KR20090102884A - Method and apparatus for selecting stock item which price is synchronized with the price index of stocks - Google Patents

Abstract

PURPOSE: A stock price and linked stock analysis service apparatus and a method thereof are provided to select stocks which will rise or fall, accurately, only if an investor determines that a stock price will rise or fall in the future. CONSTITUTION: A stock price and linked stock analysis service apparatus(100) comprises a main control unit and a correlation factor calculating unit. The main control unit controls so that the correlation factor between the price index of stocks and a stock price can be calculated by using price indexes and stock prices of each date, and outputs a correlation factor list when a inquiry request signal on the correlation factor list is received from a corresponding client PC(200(1-N)).

Description

Method and apparatus for selecting stock item which price is synchronized with the price index of stocks}

The present invention relates to a method and apparatus for selecting a stock item that moves in close proximity to a change in a stock price index. The objective is that, at the time the market forecasts that the stock index will rebound in the future, if the securities firm offers the investor a list of stocks that have moved most closely with the stock index during the past index rise and the investor buys highly linked stocks, In other words, if the index rises, the stock price will rise in conjunction with the stocks. The order of stock trading is to open stock accounts, deposit, select stocks (market and news analysis, search for stock information, asset value analysis, technical analysis, simulation, etc.), buy orders, buy orders, share price fluctuations, profit and loss, sell orders, The process proceeds with the conclusion of the sale, withdrawal or repurchase.

In the above process, the first step of stock investment is to select stocks to buy, and the most worrying thing for investors is the selection of stocks to buy and timing of buying. When deciding on a buy timing, the stock investor must judge the market situation, the most commonly referred to as the KOSPI.

Newspaper articles saying that stock selection is difficult are as follows.

Resource 1.

<Difficult to select stocks. Losses of stocks of 'ants' this year>

(Seoul = Yonhap News, 2006-09-16 Songgo) Reporter Yoon Sun-hee = The KOSPI index was adjusted only 2% in the stock market this year, but 7 out of 10 listed stocks fell and most stock investors including 'ant' Allegedly lost. In addition, as the differentiation of stocks continued in the earnings market, investors seem to have difficulty in selecting stocks.

◇ The index is down 7 out of 10 = According to Daishin Securities, the KOSPI index has fallen only 2.03% from 1,380 to 1,360 until the 15th of this year, but the stock price has been the same as last year. There were only 270 stocks in total and the remaining 571 stocks all fell.

As a result, during the last year's upswing, the 'half-and-a-half' market was formed, which made it possible for most investors to make a profit. However, this year's index fluctuations are small, but most investors point out that investment losses are inevitable. At the same time, equity managers at asset managers were able to manage yields using KOSPI's short-term trading in box stocks, experts said. End.

The implication of Data 1 above is that stocks tend to fluctuate, but stocks tend to fall. If investors choose the wrong stocks, they can also lose sideways.

Resource 2.

<The return on equity investment decreases with age>

(New York = Yonhap News, 2005-12-05) Correspondent Kim, Kye-hwan = The New York Times reported that private investors tend to show lower stock investment returns as they get older, revealing problems in judgment.

As stock investors grow older, their portfolio diversifies based on their extensive experience in market movements, reducing their losses, while lowering their ability to pick market-leading stocks.

These findings come from the analysis of stock trading by more than 7,000 private investors between 1991 and 1996 by economists George Corniotis and Alok Kumal at the University of Notre Dame.

As a result of comparing the group of individual investors in their 30s and those over 65 years of age, the difference in returns due to diversification of the portfolio was only 0.7%. 1.1% lower than the end.

The implication of Exhibit 2 is that the stock investment of the elderly population is expected to increase in an aging society, and a stock selection service for the elderly is needed.

Resource 3.

Also excellent for foreigners ..

1,000 return higher than the lead organization

(Seoul = Yonhap News 2005-06-18 14:36 Songgo) Shin, Kyung-kyung = Institutional investors led the recovery of the 1,000 stock price index in three months, but foreigners who took the lead in terms of profitability were ahead of the institutions. .

Institutions generally hired super-cap stocks in the top 20 market caps, while foreigners showed interest in the 'net' stocks in the top 50-100 market caps, including NCsoft, Woongjin Coway, and Orion.

Since April 29, when the index hit its previous low (911.30), the agency has raised the index by net buying a total of 1.9 trillion won ($ 1,003.14).

During this period, the average rate of increase in the top 20 stocks of foreigners reached 18.21%, compared to the market average of 14.99% in the top 20 stocks of institutional net buying, and 12.45% in the 20 stocks with the most pension funds. However, it did not meet foreigners' preference. On the other hand, during the rebound, 20 individual stocks rose by 2.6% on average, far below the market average.

The implications of Example 3 indicate that individuals only make a 2.6% return on their wrong choice of stock, while foreigners return 18.21%, which is determined by the selection of the stock and the gap is widened.

As seen in Cases 1, 2, and 3 above, what kind of stock you buy during the rebound will have a direct impact on yield. Foreign investors, institutions, etc., are good at picking stocks, and high returns, while individual investors made poor choices. The reason why an individual chooses an item incorrectly is that the ability to select an item decreases with age, and when the index rebounds, it is difficult to know what is the rising stock and the number of items is too large. It would be because of this lack.

Because of the large number of variables interacting with stock price decisions, it is difficult to predict the future, and as a result, the loss of most individuals is frequently published in newspapers. However, it is difficult to analyze the effects and effects of many variables, but the final conclusion is only one price.

As professional institutions predict that stock investment will become more important as a means of aging society and financial technology in the future, it is necessary to improve stock selection for individuals.

In the present invention, as a method of predicting which stock will rebound during the index rebound, the present invention intends to estimate using historical statistics. As stocks are not changeable but fluctuate depending on the company's performance and do not change much in the short term, they progress slowly.

To verify whether the above hypothesis is correct, the past statistical data is used to quantitatively identify the stocks that have risen closely linked to the index during the rebound period, and apply the results in the future to compare the results. The hypothesis will be adopted and the return on investment will be high.

The first step in investing in stocks is to choose which stocks to buy. At this point, stock investors are most concerned about which stocks to choose and when to buy. When deciding on a buy timing, stock investors need to know whether the market is in a good or bad situation. When the stock index falls, broadcasters and experts advise to refrain from buying as much as possible, since most stocks show a downtrend, and thus the risk of loss is high. When the stock index rises, it is highly likely that any stock will rise, so experts advise buying mixed stocks of industry representatives, securities stocks, and construction stocks.

However, despite the personal experience of stock selection, various know-how, corporate financial analysis, technical analysis of charts, advice, recommendation stocks, and stock academy, a lot of information comes from investors.

If you choose the wrong stock, the stock index and all stocks will rise, and only the one you bought will fall or go to the side. On the contrary, the stock index will fall and the stock price will rise. As mentioned above, numerous efforts have been made to select stocks, but investors have been troubled since the pointed method for selecting stocks has not been presented. The result is statistically represented by the low returns of individual investors. Picking stock is the one that determines yield.

Recently, investors have been flocking to stock index futures and options. The reason for this is that futures and options sell and buy stock quotes and move in the same way as stock quotes. In addition, even if the stock index falls, profits can be gained by hitting the index by hitting a new sell-off. The futures and options are simple because there is no need to worry about the choice of stocks because the number of stocks is within one to several.

Investors who used to do stocks in the past, when they make futures and options, are different. 1) You do not have to worry about what kind of item to buy. 2) Earnings can be made even if the stock index falls. 3) There is no need for efforts to analyze the company's financial structure, news and disclosures. 4) It's simple because you can predict whether the market is good or bad in the future. 5) High risk, high return.

If there are many stocks that fluctuate equally to stock indices, such as futures and options, the investor will continue to generate profits if the stock index rises or falls in the future. Investors will be able to predict the decline in the index, to some extent, through the news of world conditions, oil price fluctuations, won-dollar exchange rate, security, domestic demand, economic indicators and stock market outlook this week. However, it is difficult to select an event because no one knows which event will go up or down in the future. For investors, the direction of the stock index can be predicted to some extent, but it is difficult to predict the direction of individual stocks.

Therefore, if the stock index can be predicted to some extent and the stocks moving in the same way as the stock index (market situation) can be found, the return on stock investment will increase.

Therefore, the present invention intends to establish the following hypothesis. If you can scientifically find stocks that work in the same way as the stock index (market situation), your return on investment will be high.

If you know the stocks that move in the same way as the stock index, you can buy the stock that is most closely linked if the stock index rises or is expected to rise, and sell the stock if the stock index falls. In the present invention, by providing statistically calculated items in the order of movement (interoperability) closely related to the past stock index, the investor easily selects the stock, to provide a service that increases the return on investment.

Conventional applications and background related to the identification of factors influencing returns and stock prices based on stock selection or buy and sell simulation are as follows.

Korean Laid-Open Patent Publication No. 2000-0054632 shares the stock portfolio, yield, and opinions with others, learns from others, and maximizes return on investment.

Publication No. 2002-0034296 discloses that the average rate of change exceeds the normal rate of change set by the user based on the historical fluctuation of the item and the fluctuation of the day. The following shows how to select securities that can be traded daily by checking whether the normal rate of change is exceeded.

 In Korean Patent Publication No. 2002-0073864, an item matching the search conditions determined by the investor is provided from the company information and the corresponding item is provided.

According to Korean Patent Publication No. 10-2006-0099673, the above methods provide indicators for providing stock price charts for individual stocks through technical analysis, offline online, and know-how in selecting stocks for short-term trading. It claims to be unable.

In Korean Patent Application Publication No. 10-2006-0099673 (a securities investment stock selection system using the Internet and a stock selection method using the above system), the investor knows how to select investment stocks to save time and effort for investing stocks. I can do it. The input variables for the selection of stocks are disclosure information, investment indicators, and investors' own conditions (investment indicators, scope, mandatoryness and priority), and the result is the cumulative yield according to the simulation of trading.

Its composition consists of the company's disclosure information, the current stock price database, which stores investment indicators prepared on the basis of the current stock price, and the current investment index, which is converted into a circular form or converted from the information of the database,

Stocks that satisfy their investment indicators by setting their own investment indicators and conditions (① variable: management item status, PER, ROE, capital (billion) value, ② range of data value: upper-lower limit, ③ priority search item) Guided return database that selects list (10 ~ 20 stocks), calculates virtual cumulative return, calculates statistically correlation between cumulative return and investment indices in regulation group, and prioritizes and saves them in order of highest correlation. ,

When entering keywords (growing stocks, undervalued stocks), the guide yield database provides investment indicators, scopes, priorities and stock lists for the regulatory group.

For example, if you are looking for a company, you can search for companies that meet the conditions by setting the PER below 5, the non-managed item, the capital below 20 billion, the ESP above 6000, and the ROE in the range of 0-50%. will be.

Patent Publication No. 10-2006-0126236 (simulated securities information providing system and method)

Recommends stocks according to the investor's type (attack, stable, etc.) and recommends stocks that meet the search criteria (operating income growth rate, earnings per share, and earnings per share). Then, based on the data of the past five years of the stock, the variable value is adjusted to set the buying point at the market shortly after the upper limit price 2 times. We then repeat the buy and sell calculations to simulate a five-year return. Simulate other stocks and adjust the portfolio (the amount invested in stocks) based on the returns. In other words, it is a way to give more weight to stocks with higher yields by simulating according to the buying and selling conditions based on historical data.

In Korean Patent Publication No. 10-2007-0014616 (a method of selecting stock items by portfolio and quantitative method in a stock asset management system), a relationship between various variables and results related to profit is selected to select a variable having high relation. . The contents are displayed when the user selects the desired factors from the simulation conditions and the number of factors, and then executes the simulation, thereby displaying a simulation result view screen that enables filtering of a large number of factor combinations and their combinations. 20 steps;

Input Variable: A plurality of factors (25 items) displayed through the simulation main screen in the twentieth step,

1) Fundamental factors measuring the actual financial position of an entity

2) Estimates using future financial indicators

3) momentum factors

4) Evaluation factor.

1) Fundamental Factors (8): Changes in Return on Equity, Dividend Yield, Return on Profit, Cash Flow, Sales Growth, Sustainable ROE, Return on Equity, Share-to-Book Ratio

2) Estimated factors (9): FY1 estimated EPS change [3 months], FY1 estimated EPS change [6 months], 12-month forward EPS change [3 months], 12-month forward profit growth, 24-month forward profit growth, FY1 FY2 Estimated EPS Change vs. Estimated EPS, 12-Month Forward Profit Return, Profit Estimate Change Ratio, Estimated Return on Equity

3) Momentum factors (6): 1 month price change rate, 3 month price change rate, 6 month price change rate, 12 month price change rate, past 1 year profit growth rate and past 5 year profit growth rate

4) Valuation factors (2): Market capitalization, debt ratio

Output: A number of performance indicators, which measure a variety of simulation results, consist of 29 performance indicators.

These include annualized average return, cumulative return, standard deviation of portfolio returns (risk), average annual excess return (Rm), and risk-free return. Average annual excess return (Rf), risk of excess return relative to benchmark, risk of excess return relative to risk-free return, T-test (statistic that verifies whether average excess return is different from "0"), systematic risk (Beta), Alpha (alpha, the y-axis deviation of the regression equation), Coefficient of determination Average market cap, and percentage of portfolio returns above benchmark returns over the entire simulation period. Periods> Benchmark), the rate at which portfolio returns were higher than benchmark returns during the period that benchmark returns increased over the entire simulation period (% Periods> Benchup Mkt), The rate at which portfolio returns were higher than benchmark returns (% Periods> Bench Down Mkt) during the period of decline in benchmark returns over the entire simulation period, successive and maximum times over benchmarks, maximum and positive returns, and maximum and negative returns. , Negative rate of return to positive rate of return, negative rate of return period, maximum negative rate of return, maximum positive rate of return, yearly cumulative return, relative performance, cumulative for the last two and five years. Yield, factor mean, factor median, and factor standard deviation (risk).

In Korean Patent Application Publication No. 10-2005-0108543 (Method of Detection of Optimum Indicators by Event), the input variables are 25 technical indicators, and the results are the variables that most influence the results by the rate of return and the winning rate (the number of profitable transactions / total transactions). To select.

Input variables are simple moving average, exponential moving average, Bollinger Band, Envelope, Channel, Parabolic SAR, Price ROC (%), MAC, TRIX, CCI, SONAR, Stochastic Fast, Stochastic Slow, William's% R, RSI Exponential , DMI, ADXR, LRS, separation, investment psychology, A / D Line, CO, MFI, Volume Oscillator, Volume Ratio.

Buy Sell point is based on the buy or sell signal represented by the variable. The signal occurs when the stock price exceeds the moving average or the value of each variable exceeds the signal line. The simulation results are calculated by simulating a number of buys and sells based on the trade signal. These variables are well known and can be seen by adding indicators in the stock price graph on HTS.

The methods of the existing application provide conditions for selecting items with the corresponding data among many data, learning through online sharing of information, alarming when the variation is specified and exceeded, hypothetical yield and investment index conditions, It is to select stocks from the relationship.

Limitations and problems of existing applications and conventional techniques are as follows.

① Technical Chart Analysis

There are so many dozens of techniques used to analyze stock price charts and it is difficult to study the theoretical background. The easiest and best known moving average, MACE, Stocastic, etc. are not always correct. Moving average, MACD, etc. are always cumulative average values, so one beat is slow. Moving averages only represent the progress of the sport itself, and do not reflect the market situation (external environment). Even if a stock's moving average is rising, the stock will fall if the index falls, so the moving average will not match. It is difficult to know which method is correct because the variable values (moving average of 5, 13, 20, etc.) for each method for chart analysis are various and the analysis results for each method are different for the same event.

② Financial Analysis

Although PE and ROE are mainly used as financial analysis techniques, share prices cannot rise despite the low PER.

③ Buy / Sell Simulation

Existing applications simulate virtual purchases according to various conditions or signal generation and select input variables with good results. However, it is difficult to objectively verify the results because no specific actual data values are provided for when and how many times and how and under what conditions. Selling behavior varies according to individual investment tendencies, investment amount, and psychological situation, so the theoretical and actual values may be different.

④ Only internal factors are considered.

Share prices are determined by a number of internal and external variables. Most stocks fall when the index falls, so if the index falls, so do stocks. However, existing techniques are limited to their respective items and do not reflect external factors and impacts.

⑤ There are many missing variables.

Existing variables are mostly financial and technical analysis, but many other variables (psychological factors, forces, advertising effects, brand awareness, etc.) are not taken into consideration, and it is difficult to reflect the complex interactions among these variables. In addition, the existing variables are analyzed using only one variable, and multivariate statistical analysis (correlation analysis, regression analysis) for a plurality of variables is not performed.

⑥ Too professional.

Existing methods are too popular for beginners, senior citizens, the general public, and so on, and are not popular. Conventional methods require many types of variables, theoretical understanding of variables, variable variable settings, repeated buying and selling, and the use of simulation systems and the ability to accurately interpret the results obtained.

⑦ Variable that fuses everything

Existing methods focus on individual analysis of individually known variables and psychological factors, and fail to select representative variables in which they interact in combination.

⑧ The index is a comprehensive variable that fuses everything

An index is the sum total of stock prices, which is the overall and final result of a number of known and unknown variables. The statistical result that stock prices are difficult to predict and that most of the ant investor loses is proving that the variables that determine stock prices and their effects are very complex. However, the index is the most decisive factor in determining the stock price because it is a result of convergence that includes all variables such as economic, oil price, security, exchange rate, and psychological factors.

Other problems with stock options include:

end. Technical analysis method of graph:

1) Although the stock price graph seems to go up in the future, the stock price index rises and falls.

2) You have to look at the graphs of many events one by one.

3) The ability to analyze graphs varies greatly from person to person.

4) Differences in graphs between stocks cannot be compared as numerical values.

5) This is not a method that can be used by stock novices to select stocks easily.

6) Over 170 trend analysis, signal generation, and pattern analysis techniques such as moving average, MACD, and Stocastic are difficult to understand and verify whether they actually fit, and the general public uses only a few such as moving average, MACD, and Stocastic.

7) Technical analysis requires a lot of time and study, and is not an easy approach for anyone.

I. Estimation method of stock price rise and fall

1) The stock index is likely to rise, but there is no general data on which stocks to buy.

2) The stock index is likely to fall, but it is not clear which stocks fell even during the decline.

All. Expert Recommendation

1) There is no time to watch securities related broadcasts every day.

2) One or three recommended events.

3) The similarity between the composite index and the stock price is explained, but its closeness cannot be known numerically.

4) We do not suggest which stocks are the closest to the stock index for all stocks.

la. Own experience

1) I can't experience more than 1,500 events, so I can see only a few revisions.

2) If it is not continuously observed, recent trends and patterns cannot be known.

3) The result depends on your personal capacity. Especially for the elderly and beginners

4) You cannot compare a number of stocks relatively numerically.

Therefore, in the present invention, the index is selected as the most important and direct variable for determining the stock price. This is because the index is a combination of all the known and unknown variables described above, which may explain the parts that cannot be interpreted by current statistical methods. In other words, the stock price is the most influential variable as a result of statistically multi-correlated analysis of dozens of variables and even unknown variables.

The statistical technique for determining the correlation between the known variable and the stock price is limited and the present invention does not propose a new statistical technique, but uses the existing technique but selects the index as the most influential variable, Is the easiest and key variable.

The present invention provides a method of increasing the return on the stock investment, selects the stock index as the most influential variable among the many variables that determine the stock price, and statistically finds the stocks rising in close correlation with the stock index to investors to provide. When the stock price index is expected to increase in the future due to the improvement of market conditions, investors can grasp the rising stock group quantitatively (correlation coefficient) as the stock index suggested in the present invention, and buy the stock group and increase the stock index in the future. The share price of the stock will also rise in tandem, increasing the return on investment.

Calculate the correlation coefficient by using the stock index by date of a specific period as the independent variable, and the stock price by date of each item as a dependent variable, and then provide the investor with the sequence number and the correlation coefficient value in the form of a table in the form of the correlation coefficient value of each stock. do.

According to the present invention, the stocks that have risen most closely in the past when the stock index rebounds can be identified in a quantitative order, so that the stock investor can easily identify the stocks that had high interlocking performance in the next index rebound and buy the stocks. You can increase your return. In addition, stocks with a progressively lower linkage tendency can be minimized to minimize risk management and losses.

An apparatus and method for index-linked stock selection analysis service according to an embodiment of the present invention have the following specific effects.

1) Identify the stocks that most closely link with the stock index

Investors can identify the stocks that move closest to the stock index in order, making it easier to select stocks with a high probability of rising in the future.

2) Accuracy of item selection

As long as investors decide whether stocks will rise or fall in the future, they can choose exactly which stocks to go up or down.

3) Simplification of item selection

Investors can find stocks that move in the same way as stock indices, such as futures and options, simplifying stock selection.

4) Scientificization of item selection

Investors use accurate statistical data based on historical data, making them scientific choices.

5) Popular service of professional techniques

Even beginners and senior citizens who do not have specialized knowledge can easily receive professional analysis such as stock index, correlation coefficient, ranking analysis.

6) Guarantee of objective reliability of item selection

Compared to the existing chart selection method, expert advice, and securities company recommendation method, it provides more reliable analysis because it provides objective analysis data for all stocks.

7) Identifying stock index correlation coefficient

Investors can grasp stock index linked stocks in order, which is simple and accurate because they simply select stocks based on rankings.

8) Save effort and time for picking

 Investors need to invest a lot of time in a number of stocks, as the work of stock chart analysis, trend analysis, high / low analysis, news, disclosure, and expert opinion are almost unnecessary for stock selection. And time is saved.

9) Expansion of items under review

 Investors can receive analysis and selection services for stocks throughout the stock market. This is a broadening of the scope of the investor's analysis of only a few to 20 stocks.

11) No input from investors

Investors are provided with data analyzed by the system operator by simply pressing the inquiry button. As a result, investors can get the most benefit from the least amount of movement, time and effort. Existing stock selection methods have been difficult to use by the general public because they have to define a number of professional variables such as PER and ROE, but the method of targeting the index of this application is the most familiar as the most familiar one.

12) Minimize Loss

The daily trends in the coefficients of holding stocks that are negatively (negative and negative) opposite to the exponential period during the index rebound show that they are quantitatively indicating that the losses will continue to increase in the future as the coefficient moves quantitatively in the future. Help make decisions to minimize risk of loss through stop loss.

13) Among the many variables that determine stock prices, the index is the strongest and the result of the fusion of many variables. Investors can now easily and accurately understand the relationship between indices and stock prices, providing them with improved stock selection services for all stocks, either from personal experience or a limited number of stock selection options.

14) Futures and Options of Stock Investment

 Futures and options are simple in terms of stock selection and movement, depending on the number of stocks and moving almost identical to the stock index. The characteristics of the stock selection of the present invention can also identify stocks that move in the same way as stock indexes, such as futures and options, among many stocks, and investors can select stocks as futures and options. As a result, stock selection is easy and future movements can be predicted, enabling investors to make high profits without having to invest a lot and high risk like futures and options.

15) Gain of return

During the index rebound, you can select stocks that can at least guarantee a certain level of returns, so you can avoid the case that the index is rising but you choose the wrong stocks to lose or lose.

1 is a block diagram of an index indexed item analysis service apparatus according to an embodiment of the present invention.

2 illustrates a configuration of an index interlocked event analysis service apparatus 100 of a securities company according to an exemplary embodiment of the present invention.

Figure 3 shows the detailed configuration of the correlation coefficient calculation program DB 140 of the index linked item analysis service apparatus 100 of the embodiment of the present invention.

FIG. 4 shows the components related to the index-share index ranking and display of the cyber trading system 200 in the client PC.

5 illustrates the index-share price ranking screen 230 of the cyber trading system 200 in the client PC in the cyber trading service apparatus according to the embodiment of the present invention.

6 is a flowchart illustrating a process of analyzing index-linked items in a securities firm as an operation flowchart of an index-price correlation analysis service method.

7 is a flowchart illustrating an operation of an index-share correlation analysis service method, and a process of analyzing index-linked items at a securities company (continued).

FIG. 8 is a flowchart illustrating a process of analyzing index-linked items in a securities firm as an operation flowchart of an index-price correlation analysis service method (cont.).

9 illustrates a process of inquiring the correlation coefficient from the client PC 200 by the user of the correlation coefficient value and the rank information for each item calculated as described above.

10 is a chart of the KOSPI Daily Index.

11 illustrates a list of target stocks used for correlation coefficient analysis.

12 is a daily KOSPI index and stock price by day.

FIG. 13 illustrates the correlation coefficient between the daily stock price index and the daily stock price index of Samsung Electronics, which ranks first in the market cap from June 14, 2006 to December 22, 2006.

14 illustrates the correlation coefficient of POSCO (POSCO), which is ranked second in the market cap.

15 shows the correlation coefficient of Green Cross Holdings, which is ranked 200th in the market cap.

16 shows correlation coefficient values for each item calculated as described above.

17 is a reference for calculating the correlation coefficient and the yield thereafter.

18 shows the relationship between the correlation coefficient value and the stock price return.

19 shows a longer analysis of the yield calculation period.

20 shows the relationship between the correlation coefficient value for each item and the stock price return rate during the ② yield calculation period of FIG.

FIG. 21 is data of FIG. 20.

22 illustrates an example of the index-share price ranking screen 230.

FIG. 23 shows the ranking of the day after the day after FIG.

FIG. 24 illustrates the trend of correlation coefficient sequence rank and correlation coefficient value of Samsung Electronics, which is ranked first in market cap.

25 illustrates how the daily ranking results of Samsung Electronics shown in FIG. 24 influenced the stock price in the future.

Fig. 26 shows the relationship between the size of market cap and stock price return.

An index linked item analysis service apparatus according to an aspect of the present invention for achieving the technical problem is an item selection service apparatus that analyzes and provides a degree of linkage between a stock index and a stock price, and calculates an index-stock correlation coefficient. A main controller configured to control the controller to output and store the calculation result data after the storage;

A correlation coefficient calculation unit for calculating a correlation coefficient for each item from a daily stock index, a daily stock price of a stock, and a correlation coefficient calculation program through the main controller, and outputting the calculation result to the main controller;

A correlation coefficient calculation program DB for calculating the degree of linkage between the index and the stock price from the daily stock index and the stock price per stock;

Main memory for temporarily storing data;

Stock index for each day, the stock index / stock price DB for storing the stock price data of each day;

A correlation coefficient DB for storing correlation coefficient data for each item;

And a display unit for displaying the correlation coefficient calculation result outputted through the main controller on the client PC.

Index indexed service analysis method according to one feature of the present invention for achieving the technical problem,

An index-to-stock correlation analysis service method that calculates and provides a correlation coefficient between stock index and stock price.

If the system operator commands to perform a correlation coefficient calculation, calculating a correlation coefficient between the stock price index by day and the stock price by date per item, and storing the calculation result in the correlation coefficient DB;

Receiving a correlation coefficient inquiry signal of a user from a client PC, and outputting a correlation coefficient for each item of the correlation coefficient DB to the corresponding client PC.

Then, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that the present invention can be easily implemented by those skilled in the art.

1 is a block diagram of an index linked item analysis service apparatus according to an embodiment of the present invention. As shown in FIG. 1, the index linked item analysis service apparatus according to an exemplary embodiment of the present invention includes a plurality of client PCs 200 (1, 200, N), a network communication network 300, and an index linked. The item analysis service device 100 is included.

The plurality of client PCs 200 (1), ..., 200 (N) are provided with a dedicated emulator or a web browser capable of cyber trading through a network communication network 300 from a securities company or the like. When a dedicated emulator or a web browser is executed, the plurality of client PCs 200 (1), ..., 200 (N) are connected to the index linked item analysis service device 100, and each user has an index-share price ranking screen. When the inquiry is selected at 230, the correlation inquiry key signal is output to the index interworking event analysis service device 100 through the network communication network 300.

The plurality of client PCs 200 (1),... 200 (N) receive a correlation coefficient list and an index-share list from the index-linked stock analysis service device 100, and index-share prices on the client PC 200 are linked. The ranking screen 230 is displayed.

The network communication network 300 connects the communication lines between the plurality of client PCs 200 (1), ..., 200 (N) described above and the index-linked item analysis service apparatus 100 of each securities company to be described later. The data communication related to the correlation coefficient list is made.

When the system operator performs a correlation coefficient calculation command, the index interlocked item analysis service device 100 inputs a stock price index by date and a stock price by date of a number of items in a preset correlation coefficient calculation program to calculate a stock index and a correlation coefficient between each item. And store the result of the calculation, and request a correlation coefficient inquiry from the plurality of client PCs 200 (1) and 200 (N). Output to 200 (N)).

2 illustrates a configuration of an index interlocked event analysis service apparatus 100 of a securities company according to an exemplary embodiment of the present invention.

Referring to FIG. 2, the index interlocking event analysis service device 100 includes a main controller 110, a communication controller 120, an operation program input unit 130, a correlation coefficient calculation program DB 140, and a correlation coefficient calculation unit 150. , Correlation coefficient DB 160, and stock index / stock price DB 170.

The communication control unit 120 is a device that executes wired or wireless communication related to the correlation coefficient list between the plurality of client PCs 200 (1),..., 200 (N) and the index linked item analysis service device 100. When the inquiry key signal according to the correlation coefficient inquiry button selection of each user is input, the communication controller 120 receives data and sends the data to the main controller 110, and transmits a correlation coefficient list according to the control of the main controller 110. Outputs to the client PC 200 (1,... 200 (N)) through 300.

The main controller 110 uses a correlation coefficient calculation program 140a of the correlation coefficient calculation program DB 140 inputted through the operating program input unit 130, and a correlation coefficient calculation unit using corresponding data on the stock index / stock price DB 170. After the correlation coefficient is calculated on 150, the control result is stored in the correlation coefficient DB 160. In addition, the main controller 110 determines whether an inquiry key signal is selected according to the selection of the correlation coefficient inquiry button of the users of the client PCs 200 (1) and 200 (N), and is determined from the correlation coefficient DB 160. Control of outputting the correlation coefficient data to the client PCs 200 (1), ..., 200 (N).

The operating program input unit 130 is a correlation coefficient analysis program 140a for calculating a correlation coefficient together with various operation programs related to cyber securities transactions used by the main controller 110 by the manager of the index linked item analysis service device 100. It is a device for inputting.

The correlation coefficient calculation program DB 140 is a device that stores the correlation coefficient calculation program 140a input from the operating program input unit 130. The correlation coefficient calculation program 140a on the correlation coefficient calculation program DB 140 is sent to the correlation coefficient calculation unit 150 according to the instructions of the main controller 110. The correlation coefficient calculation program 140a calculates a correlation coefficient between the daily stock index and the daily stock price, and a detailed description of the process of calculating the correlation coefficient is described below.

The correlation coefficient calculating unit 150 inputs two input variables, namely a daily stock index and a stock price, to the correlation coefficient calculating program 140a under the control of the main controller 110 to perform an operation. The calculation is performed using the correlation coefficient calculation program 140a on the correlation coefficient calculation program DB 140 inputted from the operating program input unit 130, the daily stock index inputted from the stock index / share price DB 170, and the daily stock price of a number of items. It is responsible for the function of calculating the correlation coefficient and sending the calculation result to the main control unit (110).

The correlation coefficient DB 160 is a device that stores correlation coefficients and calculation conditions (target period, target item) for each item. The correlation coefficient DB 160 stores correlation coefficients for each item coming from the correlation coefficient calculation unit 150 according to the instructions of the main controller 110, and stores the correlation coefficient list in the client PC 200 (1), ..., 200 (N). )

The stock index / stock price DB 170 is a device for storing stock price information and stock price information for each item input by the Korea Securities Computer Network 400 by date and time. The stock index / stock DB 170 sends the stock index and stock price information to the correlation coefficient calculation unit 150 according to the instructions of the main controller 110, and sends the stock index and stock price information to the client PC 200 (1), It is in charge of the function of outputting to 200 (N)).

Figure 3 shows the detailed configuration of the correlation coefficient calculation program DB 140 of the index linked item analysis service apparatus 100 of the embodiment of the present invention. Correlation coefficient calculation program DB 140 is a correlation coefficient calculation program 140a that calculates the correlation coefficient between the stock price index by day and the stock price by date of a number of items, the rank number which ranks and ranks the items in the order of the correlation coefficient for each item The grant program 140b is included.

The operation of each calculation program on the correlation coefficient calculation program DB 140 is as follows.

The correlation coefficient calculation program 140a of the correlation coefficient calculation program DB 140 converts the stock index by date of a specific period (for example, from the previous day to the past six months) as an independent variable (x) and the stock price by date of the item as a dependent variable (y). Correlation analysis is performed and the correlation coefficient (r; correlation coeffecient) of the sample is calculated.

The item to be calculated is set by the system operator and generally covers the entire stock market.

The rank number assigning program 140b sorts the items in descending order based on the correlation coefficient for each item calculated by the correlation calculation program 140a, and assigns the rank numbers 1 to n.

FIG. 4 shows the components related to the index-share index ranking and display of the cyber trading system 200 in the client PC.

The central processing unit 210 controls the display of the index-share price ranking index 230 after sending the index-share price ranking signal to the index-share price correlation analysis service device 100 and receiving a correlation coefficient list. .

The communication unit 220 is responsible for receiving an index-stock interworking ranking inquiry signal to the index-stock correlation analysis service apparatus 100 and then receiving a correlation coefficient list.

Index-share price ranking screen 230 is composed of an inquiry button, a condition display window, a rank number column, a column name column, a correlation coefficient column for a moving item linked to the index.

5 illustrates the index-share price ranking screen 230 of the cyber trading system 200 in the client PC in the cyber trading service apparatus according to the embodiment of the present invention. Index-share price ranking screen 230 is composed of index-share correlation coefficient inquiry button 230b, condition display window (230a), ranking number column (230c), item name column (230d), correlation coefficient value column (230e) do.

Here, the index-quote correlation coefficient inquiry button 230b is a button for inquiring which item moves most closely in conjunction with the stock index.

The condition display window 230a shows the period of sample data (eg, June 14, 2006 to December 22, 2006) and the number of target items (eg 200 KOSPI 200 items) used to analyze the correlation coefficient.

The rank number column 230c is a column showing rank numbers (1st to nth) in order of magnitude of the correlation coefficient.

The item name column 230d is a column displaying the item name.

The correlation coefficient column 230e is a column displaying correlation coefficient values of the corresponding item.

Index-share price ranking screen 230 is responsible only for the display unit function to query only the correlation coefficient data already prepared by the securities company from the securities company server, there is no function for the user to enter or set conditions.

Next, an operation of an index-price correlation service apparatus and method according to an embodiment of the present invention having such a configuration will be described in detail with reference to the accompanying drawings.

6 through 9 are flowcharts illustrating operations of an index-price correlation analysis service method according to an exemplary embodiment of the present invention.

As shown in Figs. 6 to 9, the system operator first creates two correlation coefficient calculation programs 140a and two rank number assignment programs 140b (S1).

A group of securities firms' market analysis experts determine past periods to analyze the correlation between indices to be analyzed and stock prices. For example, the period during which the stock index was on the rise (June 14 ~ 2006. 12.2006) was a rise that can be easily understood by experts. 12. Set 22 as the correlation analysis period (S2).

The system operator stores the created correlation coefficient calculation program 140a, the rank number assigning program 140b, and the period to be analyzed in the correlation coefficient calculation program DB 140 through the operation program input unit 130 (S3).

Thereafter, when the operator issues a correlation coefficient calculation command (S4), the main controller 110 sends (1) the correlation coefficient calculation program 140a on the correlation coefficient calculation program DB 140 to the correlation coefficient calculation unit 150. , (2) June 14 ~ 2006. The stock price data of each day up to 12. 22 and the stock price data of each item (eg, KOSPI 200 stocks) are sent to the correlation coefficient calculating unit 150, and commands to perform the calculation (S5).

Then, the correlation coefficient calculating unit 150 inputs a stock price index (KOSPI index) list by day and a stock price list of the first item (eg, the top market cap: Samsung Electronics) by date in the correlation coefficient calculation program 140a, and by date. A correlation coefficient (r; correlation coeffecient) is calculated using the stock index as the independent variable (x) and the daily stock price of the first item as the dependent variable (y) (S6).

In addition, the correlation coefficient calculating unit 150 inputs a stock price by date and a stock price of a second item (e.g., top 2 market cap: KEPCO) into the correlation coefficient calculation program 140a, and converts the stock price index by an independent variable ( x), a correlation coefficient is calculated using the daily stock price of the second item as the dependent variable y (S7).

In this way, the correlation coefficient calculating unit 150 repeats the process of calculating the correlation coefficient for each item from the third item (eg POSCO 3rd) to the nth item (eg KOSPI 200th Unification Heavy Industries). Calculate the correlation coefficient of (S8).

When the correlation coefficient calculation to the nth item is completed (S9), the main controller 110 inputs each item name and the corresponding correlation coefficient value into the rank number assigning program 140b and executes the rank number assigning command (S10). The ranking numbering program 140b sorts the items in descending order based on the correlation coefficient value (the item having the largest correlation value is moved upward; the item having the highest index-share price is moved up) and 1 to n from the top. Give the rank number up to (S11).

When the correlation coefficient calculation unit 150 transmits a correlation coefficient and a ranking number calculation completion signal (S12), the main controller 110 transmits the calculated ranking number and correlation coefficient for each item to the correlation coefficient DB 160 for an analysis period. , Analysis target, rank number, item name, correlation coefficient is stored in the correlation coefficient DB (160) (S13).

Through the above process, the calculation of the correlation coefficient value and rank by stock index and stock was completed.

9 illustrates a process of inquiring the correlation coefficient from the client PC 200 by the user of the correlation coefficient value and the rank information for each item calculated as described above.

The characteristics of the inquiry are simply the inquiry of the data analyzed by the operator. Therefore, the investor can select the stock in a new way quickly and simply because the investor needs to view and select visually without any input or condition setting.

When each user executes a cyber trading emulator or a web browser installed in the client PC 200, the client PC 200 connects to the index linked item analysis service device 100 of each securities company through the network communication network 300. Proceed.

Thereafter, when the user selects the index-share price ranking screen 230, the central processing unit 210 calls up the index-share price ranking screen 230 and displays it on the screen.

When the user selects the index-share price ranking button 230b of the index-share price ranking screen 230 to find out the items moving in closest association with the stock price index to select the item to buy (T1), the central processing unit 210 transmits the correlation coefficient inquiry key signal to the index linked item analysis service apparatus 100 through the communication unit 220 (T2).

The main controller 110 of the index linked item analysis service apparatus 100 determines whether a correlation coefficient inquiry key signal is input from the client PC 200 through the communication controller 120 (T3).

As a result of determination, when the correlation coefficient inquiry key signal is input, the main controller 110 reads the analysis period, the target item, the rank number, the item name, the correlation coefficient list from the correlation coefficient DB 160 (T4), and the communication control unit 120. And outputs to the client PC (200) (T5).

Thereafter, the process of displaying the correlation coefficient list data input to the client PC 200 is as follows.

When the correlation coefficient list data is input from the exponential indexed item analysis service device 100 to the client PC 200 through the communication unit 220, the central processing unit 210 of the client PC indexes the input correlation coefficient list data. The condition display window 230a, the rank number column 230c, the item name column 230d, and the correlation coefficient column 230e of the rank screen 230 are respectively displayed.

The user can then identify the stocks that have moved most closely to the stock indices in the past. After that, the user can use the right mouse to search the daily stock graph for a specific item or buy an order with a relatively high correlation coefficient, or the correlation coefficient value of his own item is low or negative (negative, negative). If the price falls, you can place a stop selling order. This order linkage process is well known in the art and thus is omitted in the present application.

The following is an application example according to an embodiment of the present invention.

10 is a chart of the KOSPI Daily Index. A securities firm's market analysis expert (system administrator) selects the rebound by looking at the chart of the daily KOSPI index. Or use data from the past six months from the current date. As shown in FIG. 8, the daily low of the KOSPI index is June 14, 2006 and the KOSPI index is 1,192 points.

The range of samples for calculating the correlation coefficient is about 7 months from ① June 14, 2006 (index 1,192 points) to December 22, 2006 (index 1,438 points, 246 points) as shown in the index chart. The number of is 133 days except public holidays.

11 shows a list of target stocks used for correlation coefficient analysis, and the number of stocks was based on 400 stocks with the highest market cap. Samsung Electronics ranked first in market cap and Shinyoung Wacoal in 400th.

12 is a daily KOSPI index and stock price by day. 12 is basic data to be used to calculate the daily KOSPI index and stock correlation coefficient. The number of data is the product of the number of days on the vertical axis (220 days) and the number of events on the horizontal axis (400 events), with 200 x 400 = 88,000.

 FIG. 13 illustrates the correlation coefficient between the daily stock price index and the daily stock price index of Samsung Electronics, which ranks first in the market cap from June 14, 2006 to December 22, 2006. The correlation coefficient was a correlation coefficient function (correl) of Microsoft Excel. The formula is ‘= correl (data1, data2), where data1 = date index and data2 = share price by date. The correlation coefficient of Samsung Electronics was 0.644.

14 illustrates the correlation coefficient of POSCO (POSCO), which is ranked second in the market cap. The correlation coefficient is 0.81, higher than that of Samsung's 0.644. This means that POSCO's daily stock price moved more closely with the stock price index than Samsung Electronics.

15 shows the correlation coefficient of Green Cross Holdings, which is ranked 200th in the market cap. The correlation coefficient of Green Cross Holdings is 0.55, indicating that the correlation coefficients are different for each stock.

16 shows correlation coefficient values for each item calculated as described above. It is also sorted in descending order of highest correlation coefficient. As a result, the KODEX200 stocks that traded most closely with the stock price index from June 14, 2006 to December 22, 2006 for the 400 stocks with market cap are the KODEX200 stocks. The correlation coefficient is 0.99. The item with the lowest correlation coefficient is Cognitive Controls and the correlation coefficient is -0.82. The correlation coefficient ranged from 1.0 to -1.0 and the cognitive control item was -0.82, which indicates that the stock price moved freely regardless of the stock index. On the other hand, if you look at the market cap and the correlation coefficient, you can see that a large market cap does not mean that the correlation coefficient is high because there are a lot of stocks that rank below the top 100 in the market cap.

17 is a reference for calculating the correlation coefficient and the yield thereafter. In the graph, the correlation coefficient values for each item were calculated based on the stock price index by date and stock price per item within the calculation period. The share price return is the share price return within the yield calculation period. Therefore, if a stock with a high correlation coefficient within the calculation period of ① shows a high return within the calculation period, it can be said that the correlation coefficient and the return are related. The stock index rose by about 200 points (1,360 → 1,560) during the yield calculation period, with the index rising about 14%.

18 shows the relationship between the correlation coefficient value and the stock price return. As shown in FIG. 17, the correlation coefficient value of the X-axis is a correlation coefficient value between the stock price index and the stock price of the top 200 stocks from June 14, 2006 to December 22, 2006. Y-axis is the stock price return from January 23, 2007 to April 26, 2007. The stock price return is derived by dividing the stock price on April 26, 2007 by the stock price on January 23, 2007.

For example, stocks with a correlation coefficient of 0.8 have an average return of about 25%. The solid line in the graph represents the regression line. As a result, stocks with high correlation coefficients show higher returns than stocks with low correlation coefficients. Therefore, if you buy a stock with a high correlation coefficient on January 23, 2007, it will have a high yield on April 26. Conversely, if you buy stocks with low correlation coefficients on January 23, you are likely to have low returns on April 26. In the graph, the stocks with a correlation coefficient of 0.9 show more than 30% return, which is higher than the stock price index's 14% increase (from 1,360 to 1,560) over the same period.

19 shows a longer analysis of the yield calculation period. In the graph, the correlation coefficient values for each item were calculated based on the stock price index by date and stock price per item within the calculation period. The share price yield is calculated from January 23, 2007 to July 25, 2007, the historical high. The stock index rose by approximately 650 points (1,360 → 2,011) during the yield calculation period, with the index rising by 48%.

20 shows the relationship between the correlation coefficient value for each item and the stock price return rate during the ② yield calculation period of FIG. As shown in FIG. 17, the correlation coefficient value of the X-axis is a correlation coefficient value between the stock price index and the stock price of the top 200 stocks from June 14, 2006 to December 22, 2006. The Y-axis is the stock price return from January 23, 2007 to July 25, 2007. The stock price return is calculated by dividing the stock price on July 25, 2007 by the stock price on January 23, 2007. For example, an average return of 0.6 for an item with a correlation coefficient of 0.6 is about 100% and an average return of about 100% for an item with a correlation coefficient of 0.9. The solid line in the graph represents the regression line.

As a result, stocks with high correlation coefficients show higher returns than stocks with low correlation coefficients. Thus, if a stock with a correlation coefficient of 0.9 was purchased on January 23, 2007, the return would be twice as high as with a stock with a correlation coefficient of 0.6. In the graph, the stocks with a correlation coefficient of 0.9 show more than 100% return, which is on average twice as high as the 48% increase in the same period. Thus, if the index rebounds, investments in stocks with high correlation coefficients (highly linked stocks) will yield high returns.

That is, FIG. 20 demonstrates that the stocks having high correlation coefficients in the past index rise showed high returns in the future index rise as a core result of the present invention. Therefore, it can be said that the index interoperability as a stock decision variable selected in the present invention is very effective.

FIG. 21 is data of FIG. 20. For example, Daewoo Securities, which ranks third in the correlation coefficient ranking, has a correlation coefficient of 0.95 and its share price rose from KRW15,600 on January 23, 2007 to KRW36,150 on July 25. During this period, the stock price rose 131.7% (W36,150 ÷ W15,600).

22 illustrates an example of the index-share price ranking screen 230. When the correlation coefficient list data is input from the exponential indexed item analysis service device 100 to the client PC 200 through the communication unit 220, the central processing unit 210 of the client PC indexes the input correlation coefficient list data. The condition display window 230a, the rank number 230c, the item name 230d, and the correlation coefficient 230e of the rank screen 230 are respectively displayed. In FIG. 20, when the user selects the inquiry button 230b, the user receives data from the index linked item analysis service device 100 and displays the data. The analysis period of correlation coefficient was from June 14, 2006 to December 22, 2006. The third largest correlation coefficient was Daewoo Securities and the correlation coefficient value was 0.953. Investors can select stocks with high correlation coefficients and buy them by referring to the stock price graph.

FIG. 23 shows the ranking of the day after the day after FIG. The analysis period increased by one day until December 23, 2006. Daewoo Securities rose from 3rd place to 2nd place and the correlation coefficient rose from 0.953 to 0.962. The second place, Samsung Electro-Mechanics, fell to third place. As described above, securities firms re-analyze the correlation coefficients after the intraday or after the end of the day, and provide them to investors. Investors continue to take stocks that report the ranking or correlation coefficient of Daewoo Securities. If the index rises and Daewoo Securities's ranking falls significantly, it can be seen as a signal to clear some volumes. On the other hand, when the index rises and the rank of a specific subordinate stock rises, it is identified as a buy signal and buys.

FIG. 24 illustrates the trend of correlation coefficient sequence rank and correlation coefficient value of Samsung Electronics, which is ranked first in market cap. As shown in Fig. 22, Samsung recorded the highest rank of the correlation coefficient value of 0.9 or more since July 4, 2006, but since October, the sequence rank and the correlation coefficient value have greatly decreased. On the other hand, the stock index rose at this point. Therefore, if the index rises but the correlation coefficient rank or correlation coefficient decreases, it means Samsung Electronics' share price does not correlate with the index. In other words, since October 2006, Samsung Electronics has been eliminated from the top ranking group and has gone on its own regardless of the index. As a result, as of December 22, 2006, Samsung Electronics ranked 0.644 and ranked 250th in the correlation coefficient ranking.

25 illustrates how the daily ranking results of Samsung Electronics shown in FIG. 24 influenced the stock price in the future. In Figure 24, we have already seen that the correlation coefficient and ranking of Samsung Electronics have deteriorated until December 22, 2006. What happened after December 22, 2006? After December 22, have you been alone or downside, regardless of the index? Let's check. The result is that the fall in the correlation coefficient makes sense when the stock price remains weak regardless of the index's rise. In other words, the stock price should proceed in tandem with the index. Samsung Electronics' share price since Dec. 22 was only 8.7% up from W596,000 on Jan. 23, 2007 to W648,000 on Jul. 25, 2007. On the other hand, during this period, the index increased by about 650 points by 48%, as shown in Fig. 17, and the items with high correlation coefficient recorded more than 100% during that period. Why did Samsung's stock market most believe that it will be linked to the index as the number one market cap in the market? This is because, as shown in Fig. 24, the decrease in the sequence rank and the correlation coefficient value indicates its precursor. In other words, in the period shown in FIG. 24, the market environment of Samsung Electronics is deteriorated due to the drop in semiconductor prices.

24 and 25 suggest that FIG. 24 shows that the sequence rank and the correlation coefficient have a function of predicting the stock price of Samsung Electronics in FIG. 25 in advance. As shown in FIG. 22, investors holding Samsung Electronics should understand that if the index goes down while the index goes up, it should be identified as a bad signal, and some of the stocks should be changed or stocks changed. In addition, through FIG. 24, an investor who buys stocks can see that it is profitable to select other stocks with higher rankings instead of selecting Samsung stocks whose correlation coefficients fall when the index rises.

Fig. 26 shows the relationship between the size of market cap and stock price return. As shown in FIG. 26, the size and the profit of the market cap have little relationship. During the index's upswing, general investors may select large caps (Samsung Electronics), mid caps, and small caps as their options. However, as shown in FIG. 26, the yields are spread evenly over the market cap ranking. Therefore, it is profitable to pick up stocks based on individual characteristics rather than large stocks.

The opinion of the present application regarding the difference between the correlation coefficient and the regression analysis is as follows. The correlation coefficient is a measure of the linearity of two continuous variables (index, stock price). If one variable (X, exponential rise) has a linear relationship between exactly another variable (Y, share price rise), the correlation coefficient is 1 (share price rise) or -1 (share price decline). The coefficient of determination in the regression analysis is the square of the correlation coefficient. The correlation coefficient and the coefficient of determination are the same. Regression analysis is suitable for estimating future stock price trends. In the present invention, only the correlation coefficient is used and the determination coefficient value of the regression analysis is not used, but it is pointed out that even if the regression analysis method is used, it belongs to the concept of the present application.

Claims (9)

A cyber trading service apparatus that provides an index linked item analysis service according to a request of a plurality of client PCs, Control the correlation coefficient between the index and the stock price using the daily stock price index and the stock price of the stock as the input variable, and output the correlation coefficient list when an inquiry request signal for the correlation coefficient list is received from the client PC. A main controller for controlling; When the stock price index by day and the stock price of each item of a specific period are input through the main control unit, an index linked item analysis including a correlation coefficient calculation unit for calculating a correlation coefficient between the stock index and the stock price and outputting the calculation result to the corresponding client PC Service device. The method of claim 1, When the inquiry key signal is input from the client PC according to the selection of the correlation coefficient inquiry button of each user, the data is sent to the main controller, and the correlation coefficient list is output to the client PC through the network communication network under the control of the main controller. Index interworking event analysis service device further comprising a communication control unit. The method according to claim 1 or 2, A stock index / stock database for storing stock price information by date and stock price by date; Index indexed item analysis service device further comprising a correlation coefficient DB that stores analysis conditions, correlation coefficient, ranking number data for each item. The method of claim 2, An operating program input unit for receiving a correlation coefficient calculation program for calculating a correlation coefficient by various indexes related to cyber securities transactions used in the main controller by the index linked item analysis service system administrator; A correlation coefficient calculation program DB that stores a correlation coefficient calculation program inputted from the operating program input unit; And a correlation coefficient calculation unit configured to calculate a correlation coefficient using a calculation program, a stock price index per day of a specific period, and a stock price by date per item under a control of the main controller. The method of claim 4, wherein The correlation coefficient calculation program DB, A correlation coefficient calculation program for calculating a correlation coefficient by using the stock index for each day of a specific period as an independent variable and the daily stock price of the stock as a dependent variable; Index indexed item analysis service device further comprising a ranking number assigning program for assigning a ranking number in descending order based on the correlation coefficient value for each item. The method of claim 1, The client PC outputs an inquiry key signal to the outside when the user selects an inquiry button on the correlation coefficient inquiry screen of the cyber trading system, and receives a correlation coefficient calculation result from the outside and displays the index-share price on the index ranking screen. Index linked item analysis service device. The method of claim 6, The index-share price link ranking screen of the client PC comprises a condition display window, correlation coefficient rank number column, item name column, correlation coefficient column characterized in that the index linked item analysis service device. A method of providing an index-share correlation coefficient analysis service according to a request of a plurality of client PCs, A first step of calculating and storing a correlation coefficient between the stock index and the stock price by inputting the stock price by date and the stock price by date of a specific period in a preset correlation coefficient calculation program; A second step of receiving a correlation coefficient inquiry key signal from the client PC and outputting a correlation coefficient list to the client PC; And a third step of displaying the input data of the client PC in the condition display window, the rank number, the item name, and the correlation coefficient column of the index-share price ranking screen. The method of claim 9, In the first step, the correlation coefficient calculation is an index linked item analysis service method characterized in that to perform the correlation coefficient analysis and calculate the correlation coefficient by using the daily index as the independent variable, the stock price by day as the dependent variable.