CN101739649A - Method for grading open securities investment funds based on computer simulation technique - Google Patents

Method for grading open securities investment funds based on computer simulation technique Download PDF

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CN101739649A
CN101739649A CN200810202589A CN200810202589A CN101739649A CN 101739649 A CN101739649 A CN 101739649A CN 200810202589 A CN200810202589 A CN 200810202589A CN 200810202589 A CN200810202589 A CN 200810202589A CN 101739649 A CN101739649 A CN 101739649A
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闫景园
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Abstract

The invention discloses a new method for performance evaluation and grading of open securities investment funds based on a computer simulation technique, which is mainly based on a Monte-Carlo method to process the distribution of investment industries, the asset allocation and market industry indexes of the open securities investment funds through programming of an SAS software program, and perform a plurality of times of computer simulations so as to acquire performance simulation data in quarterly, annual and even longer time intervals of the funds. Based on the simulation data, the method performs risk-income evaluation on the funds, evaluates the investment market timing capacity, evaluates risk factors, analyzes the persistency and the survival bias of performance of the funds, and predicts and evaluates the future performance of the funds.

Description

Open securities investment fund rating method based on computer simulation technology
Technical Field
The invention relates to a method for grading open securities investment funds, in particular to a new method for evaluating and grading the performance of open securities investment funds based on a computer simulation technology, belonging to the field of financial investment and consultation.
Background
Various types of securities investment funds in China show explosive growth in recent years. Among many securities investment funds, the open funds occupy most of the mountains, but the open funds are numerous in quantity, large in scale and changed frequently, and complex investment concepts, various fund types and fund marketing means with renovation of patterns all bring great obstacles to individual investors to invest the open funds. Because the combination mode of fund investment is the comprehensive embodiment of the fund manager's investment concept, investment style, asset allocation capability stock selection capability, timing selection capability and the like, the investment performance of different funds has great difference, and how to reasonably measure the investment performance of the fund is a problem which is urgently concerned by the masses of investors. And the performance evaluation and rating of the fund are auxiliary interest devices for the investor to make rational investment.
However, the core of performance evaluation and rating of various types of funds is generally historical performance evaluation, and the evaluation of performance of funds in early domestic is mainly to evaluate the unit equity and the growth rate of the unit equity. The historical performance evaluation based on the unit equity and the growth rate can only faithfully reflect the investment results of different funds in the past time, the results have contingency, the performance of the funds in different market conditions cannot be comprehensively reflected, and the prediction of the future performance of the funds is lack of persuasion. As operating time increases, various quantitative models and methods are also being applied to empirical studies. The Monte Carlo simulation method, also called random simulation, is an advanced digital simulation technique that simulates the various conditions that may occur in practice by means of a random number generator that generates a certain probability distribution number. The method makes it possible to simulate market conditions by a computer and evaluate the comprehensive performance of the securities investment fund under various market conditions.
Disclosure of Invention
The invention aims to provide an open securities investment fund rating method based on a computer simulation technology, which is mainly based on a Monte-Carlo (Monte-Carlo) method, processes investment industry distribution, asset allocation and market industry indexes of the open securities investment fund through SAS software program programming, and carries out computer simulation for multiple times so as to obtain performance simulation data of the fund in the quarter, year and longer time interval.
The technical scheme of the invention is realized by the following technical scheme:
a method for rating the investment fund of open-type securities based on computer simulation technique features that Monte-Carlo method is used, the historical data of investment distribution, asset allocation and market index of investment fund of open-type securities are used, the performance of fund is simulated in large scale, and the result of simulation is used for the evaluation, rating and performance prediction of fund.
The Monte Carlo (Monte-Carlo) method, its application in fund performance evaluation and rating, forms a new approach.
Investment industry distribution data, its application in fund performance evaluation and rating, forms a new approach.
Market industry index data, its application in fund performance evaluation and rating, forms a new approach.
Data extraction parameter calculation and simulation processes are performed on fund investment industry distribution data and market industry index data as claimed in claim 3, and performance simulation data of open securities investment fund is obtained.
The simulation data obtained by data extraction is used for fund performance evaluation and rating instead of using fund historical performance for evaluating and rating, and is used for fund performance prediction, thereby playing a guiding role in fund investment.
The invention simulates the operation performance of stock market conditions and open securities investment funds under different market conditions through a computer, thereby performing performance evaluation and rating on the funds, changing the performance evaluation status of the funds which only depends on historical data, comprehensively reflecting the operation investment capacity of the funds, and ensuring that the performance prediction of the funds has a more convincing method and basis.
Detailed Description
The invention realizes the performance evaluation and rating of the fund by the following method:
(1) data extraction
And selecting the accumulated net income rate, the witness and prison trade index data, the fund investment trade distribution and the asset allocation condition after adjusting the dividend allocation and the like in a certain period of time of the fund to be researched.
The fund investment industry distribution refers to the industry investment proportion of the stock investment in the company listed in the twenty-three industries of the witness guild reported by the fund in the quarterly performance report.
Asset allocation refers to the proportion of funds investments and holding stocks, bonds and cash that funds report in the quarterly performance report.
(2) Parameter estimation
The stock index yield distribution of the two places stock market in Shanghai shows the inverse positive characteristic of 'peak thick tail', and the fitting efficiency of Laplace distribution is obviously improved compared with that of normal distribution. The invention assumes that the witness industry index profitability conforms to the asymmetric Laplace distribution.
Laplace distribution was first discovered by the famous mathematician Laplace in 1774, when he noted a maximum likelihood estimate using the median of the sample as a location parameter, the distribution having the following form:
<math><mrow><msub><mi>f</mi><mi>&xi;</mi></msub><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow><mo>=</mo><mfrac><mn>1</mn><mrow><msqrt><mn>2</mn></msqrt><mi>&sigma;</mi></mrow></mfrac><mi>exp</mi><mrow><mo>(</mo><mo>-</mo><msqrt><mn>2</mn></msqrt><mfrac><mrow><mo>|</mo><mi>x</mi><mo>-</mo><mi>&mu;</mi><mo>|</mo></mrow><mi>&sigma;</mi></mfrac><mo>)</mo></mrow></mrow></math>
asymmetric Laplace distribution is a generalization of traditional Laplace distribution, and is defined as follows:
<math><mrow><mi>f</mi><mrow><mo>(</mo><mi>x</mi><mo>|</mo><mi>&mu;</mi><mo>,</mo><mi>&sigma;</mi><mo>,</mo><mi>p</mi><mo>)</mo></mrow><mo>=</mo><mfrac><mi>k</mi><mi>&sigma;</mi></mfrac><mi>exp</mi><mo>{</mo><mo>-</mo><mrow><mo>(</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>-</mo><mi>p</mi></mrow></mfrac><msub><mi>I</mi><mrow><mo>[</mo><mi>x</mi><mo>></mo><mi>&mu;</mi><mo>]</mo></mrow></msub><mo>+</mo><mfrac><mn>1</mn><mi>p</mi></mfrac><msub><mi>I</mi><mrow><mo>[</mo><mi>x</mi><mo>&lt;</mo><mi>&mu;</mi><mo>]</mo></mrow></msub><mo>)</mo></mrow><mfrac><mi>k</mi><mi>&sigma;</mi></mfrac><mo>|</mo><mi>x</mi><mo>-</mo><mi>&mu;</mi><mo>|</mo><mo>}</mo></mrow></math>
wherein k = p 2 + ( 1 - p ) 2 .
μ is the position parameter and σ is the standard deviation. p is a shape parameter between 0 and 1, and controls skewness and kurtosis, and different values of p enable the skewness to be positive or negative.
Since the SAS software cannot directly simulate the asymmetric Laplace distribution, certain data processing is required. The basis of data processing is that asymmetric Laplace can be obtained by exponential distribution:
if xi and eta are two independent random variables in the same distribution, both obey the standard exponential distribution f (x) exp (-x), x e [0, ∞), if z (1-p) xi-p eta, then z □ AL (0, k, p), where
Figure G2008102025898D0000034
I.e. having the following density function form:
<math><mrow><mi>f</mi><mrow><mo>(</mo><mi>z</mi><mo>|</mo><mo>&CenterDot;</mo><mo>)</mo></mrow><mo>=</mo><mi>exp</mi><mo>{</mo><mo>-</mo><mrow><mo>(</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>-</mo><mi>p</mi></mrow></mfrac><msub><mi>I</mi><mrow><mo>[</mo><mi>x</mi><mo>></mo><mn>0</mn><mo>]</mo></mrow></msub><mo>+</mo><mfrac><mn>1</mn><mi>p</mi></mfrac><msub><mi>I</mi><mrow><mo>[</mo><mi>x</mi><mo>&lt;</mo><mn>0</mn><mo>]</mo></mrow></msub><mo>)</mo></mrow><mfrac><mi>k</mi><mi>&sigma;</mi></mfrac><mo>|</mo><mi>z</mi><mo>|</mo><mo>}</mo></mrow></math>
estimation of parameters mu, pKnowing and considering the kurtosis problem, it is necessary to estimate another parameter of the two-parameter exponential distribution: let x be a random variable subject to a two-parameter exponential distribution, i.e.
Figure G2008102025898D0000037
Figure G2008102025898D0000038
And λ > 0. The following results are obtained using maximum likelihood estimation:
<math><mrow><mover><mi>&lambda;</mi><mo>^</mo></mover><mo>=</mo><mfrac><mn>1</mn><mrow><mover><mi>x</mi><mo>&OverBar;</mo></mover><mo>-</mo><mi>min</mi><mo>{</mo><msub><mi>x</mi><mi>i</mi></msub><mo>}</mo></mrow></mfrac><mo>;</mo></mrow></math>
according to the property of Laplace distribution, making both xi and eta obey the above-mentioned two-parameter exponential distribution
Figure G2008102025898D0000042
Then
Figure G2008102025898D0000043
Sigma is composed ofAnd other parameters.
Therefore, after parameter estimation is completed, the above properties can be utilized to generate independent same-distribution sequences obeying the same asymmetric Laplace distribution by adopting Monte-Carlo (Monte-Carlo) method simulation.
(3) Revenue calculation and simulation
Suppose a certain fund F, its Q0The quarterly report shows that its asset configuration is equity M0Therein cash C0Stock market value S0Bond market value B0Local value of the ticket W0(ii) a The percentage of the industry configuration to the position market value of the industry A to the fund net asset is a0For the B industry, the percentage of the position market value to the fund net asset is B0For the C industry, the percentage of the position market value to the fund net asset is C0… … percentage of the position market value of N industry to the capital net asset is N0。
To Q1Quarterly, the fund periodic report shows that its asset disposition has changed to net worth M1Therein cash C1Stock market value S1Bond market value B1Local value of the ticket W1(ii) a The percentage of the industry configuration to the position market value of the industry A to the fund net asset is a1For the B industry, the percentage of the position market value to the fund net asset is B1For the C industry, the percentage of the position market value to the fund net asset is C1… … percentage of the position market value of N industry to the capital net asset is N1。
For the industry index of the A, B, C … … N industry, at Q0Quarterly reporting deadline and Q for published data1Quarterly reports that within three months of the deadline for published data, there are different amplitudes of fluctuation and fluctuations, respectively. And (3) estimating corresponding parameters according to historical data of the industries under the assumption that the industries accord with asymmetric Laplace distribution on the yield. After the parameters are possessed, Monte Carlo (Monte-Carlo) method can be adopted to simulate the operation condition of each industry index.
For convenience, the fund industry configuration change is considered to be linearly adjusted during the two periodic report publications (the length of time and the continuing stability of the fund industry adjustment strategy ensures that this assumption does not deviate too much from reality),
in addition, since the allocation ratio and the profitability of the stock-type fund non-stock assets are low, the profitability of cash is calculated according to the market standard interest rate R (fixed value) and the bond profitability according to the profitability of the forensics bond index section.
Thus, the total rate of benefit of fund F between two adjacent periodic reports is:
<math><mrow><mi>r</mi><mo>=</mo><munderover><mi>&Pi;</mi><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>t</mi><mo>=</mo><msub><mi>t</mi><mn>0</mn></msub></mrow></munderover><mo>{</mo><mn>1</mn><mo>+</mo><mfrac><mrow><msub><mi>C</mi><mi>t</mi></msub><msub><mi>R</mi><mi>c</mi></msub></mrow><mrow><msub><mi>M</mi><mi>t</mi></msub><msub><mi>t</mi><mn>0</mn></msub></mrow></mfrac><mo>+</mo><mfrac><mrow><msub><mi>B</mi><mi>t</mi></msub><msub><mi>R</mi><mi>b</mi></msub></mrow><mrow><msub><mi>M</mi><mi>t</mi></msub><msub><mi>t</mi><mn>0</mn></msub></mrow></mfrac><mo>+</mo><munderover><mi>&Sigma;</mi><mrow><mi>i</mi><mo>=</mo><mi>A</mi></mrow><mrow><mi>i</mi><mo>=</mo><mi>N</mi></mrow></munderover><mrow><mo>(</mo><msub><mi>n</mi><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>t</mi><mo>)</mo></mrow></msub><msub><mi>R</mi><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>t</mi><mo>)</mo></mrow></msub><mo>)</mo></mrow><mo>}</mo></mrow></math>
wherein,
Figure G2008102025898D0000051
indicating the net cash value on day t,
Figure G2008102025898D0000052
then watchShowing the contribution of the cash assets to the net fund growth rate on the t day;
Figure G2008102025898D0000053
representing the net bond value on the t-th day,
Figure G2008102025898D0000054
then the contribution of the bond assets to the net value growth rate of the fund on the t day is represented;
representing the percentage of the fund's assets allocated in the i industry to the total fund assets on t days, R(i，t)The daily growth rate of the industry is simulated for Monte Carlo (Monte-Carlo). n is(i，t)R(i，t)It represents the contribution of the industry to the net-value growth rate of the fund on the current day.
Due to R(i，t)Is generated by computer simulation, therefore, r will also generate different simulation results accordingly. Through the research on the distribution of the results, the profitability and the risk of different funds can be compared and the subsequent performance of a single fund can be predicted.
(4) Performance assessment and prediction
Based on the simulation results, conventional methods may be utilized to perform performance evaluations on the fund.
The formula and program used for predicting the fund performance are basically the same as those of the method, when prediction is carried out, parameters such as future risk-free interest rate, asset allocation of different funds, actual transaction days, simulation times and the like are set according to personal prediction, and the operation of the fund in the next time interval is simulated according to initial fund industry distribution and witness trade index characteristics. The shorter the simulation time, the more efficient the prediction is the closer to the base data extraction period.
For example, the operating situation of the fund in the year 1 of 2008 can be simulated by changing the asset allocation situation of the fund on the basis of fund industry distribution data of the fund in 12 and 31 days of 2007 and 3 and 31 days of 2008 without changing parameters such as risk-free interest rate, actual transaction days, simulation times and the like and assuming that the funds are not red, split and the scale is kept unchanged during the simulation. And (4) evaluating the performance of the simulation result by adopting a traditional method.

Claims (3)

1. A method for rating the investment fund of open-type securities based on computer simulation technique features that Monte-Carlo method is used to utilize the historical data of distribution of investment trade, asset allocation and market trade index of investment fund of open-type securities, and the large-scale simulation of fund performance is realized by simulating the trade index.
2. A rating method as in claim 1, wherein said data extraction comprises a market industry index data parameter calculation and simulation process and obtaining performance simulation data for open securities investment funds.
3. A rating method as claimed in claim 1, wherein said performance assessment and prediction is performed using performance simulation data obtained by parameter extraction for fund performance assessment and rating.
CN200810202589A 2008-11-12 2008-11-12 Method for grading open securities investment funds based on computer simulation technique Pending CN101739649A (en)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102722849A (en) * 2012-05-18 2012-10-10 苏州万图明电子软件有限公司 Security consulting system
CN103544649A (en) * 2013-10-17 2014-01-29 常熟市华安电子工程有限公司 Stock client side
CN108573366A (en) * 2017-03-09 2018-09-25 派衍信息科技(苏州)有限公司 A kind of NAV simulations calculation processing system
CN108648121A (en) * 2018-05-11 2018-10-12 阿里巴巴集团控股有限公司 Generation of simulating data method and device and electronic equipment
WO2019119627A1 (en) * 2017-12-21 2019-06-27 平安科技（深圳）有限公司 Fof asset industry analysis method, terminal, and computer readable storage medium

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102722849A (en) * 2012-05-18 2012-10-10 苏州万图明电子软件有限公司 Security consulting system
CN103544649A (en) * 2013-10-17 2014-01-29 常熟市华安电子工程有限公司 Stock client side
CN108573366A (en) * 2017-03-09 2018-09-25 派衍信息科技(苏州)有限公司 A kind of NAV simulations calculation processing system
CN108573366B (en) * 2017-03-09 2021-09-17 派衍信息科技(苏州)有限公司 NAV simulation calculation processing system
WO2019119627A1 (en) * 2017-12-21 2019-06-27 平安科技（深圳）有限公司 Fof asset industry analysis method, terminal, and computer readable storage medium
CN108648121A (en) * 2018-05-11 2018-10-12 阿里巴巴集团控股有限公司 Generation of simulating data method and device and electronic equipment

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