JPH0736976A - Dealing discrimination processing system - Google Patents

Abstract

PURPOSE:To reduce the burden to the dealing discrimination processing of the front system by simulating external information and the condition of possessed assets in the back system to output middle-term and long-term apportionment results of dealing data to the front system. CONSTITUTION:A front system 302 transmits the file, to which data of bonds, stocks, etc., capable of dealing is inputted, to a back system to write it in a file for sale. The back system 301 writes data of the rate of interest, stock prices, etc., from external information venders in an actual result value file by a communication server work station 303 and writes forecasted data of the rate of interest, stock prices, etc., in a forecasted value file. Possessed assets are simulated for each circumstance scenario by work stations 304 to 306, and the amout of money to be apportioned is determined by an optimizing program, and the result is written in an apportionment result file. Work stations 307 to 309 in the front system 302 read in this file to display data to be the dealing object on screens.

Description

Detailed Description of the Invention

[0001]

TECHNICAL FIELD The present invention relates to a trading decision processing system including a communication server workstation for reading market environment data, a back-end workstation having a database of owned assets, and a front system for executing trading decision processing.

[0002]

2. Description of the Related Art Conventionally, there is a digital feed system as a system for fetching external information such as stock price information and analyzing the analysis of trading. In the digital feed system, an IFP (InterFaceProcessor), which is a communication server workstation that captures the status of external information, and an MMS (Man Ma), which is a workstation that analyzes external information.
chine Server). In IFP, from information sources such as TSE, BB, Reuters, and Telerate,
Price information data such as bonds and stocks are captured in real time. The MMS takes in digital data in the IFP and executes various analysis programs.

The analysis program includes a P & F (point & figure) method as a chart analysis method. These analysis methods are methods of analyzing external information such as stock prices, predicting future price changes, and making trading decisions.
As described above, in financial institutions such as banks, currently, a method is adopted in which external information is taken in by a front system, the situation of the external information is analyzed, and trading is judged.

However, at present, institutional investors such as insurance companies are not limited to the situation of changes in external information due to the sophistication of asset management, but also the overall position (owned assets).
It is necessary to grasp the situation of changes in the above, and then execute trading. For this reason, it is necessary not only to analyze in the front system, but also to comprehensively judge the situation of external information and the situation of the overall position based on the data of the database server that has the overall position in the back system. ing.

[0005]

In the conventional digital feed system, an IFP (InterFace Processor) which is a communication server workstation for fetching external information.
And an MMS (Man Machine Server) that is a back-end workstation that analyzes external information. In the conventional system, the external information is analyzed according to the situation of the external information captured by the IFP, and whether the external information such as the stock price rises or falls in the future, and the stock is traded.

[0006] On the other hand, institutional investors such as insurance companies change the market environment and the total position (owned assets).
Understand the changes in the market, and execute trading based on medium- to long-term decisions, not short-term decisions. In addition, institutional investors such as insurance companies have restrictions on overall positions, and it is necessary to make trading decisions while clearing these restrictions. In the conventional system, the front system is a system for analyzing the situation of external information,
It was not a system for grasping the entire position and making trading decisions.

According to the present invention, in the system configuration including the front system and the back system having the entire position, the back system executes a simulation process for grasping the state of external information and the state of the owned assets.
Provides the front system with medium- to long-term allocation results for trading data. As a result, not only the analysis of the front system but also the processing result of the back system,
The purpose is to support the trading decision and reduce the load of the trading decision process on the front system.

[0008]

The above object of the present invention is to provide a back-end system comprising a communication server workstation for acquiring external information data such as interest rates and exchange rates, and a database server having overall position data, and a trading process. In a computer system that consists of a front system consisting of workstations that perform the above, first, in the workstation of the back system, an external information acquisition means that acquires data such as interest rates and exchange rates from an external information vendor and stores it in the actual value file. , Read the actual value file that stores the actual data of the external information based on the external data of the external information, and predict the future data by the prediction program part that predicts the future data, and the predicted data is the predicted file. External information prediction means and external information prediction data to be stored in Predicted value file to be stored and holding asset database that stores parameters such as redemption period and interest payment interval of holding asset, executes a simulation of the held asset, and holds the result of simulation in the result database Optimal allocation processing is performed based on the predicted value file that stores the data, the result database that stores the simulation results, and the data that stores the parameters for the trading data that is the target of the allocation, and the distributed results are the distribution results. An optimal asset allocation means for storing in a file is provided.The workstation of the front system registers an input file for trading data, sends it to the back system, and takes in the distribution result file of the trading data to determine the user's trading. To establish a transaction processing means to support , To achieve.

[0009]

According to the present invention, a back-end system including a communication server workstation for acquiring external information data such as interest rates and foreign exchange rates, a database server for storing all position data, and a front system including a workstation for trading transactions. In the computer system that consists of, first, in the front system, the user inputs data that can buy and sell bonds and stocks,
Write to the input file. The input file is sent to the back system and written in the trading file.

In the back system, first, at the communication server workstation, TSE, BB (Broker's Broker)
Data such as interest rates, bond interest rates, stock prices, and exchange rates from external information vendors, etc. are written and written in the actual value file.

In the workstation of the back system, the actual value file of the external information data is read, the interest rate, the bond interest rate, the stock price, and the exchange rate are estimated by the estimation program unit, and the estimated data is written in the estimated value file.

At the workstation of the back system, the created forecast value file of external information is loaded, and based on the data of the owned assets (owned asset database), the simulation program section holds the Perform an asset simulation. The simulation result is written in the simulation result database.

In the workstation of the back system, the created forecast value file of external information and the created database of simulation results of owned assets are loaded, and these objective functions and coefficient of constraint conditions for allocating trading data are stored. To generate. Based on these coefficients, the optimization program determines the amount to be allocated for each asset and each period. The result of this allocation is written in the allocation result file.

In the workstation of the front system, the trading result processing section takes in the distribution result file created in the back system. The workstation of the front system reads the distribution result file and displays the data to be traded on the screen. The user executes buying and selling while referring to this screen.

With respect to trade data obtained by executing trades at the workstation of the front system, parameters are input to the system. The input file is sent to the back system and written in the owned asset database.

As a result, in the back system having the owned asset database, based on the situation of the external information,
By running a simulation of the assets held,
It is possible to output the distribution result of the trading data, and according to this result, the front system can make a trading decision based on the status of the owned assets, and it is possible to reduce the load of the trading decision processing of the front system. Become.

[0017]

Embodiments of the present invention will now be described in detail with reference to the drawings.

FIG. 1 is a flowchart showing the overall processing procedure of this embodiment.

Step 101: In the front system,
The user inputs data such as bonds and stocks that can be bought and sold, and writes it in the input file. The input file is sent to the back system and written in the trading file. After that, the back system executes the processes of the following steps 102 to 105 to determine the data to be traded from the trade file. The front system fetches the data, and steps 106 to 108 are carried out.
Buy and sell according to the processing of.

Step 102: In the back system, first, at the communication server workstation, TSE, BB (Bro
Data such as interest rates, bond interest rates, stock prices, and exchange rates are imported from external information vendors such as ker's broker) and written in the actual value file.

Step 103: In the workstation of the back system, the actual value file of the external information data created in Step 102 is read, and the forecasting program section predicts interest rates, bond interest rates, stock prices, and exchange rates, and makes a forecast. Write the data to the predictor file.

Step 104: In the workstation of the back system, the predicted value file of the external information created in step 103 is fetched, and based on the data of the owned assets (owned assets database), the simulation program section Simulation of owned assets is executed for each environmental scenario. The simulation result is written in the simulation result database.

Step 105: In the workstation of the back system, the predicted value file of the external information created in step 103 and the simulation result database of the owned assets created in step 104 are loaded,
These objective functions for allocating trading data,
Generate constraints coefficients. Based on these coefficients, the optimization program determines the amount to be allocated for each asset and each period. The result of this allocation is written in the allocation result file.

Step 106: In the workstation of the front system, the trading result processing unit loads the distribution result file created by the back system.

Step 107: The workstation of the front system reads the distribution result file and displays the data to be traded on the screen. While referring to the screen, the user executes buying and selling.

Step 108: In the workstation of the front system, for the trading data executed by the trading, the parameters are input to the system. The input file is sent to the back system and written in the owned asset database.

FIG. 2 is a block diagram of a workstation on which each program operates. In this configuration diagram, an example of the present invention including two workstations is shown.

In the figure, 201 and 208 are system units, 202 and 209 are central processing units for executing programs, 203 and 210 are storage devices for storing programs and data, and 204 and 211 are data for other workstations. Input / output unit 205, 21 for controlling input / output of
2 is a display device for displaying the processing result, 206, 21
3 is an input device for inputting data, 207, 214
Is a pointing device that indicates a position on the display screen. The two workstations are connected by a local area network (LAN) 215.

FIG. 3 is a block diagram of a system for realizing an embodiment of the present invention.

In the figure, 301 is a front system,
302 is a back system. The front system and the back system are composed of a plurality of workstations. In the backend system, 303 is
Communication server workstations for fetching information from external information vendors 304, 305, and 306 are workstations on which a prediction program, a simulation program, an optimization program, and the like operate. In the front system, reference numerals 307, 308 and 309 are workstations on which a trading processing program operates.

FIG. 4 is a functional block diagram showing functions in one embodiment of the present invention.

In one embodiment of the present invention, 401 is a back system and 402 is a front system. In the back system, an external information acquisition unit 403 for acquiring external information, an external information prediction unit 404 for predicting external information, an owned asset simulation unit 405 for executing an owned asset simulation, and an optimal asset allocation unit 4
It is composed of 06. The front system includes a trading processing unit 407 for executing trading processing.

In the external information acquisition unit 403, 40
Reference numeral 8 is a data acquisition unit that acquires data such as interest rates and exchange rates from the outside, and 409 is a performance value file that stores performance data.

In the external information prediction unit 404, 41
Reference numeral 0 denotes a prediction program unit that predicts future data based on the actual data of the external information, reference numeral 411 denotes a performance value file that stores the performance data of the external information, and reference numeral 412 denotes a prediction value file that stores the prediction data.

In the possessed asset simulation unit 405, 413 is a simulation program unit for predicting future profits, 414 is a forecast value file for storing forecast data, and 415 is a parameter such as a redemption period or interest payment interval of the possessed assets. The database 416 is a result database that stores the results of the simulation.

In the asset optimal allocation unit 406, 41
7 is an optimization program unit for allocating new assets, 418
Is a result database that stores simulation results, 419 is a prediction value file that stores prediction data, 4
Reference numeral 20 is a trading file that stores the parameters of assets to be distributed, and 421 is a distribution result file that stores the distribution result.

In the trading unit 407, 422 is a trading unit for controlling the trading process, 423 is an input file of trading data, 424 is a distribution result file of trading data, 425 is a trading result of the trading process. This is the result file.

FIG. 5 is a flowchart showing a procedure for acquiring external information at the communication server station.

Step 501: TSE and BB (Broker's B
Import external information from external information vendors such as roker).

Step 502: The types of interest rate, bond interest rate, stock price, exchange rate, etc. of the external information taken in are determined.

Step 503: The interest rate ID and the bond interest rate ID are stored in the actual value file in time series. For example, for interest rate data, the interest rate data is stored for each interest rate ID. Bond interest rate data, bond interest rate ID
And bond interest rate data is stored for each remaining period.

6 to 9, interest rates, bond interest rates, exchange rates,
The data structure of the actual value file of the stock price external information is shown.
The predicted value file is one in which predicted data is stored in the data structure of the actual value file.

FIG. 6 shows the data structure of the actual value file of the interest rate data of the external information. In the actual value file, the interest rate data taken in by the external information acquisition unit is accumulated in time series. Reference numeral 601 is a file that stores actual values relating to interest rate data, 602 is data that indicates time (monthly, yearly, etc.), 603 is an interest rate ID that indicates the type of interest rate, and 604 is interest rate data for interest rates.

FIG. 7 shows the data structure of the actual value file of bond interest rate data of external information. In the actual value file, the bond interest rate data taken in by the external information acquisition unit is accumulated in time series. 701 is a file that stores actual values relating to bond interest rate data, 702 is data that indicates time (monthly, yearly, etc.), 703 is a bond interest rate ID that indicates the type of bond interest rate, 704 is the remaining period of the bond, and 705 is a bond. This is interest rate data of interest rates.

FIG. 8 shows the data structure of the actual value file of the exchange rate of the external information.

In the figure, 801 is a file storing values relating to exchange rates, 802 is data indicating time (monthly, yearly, etc.), 803 is a currency code indicating the type of currency, and 804 is exchange rate data. .

FIG. 9 shows the data structure of the actual value file of the stock price index of the external information.

Reference numeral 901 is a file storing values relating to the stock price index, 902 is data indicating time (monthly, yearly, etc.), 903 is a stock price index ID indicating the type of stock price index, and 904 is a stock price index value.

FIG. 10 is a flow chart showing a procedure for predicting future external information.

Step 1001: The user selects data to be predicted from interest rates, bond interest rates, exchange rates, and stock prices, and selects a forecast period and unit (monthly, yearly, etc.).

Step 1002: According to the period to be predicted, the data of the required past actual value period is read from the actual value file.

Step 1003: From the autoregressive model, moving average model, autoregressive moving average model, etc., determine a prediction model that fits the data.

Step 1004: Using the prediction model determined in step 1003, generate prediction data for the period and unit to be predicted. The predicted result is stored in the predicted value file.

FIG. 11 is a prediction condition setting screen for generating prediction data.

In the figure, 1101 is a forecast condition setting screen, 1102 is a forecast target interest rate, bond interest rate, exchange rate,
A menu for selecting the type of stock price 1103 is a menu for selecting the type of ID of the scenario selected in 1102, 1
Reference numeral 103 is a period type of the prediction data, and 1104 is a menu for selecting a period for prediction. With this menu, the prediction data is generated by the prediction program for the set data. FIG. 12 is a flowchart showing a procedure for simulating the owned assets based on the market environment prediction data.

Step 1201: Parameters such as a redemption loan period, a deferral period, and an interest payment period are read from the possessed asset database storing the attributes of the possessed assets for each operation item.

Step 1202: Read the predicted data required for the simulation from the predicted value file (environment scenario).

Step 1203: Using the parameters of the owned assets, determine the calculation pattern defined for each operation item based on the future prediction data, and calculate the face value, book value, market value, valuation loss, Simulation is performed for each item such as income gain and capital gain.

Step 1204: As a simulation result, the face value, book value, market value, valuation loss, income gain,
The data of each item of capital gain is stored in the simulation result database.

Step 1205: When the operation item to be simulated is completed, the processing is completed. If not completed, steps 1201 to 1204 are repeated.

FIG. 13 shows the data structure of the database of owned assets. This database stores data indicating the attributes of the owned assets required for the simulation.

In the figure, 1301 is a database of owned assets, 1302 is an operation item code, 1303 is a listing category, 1304 is a currency category, 1305 is an execution date, 1306.
Is a face value balance, and 1307 is a purchase date.

FIG. 14 shows the data structure of the result database of the simulation of owned assets. This database stores simulation results regarding future conditions of owned assets. In the figure, 1401 is a simulation result database of owned assets, 1402 is an operation item code, 1403 is an application date, 1404 is a face value balance,
1405 is a market price, 1406 is an income gain, 1407.
Is the par value and repayment amount.

FIG. 15 is a flow chart showing the procedure for executing the allocation processing of new assets. With this flow, how to allocate to the new asset set by the user is determined based on the forecast data and the future situation of the owned asset.

Step 1501: The simulation section reads data from a database of results of simulation of owned assets.

Step 1502: The environment predicting unit reads the predicted value files of the predicted interest rate, bond interest rate, exchange rate and stock price.

Step 1503: The tradable trading file transmitted from the front system is read.

Step 1504: Based on the setting screen of FIG. 16, the user sets necessary constraint conditions when allocating the funds.

Step 1505: Based on the above-mentioned read data, a coefficient for delivering to the optimization processing is created in accordance with the set constraint condition.

Here, in this embodiment, the objective function is to maximize the yield generated from assets, and the constraint conditions are conditions relating to operational regulations, operational policies, and liquidity. The coefficient for each condition is generated. The yield coefficient is generated from the parameters of the trading file and the forecast value file. The coefficient of operation regulation is obtained from the simulation result of the owned assets, the parameters of the trading file, and the constraint conditions. The coefficient of the operation policy is obtained from the simulation result of the owned assets, the parameters of the trading file, and the constraint conditions. The liquidity coefficient is obtained from the simulation result of the owned assets and the parameters of the trading file.

Step 1506: The optimization program is executed based on the delivered coefficient. By doing this, we ask which asset, in what period and how much to invest.

Step 1507: The result of the optimization process is
Store in the distribution result file.

FIG. 16 is a screen for setting constraint conditions for distribution processing. For example, there are restrictions on insurance companies such that shares are 30% or less of total assets and loans are 55% or less of total assets. In the figure, 1601 is a type of constraint condition, 1602 is a lower limit value, and 1603 is an upper limit value.

FIG. 17 shows the data structure of a trading file. The trading file is a file that stores parameters relating to new assets to be traded.

In the figure, 1701 is a trading file,
1702 is an operation item code, 1703 is a listing category, 17
04 is the currency classification, 1705 is the execution date, 1706 is the current investment amount, 1707 is the investment amount after one month, 1708.
Is the investment amount after 35 months. In this file, first, the parameters of the investment target asset set by the user are stored as the above parameters 1702-1705. 1706 to 1 for the processing result
708.

FIG. 18 is a flow chart showing the procedure for executing the trading process in the workstation of the front system. This flow shows the execution procedure of the trading process based on the result determined from the situation of the entire position in the back system.

Step 1801: In the front system,
The user inputs the tradable data into the system and stores it in a file. This input file is sent to the trading file of the back system.

Step 1802: In the back system, the simulator of Step 102 to Step 105 of FIG. 1,
The distribution process is executed and the distribution result file is output.

Step 1803: The front system fetches the distribution result data created by the back system.

Step 1804: Based on the distribution result data, the distribution result data screen shown in FIG. 19 and the parameter data screen shown in FIG. 20 are displayed. The user refers to the screen to see which asset, when, and
Determine how much assets to buy and sell.

Step 1805: After executing the sale and purchase, the user uses the registration screen of FIG. 21 to register the actual values of the parameters of the bought and sold assets.

Step 1806: The actual trading value is transmitted to the back system and stored in the owned asset database.

FIG. 19 is a screen displaying the distribution result for the trading data.

In the figure, 1901 is a distribution result screen, 1
Reference numeral 902 is an operation item code, 1903 is a column showing the investment amount at the present time of the first year, 1904 is a column showing the investment amount after January of the first year, and 1905 is a column showing May after the first year. The current column changes color to allow you to check the data to trade. Further, 1906 is an area for switching data contents. By picking the area 1906 with the mouse, the display screen shown in FIG. 20 is displayed.

FIG. 20 shows a screen for displaying parameters of trading data.

In the figure, reference numeral 2001 denotes a parameter screen,
2002 is an operation item code, 2003 is a listing category, 20
Reference numeral 04 indicates a currency classification, and reference numeral 2005 indicates a stationary period.

Reference numeral 2006 is an area for switching data contents. By picking the area 2006 with the mouse, the display screen shown in FIG. 19 is displayed.

FIG. 21 is a screen for registering trading results.
On this screen, you can set the parameters of the bought and sold assets. In the figure, 2101 is a trade registration screen, 2102 is an operation item code, 2103 is a listing category, 2104 is a currency category, 2105 is a deferral period, 2106 is an execution date, and 210.
Reference numeral 7 indicates a purchase month, and 2108 indicates an investment amount.

[0089]

According to the present invention, a communication server workstation that reads market environment data such as interest rates and exchange rates, a back-end workstation that has a database of owned assets, and a front system work that executes a trading decision process. In a trading decision processing system composed of stations, first, in a back system having a holding asset database, simulation of holding assets based on external information captured by a communication server workstation is performed to output trading data distribution results. According to this result, the front system can make a trading decision based on the status of the owned assets, and the load of the trading decision process of the front system can be reduced.

[Brief description of drawings]

FIG. 1 is a flowchart showing the overall processing procedure of this embodiment.

FIG. 2 is a block diagram of a workstation on which each program operates.

FIG. 3 is a configuration diagram of a system that realizes an embodiment of the present invention.

FIG. 4 is a functional configuration diagram showing functions in one embodiment of the present invention.

FIG. 5 is a flowchart showing a procedure for acquiring external information.

FIG. 6 is a data structure of an actual value file of interest rate data.

[FIG. 7] Data structure of actual value file of bond interest rate data.

FIG. 8 is a data structure of an actual value file of exchange rates.

FIG. 9 is a data structure of a stock price index performance value file.

FIG. 10 is a flowchart showing a procedure for predicting external information.

FIG. 11 is a prediction condition setting screen for generating prediction data.

FIG. 12 is a flowchart showing a procedure for simulating owned assets.

FIG. 13 is a data structure of a database of owned assets.

FIG. 14 is a data structure of a result database of a simulation of owned assets.

FIG. 15 is a flowchart showing a procedure for executing a new asset allocation process.

FIG. 16 is a screen for setting constraint conditions for distribution processing.

FIG. 17 is a data structure of a trading file.

FIG. 18 is a flowchart showing a procedure for executing a buying and selling process.

FIG. 19 is a screen displaying distribution results for trading data.

FIG. 20 is a screen displaying parameters of trading data.

FIG. 21 is a screen for registering trading results.

[Explanation of symbols]

201, 208 ... System unit, 202, 209 ... Central processing unit for executing program, 203, 210
... Storage device for storing programs and data, 204, 2
11 ... Input / output unit for controlling input / output of data with other workstations, 205, 212 ... Display device for displaying processing result, 206, 213 ... Input device for inputting data, 207, 214 ... Display Pointing device for indicating the position on the screen, 215 ... Local area network (LAN), 301 ... Front system,
302 ... Back system, 303 ... Communication server workstation for fetching information from external information vendor, 30
4, 305, 306 ... Workstations in which prediction programs, simulation programs, optimization programs, etc. run, 307, 308, 309 ... Workstations in which trading processing programs run, 401 ... Back system, 402 ... Front system, 403 ... External Information acquisition unit, 404 ... External information prediction unit, 405 ... Owned asset simulation unit, 406 ... Optimal asset allocation unit, 407
... trading processing unit, 408 ... data acquisition unit, 409 ... actual value file, 410 ... prediction program unit, 411 ... actual value file, 412 ... predicted value file, 413 ... simulation program unit, 414 ... predicted value file, 41
5 ... possessed asset database, 416 ... simulation result database, 417 ... optimization program section, 41
8 ... Simulation result database, 419 ... Prediction value file, 420 ... Trading file, 421 ... Allocation result file, 422 ... Trading processor, 423 ... Trading file, 424 ... Allocation result file, 425 ... Trading result file.

Claims (1)

[Claims] 1. A trading decision made by a communication server workstation that reads market environment data such as interest rates and exchange rates, a back-end workstation that has a database of owned assets, and a front system workstation that executes trading decision processing. In the processing system, the workstation of the front system registers the tradable product data, imports the distribution result file from the backend system, and establishes the trading processing means for making a trading decision. A communication server workstation that captures external information such as, external information prediction means that predicts future predicted values based on the actual value of this external information, and a database of external information predicted values and owned assets A stain that simulates an asset A back system that has a database of owned assets by means of optimization means, optimization processing of output of distribution results by performing optimization processing of distribution to tradable data based on predicted value of external information and simulation result data. At this point, it becomes possible to perform allocation processing to the tradable data by simulating the owned assets based on the forecast data of external information, and the front system can perform the trading decision processing by incorporating this result. Becomes possible,
A trading decision processing system characterized by reducing the load on a workstation of a front system involved in the trading decision processing.