Information processing device, information processing method, and program

Abstract

[Problem] To address the problem that information pertaining to the fluctuation in value of an asset or the value of the asset cannot be acquired conventionally. [Solution] Information pertaining to the fluctuation in value of an asset or the value of the asset can be acquired by an information processing device 1 that comprises: a price variation distribution acquisition unit 131 that acquires, for each of two or more assets including a reference asset, price variation distribution information indicating a time-series price variation distribution based on the prices of the two or more assets; a reference value variation acquisition unit (132) that acquires value variation information by substituting the price variation information into a first arithmetic expression, which calculates, by using the price variation distribution information and a covariance matrix, reference value variation information for identifying the fluctuation in value of the reference asset, and by executing the first arithmetic expression; and a value variation output unit 141 that outputs the reference value variation information.

Description

Information Processing Apparatus, Information Processing Method, and Program

[0001] The present invention relates to an information processing apparatus that processes information on a plurality of assets and the like.

[0002] Conventionally, there has been a system that provides advice on investments (see Patent Document 1).

[0003] Japanese Patent No. 6958954

[0004] However, in the prior art, information on fluctuations in the value of an asset or the value of an asset could not be obtained. Note that the value of an asset is different from the price of an asset.

[0005] The information processing apparatus of the first invention of the present invention includes a price fluctuation distribution acquisition unit that acquires price fluctuation distribution information indicating a distribution of fluctuations in time-series prices based on prices at two or more times of two or more assets including a reference asset, a reference value fluctuation acquisition unit that acquires reference value fluctuation information for specifying fluctuations in the value of the reference asset using the price fluctuation distribution information, and a value fluctuation output unit that outputs the reference value fluctuation information.

[0006] With such a configuration, information regarding fluctuations in the value of an asset can be obtained.

[0007] Further, in the information processing apparatus of the second invention of the present invention, with respect to the first invention, the reference value fluctuation acquisition unit substitutes two or more price fluctuation information into a first arithmetic expression for calculating value fluctuation information using the price fluctuation distribution information and a covariance matrix, executes the first arithmetic expression, and acquires the value fluctuation information.

[0008] With such a configuration, information regarding fluctuations in the value of an asset can be obtained.

[0009] Further, in the information processing apparatus of the third invention of the present invention, with respect to the second invention, the two or more assets are N + 1 assets, the price fluctuation distribution acquisition unit acquires price fluctuation distribution information (X → ) that is the logarithmic return of a set of prices of two or more assets, the reference value fluctuation acquisition unit acquires a one vector (1 vector) in which elements corresponding to each of the N + 1 assets are 1 and a covariance matrix (Σ ←→ ), and the price fluctuation distribution information (X→ ) and one vector and covariance matrix (Σ ←→ This is an information processing device that substitutes the above into the first calculation formula, which is formula 12 described later, and obtains the reference value fluctuation information (ξ).

[0010] This configuration allows for the acquisition of information regarding fluctuations in asset value.

[0011] Furthermore, the information processing device of the fourth invention further comprises a first value receiving unit that receives a first value which is the value of a reference asset at a first point in time, a second value acquisition unit that acquires a second value which is the value of the reference asset at a second point in time, using reference value fluctuation information from the first point in time to a second point in time that precedes the first point in time and the first value, and a second value output unit that outputs the second value of the reference asset.

[0012] This configuration allows for the acquisition of asset value.

[0013] Furthermore, the information processing device of the fifth invention, compared to the fourth invention, is an information processing device in which the first value receiving unit receives the second value acquired by the second value acquisition unit as the first value, the second value acquisition unit receives reference value fluctuation information for a third time point prior to the second time point from the value fluctuation output unit, and uses the reference value fluctuation information and the first value received by the first value receiving unit to acquire the second value, which is the value of the reference asset at the third time point, and the processing of the first value receiving unit and the processing of the second value acquisition unit are repeated one or more times, and the second value output unit outputs the second value for each of two or more time points including the second time point and the third time point.

[0014] This configuration allows us to obtain data on the changes in asset value over time.

[0015] Furthermore, the information processing device of this sixth invention, in relation to any one of the first to fifth inventions, has two or more assets, which are N+1 assets, and the base value fluctuation acquisition unit acquires a base value fluctuation time series which is base value fluctuation information at two or more points in time, and a parameter storage unit which stores two or more parameters including the expected return (R) and the projected return (E) of the N+1 assets, and uses the base value fluctuation time series to calculate the covariance matrix (Σ ←→A covariance matrix acquisition unit that acquires a covariance matrix, an expected return (R), a predicted return (E), and a covariance matrix (Σ ←→ ), and a portfolio acquisition unit that acquires portfolio information that is N elements of an (N + 1)-dimensional weight vector (w) having three or more elements with a weight of "-1" for the reference asset, and a portfolio output unit that outputs the portfolio information.

[0016] With such a configuration, an appropriate portfolio can be proposed.

[0017] Further, in the information processing apparatus of the seventh invention, with respect to the sixth invention, the covariance matrix acquisition unit uses the identity matrix as a temporary covariance matrix (Σ ←→ ), substitutes the covariance matrix (Σ ←→ ) and the price fluctuation distribution information (X → ) into a first arithmetic expression, executes the first arithmetic expression, and acquires temporary value fluctuation information (ξ) as a first means, and uses the temporary value fluctuation information (ξ) to acquire a temporary covariance matrix (Σ ←→ ) as a second means, substitutes the temporary covariance matrix (Σ ←→ ) and the price fluctuation distribution information (X → ) into the first arithmetic expression, executes the first arithmetic expression, and acquires temporary value fluctuation information (ξ) as a third means, and a fourth means for determining whether difference information regarding the difference between the covariance matrix (Σ ←→ ) acquired immediately before and the covariance matrix (Σ') acquired before that satisfies a convergence condition, and repeats the processing of the second means, the third means, and the fourth means until the fourth means determines that the convergence condition is satisfied.

[0018] With such a configuration, an appropriate portfolio can be proposed.

[0019] Further, in the information processing apparatus of the eighth invention, with respect to the sixth or seventh invention, the portfolio acquisition unit substitutes the expected return (R → ), the predicted return (E → ), the covariance matrix (Σ ←→ ), and a weight of "-1" for the reference asset, and an (N + 1)-dimensional weight vector (w →This is an information processing device that obtains portfolio information by substituting ) and solving the equation that minimizes L in equation 16.

[0020] This configuration allows us to propose an appropriate portfolio.

[0021] Furthermore, the information processing device of the ninth invention further comprises an asset information receiving unit that receives asset information from a server having two or more prices for each of two or more assets, in addition to any one of the first to eighth inventions, and the price fluctuation distribution acquisition unit is an information processing device that acquires price fluctuation distribution information using the two or more prices that the two or more asset information received by the asset information receiving unit has.

[0022] This configuration allows for the acquisition of information regarding fluctuations in asset value.

[0023] According to the information processing device of the present invention, information regarding fluctuations in the value of an asset and the value of the asset can be obtained.

[0024] Conceptual diagram of information system A in Embodiment 1 Block diagram of information system A Block diagram of information processing device 1 Flowchart explaining an example of operation of information processing device 1 Flowchart explaining an example of value fluctuation acquisition process Flowchart explaining an example of covariance matrix acquisition process Flowchart explaining an example of operation of terminal device 2 Diagram showing an example of experimental results Diagram showing an example of experimental results Diagram showing the relationship between devices of information system B in Embodiment 2 Diagram showing the relationship between devices of information system C Block diagram of the computer system in the above embodiment Diagram showing the probability distribution of price fluctuation and value fluctuation

[0025] The embodiments of the information processing device, etc., will be described below with reference to the drawings. In the embodiments, components that are denoted by the same reference numerals perform the same operation, and therefore, further explanation may be omitted.

[0026] (Embodiment 1) In this embodiment, an information processing device that acquires and outputs value fluctuation information of a reference asset using time-series price fluctuation information of multiple assets will be described. Note that the value of the reference asset is information that minimizes the influence of fluctuations in the value of the price evaluation standard (currency, etc.). Therefore, it can be said that the value of one asset is the intrinsic price of that one asset.

[0027] In this embodiment, an information processing device that acquires and outputs the current value of a reference asset based on information on the fluctuations in the value of the reference asset and the past value of the reference asset is described. The reference asset is the asset from which the value is to be acquired. The reference asset may also be called the target asset.

[0028] In this embodiment, an information processing device that outputs the value transition of a reference asset will be described.

[0029] In this embodiment, an information processing device that proposes a portfolio will be described.

[0030] In this specification, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is irrelevant. Information X and information Y may be linked, may exist in the same buffer, may information X be contained in information Y, or information Y may be contained in information X, and so on.

[0031] Furthermore, in this specification, selecting or determining information Z means obtaining information Z, obtaining a pointer to information Z, obtaining the ID of information Z, setting a flag on information Z, etc., and it is sufficient to be able to access information Z.

[0032] Figure 1 is a conceptual diagram of information system A in this embodiment. Information system A comprises an information processing device 1 and a terminal device 2.

[0033] Information processing device 1 is a device that acquires and outputs information about assets. Information processing device 1 can be, for example, a cloud server or an ASP server, but the type is not limited. Information processing device 1 may also be a terminal. If information processing device 1 is a terminal, terminal device 2 is not required.

[0034] Terminal device 2 is the device used by the user. Terminal device 2 can be a personal computer, smartphone, or tablet device, but the type is not specified. In this context, the user is the person who obtains information about the assets.

[0035] The following describes how to estimate the Maximum Likelihood Value (MLV) in two stages. The first stage involves deriving the relationship between the price fluctuation distribution and the value fluctuation distribution using the relationship between price and value. The second stage involves deriving the MLV calculation formula using maximum likelihood estimation based on the properties of the multivariate joint distribution of value fluctuations. The information processing device 1 performs processing using a mathematical formula for estimating the Maximum Likelihood Value (MLV), for example. The Maximum Likelihood Value (MLV) is the value that maximizes the joint probability density of value fluctuations based on the time series of price fluctuations of a large group of assets. (1) First Stage

[0036] Here, we will discuss assets whose prices are updated in real time and at high frequency in financial markets. An asset is defined as something that has economic value. Examples of assets in this context include stocks (securities) and currencies.

[0037] R is the exchange ratio (or price if A is a currency) between asset A and asset B at time t. ＢＡ，ｔ The value V of asset A is Ａ The value V of asset B Ｂ Using and , it can be expressed by the following equation 1.

[0038]

[0039] For simplicity, we denote the logarithmic return normalized by variance as Δ, and by rearranging Equation 1, we obtain Equations 2 and 3.

[0040]

[0041]

[0042] Furthermore, we assume that the marginal distribution of the price fluctuation ΔR with normalized variance is q-gaussian, and that the marginal distribution of the value fluctuation ΔV with normalized variance is also the same distribution. In this case, the characteristic function of the sum of variables is the product of the characteristic functions of the variables, so if we let the probability density function be f and the characteristic function be φ, then equation 3 can be transformed into equation 4.

[0043]

[0044] Since the assumptions are that the functions are identically distributed and even, equation 4 can be transformed into equation 5.

[0045]

[0046] By swapping both sides of equation 5 and raising each side to the power of 1 / 2, equation 5 becomes equation 6.

[0047]

[0048] Probability density function (f ΔＶ ) is obtained by performing an inverse Fourier transform on equation 6. Its probability density function (f ｖ ) is equation 7.

[0049]

[0050] Note that in formula 7, "F －１ " represents the inverse Fourier transform.

[0051] From the above, it can be seen that the marginal distribution of value fluctuations is uniquely determined by the marginal distribution of price fluctuations. Furthermore, it can be seen that the characteristic function is the price fluctuation raised to the power of 1 / 2. Note that when the characteristic function is raised to the power of 1 / 2, if the original density function was a normal distribution, it becomes a normal distribution with half the variance, and if it was a Cauchy distribution, it becomes a Cauchy distribution with half the scale parameters.

[0052] Furthermore, by substituting the characteristic function of value fluctuation (q-gaussian), equation 7 becomes equation 8.

[0053]

[0054] Furthermore, it can be seen that value fluctuations have a smaller variance compared to price fluctuations and follow a stable elliptic distribution (see Figure 13). Figure 13 shows the probability distributions of price fluctuations and value fluctuations. (2) Second stage

[0055] As described above, the value that maximizes the joint probability density of value fluctuations based on the time series of price fluctuations of a large asset group will be called the Maximum Likelihood Value (MLV). The formula for calculating MLV is uniquely determined regardless of the shape of the marginal distribution, as long as the marginal distribution of value belongs to the family of elliptic distributions. This is because the multivariate joint probability density function (F) of the elliptic distribution can be expressed as shown in equation 9 below, and X Ｔ Σ ?１ This is because the maximum value is taken when X (which has an arrow above it) is at its minimum.

[0056]

[0057] In equation 9, X → Σ is a probability vector. ←→ Here, is a positive semidefinite matrix, and k represents the normalization constant. Also, in equation 8, g is an arbitrary function. → The superscript arrow → is placed directly above X. Similarly, the superscript back-and-forth arrow → is placed directly above Σ. The same rules apply to other parts of the text.

[0058] As an example, the MLV calculation formula assuming a t-distribution for the marginal distribution of value is shown below. The log-return of the asset price group is X. → Let ξ be the logarithmic return of the value of the base currency, ν be the marginal distribution of the value (a t-distribution with ν > 2 degrees of freedom), and Σ be the covariance matrix. ←→ Therefore, the joint probability density function (F) is given by equation 10.

[0059]

[0060] Note that the base currency itself is also included as an asset, and its value is set to 0. If N is the number of prices observed, then X → The dimension of is N+1, Σ ←→ This will result in an N+1 × N+1 matrix.

[0061] Furthermore, in equation 10, N is the number of observed prices (number of assets excluding the base currency), and ν is the degree of freedom of the t-distribution, "1 → " represents a vector with length N+1 and all elements being 1 (a one-vector).

[0062] To find the ξ that maximizes the joint probability density (F), we can solve equation 11 below.

[0063]

[0064] Solving equation 11 yields equation 12 below.

[0065]

[0066] Note that in equation 12, "Σ ←→ This is the covariance matrix.

[0067] Based on the above, we can calculate the time series of the logarithmic return of the maximum likelihood value of the base currency. Since Equation 1 shows that price (= observed value) is the ratio of the base currency to the asset value, any asset price time series can be transformed into a time series of maximum likelihood value.

[0068] Furthermore, if you want to define the absolute value of a base currency, you need to set a reference point (for example, the value of 1 yen on January 1, 2000, is set to 1).

[0069] Reference time t ０ The maximum likelihood value V of the base currency C and the arbitrary asset A at any point in time t thereafter. Ｃ，ｔ , V Ａ，ｔ This can be shown by equations 13 and 14, assuming that the maximum possible value of the base currency at the base date is 1.

[0070]

[0071]

[0072] In addition, in formula 13, R ＡＣ，ｔ This represents the price of A in C terms at time t.

[0073] Figure 2 is a block diagram of information system A in this embodiment. Figure 3 is a block diagram of information processing device 1.

[0074] The information processing device 1 comprises a storage unit 11, a receiving unit 12, a processing unit 13, and an output unit 14.

[0075] The storage unit 11 includes an asset information storage unit 111 and a parameter storage unit 112. The receiving unit 12 includes an asset information receiving unit 121 and a first value receiving unit 122. The processing unit 13 includes a price fluctuation distribution acquisition unit 131, a base value fluctuation acquisition unit 132, a second value acquisition unit 133, a covariance matrix acquisition unit 134, and a portfolio acquisition unit 135. The covariance matrix acquisition unit 134 includes a first means 1341, a second means 1342, a third means 1343, and a fourth means 1344. The output unit 14 includes a value fluctuation output unit 141, a second value output unit 142, and a portfolio output unit 143.

[0076] The terminal device 2 includes a terminal storage unit 21, a terminal receiving unit 22, a terminal processing unit 23, a terminal transmission unit 24, a terminal receiving unit 25, and a terminal output unit 26.

[0077] The storage unit 11, which constitutes the information processing device 1, stores various types of information. These types of information include, for example, asset information and parameters, which will be described later.

[0078] The asset information storage unit 111 stores two or more asset information records. Asset information is information about an asset. Asset information includes the price of the asset. Asset information has the price of the asset at two or more points in time. In other words, asset information has the price of the asset over time. Asset information is usually associated with an asset identifier.

[0079] The parameter storage unit 112 stores two or more parameters. The parameters are, for example, the expected return (R) of N+1 assets. → ) and the expected return (E) of the N+1 assets → ) includes. Note that the expected return (R → ) and projected earnings (E → ) is an N+1-dimensional vector.

[0080] The reception unit 12 receives various types of information and instructions. These types of information and instructions include, for example, asset information and primary value, which will be described later.

[0081] The asset information receiving unit 121 receives asset information for one or more assets from a server (not shown). It is preferable for the asset information receiving unit 121 to receive asset information for N+1 assets from a server (not shown). The asset information receiving unit 121 may also receive asset information for different assets from two or more servers.

[0082] The first value receiving unit 122 receives the first value, which is the value of the reference asset at the first point in time. The source of the first value received by the first value receiving unit 122 is not considered.

[0083] It is preferable for the first value receiving unit 122 to accept the second value acquired by the second value acquisition unit 133 (described later) as the first value.

[0084] Here, "reception" typically refers to the reception of information transmitted via wired or wireless communication lines, but it may also be a concept that includes the reception of information input from input devices such as keyboards, mice, and touch panels, as well as the reception of information read from recording media such as optical discs, magnetic discs, and semiconductor memory.

[0085] The processing unit 13 performs various processes. These processes include, for example, those performed by the price fluctuation distribution acquisition unit 131, the base value fluctuation acquisition unit 132, the second value acquisition unit 133, the covariance matrix acquisition unit 134, or the portfolio acquisition unit 135.

[0086] The price fluctuation distribution acquisition unit 131 acquires price fluctuation distribution information (X) for each of two or more assets, including the reference asset, which shows the distribution of price fluctuations over time based on the prices of the asset at two or more different times. → ) is obtained. Here, it is preferable that the two or more assets are (N+1) assets that include the base asset and N assets (where N is a natural number greater than or equal to 1).

[0087] The price fluctuation distribution acquisition unit 131 acquires price fluctuation distribution information using, for example, the prices of two or more asset information received by the asset information receiving unit 121.

[0088] The price fluctuation distribution acquisition unit 131 acquires price fluctuation distribution information (X), which is, for example, the logarithmic return of a set of prices of two or more assets. → ) obtain.

[0089] The benchmark value fluctuation acquisition unit 132 acquires benchmark value fluctuation information that identifies fluctuations in the value of a benchmark asset using two or more price fluctuation distribution information.

[0090] The base value fluctuation acquisition unit 132 substitutes two or more price fluctuation information into a first calculation formula that calculates value fluctuation information using price fluctuation distribution information and a covariance matrix, executes the first calculation formula, and acquires value fluctuation information.

[0091] The first calculation formula is, for example, formula 12. The base value fluctuation acquisition unit 132 is, for example, a one-vector (1) in which each of the N+1 assets has an element of 1. → ) and covariance matrix (Σ ←→ ) and obtain price fluctuation distribution information (X → ) and one vector (1 → ) and covariance matrix (Σ ←→ Substitute the values ​​of ) into the first calculation formula, which is formula 12, perform the calculation on formula 12, and obtain the reference value fluctuation information (ξ).

[0092] The benchmark value fluctuation acquisition unit 132 may acquire a benchmark value fluctuation time series, which is benchmark value fluctuation information for two or more points in time.

[0093] The second value acquisition unit 133 receives base value fluctuation information from the value fluctuation output unit 141 from the first time point to the second time point, and uses this base value fluctuation information and the first value to acquire the second value, which is the value of the base asset at the second time point. The second time point is a time point later (in the future) than the first time point. The first value used by the second value acquisition unit 133 is either the first value received by the first value receiving unit 122, or the second value acquired by the second value acquisition unit 133 immediately before.

[0094] The second value acquisition unit 133 receives base value fluctuation information from the value fluctuation output unit 141 for a time point after the second time point (third time point), and uses the base value fluctuation information and the first value received by the first value receiving unit 122 to acquire the second value, which is the value of the base asset at the earlier time point (third time point).

[0095] The second value acquisition unit 133 repeats the above process to acquire the second value, which is the value of the reference asset at a point in time after the third point in time (the fourth point in time). In other words, it is preferable for the second value acquisition unit 133 to acquire the second value of the reference asset at two or more points in time.

[0096] The covariance matrix acquisition unit 134 uses the base value fluctuation time series (ξ) to obtain the covariance matrix (Σ ←→ The covariance matrix acquisition unit 134 obtains the covariance matrix (Σ ←→ ) the components of σ ｉ，ｊ In this case, it can be expressed as shown in the following equation 15. In equation 15, ΔR ｉ → , ΔR ｊ → This is the time series of price logarithmic returns for asset i and asset j. In equation 15, ξ → This is the time series of the logarithmic return of the benchmark asset.

[0097]

[0098] The processing of the covariance matrix acquisition unit 134 is carried out, for example, by the first means 1341, the second means 1342, the third means 1343, and the fourth means 1344.

[0099] The first method 1341 is to use the identity matrix as a provisional covariance matrix (Σ ←→ ) and the provisional covariance matrix (Σ ←→ ) and price fluctuation distribution information (X → Substitute ) into the first calculation formula (formula 12), execute the first calculation formula, and obtain provisional value change information (ξ).

[0100] The second means 1342 uses the provisional value fluctuation information (ξ) obtained by the first means 1341 to create a provisional covariance matrix (Σ ←→ ) obtain.

[0101] The third means 1343 uses the provisional covariance matrix (Σ) obtained by the second means 1342. ←→ ) and price fluctuation distribution information (X → Substitute ) into the first calculation formula (formula 12), execute the first calculation formula, and obtain provisional value change information (ξ).

[0102] The fourth means 1344 is the covariance matrix (Σ) obtained immediately before. ←→ It determines whether the difference information regarding the difference between ( ) and the previously obtained covariance matrix (Σ') satisfies the convergence condition. The convergence condition is that the covariance matrix (Σ ←→ The conditions for determining that the model has converged are as follows: The convergence condition is usually that the difference between the two covariance matrices is less than or equal to a threshold. The specific method for calculating the difference is not specified. For example, the convergence condition is that the sum of the squares of the differences between the elements of the two covariance matrices is less than or equal to a threshold. For example, the convergence condition is that the sum of the absolute values ​​of the differences between the elements of the two covariance matrices is less than or equal to a threshold.

[0103] It is preferable that the processes of the second means 1342, the third means 1343, and the fourth means 1344 are repeated until it is determined that the fourth means 1344 satisfies the convergence condition.

[0104] The portfolio acquisition unit 135 calculates the expected return (R → ) and projected earnings (E → The expected return (R) is obtained from the parameter storage unit 112. The portfolio acquisition unit 135 obtains the expected return (R) from the parameter storage unit 112. → ) and the projected earnings (E → ) and the covariance matrix (Σ) obtained by the covariance matrix acquisition unit 134 ←→ Using this, we obtain portfolio information, which is the N element of a (N+1) dimensional weight vector (w) having three or more elements, each with a weight of "-1" relative to the base asset.

[0105] The portfolio acquisition unit 135, for example, calculates the expected return (R → ) and expected earnings (E → ) and the covariance matrix (Σ ←→ ) and set the weight of the reference asset to "-1", and a (N+1) dimensional weight vector (w) with N elements as variables. → Substitute ) into the following equation 16 and solve the calculation that minimizes L in equation 16 to obtain portfolio information. Note that portfolio information is obtained using a weight vector (w →The elements of N are those of the base asset that constitutes the portfolio, excluding the element with a weight of "-1". Portfolio information is, for example, a vector having the elements of N. Each element of N is the weight of each asset in N. The weight of an asset is the investment ratio in that asset. The sum of the elements of N is usually "1".

[0106]

[0107] Furthermore, using formula 16, the portfolio acquisition unit 135 can acquire a minimum diversified portfolio in MLV terms by adding the target stock to the portfolio constituent stocks.

[0108] However, if the number of stocks in the portfolio is N, then w is a weight vector of length N+1, and the 0th element is the weight of the stock to be imitated. In this case, the portfolio consisting of stocks other than the target stock and the target stock with quantity w0 are balanced so that their fluctuations cancel each other out. Therefore, the portfolio acquisition unit 135 can create a portfolio that imitates the target using the weights w of the N stocks obtained by solving equation 13 with "Wo = -1". Note that w represents the investment ratio (weight vector) to the asset. w is given by equation 17.

[0109]

[0110] The output unit 14 outputs various types of information. These types of information include, for example, information on changes in the base value, secondary value, time-series secondary value, and portfolio information. The output unit 14 usually transmits these types of information to the terminal device 2.

[0111] Here, output usually refers to transmission to terminal device 2, but it may also be a concept that includes display on a screen, projection using a projector, printing with a printer, sound output, transmission to an external device, storage on a recording medium, and delivery of processing results to other processing devices or other programs.

[0112] The value fluctuation output unit 141 outputs the base value fluctuation information (ξ) acquired by the base value fluctuation acquisition unit 132.

[0113] The second value output unit 142 outputs the second value acquired by the second value acquisition unit 133. The second value is information indicating the value of the reference asset.

[0114] The second value output unit 142 preferably outputs the second value for each of two or more time points. The second value for each of the two or more time points is the second value in the time series.

[0115] The portfolio output unit 143 outputs the portfolio information acquired by the portfolio acquisition unit 135.

[0116] The terminal storage unit 21, which constitutes the terminal device 2, stores various types of information. These types of information include, for example, identifiers for reference assets.

[0117] The terminal reception unit 22 receives input such as instructions and information from the user. Instructions and information include, for example, the identifier of a reference asset. The means of inputting instructions and information can be anything, such as a touch panel, keyboard, mouse, or menu screen.

[0118] The terminal processing unit 23 performs various processes. These processes include, for example, changing instructions and information received by the terminal receiving unit 22 into instructions and information in a structure to be transmitted, and changing the information received by the terminal receiving unit 25 into a structure to be output.

[0119] The terminal transmission unit 24 transmits various information and instructions to the information processing device 1. These various information and instructions include, for example, output instructions.

[0120] The terminal receiving unit 25 receives various types of information from the information processing device 1. These types of information include, for example, benchmark value fluctuation information (ξ), secondary value, time-series secondary value, and portfolio information.

[0121] The terminal output unit 26 outputs various types of information. These types of information include, for example, benchmark value fluctuation information (ξ), secondary value, time-series secondary value, and portfolio information.

[0122] The storage unit 11, asset information storage unit 111, parameter storage unit 112, and terminal storage unit 21 are preferably made of non-volatile recording media, but can also be made of volatile recording media.

[0123] The process by which information is stored in the storage unit 11, etc. is not relevant. For example, information may be stored in the storage unit 11, etc. via a recording medium, information transmitted via a communication line, etc. may be stored in the storage unit 11, etc., or information input via an input device may be stored in the storage unit 11, etc.

[0124] The reception unit 12 and the first value reception unit 122 are preferably implemented by wireless or wired communication means, but may also be implemented by means of receiving broadcasts, device drivers for input means such as touch panels and keyboards, or control software for menu screens.

[0125] The asset information receiving unit 121 is usually implemented by wireless or wired communication means, but it may also be implemented by means of receiving broadcasts.

[0126] The processing unit 13, price fluctuation distribution acquisition unit 131, base value fluctuation acquisition unit 132, second value acquisition unit 133, covariance matrix acquisition unit 134, portfolio acquisition unit 135, first means 1341, second means 1342, third means 1343, and fourth means 1344 can usually be implemented using a processor, memory, etc. The processing procedures of the processing unit 13, etc., are usually implemented in software, and this software is recorded on a recording medium such as ROM. However, it may also be implemented in hardware (dedicated circuitry). The processor can be a CPU, MPU, GPU, etc., and the type is not limited.

[0127] The output unit 14, the value fluctuation output unit 141, the second value output unit 142, and the portfolio output unit 143 are typically implemented by wireless or wired communication means. However, the output unit 14, etc., may also be implemented by driver software for an output device such as a display or speaker, or by driver software for an output device and an output device.

[0128] The terminal reception unit 22 can be implemented using device drivers for input means such as touch panels and keyboards, or control software for menu screens, etc.

[0129] The terminal transmission unit 24 is usually implemented by wireless or wired communication means, but it may also be implemented by broadcasting means.

[0130] The terminal receiving unit 25 is usually implemented by wireless or wired communication means, but it may also be implemented by means of receiving broadcasts.

[0131] The terminal output unit 26 may or may not be considered to include output devices such as a display or speakers. The terminal output unit 26 can be implemented using driver software for an output device, or driver software for an output device and an output device.

[0132] Next, an example of the operation of the information processing device 1 will be explained using the flowchart in Figure 4.

[0133] (Step S401) The processing unit 13 determines whether or not it is time to acquire asset information. If it is time to acquire asset information, the process proceeds to step S402; otherwise, it proceeds to step S403. The time to acquire asset information is, for example, a predetermined time. The time to acquire asset information is, for example, a predetermined time each day (for example, 18:00). The time to acquire asset information is, for example, when an instruction is received from terminal device 2.

[0134] (Step S402) The processing unit 13 obtains the prices of two or more assets from one or more servers (not shown), and for each of the two or more assets, it constructs asset information including the price and stores it in the asset information storage unit 111. The process returns to step S401. The asset information includes, for example, time information.

[0135] (Step S403) The reception unit 12 determines whether or not it has received a value change output instruction from the terminal device 2. If a value change output instruction has been received, the unit proceeds to step S404; otherwise, it proceeds to step S406.

[0136] (Step S404) The base value change acquisition unit 132 performs value change acquisition processing. An example of such value change acquisition processing will be explained using the flowchart in Figure 5.

[0137] (Step S405) The value change output unit 141 outputs the base value change information obtained in step S404. Return to step S401.

[0138] (Step S406) The reception unit 12 determines whether or not it has received a second value output instruction for the reference asset from the terminal device 2. If a second value output instruction is received, the unit proceeds to step S407; otherwise, it proceeds to step S411.

[0139] (Step S407) The first value receiving unit 122 acquires the first value. The first value is stored, for example, in the storage unit 11.

[0140] (Step S408) The base value change acquisition unit 132 performs value change acquisition processing. An example of such value change acquisition processing will be explained using the flowchart in Figure 5.

[0141] (Step S409) The second value acquisition unit 133 calculates the second value using the first value acquired in step S407 and the base value fluctuation information acquired in step S408. The second value acquisition unit 133 calculates the second value, for example, using the formula "second value = first value × exp(ξ)".

[0142] (Step S410) The second value output unit 142 outputs the second value obtained in step S409. Return to step S401.

[0143] (Step S411) The reception unit 12 determines whether or not it has received a portfolio output instruction from the terminal device 2. If a portfolio output instruction is received, the system proceeds to step S412; otherwise, it returns to step S401.

[0144] (Step S412) The portfolio acquisition unit 135 retrieves the expected return (R) from the parameter storage unit 112. → ) obtain.

[0145] (Step S413) The portfolio acquisition unit 135 retrieves the expected earnings (E) from the parameter storage unit 112. → ) obtain.

[0146] (Step S414) The covariance matrix acquisition unit 134 acquires the covariance matrix. An example of such covariance matrix acquisition process will be explained using the flowchart in Figure 6.

[0147] (Step S415) The portfolio acquisition unit 135 uses the covariance matrix to obtain the weight vector (w → ) obtain.

[0148] (Step S416) The portfolio acquisition unit 135 generates a weight vector (w → These are the elements of the reference asset, and the weights (w ０ Portfolio information is constructed using the weights of each element of N, excluding the element with a value of -1.

[0149] (Step S417) The portfolio output unit 143 outputs the portfolio information configured in step S416. Return to step S401.

[0150] In the flowchart shown in Figure 4, processing is terminated by power-off or processing termination interrupts.

[0151] Next, an example of the value change acquisition process in step S404 will be explained using the flowchart in Figure 5.

[0152] (Step S501) The base value fluctuation acquisition unit 132 acquires the prices of (N+1) assets at two or more points in time from the asset information storage unit 111.

[0153] (Step S502) The base value fluctuation acquisition unit 132 acquires price fluctuation distribution information using the prices of each of the (N+1) assets at two or more points in time obtained in step S501.

[0154] (Step S503) The base value change acquisition unit 132 acquires the covariance matrix. Alternatively, the covariance matrix acquisition unit 134 may acquire the covariance matrix by the operation described using the flowchart in Figure 6. The base value change acquisition unit 132 may also read the covariance matrix from the storage unit 11.

[0155] (Step S504) The base value change acquisition unit 132 acquires the first calculation formula (formula 12) from the storage unit 11.

[0156] (Step S505) The base value fluctuation acquisition unit 132 substitutes the price fluctuation distribution information acquired in step S502, the covariance matrix and the 1 vector acquired in step S503 into the first calculation formula.

[0157] (Step S506) The base value change acquisition unit 132 executes the first calculation formula.

[0158] (Step S507) The base value change acquisition unit 132 acquires the base value change information (ξ in formula 11), which is the result of the execution in step S506. It returns to the higher-level processing.

[0159] Next, an example of the covariance matrix acquisition process in step S414 will be explained using the flowchart in Figure 6.

[0160] (Step S601) The first means 1341 is a provisional covariance matrix (Σ ←→ Obtain the identity matrix which is ).

[0161] (Step S602) The first means 1341 obtains the prices of (N+1) assets at two or more points in time from the asset information storage unit 111.

[0162] (Step S603) The first means 1341 uses the prices of the (N+1) assets obtained in step S602 at two or more points in time to obtain price fluctuation distribution information (X → ) obtain.

[0163] (Step S604) The first means 1341 is a provisional covariance matrix (Σ ←→ ) and price fluctuation distribution information (X → Substitute ) into the first calculation formula, execute the first calculation formula, and obtain provisional value change information (ξ).

[0164] (Step S605) The second means 1342 uses the provisional reference value fluctuation information (ξ) obtained in step S604 to form a provisional covariance matrix (Σ ←→ ) obtain.

[0165] (Step S606) The third means 1343 is a provisional covariance matrix (Σ ←→ ) and price fluctuation distribution information (X → Substitute ) into the first calculation formula, execute the first calculation formula, and obtain provisional value change information (ξ).

[0166] (Step S607) The fourth means 1344 determines whether the convergence condition is satisfied. If the convergence condition is satisfied, it proceeds to step S608; if not, it returns to step S605.

[0167] Here, for example, the fourth means 1344 uses the difference information between the latest temporary covariance matrix (Σ ←→ ) and the previous temporary covariance matrix (Σ ←→ ) to determine whether the covariance matrix (Σ ←→ ) satisfies the convergence condition. The difference information is, for example, the sum of the absolute values of the differences of each element of the two covariance matrices. The convergence condition is, for example, that the difference information is less than or equal to a threshold value.

[0168] (Step S608) The covariance matrix acquisition unit 134 acquires the latest temporary covariance matrix (Σ ←→ ) as the final covariance matrix (Σ ←→ ). Return to the upper - level process.

[0169] Next, an operation example of the terminal device 2 will be described using the flowchart of FIG. 7.

[0170] (Step S701) The terminal reception unit 22 determines whether a value - fluctuation output instruction has been received. If a value - fluctuation output instruction has been received, it proceeds to step S702; if not, it proceeds to step S705.

[0171] (Step S702) The terminal processing unit 23 constructs a value - fluctuation output instruction to be transmitted. The terminal transmission unit 24 transmits the value - fluctuation output instruction to the information processing device 1.

[0172] (Step S703) The terminal reception unit 25 determines whether reference value - fluctuation information has been received from the information processing device 1. If reference value - fluctuation information has been received, it proceeds to step S704; if not, it returns to step S703.

[0173] (Step S704) The terminal processing unit 23 constructs reference value - fluctuation information to be output. The terminal output unit 26 outputs the reference value - fluctuation information. Return to step S701. [[ID=3(Step S705) The terminal reception unit 22 determines whether or not it has received a second value output instruction for the reference asset. If it has received a second value output instruction, it proceeds to step S706; otherwise, it proceeds to step S709.

[0175] (Step S706) The terminal processing unit 23 configures the second value output instruction to be transmitted. The terminal transmission unit 24 transmits the second value output instruction to the information processing device 1.

[0176] (Step S707) The terminal receiving unit 25 determines whether or not it has received the second value from the information processing device 1. If the second value has been received, the process proceeds to step S708; otherwise, it returns to step S707.

[0177] (Step S708) The terminal processing unit 23 configures the second value to be output. The terminal output unit 26 outputs the second value. The process returns to step S701.

[0178] (Step S709) The terminal reception unit 22 determines whether or not it has received a portfolio output instruction. If it has received a portfolio output instruction, it proceeds to step S710; otherwise, it returns to step S701.

[0179] (Step S710) The terminal processing unit 23 configures the portfolio output instruction to be transmitted. The terminal transmission unit 24 transmits the portfolio output instruction to the information processing device 1.

[0180] (Step S711) The terminal receiving unit 25 determines whether or not it has received portfolio information from the information processing device 1. If portfolio information has been received, the process proceeds to step S712; otherwise, it returns to step S711.

[0181] (Step S712) The terminal processing unit 23 configures the portfolio information to be output. The terminal output unit 26 outputs the portfolio information. The process returns to step S701.

[0182] In the flowchart shown in Figure 7, processing is terminated by power-off or processing termination interrupts.

[0183] The following describes an example of the results of a specific operational experiment of the information processing device 1 in this embodiment.

[0184] Figure 8 is a graph showing a comparison between the returns of 20 selected assets and the returns in Japanese yen, using the information processing device 1, with the benchmark asset set to MLV (Metal Value). The horizontal axis of Figure 8 represents logarithmic return, and the vertical axis represents the number of days.

[0185] Figure 9 shows an example of how the information processing device 1 randomly selects 10 asset stocks, sets the benchmark asset to "yen," and manages them for 60 days to obtain and output information on the minimum variance portfolio under non-negative constraints. 901 shows the investment results using the method of the information processing device 1. 902 shows the investment results using the conventional method. Note that in Figure 9, risk is on the horizontal axis and return is on the vertical axis.

[0186] As described above, according to this embodiment, information regarding fluctuations in the value of an asset can be obtained.

[0187] Furthermore, according to this embodiment, the value of the asset can be obtained. Furthermore, according to this embodiment, the trend in the value of the asset can be obtained.

[0188] Furthermore, according to this embodiment, an appropriate portfolio can be proposed.

[0189] The processing in this embodiment may be implemented by software. This software may be distributed by software download or the like. Alternatively, this software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software that implements the information processing device 1 in this embodiment is the following program. In other words, this program is a program that causes a computer to function as a price fluctuation distribution acquisition unit that acquires price fluctuation distribution information showing the distribution of time-series price fluctuations based on the prices of two or more assets, including a reference asset, for each of two or more assets; a reference value fluctuation acquisition unit that substitutes the price fluctuation information into a first calculation formula that calculates reference value fluctuation information that identifies the fluctuation in the value of the reference asset using the price fluctuation distribution information and a covariance matrix, executes the first calculation formula, and acquires the value fluctuation information; and a value fluctuation output unit that outputs the reference value fluctuation information. (Embodiment 2) In Embodiment 2, an information system B that uses the value fluctuation information of the reference asset acquired by the information processing device 1 will be described. Information system B is a system that issues and manages digital assets based on real value using the stability index (MLV) of the present invention. In this second embodiment, we will describe an information system B that issues and manages digital assets pegged to a real value evaluated by a stability index (MLV). In this second embodiment, we will describe an information system B that can stabilize the value of issued digital assets on a real basis, without depending on a specific fiat currency, by determining the issuance amount by referring to the value of the benchmark asset indicated by the MLV. In such a system, it becomes possible to provide digital assets based on a new unit of value that is less susceptible to the effects of price fluctuations and exchange rate fluctuations, and it has the effect of being usable as an international settlement and store of value. In this second embodiment, we will describe an information system B that issues and manages digital assets pegged to a real value (hereinafter referred to as the benchmark value) evaluated by a stability index (MLV) calculated in the first embodiment.In this embodiment, the real value of each legal tender is estimated based on the MLV value calculated by the information processing device 1. The issuer determines the issuance amount based on this estimated real value, thereby enabling the issuance of digital assets with a stable real value that are not dependent on a specific unit of legal tender. This provides digital assets based on a new measure of value that is less susceptible to the effects of price fluctuations and exchange rate fluctuations, and has the effect of realizing a stable means of value transmission in international settlements, savings, lending transactions, etc. Furthermore, the digital assets according to the present invention can correct purchasing power disparities among national currencies and can also function as a global stable value asset. Information system B includes one or more user terminals 3, an issuance management server 4, an issuer server 5, a ledger management server 6, an operation management server 7, and one or more operation systems 8. Figure 10 shows the relationships between the devices of information system B. Each server communicates with each other via the network and exchanges information such as deposit information from users, issuance instructions, exchange rate information, issuance amount, balance, and operation results. User terminal 3 is a terminal used by a user. User terminal 3 is a terminal used by users to issue, redeem, and inquire about benchmark assets. Users are those who wish to deposit their own legal tender (yen, dollars, etc.) and acquire stable digital assets based on their real value. User terminal 3 can be a personal computer, smartphone, or tablet, but the type is not specified. Issuance management server 4 is a device that manages the issuance of digital assets held by users. Issuer server 5 is a device that issues digital assets based on instructions from issuance management server 4. Ledger management server 6 is a server that manages the owners of digital assets issued by issuer server 5 in a ledger. Ledger management server 6 is a device that receives recording instructions transmitted from issuer server 5 and registers the user identifier and the quantity of digital assets in the electronic ledger. The electronic ledger is a database that consistently manages each user's digital asset balance, issuance history, redemption history, and transaction history. The ledger management server 6 applies an electronic signature and timestamp to the registered record data, enabling the detection of tampering with the record content.The recorded data includes, for example, the date and time of issuance, issuer identifier, issuance amount, balance, and transaction identifier. Furthermore, the electronic ledger is distributed and stored on multiple backup servers, making it resistant to data loss and unauthorized modification. In addition, the ledger management server 6 communicates with the issuance management server 4 and the operation management server 7 to periodically perform balance verification of issued assets and consistency verification with the underlying assets. As a result, the correspondence between the total amount of issued digital assets and the valuation of the underlying assets is continuously monitored, maintaining the value stability of the benchmark asset. For example, the ledger management server 6 records the user identifier of a user using the user terminal 3 on the blockchain in association with the issuance amount of digital assets. The operation management server 7 is a server that manages deposited fiat currency as underlying assets and controls asset management so that its value is consistent with the benchmark value. The operation management server 7 is a device that manages the operation of fiat currency deposited from the user terminal 3. The operation management server 7 is a device that determines an appropriate portfolio using the fiat currency deposited from the user terminal 3 and issues instructions to one or more investment systems 8 according to that portfolio. For example, the operation management server 7 manages the deposited assets in a distributed manner through multiple investment systems 8 and adjusts them so that the value of the entire outstanding benchmark asset is consistent with the sum of the benchmark values ​​of the deposited assets. For example, the operation management server 7 constructs a minimum diversified portfolio with risk tolerance θ as a constraint and performs periodic rebalancing based on the evaluation results. Specifically, the operation management server 7 determines the portfolio by solving the optimization problem of formula 18, which will be described later. An investment system 8 is a system that receives instructions from the operation management server 7 and buys and sells assets or executes asset management according to those instructions. The results of its operations are transmitted to the operation management server 7 and reflected in the evaluation of the portfolio and the next adjustment. Note that an investment system 8 is, for example, a server of a securities company. The issuance management server 4, issuer server 5, ledger management server 6, operation management server 7, and operation system 8 are, for example, cloud servers and ASP servers, but the type is not limited. The following explanation of information exchange between each device constituting information system B will be explained using Figure 10.First, the user requests the issuance of their digital assets from the user terminal 3. In other words, the user terminal 3 receives the issuance request and transmits it to the issuance management server 4 (1001 in Figure 10). The transmission of the issuance request is equivalent to a payment from the user. The transmitted issuance request includes the payment amount. The issuance request also includes a user identifier. Next, the issuance management server 4 receives the issuance request transmitted from the user terminal 3 and confirms that the payment amount specified in the issuance request has been received (1002). The issuance management server 4 also requests the issuance of base assets equivalent to the payment amount. In other words, the issuance management server 4 transmits an issuance instruction to the issuer server 5 (1003). The issuance instruction includes the payment amount. Next, the issuer server 5 receives the issuance instruction. Next, the issuer server 5 retrieves the payment amount from the issuance instruction. Next, the issuer server 5 refers to an exchange rate table (not shown) between legal tender and the base value, and calculates the amount of digital assets to be issued using the amount of legal tender deposited and the exchange rate (1004). Next, the issuer server 5 sends a record instruction to the ledger management server 6 (1005). A record instruction is an instruction to record that digital assets equal to the amount of digital assets issued have been issued in relation to the amount deposited by the user. The record instruction includes, for example, a user identifier, the amount deposited, and the amount of digital assets issued. The exchange rate table is a table that manages the rates between legal tender and the base value. The exchange rate table has rate information for one or more legal tenders. The rate information is information on the rate between legal tender and the base asset. The exchange rate table is, for example, a table created by the information processing device 1. That is, for one or more legal tenders, the information processing device 1 acquires rate information showing the ratio between the acquired value of legal tender and the value of the base value, and constructs and stores an exchange rate table with this rate information as a record. Next, the ledger management server 6 receives a record instruction. Then, the ledger management server 6 reflects the information containing the user identifier and the amount of assets issued in the record instruction into the ledger (1006). The information reflected in the ledger may also include the amount of money received. The ledger management server 6 then sends a report to the issuance management server 4 indicating that the information has been reflected in the ledger (1007).Furthermore, the issuance management server 4 sends a result report to the user terminal 3 (1008). The reflection report and result report include, for example, the deposit amount and the amount of digital assets issued. The ledger management server 6 also sends a balance report to the operation management server 7 (1009). The balance report includes, for example, the user identifier and the deposit amount. Next, the operation management server 7 receives the balance report. Next, the operation management server 7 performs a consistency check (1010). The consistency check is a process to determine whether portfolio adjustment is necessary. The consistency check is a process to determine whether the deviation between the ideal portfolio and the current portfolio is above a certain level. The operation management server 7 sends operation instructions to the operation system 8 to operate according to the constructed portfolio (1011). More specifically, the operation management server 7, for example, evaluates the optimal portfolio (minimum variance portfolio) at a predetermined time each day, determines whether the difference with the current portfolio (rebalancing amount) exceeds a threshold, and performs rebalancing (asset buying and selling) if the rebalancing amount exceeds the threshold. The formula for calculating the optimal portfolio is given by the following equation 18. In equation 18, ω is the portfolio weight vector (fund allocation ratio), μ is the expected return from dividends, C is the trading cost, ω' is the current fund allocation ratio, Σ is the covariance matrix, and θ is the risk tolerance (threshold). The double vertical line and the 1 in the lower right corner represent the L1 norm, which is the sum of the absolute values ​​of all elements of the vector. Also, equation (4) of equation 18 indicates that all elements of the weight vector are non-negative. Next, the trading system 8 receives trading instructions. The trading system 8 then performs trading operations according to the trading instructions (1012). The trading system 8 then sends a status report of the trading results to the trading management server 7 (1013). Also, if a user enters a balance inquiry into the user terminal 3, the user terminal 3 receives the balance inquiry and sends it to the ledger management server 6 (1014). Next, the ledger management server 6 obtains the balance response to the balance inquiry and sends it to the user terminal 3 (1015). Furthermore, the issuance management server 4 and the issuer server 5 in information system B may be integrated into a single issuer server 9. Also, the ledger management server 6 in information system B does not need to exist. Moreover, the operation management server 7 and the operation system 8 may be integrated into a single operation server 10. In other words, information system C may be configured having one or more user terminals 3, issuer servers 9, and operation servers 10 (see Figure 11). In such a case, the issuer server 9 may also have the role of ledger management server 6. In this way, it is possible to configure an information system by integrating or distributing multiple components. According to this embodiment, by determining the issuance amount based on MLV and centrally managing the balance and underlying assets with an electronic ledger, a real value stable digital asset issuance system that does not depend on legal tender or market prices can be realized. In other words, in this embodiment, a real value pegged digital asset based on MLV value can be issued. This has the excellent effect of reducing price fluctuation risk and providing a stable value measure based on real purchasing power in international settlements, savings, and loans.

[0190] Figure 12 is a block diagram of a computer system 300 that executes the program described herein to realize the various embodiments of the information processing device 1 described above.

[0191] In Figure 12, the computer system 300 includes a computer 301 with a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0192] In Figure 12, the computer 301 includes, in addition to the CD-ROM drive 3012, an MPU 3013, a bus 3014 connected to the CD-ROM drive 3012, a ROM 3015 for storing programs such as boot-up programs, a RAM 3016 connected to the MPU 3013 for temporarily storing application program instructions and providing temporary storage space, and a hard disk 3017 for storing application programs, system programs, and data. Although not shown here, the computer 301 may further include a network card that provides connectivity to a LAN.

[0193] The program that causes the computer system 300 to execute the functions of the information processing device 1, etc., as described above, may be stored on the CD-ROM 3101, inserted into the CD-ROM drive 3012, and then transferred to the hard disk 3017. Alternatively, the program may be transmitted to the computer 301 via a network (not shown) and stored on the hard disk 3017. The program is loaded into the RAM 3016 when executed. The program may also be loaded directly from the CD-ROM 3101 or the network.

[0194] The program does not necessarily have to include an operating system (OS) or third-party program that causes the computer 301 to execute the functions of the information processing device 1, etc., as described above. The program only needs to include the instruction portion that calls the appropriate function (module) in a controlled manner and obtains the desired result. How the computer system 300 operates is well known, so a detailed explanation is omitted.

[0195] Furthermore, the computer running the above program may be a single computer or multiple computers. In other words, it may perform centralized processing or distributed processing.

[0196] Furthermore, it goes without saying that in each of the above embodiments, two or more communication means present in a single device may be physically implemented in a single medium.

[0197] Furthermore, in each of the above embodiments, each process may be implemented by centralized processing by a single device, or by distributed processing by multiple devices.

[0198] It goes without saying that the present invention is not limited to the embodiments described above, and various modifications are possible, all of which are also included within the scope of the present invention.

[0199] As described above, the information processing device 1 according to the present invention has the effect of being able to acquire information regarding fluctuations in the value of assets and the value of assets, and is useful as a server or the like for processing asset information.

Claims

1. An information processing device comprising at least one processor, the device comprising: a price fluctuation distribution acquisition unit that acquires price fluctuation distribution information showing the distribution of time-series price fluctuations based on the prices of two or more assets, including a reference asset; a reference value fluctuation acquisition unit that substitutes the price fluctuation information into a first calculation formula that uses the price fluctuation distribution information and a covariance matrix to calculate reference value fluctuation information that identifies the fluctuation in the value of the reference asset, executes the first calculation formula, and acquires the value fluctuation information; and a value fluctuation output unit that outputs the reference value fluctuation information.

2. The two or more assets are N+1 assets, and the price fluctuation distribution acquisition unit obtains the price fluctuation distribution information (X) which is the logarithmic return of the set of prices of the two or more assets. → The unit obtains the following: The unit obtains a one-vector (1 vector) in which each of the N+1 assets has an element of 1 and a covariance matrix (Σ ←→ ) and obtain the price fluctuation distribution information (X → ) and the one vector (Σ ←→ The information processing apparatus according to claim 1, which obtains reference value fluctuation information (ξ) by substituting ) into the first calculation formula which is formula 19.

3. The information processing apparatus according to claim 1, further comprising: a first value receiving unit that receives a first value which is the value of the reference asset at a first point in time; a second value acquisition unit that acquires a second value which is the value of the reference asset at a second point in time using the reference value fluctuation information from the first point in time to a second point in time beyond the first point in time and the first value; and a second value output unit that outputs the second value of the reference asset.

4. The information processing apparatus according to claim 3, wherein the first value receiving unit receives the second value acquired by the second value acquisition unit as the first value; the second value acquisition unit receives the reference value fluctuation information for a third time point prior to the second time point from the value fluctuation output unit, and uses the reference value fluctuation information and the first value received by the first value receiving unit to acquire the second value, which is the value of the reference asset at the third time point; the processing of the first value receiving unit and the processing of the second value acquisition unit are repeated one or more times; and the second value output unit outputs the second value for each of two or more time points including the second time point and the third time point.

5. The above two or more assets are N + 1 assets, and the reference value change acquisition unit acquires a reference value change time series, which is reference value change information at two or more time points. The expected return (R → ), and a parameter storage unit that stores two or more parameters including the expected return (E → ), a covariance matrix acquisition unit that acquires a covariance matrix (Σ ←→ ) using the reference value change time series, and the expected return (R → ), the expected return (E → ), and the covariance matrix (Σ ←→ ), a portfolio acquisition unit that acquires portfolio information, which is N elements of an (N + 1)-dimensional weight vector (w → ) having three or more elements with a weight of "-1" for the reference asset, and a portfolio output unit that outputs the portfolio information. The information processing apparatus according to claim 1.

6. The covariance matrix acquisition unit uses the identity matrix as a provisional covariance matrix (Σ ←→ ) and the covariance matrix (Σ ←→ ) and the price fluctuation distribution information (X → A first means to obtain provisional value fluctuation information (ξ) by substituting ) into the first calculation formula and executing the first calculation formula, and using the provisional value fluctuation information (ξ) a provisional covariance matrix (Σ ←→ A second means of obtaining the provisional covariance matrix (Σ ←→ ) and the price fluctuation distribution information (X → A third means to substitute the above first calculation formula and execute the first calculation formula to obtain provisional value change information (ξ), and the covariance matrix (Σ) obtained immediately before ←→ The information processing apparatus according to claim 5, comprising: a fourth means for determining whether the difference information relating to the difference between the first and the previously obtained covariance matrix (Σ') satisfies a convergence condition, wherein the processing of the second, third, and fourth means is repeated until the fourth means determines that the convergence condition is satisfied.

7. The portfolio acquisition unit includes the expected return (R), the projected return (E), and the covariance matrix (Σ ←→ The information processing device according to claim 5, which obtains the portfolio information by substituting the (N+1)-dimensional weight vector (w), in which the weight for the reference asset is "-1" and the element N is a variable, into equation 18 and solving the calculation formula that minimizes L in equation 20.

8. The information processing apparatus according to claim 1, further comprising an asset information receiving unit that receives asset information from a server having the prices of the two or more assets at the two or more times, wherein the price fluctuation distribution acquisition unit acquires the price fluctuation distribution information using the prices of the two or more assets that the asset information of the two or more assets received by the asset information receiving unit has at the two or more times.

9. An information processing method implemented by an information processing device that includes at least one processor and comprises a price fluctuation distribution acquisition unit, a reference value fluctuation acquisition unit, and a value fluctuation output unit, the information processing method comprising: a price fluctuation distribution acquisition step in which the price fluctuation distribution acquisition unit acquires price fluctuation distribution information showing the distribution of time-series price fluctuations based on the prices of an asset at two or more times for each of two or more assets, including a reference asset; a reference value fluctuation acquisition step in which the reference value fluctuation acquisition unit substitutes the price fluctuation information into a first calculation formula that uses the price fluctuation distribution information and a covariance matrix to calculate reference value fluctuation information that identifies the fluctuation in the value of the reference asset, executes the first calculation formula, and acquires the value fluctuation information; and a value fluctuation output step in which the value fluctuation output unit outputs the reference value fluctuation information.

10. A program to cause a computer to function as: a price fluctuation distribution acquisition unit that acquires price fluctuation distribution information showing the distribution of time-series price fluctuations based on the prices of two or more assets, including a reference asset; a reference value fluctuation acquisition unit that substitutes the price fluctuation information into a first calculation formula that uses the price fluctuation distribution information and a covariance matrix to calculate reference value fluctuation information that identifies the fluctuation in the value of the reference asset, executes the first calculation formula, and acquires the value fluctuation information; and a value fluctuation output unit that outputs the reference value fluctuation information.

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