eight determinants shown in FIG. 5A can be further divided into a total of 10 factors by further adding determinants of the cumulative foreign net buying and the institutional cumulative net buying.

Further, it is possible to control the specific gravity of the factor by quarter. For example, using the weekly returns of the highest score group for each factor, adjust the weighted value through an optimizer that maximizes the expected return. In addition, we use 12 weekly returns of three months, divide groups by each factor, calculate returns, and calculate the weighted value in the optimizer.

Each component is described in detail below.

(1) If the price change rate in the fourth quarter is higher than the average of the universe, the price increases. The price change rate of the recent quarter is important as an index comparing the rate of price change between the 4th quarter of the individual company and the whole universe. For example, a relatively high score is given to an item that has a higher price change rate than the universe average value.

(2) The rising reversal gives a high score if the past price change rate divided by the recent price change rate is large. It is an indicator that reflects the tendency of over-bought and over-sold stocks. Comparing the recent price change rate with the past price change rate, we give relatively high score to high value stocks.

(3) The 120MA spacing is the price / 120 moving average, and the higher the closing price of the previous day than 120 moving average, the higher the score.

(4) 20MA spacing, price / 20-day moving average. The higher the closing price of the previous day than the 20th moving average, the higher the score.

 Positive indicators are more than 120%, and negative indicators are less than 90%.

(5) Foreign net buying is the ratio of foreign net buying on the same day to the average trading volume for two weeks, giving relatively high scores to companies with high ratios.

 This is due to the unique characteristics of the Korean stock market as a factor of the technology model. Since foreign investors 'transactions have been liberalized from the past to the present, the form of foreign investors' trading has shown significant influence on stock prices of individual companies as an important factor in judging sentiment ranking. For example, buying foreigners is a positive factor, and selling foreigners is a negative factor. The average trading volume of individual companies for two weeks is calculated, and then the foreign trading volume is divided by the average trading volume on that day. Rank from + to -. If there is no foreigner trading, ranking becomes 0, and it becomes a neutral factor.

(6) Institutional net buying is the ratio of institutional net buying to average trading volume over the two-week period, giving a relatively high score to firms with high ratios.

 This is because stocks that institutional investors net bought in the market act as positive factors, which is an important factor in supply and demand analysis. After calculating the average of two-week trading volume of each company, the trading volume of the same day is divided by the average trading volume. Rank from + to -. If there are no institutional trades, the ranking becomes zero, and it becomes a neutral factor.

(7) The efficiency ratio is an index that measures the momentum of a price. It is a numerical value indicating how long it took to change from A price to B price. If the efficiency ratio reaches 1, This implies that price momentum is added. For example, the lower the volatility, the higher the score.

(8) An industrial combo model is a model that assigns weights based on 24 industries as factors that measure the attractiveness of the industries in which each company belongs. Standard indicators include six sectors: net profit forecast change / surprise, price / relative strength, PBR, PCR, PSR, and dividend yield. Universe stocks are sorted by sector (if the IBES sector classification and aggregate data are not used, individual stocks are weighted and calculated by the bottom-up method). For example, we believe the top 25% of the industry is attractive and the bottom 25% is not attractive.

(9) Foreign cumulative net buying refers to the cumulative number of foreign net buying days divided by the 20-day trading volume average for three days.

(10) Institutional cumulative net buying refers to the number of institutional cumulative net buying days divided by the 20-day trading volume average for three days.

In this way, the sentiment determinants are rank information scored using factors related to the investor's attitude rather than the fundamentals of the company such as stock momentum, trading volume, supply and demand, and psychology. For example, you can see which sectors / sectors / industries are giving high scores to a foreigner / organization and whether they are getting better scores compared to the previous day, 1 week before, 4 weeks before, Knowing it can help you make investment decisions.

<Comprehensive Analysis (Balanced Analysis )>

Referring to FIG. 6, the Balanced Score is a combination of the five net profit factor scores (50 points) and the eight sentiment factor scores (50 points). The highest score is 100 points and the lowest score is 0 Points, and rankings can be displayed with standardized scores. Here, the ratio of the sentiment factor score and the net profit factor score is equally distributed, but the weight can be adjusted by adding a score to either factor. For example, if it is judged that supply and demand factors are important according to the market situation, 60 points can be given to the sentiment factor and 40 points can be given to the net profit factor score.

Once the overall score is calculated and the rankings are calculated, the investor can select stocks based on this. For example, it is advisable not to buy stocks with a final score of 90 or more and not buy stocks with a score below 60

FIG. 7 is a flowchart illustrating a stock price selection process using a stock price ranking analysis system according to a preferred embodiment of the present invention.

First, a plurality of user terminals of an individual investor and an institutional investor access a stock price analysis system (server) 200 through user login and download and install a stock stock ranking program (S110). At this time, the server may be a server of a securities company providing a stock asset management service.

Thereafter, if there is a stock price ranking analysis request from the user terminal, the server calculates the score, ranking (ranking) information calculated through net profit analysis, score and ranking information calculated through sentiment analysis, The integrated score and ranking information that combine the net profit score and the sentiment score are provided to the user terminal (S120). At this time, according to the user's choice, not only the comprehensive score but also the sentiment score or the net score can be selected, so that both the individual score and the ranking as well as the comprehensive score can be provided. Through this, the user can get a high score of over 90 points and a high ranked item through a web browser, and consider a short item with a low score of 60 points or less You can increase your profitability.

In addition, the user can set the factors for calculating the score of the universe, the sentiment, the net profit, and the comprehensive analysis, and the weighting factor of each factor, thereby automatically calculating by the ranking analysis system, Two options are available.

For example, the user can set the universe through the user interface unit of the terminal. In other words, according to the user's choice, you can select the number of universes in total 7 universes and the number of stocks in the corresponding universe. Depending on the system's monetization standard, one basic is set as default and up to 5 Can be set. At this time, the maximum number of constituent items of each universe can be set to a maximum of 200 or less.

In addition, the user can set a factor of use of a sentence factor, a net profit factor, and an overall score for calculating a score and a rank through the user interface unit of the terminal. For example, a user can set up a maximum of eight factors among a total of ten sentence factors. A large number of factor combinations can take a long time to compute and may reduce predictive power due to information crossing. At this time, based on the use of trailing data, the estimated net profit is composed only of the corresponding stock and can be set as an option when selecting the universe. This is because the net profit factor makes it difficult to use the estimated data, and when the universe selection item is mixed with the estimated data and the trailing data, only the trailing data makes it possible to use the many factors. Can be limited to a maximum of eight.

In addition, the user can set the specific gravity value of each factor for score and ranking. For example, the automatic analysis system according to the present invention can be defaulted to the same specific weight value for each factor in the sentiment analysis. At this time, the user directly inputs the specific weight value for each factor and performs a sentiment or net profit analysis according to the weight .

Then, the server updates the score and rank information according to the set period (time, day, and day), and provides the updated score and rank information to the user terminal in step S130.

In addition, a transition of individual scores is also provided according to the set period (time, day, and day) (S140). In this case, if the stocks that are improving the score and ranking of the previous day, 1 week, or 4 weeks ago are found and invested in these stocks, or if the stocks in which the score starts to soar in the low score range are bought below 70 points, . In addition, investors who want high-risk, high-yielding stocks can search for stocks that are highly fluctuating in their scores and rankings, and can improve returns by predicting the rate of increase or decrease of these stocks.

As such, the final score is more useful as a complementary indicator rather than a decisive factor in stock trading decisions. If the final score can be used to determine the relative position of individual stocks, It can be used to select candidates for the buy or sell candidates.

In addition, it is meaningful to invest in ranking items that are appropriate to the market situation with each factor ranking.

In addition, rather than acting as an absolute criterion, the score can be used to select a buy / sell item based on the weight or trend of the score, so that if the score is heavily fluctuating, it can be the most important factor in the trading decision

FIG. 8 to FIG. 20 show screens of the stock price ranking program according to the present invention.

FIG. 8 shows today's stock screen showing the highest total score for each of seven universes on the search reference date. In the KF-ALL, the item with the highest total score is JW Choongwae Pharmaceutical, and in the KT-ALL, It can be seen that the high stocks are the holding companies of Sung Chang.

Referring to FIG. 8, a score type can be set according to a user's selection, and one of an overall score, a sentiment score, and a net score can be selected. At this time, the score indicates the relative score in the universe, and if the overall score is 90, it means that it is in the top 10% of the universe stocks.

It is also possible to know the trend of individual scores. Four weeks ago, one week ago, yesterday, today's score, you can see at a glance how fast your score is getting better and faster, and you can make quick decisions. For example, it is statistically proven that individual investors are more likely to increase stock prices when buying stocks below 70 points in the low-scoring stocks.

In addition, the item information can be known. The current market trends of individual stocks, foreign / institutional net buying, 52-week high / low price, and listed stocks can be seen. In addition, the market value and net profit information can be used to judge the fair value of the stock price (based on the closing price on the previous day).

Also, if you click the "View Chart" button, you can see trends of the recent 30-day trading days.

Also, if you click the "Detailed Analysis" button, you can move to the "Item Basic Information" screen of the menu and check the detailed item score in detail.

Also, if you click the "Net Profit Analysis" button, you can go to the "Net Profit Analysis" screen of the menu and check the detail item score in detail.

Also, if you click the "Sentiment Analysis" button, you can go to the "Sentiment Analysis" screen of the menu, and you can check the status of supply and demand of the market such as foreign net buying, institutional net buying score, technical score such as rising strength and short and long term separation. You can also see a chart of changes in stock price and sentiment scores.

Fig. 9 shows the "item item basic information" screen, and it is possible to confirm the detailed item score of the search item (JW Choongwae Pharmaceutical).

Referring to FIG. 9, it can be seen that the equalization graph is shown to intuitively show the five net profit and the seven coefficient of the sentiment. The right end of the equalization bar is 100 points and the left end is 0 point , And the 8th factor industrial combo model value is not shown.

On the 30-day chart, the blue bar represents the trading volume of JW Choongwae Pharmaceutical, which is the search item, the red line represents the stock price, and the 30-day chart shows the 30-day trend of the score change.

 Fig. 10 shows the "net profit analysis" screen. As a result, the score and trend of "JW Choongwae Pharma" by the net profit factor can be confirmed. Here, you can see the graphs of the scores and trends of the short-term profit growth rate and the long-term profit growth rate.

 Fig. 11 shows the "Sentiment Analysis" screen. As a search item, the score and trend of "SEMICONDUCTOR" by the sentence factor can be confirmed. Here, the graphs of the rising strength and the rising and reversing points can be confirmed.

12 shows the " Item Comparison Analysis "screen, in which a plurality of (up to three) items can be compared with each other to find a relatively good item. Here, three kinds of items can be compared by inputting "Gavia" and "Balsson" as comparative items into "JW Choongwae Pharma", which is an item of today, as an item to be searched in the item entry window.

FIG. 13 shows a "snapshot" screen. It is possible to compare the estimated EPS and the recent EPS value of the search item in the last four quarters, and analyze changes in the sentiment and the net profit in depth.

FIG. 14 shows a screen of "Increase in net profit score 50", which is a screen for retrieving 50 items having the highest increase rate of net profit score compared to four weeks ago. In the "reference screen", it is possible to select the top stocks to rise compared to four weeks ago, as well as the top stocks to rise three months and six months before.

By selecting "Mosaic", you can graphically view the net profit analysis screen for individual stocks.

If you select "List", it is possible to display 20 items in a table for fast searching.

Fig. 15 shows a screen showing the "Sentiment score increase 50" screen. It is a screen for inquiring 50 items with the highest rate of increase in sentiment scores from the previous week.

Fig. 16 shows a screen of "TOP 20 OF COMPREHENSIVE SCORE ", which is a screen for searching 20 items with the highest total score on the search reference date. If you select a universe, it displays the top 20 items in each universe.

Fig. 17 shows a "universe analysis" screen, which shows the stock coverage of each sector for each of six universes excluding KRX100. By distinguishing the industry by sector, it facilitates selection of stocks.

FIG. 18 shows a "sector analysis" screen in the order of weekly or monthly (4 weeks) EPS growth rates. The MSCI sector uses 1) essential consumer goods, 2) industrial materials, 3) materials, 4) utilities, 5) energy, 6) finance, 7) telecommunications services, 8) As shown in Fig.

Fig. 19 shows the "Industry Analysis" screen, showing the order of industries with high EPS growth rates during the week or month (4 weeks). It is based on the MSCI industry classification, which includes 1) food and tobacco, 2) materials, 3) durable consumer goods and clothing, 4) diversified financing, 5) utilities, Management services and equipment 10) food and beverage retailing 11) real estate 12) hardware and IT equipment 13) banking 14) insurance 15) retailing 16) commercial professional services 17) 19) consumer services, 20) pharmaceutical and life sciences, 21) software and IT services, 22) semiconductor and semiconductor equipment, 23) media, and 24) transportation.

FIG. 20 shows a "performance evaluation" screen. The performance evaluation screen is a screen for showing to the member what profit rate is obtained in comparison with a benchmark when an investor purchases stocks having scores of 90 points or more every day. By showing the profit rate of TOP ranks according to the present invention transparently, it is possible to establish trust with users.

You can compare the returns of the top stocks (over 90 points) and the bottom stocks (below 10 points) with the benchmark index by universe / period, and the benchmark index can use the KOSPI and KOSDAQ Index. You can choose between the 'same weight' and 'market weight' as the weight of the calculation. The same weight is calculated by investing the same weight regardless of the price, and the market share is calculated by investing the amount according to the price ratio of the stock.

Here you can select one of the seven universes via the "Universe" button. You can select one of the KOSPI or KOSDAQ through the "Benchmark" button and use the "Calculate method" You can select a time period via the "Select a time period" button (available from January 2008). You can select one of the total score, net score, or sentiment score using the "Score type" You can calculate the return on the stocks of 90 or more and 10 or less in the sentiment value. Finally, when you have finished selecting the conditions, you can calculate the performance evaluation by clicking on the inquiry button.

It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims and their equivalents. Only. The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

The present invention can be applied to a stock trading system in which stocks or derivatives can be traded on-line. In addition, the present invention can be applied to a terminal that can perform a securities transaction while the user is portable.

200: Stock stock ranking system
230: Rank analysis section
240: Universe management section
245: Data processing unit
250: ranking analysis database

Claims (15)

A method of selecting a stock item to be listed on a KOSPI and a KOSDAQ in a stock asset management system in which one or more user terminals and a server are connected through a network,
Classifying the server into a plurality of universes based on the use of the estimated data and the total market capitalization for the stocks listed on the KOSPI and the KOSDAQ;
The server calculates the score and rank (rank) information of all the stocks in each universe through the net profit analysis based on the EPS of each stock,
Based on the stock momentum, volume, and supply and demand of foreign investors and institutional investors in each stock, we calculate the score and rank information through sentiment analysis,
Estimating a total score and rank information by combining the net profit score and the sentiment score;
When the stock price analysis request is received from a user terminal connected to the server through a network, the server provides the calculated score and ranking information to each user terminal for each stock item;
Updating the score and rank information according to a set cycle for each stock tick, and providing updated score and rank information to the user terminal in real time;
The server provides the user terminal with a transition of the net profit score, the sentiment score or the total score and the transition of ranking information corresponding to the score, according to the set cycle for each stock item, And providing ranking information on a rate of increase of the score or ranking information to the user terminal by obtaining a rate of increase according to a change in information. The method according to claim 1,
Wherein the score is calculated using five factors of short-term profit growth rate, long-term profit growth rate, profit retention rate, capital profit rate, and reverse PEG value. The method according to claim 1,
The sentiment analysis is based on the assumption that the score is calculated using the eight factors of the stock price rising strength, rising reversal, 120MA spacing, 20MA spacing, foreign net buying, institutional net buying, efficiency ratio, and industrial combo model Method of stock selection in the system. The method of claim 3,
Wherein the sentiment analysis includes calculating a score using a total of ten factors including two factors, cumulative foreign net buying and institutional cumulative net buying. The method according to claim 1,
The total score is a relative score obtained by combining the net profit score of 50 and the sentiment score of 50. The highest score is 100 and the lowest score is 0, How to Select Stock Items in the Asset Management System. The method according to claim 1,
Wherein the plurality of universe excludes trading suspension items, management items, quarterly accounting data non-listing companies, ETF stocks, ELW stocks, new listed and registered stocks. The method according to claim 1,
In the case of the item in which the estimation data is not provided, weighting is performed for each quarter in the immediately preceding four quarters of the finalized financial data to generate trailing data, and the ranking analysis is performed using the generated trailing data A method for selecting a stock item in a stock asset management system. The method according to claim 1,
When the universe, the usage factor, and the specific gravity value of the usage factor for calculating the score and rank information are set from the user terminal in the stock price rank analysis request step, the server calculates the universe, And providing the calculated score and ranking information to the user terminal. &Lt; Desc / Clms Page number 20 &gt; 1. A stock asset management system in which a database storing data such as a stock price database, a financial DB, an IBES, etc. of one or more user terminals and an exchange is connected to a server via a network,
The server includes a universe management unit that classifies the stocks listed on the KOSPI's KOSPI or registered on the KOSDAQ into a plurality of universes on the basis of the use of the estimated data and the market capitalization, The results of the analysis are summarized as follows: 1) The net profit analysis unit calculates and evaluates the net profit based on the EPS of stocks, and measures sentiment based on the stock momentum, volume of transactions, and the supply and demand status of foreign investors and institutional investors. And a comprehensive analyzer for calculating an overall score and a ranking by combining the sentiment score and the net profit score,
The server updates the score and rank information according to a set cycle for each stock item, and provides updated score and rank information to the user terminal in real time,
The server provides the user terminal with a transition of the net profit score, the sentiment score or the total score and the transition of ranking information corresponding to the score, according to the set cycle for each stock item, And provides ranking information on the rate of increase of the score or ranking information to the user terminal. 10. The method of claim 9,
Wherein the score is calculated using five factors of the short-term profit growth rate, the long-term profit growth rate, the profit retention rate, the return on equity, and the reverse PEG value. 10. The method of claim 9,
The sentiment analysis is based on the stock market ranking, which is calculated by using the eight factors of the stock price rising strength, rising reversal, 120 MA spacing, 20 MA spacing, foreign net buying, institutional net buying, efficiency ratio, Analysis system. 12. The method of claim 11,
Wherein the sentiment analysis includes calculating a score using a total of ten factors including two factors of cumulative foreign net buying and institutional cumulative net buying. 10. The method of claim 9,
The total score is a relative score obtained by combining the net profit score of 50 and the sentiment score of 50. The highest score is 100 and the lowest score is 0, Item ranking analysis system. 10. The method of claim 9,
Wherein the plurality of universe excludes trading suspension items, management items, quarterly accounting data non-listing companies, ETF stocks, ELW stocks, new listed and registered stocks. 10. The method of claim 9,
Further comprising a data processing unit for generating trailing data by assigning weights to the respective quarters of the immediately preceding four quarters of the finalized financial data in the case of the item in which the estimation data is not provided and processing the same, system.

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