CN101986342A - Method for arbitraging by using inherent price discrepancy of relevant finical products - Google Patents
Method for arbitraging by using inherent price discrepancy of relevant finical products Download PDFInfo
- Publication number
- CN101986342A CN101986342A CN2010105349326A CN201010534932A CN101986342A CN 101986342 A CN101986342 A CN 101986342A CN 2010105349326 A CN2010105349326 A CN 2010105349326A CN 201010534932 A CN201010534932 A CN 201010534932A CN 101986342 A CN101986342 A CN 101986342A
- Authority
- CN
- China
- Prior art keywords
- price
- financial product
- arbitrage
- relevant
- benchmark
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention relates to technical field of financial product investment arbitrage technology and provides a method for arbitraging by using inherent price discrepancy of relevant finical products. The method comprises the following steps of: processing historical data of the relevant financial products; converting the absolute prices of various financial products with high relevance into comparable regular prices, so that two relevant financial products are comparable; and based on the comparability of the relevant financial products, identifying the price discrepancies and performing arbitrage. The method in the invention solves the problem that the price measures of futures and spot goods are inconsistent in the deadline arbitrage and can gather all priced-related financial products or investment portfolio into one group for arbitrage, so that the investment opportunity and the investment range of arbitrage investors are widened greatly and the arbitrage process becomes more accurate. Moreover, the method also can provide a time-trend chart, a price-discrepancy chart, a unique arbitrage Kreinik chart and the like corresponding to the arbitrage, so that the real-time arbitrage processes of the investors are more clear and effective.
Description
Technical field
The present invention relates to financial product investment arbitrage technical field, particularly a kind of method of utilizing the inherent price difference of relevant financial product to carry out arbitrage.
Background technology
The arbitrage deal of being mentioned on traditional sense generally refers to the transaction of futures spread, its process comprise that the Arbitrageur catches between the very relevant contract of two prices or and other products between at a time or the relative price deviation that occurs in the time period, Short Sales Mechanism by the futures market is sold the contract that outbids, buy at a low price contract, and then when the price deviation disappears, carry out again obtaining profit after (buy in and originally sell) operated in opposite transaction.In order to realize above-mentioned strategy, the condition of a most critical is exactly to make the price of two financial products have comparability, this has no difficulty naturally for striding the phase arbitrage, because equally as stock price index futures, the price of these two financial products all must be to obtain with identical preparation method; But for existing arbitrage of phase, because futures belong to dissimilar financial products with stock, price does not have direct comparability.To this, more traditional solution is the interval method of No Arbitrage, it directly compares Shanghai and Shenzhen 300 indexes and index futures contract, and the price differential that draws instructed arbitrage deal as signal, its model is roughly, make up two combinations M and N at first respectively, combination M is that a forward contract bull adds that one is Ke than number
-r (T-t)Cash, combination N is e
-q (T-t)Individual security and all incomes all reinvest these security, and wherein q is the known earning rate of these assets by continuous compounding.In combination M, Ke
-r (T-t)Cash invest with risk free rate, be (T-t) time horizon of vestment, then when to T constantly the time, because Ke
-r (T-t)e
R (T-t)=K, its amount of money will reach K, so when forward contract expired, this cash just equaled unit target security.The security quantity that combination N has then increases along with the increase that obtains bonus, at moment T, just in time has unit target security.Therefore also equate in t both value of the moment, i.e. f+Ke
-r (T-t)=Se
-q (T-t), because forward price F is exactly that to make contract be worth f be zero making up price K, promptly when f=0, K=F, so f=Se
-q (T-t)-Ke
-r (T-t)Show, pay the forward price of known earning rate assets: F=Se
(r-q) (T-t), then it is the pricing formula of stock price index futures theoretical prince.After having determined theoretical prince, as long as we add and subtract respectively on its basis, and change-over cost C(mainly comprises that cost or the like is impacted in the existing bilateral service charge of marketing of phase, dealing) just obtained No Arbitrage interval [Se
(r-q) (T-t)-C, Se
(r-q) (T-t)+ C], when the index futures price volalility goes out this interval, can implement spread strategy, reversal arbitrage when being lower than this interval---the phase of selling buys existing, otherwise, then carry out the forward arbitrage---the phase of buying sells existing.
Certainly, a lot of forefathers had once carried out certain correction to above-mentioned basic model, as adjusting composition in the change-over cost or the like.In any case but improve, this model all has a shortcoming the most basic, the basic step of realization arbitrage need be carried out the judgement of price differential, and it with Shanghai and Shenzhen 300 indexes as spot price, though the price that the phase that makes shows the two has had comparability, but this only can embody the price differential of stock price index futures and Shanghai and Shenzhen 300 indexes, if we will be with the stock of other financial product as arbitrage, such as LOF, ETF and with the Shanghai and Shenzhen discrepant stock portfolio of 300 constituent stocks during as stock, because the difference of preparation method, its price is just different fully with the yardstick of futures price, and this model just can't reflect corresponding price differential; If only think to have bigger error again, make that so not only profit is difficult to hold, and require more accurate investor particularly unfavorable for data for doing this class of short-term arbitrage with its transaction index or signal as the existing arbitrage of all phases.
Summary of the invention
Purpose of the present invention is exactly in order to overcome the weak point of above-mentioned background technology, a kind of method of utilizing the inherent price difference of relevant financial product to carry out arbitrage is provided, it not only efficiently solves futures and the skimble-scamble problem of spot price yardstick in the existing arbitrage of phase, product that can also all are relevant or investment portfolio are gathered into a class and carry out arbitrage, applicability is more extensive, and the arbitrage process is accurate and effective more.
A kind of method of utilizing the inherent price difference of relevant financial product to carry out arbitrage provided by the invention comprises the steps:
1) chooses the financial product sample, set up the total storehouse of financial product Transaction Information, storage is from the financial product transaction data of each stock exchange, this transaction data comprises transaction value, time, trading volume, transaction value, based on the transaction data in the total storehouse of Transaction Information, carry out the search of the inherent price correlativity of financial product, find out price and have the product of correlativity and carry out corresponding classification, obtain a plurality of taxonomy databases;
2) in each taxonomy database, pick out a benchmark financial product, extract all kinds of historical trading data of the relevant financial product A that needs to carry out arbitrage in this classification, relevant financial product B and benchmark financial product;
3) in conjunction with the above-mentioned data of relevant financial product A and benchmark financial product, the time period that the two relative price equates is found out in the applied statistics analysis; Relative price herein equates to refer to as return situation about equating owing to phase present price lattice return the pairwise correlation financial product price that produces near prompt date, the recurrence of phase present price lattice herein comes from the futures and the equivalence of stock on prompt date in the financial theory, if it exists the spreader then can carry out arbitrage rapidly the two relative price is equated; And for two financial products that do not have this situation, then can select the very approaching time period of undulatory property index of the two price for use, time period of equating of the two earning rate for example;
4) according to the historical trading data of relevant financial product A and benchmark financial product in the above-mentioned time period that filters out, set up regression analysis model and find out the two existing concrete correlationship formula, and be stored in the respective classified database;
5) with the price of benchmark financial product as benchmark price, utilize will the be correlated with price of financial product A of the correlationship formula that obtains in the step (4) to be converted into the price measure of benchmark price, obtain the standardization price of this financial product A;
6) relevant financial product B is repeated the operation of above-mentioned steps (3) to step (5), make its price also be converted into the price measure of benchmark price, obtain the standardization price of this financial product B;
7) will the be correlated with standardization price that draws after financial product A and the relevant financial product B conversion compares and draws price differential;
8) between relevant financial product A and relevant financial product B mutually the real-time change-over cost of arbitrage estimate, and the price measure that cost also is converted into benchmark price is estimated in this arbitrage, obtain the cost that standardizes; As signal, carry out arbitrage deal than formula with the price differential that draws and the cost that standardizes in conjunction with transaction size.
In technique scheme, step (4) is described sets up that regression analysis model is estimated two groups of historical trading data of selected mistake and then the concrete steps of finding out the correlationship formula comprise the estimation of functional form, the estimation of parameter, and to above the two carry out significance test, evaluation and prediction.
In technique scheme, after obtaining price differential in the step (7), can also utilize standardization price and corresponding price differential data to draw relevant financial product standardization price contrast timesharing trend graph, relevant financial product standardization price differential timesharing trend graph and arbitrage K line chart.
A kind of method of utilizing the inherent price difference of relevant financial product to carry out arbitrage of the present invention, mainly be that the historical data of financial product is handled, the absolute price of the financial product that all kinds of correlativitys are comparatively close all is converted into comparable standardization price, make the pairwise correlation financial product have comparability, and then identify price differential on this basis and carry out arbitrage.The inventive method has not only solved futures and the skimble-scamble problem of spot price yardstick in the existing arbitrage of phase, financial product that it can also be correlated with all prices or investment portfolio are gathered into a class and carry out arbitrage, be not limited only to existing and even futures spread of traditional phase, also will be referred to the transaction of the uncared-for many hedging portfolios of possibility once, thereby arbitrage investor's investment opportunity and investment scope have been expanded greatly, universality with height, arbitrage process be accurate and effective more also.Moreover, this method can also provide and the pairing timesharing trend graph of arbitrage, and price differential trend graph and arbitrage K line chart or the like make investor's real-time arbitrage process more clear effectively.
Description of drawings
Fig. 1 for the present invention utilizes the inherent price difference of relevant financial product carry out the general flow chart of the method for arbitrage.
Fig. 2 is the relevant financial product classification process figure in the method for the present invention the utilizes inherent price difference of relevant financial product carries out arbitrage.
Fig. 3 is the price differential identifying process flow diagram in the method for the present invention the utilizes inherent price difference of relevant financial product carries out arbitrage.
Fig. 4 is the process flow diagram flow chart that places an order of the transaction in the method for the present invention the utilizes inherent price difference of relevant financial product carries out arbitrage.
Fig. 5 is that the standardization price that method was drawn that the relevant financial product of the utilization inherence price difference of example is carried out arbitrage contrasts the timesharing trend graph for the present invention with stock price index futures and ETF.
Fig. 6 is a price differential timesharing trend graph.
Fig. 7 is arbitrage K line chart and attributed graph thereof.
Embodiment
Come a kind of method of utilizing the inherent price difference of relevant financial product to carry out arbitrage of the present invention is further described below in conjunction with accompanying drawing and specific embodiment.
As shown in Figure 1, present embodiment is described is that a kind of method of utilizing the inherent price difference of relevant financial product to carry out arbitrage of example may further comprise the steps with stock price index futures and ETF.
As shown in Figure 2, at first set up required financial product taxonomy database.Determine the financial product sample earlier, choose the sample stock price index futures in the present embodiment, commodity future, ETF, all constituent stocks of LOF and Shanghai and Shenzhen 300 are example, in the total storehouse of data importing financial product Transaction Information with the historical transactional information of aforementioned security, extract the day closing price of every stock correspondence, processing to the advanced line time correspondence of these price sequences, such as the situation that may exist certain security day or several days to carry out because of no deal, for this situation promptly the blank phase of this section is carried out polishing with the average of two days the closing price in immediate front and back during this period of time, after the time of all security can both be mapped, utilize correlativity index and analytical model thereof such as related coefficient index, the tracking error index, distance-like index or the like is found out relevant comparatively close financial product, respectively that correlativity is enough big various financial products are classified, at this promptly is that index is classified to above sample with the related coefficient, the financial product that the taking-up related coefficient reaches more than 0.8 is formed a classification, so can obtain a classification a and the classification b based on stock price index futures based on commodity future.Algorithm of definition is picked out a benchmark financial product in each classification, this algorithm of selecting the benchmark financial product can be that financial product in classifying is searched for, search out and follow the average of the related coefficient of each product in other classification to surpass certain certain value, and that financial product of standard error minimum.B uses aforementioned algorithm to classification, and the benchmark financial product of the b that obtains classifying is the of that month stock price index futures continuously of IF.For storing aforementioned information, also be necessary for each classification and set up a financial product taxonomy database, can comprise classified information in the database, both corresponding degrees of correlation in the per minute class, each financial product title, the historical trading data of each financial product etc.After the first foundation of taxonomy database, need upgrade it every one-period, again adjust the related coefficient between wherein per two financial products, will with database in surpass 60% financial product correlativity all from classification, screen out less than 0.8 financial product, and in adding and the database above the correlativity of 60% financial product all greater than 0.8 new product.
As shown in Figure 3, below be the price differential identifying.
1. be example with the ETF financial product among the classification b, ETF among the classification b comprises the 180ETF of Huaan Shanghai Stock Exchange, the Shenzhen Stock Exchange 100ETF of E Fund Management Co., Ltd and the 50ETF of China Shanghai Stock Exchange or the like, be that example gets final product with the 180ETF of Huaan Shanghai Stock Exchange in this step, need extract the historical trading data of stock price index futures, ETF and this classification benchmark financial product from this taxonomy database, the of that month stock price index futures continuously of IF is the benchmark financial product in this classification.
2. find out the time period that ETF equates with the relative price of stock price index futures, this is that price by futures contract can level off to the character of stock on prompt date and realizes.ETF is as the substitute of Shanghai and Shenzhen 300 stock, though the numerical value of fitting index such as the spot index correlativity of its price and Shanghai and Shenzhen 300 and tracking error fails to reach 1, the height that fitting degree is also suitable at last, therefore, also should have character similarly.
Yet, in view of coming out in China, stock price index futures has only the time of some months, the data of only continuing to use prompt date may cause error bigger, therefore take by the liquidity scale near the date near prompt date to be screened earlier, the mode of utilizing related coefficient that data are wherein filtered again searches out only price set, and algorithm is as follows
A, for example with the stock price index futures amount of holding position as the liquidity scale, then began to calculate the minimizing ratio of holding position of every day in initial day from each historical contract, if a certain day amount of holding position reduces and ratio accounts for that proxima luce (prox. luc) closing quotation holds position 20%, then take out its one minute line data, go out the average price of its per minute again according to the data computation of extracting until the ETF that (comprises this day and prompt date) during this period of time and the stock price index futures on prompt date.
B, obtain historical average price data set with the ETF of all above-mentioned historical contracts and the average price data of stock price index futures, employing is with the elimination method of related coefficient as index, promptly obtain Pierre's related coefficient of this two column data earlier, removed Pierre's facies relationship manifold of each minute two column data during average price more from top to bottom, the former with the latter is compared, if the former is more than or equal to the maximal value in latter's set, then computing stops, and obtains this data set; If the former is less than the maximal value in latter's set, then deletion makes and obtains the ETF of required deletion when related coefficient is maximum and minute average price of stock price index futures the new data set of two row and continue this computing.Carry out with this loop statement, resulting data set is the only price set that searches out when this computing stops.
After finishing above-mentioned algorithm, just can be that two row of ETF and stock price index futures are estimated through the curve of optimizing resulting average price data and carrying out in the simple regression analysis to this price set, here getting the ETF price is independent variable, and stock price index futures is a dependent variable, regression equation such as linear equation Y=b
0+ b
1T, indicial equation Y=b
0* e
B1*t, quadratic equation Y=b
0+ b
1T+b
2t
2, logarithmic equation Y=b
0+ b
1Ln (t) etc., obtain relational expression after, respectively it is carried out F check, T check and fitting degree R
2Judgement, select a more excellent group model relational expression.ETF price with each relational expression substitution each prompt date draws corresponding ETF index-linked price collection again, the stock price index futures set of prices on prompt date therewith ETF index-linked price set pair should subtract each other and can obtain price differential collection on prompt date, is that index compares with the price differential collection that obtains with size and the variance of value, pick out make the aforementioned value smaller relational expression also importing be stored in the financial product taxonomy database.
The form of this correlationship formula of conversion makes the benchmark price variable as the dependent variable of this formula and the ETF price variable is an independent variable provides, and will can obtain the standardization price of ETF behind the ETF price substitution independent variable then.
4. this routine stock price index futures is the benchmark financial product, then just there is no need to calculate its relative price under benchmark price, because its price itself has been the standardization price.After having obtained these two relative prices that can compare, just can directly obtain the standardization price differential of stock price index futures and ETF by simple subtraction, so far, just finished the overall process of price differential identification.
In order to finish the arbitrage process, above-mentioned price differential identifying must be real-time.From sometime,, just can begin the differentiation of process of exchange if finished the price differential identification of this time point.As shown in Figure 4, below differentiate and the process that places an order for transaction.
At first, need carry out estimating of arbitrage total cost, change-over cost mainly is divided into fixed cost and variable cost two parts.Why the former is referred to as fixed cost, is not because its value is invariable, but comes from the stationarity of its accounting mode, and in fact it generally all promptly concludes the business total charge as independent variable with transaction size; The latter then mainly comprises impact cost, fluctuation risk etc.; for these variable costs; just be not only relevant with transaction size or real time price; the more important thing is that itself and at that time various transaction data are got in touch all may be very tight; for this class cost; though forefathers have proposed some comparatively feasible models, also there is no fixing account form eventually.At this is that example is introduced next required arbitrage process with the fixed cost only just, for this reason, need extract the price data of this time point stock price index futures and ETF and the historical trading data of the two, thereby and when being based upon closing a position on the historical data price prediction model realize the estimation of real-time change-over cost.The mode of estimating as price with the simplest linear weight model is an example, calculate stock price index futures and the price average of ETF every day last month earlier, give corresponding weights by its distance apart from the current time, price the closer to the current time will be endowed higher weight according to certain linear formula, and then calculate average after the two weighting, the corresponding estimated price when so promptly having obtained closing a position.Above-mentioned estimate finish after, actual needs obtains has only four data, ETF real time price, stock price index futures real time price, ETF estimated price, stock price index futures estimated price, in conjunction with stock price index futures and ETF accordingly fixedly the formula of transaction cost can estimate and carry out the real-time change-over cost that exchange needs this moment.
After the clear and definite cost of real-time arbitrage deal, native system should carry out standardization processing with it earlier equally.Then, just need whether will implement to judge to the arbitrage process that places an order in conjunction with its value with the standardization price differential, at this moment judgement need be used the relational expression between the two that system sets, as the price differential of standardizing 〉=standardization cost+10, if above two values satisfy this relational expression, then need to determine the arbitrage scale and and then the step that places an order of enforcement; If do not satisfy this relational expression, then cycle, be taken as 5 seconds herein to set, continue to wait for next time differentiation chance and exchange meeting.In the present embodiment since the unit scale of stock price index futures much larger than ETF, therefore, optional its subject matter as the arbitrage scale promptly determines to carry out the arbitrage of a few hand stock price index futures, the transaction of then ETF being made corresponding scale gets final product.After determining the arbitrage scale, what need to implement is the process of placing an order, and the higher financial product of standardization price is taked short sales, is example continuously with IF this month here; And the lower financial product of standardization price is taked to buy in, be example here with ETF.
On the basis of having finished the arbitrage start-up course, what next need to do is the generative process of proceeding above-mentioned standardization price differential.When finding that the standardization price differential is reduced to certain degree, because this degree need be considered user's risk partiality, so it can be by acquiescence decision or User Defined, as be defaulted as 10, then can carry out arbitrage and close the trade this moment, transaction this time starts the reverse operating of concluding the business for arbitrage, will buy in the financial product stock price index futures of arbitrage start-up course short sales, and sell the financial product ETF that the arbitrage start-up course is bought in.
A kind of method of utilizing the inherent price difference of relevant financial product to carry out arbitrage described in the present embodiment also comprises relevant arbitrage instrument, promptly with the pairing timesharing trend graph of arbitrage, price differential trend graph and arbitrage K line chart.
Shown in Fig. 5,6, except independent real-time price differential identification, can also obtain the standardization price set of financial product in certain time period to obtain figure line more intuitively by the described price differential identifying of present embodiment.Still from this example, just generated phase present price lattice contrast timesharing trend graph together if the ETF of each time point data standardization price, stock price index futures price be that is to say that stock price index futures standardization price is presented on, Time Index Chart mainly comprises a minute figure, hour figure, Day Line Chart or the like.
As shown in Figure 7, arbitrage K line in the present embodiment is based upon on the basis of traditional K line, its shape is still continued to use the typical candlestick chart of traditional K line, but in order to make the user obtain important and more to play the information of directive function to arbitrage, its content has been carried out bigger correction.Crucial part is to introduce two exclusive indexs of arbitrage high price and arbitrage low price, meanwhile, the highest and minimum two indexs of traditional K line have also been inherited, because arbitrage K dimension amount standard is a price differential, rather than price, so these two indexs of traditional K line highest price and lowest price, being reflected on the arbitrage K line then is highest price difference and minimum price differential.
Wherein arbitrage is defined as making every day or certain one-period to be higher than the price differential that arbitrage price differential at high price accounts for all price differentials 10% at high price, this value also can be according to user's oneself preference, set up such as being set at 5% feasible in theory but difficult the catching in the practical operation of the price differential between highest price difference and the arbitrage high price on their own according to actual conditions; Arbitrage is defined as making every day or certain one-period to be lower than arbitrage price differential at a low price at a low price and accounts for all price differentials 10%, this value also can be according to user's oneself preference, set up such as being set at 7% feasible in theory but difficult the catching in the practical operation of the price differential between minimum price differential and the arbitrage at a low price on their own according to actual conditions.
The black and white cylinder refers to the negative line and the land of arbitrage K line respectively among Fig. 7, to this, we need by the mode of weighting obtain price differential surpass the arbitrage high price averaging time T_1 and price differential be lower than arbitrage T_2 averaging time at a low price; Then, more aforementioned two times are compared, as T_1 during T_2, the arbitrage K line of this day is judged to be land; Otherwise, then be judged to be negative line.
Claims (3)
1. a method of utilizing the inherent price difference of relevant financial product to carry out arbitrage is characterized in that this method comprises the steps:
1) chooses the financial product sample, set up the total storehouse of financial product Transaction Information, storage is from the financial product transaction data of each stock exchange, this transaction data comprises transaction value, time, trading volume, transaction value, based on the transaction data in the total storehouse of Transaction Information, carry out the search of the inherent price correlativity of financial product, find out price and have the product of correlativity and carry out corresponding classification, obtain a plurality of taxonomy databases;
2) in each taxonomy database, pick out a benchmark financial product, extract all kinds of historical trading data of the relevant financial product A that needs to carry out arbitrage in this classification, relevant financial product B and benchmark financial product;
3) in conjunction with the above-mentioned data of relevant financial product A and benchmark financial product, the time period that the two relative price equates is found out in the applied statistics analysis;
4) according to the historical trading data of relevant financial product A and benchmark financial product in the above-mentioned time period that filters out, set up regression analysis model and find out the two existing concrete correlationship formula, and be stored in this taxonomy database;
5) with the price of benchmark financial product as benchmark price, utilize will the be correlated with price of financial product A of the correlationship formula that obtains in the step (4) to be converted into the price measure of benchmark price, obtain the standardization price of this financial product A;
6) relevant financial product B is repeated the operation of above-mentioned steps (3) to step (5), make its price also be converted into the price measure of benchmark price, obtain the standardization price of this financial product B;
7) will the be correlated with standardization price that draws after financial product A and the relevant financial product B conversion compares and draws price differential;
8) between relevant financial product A and relevant financial product B mutually the real-time change-over cost of arbitrage estimate, and the price measure that cost also is converted into benchmark price is estimated in this arbitrage, obtain the cost that standardizes; As signal, carry out arbitrage deal than formula with the price differential that draws and the cost that standardizes in conjunction with transaction size.
2. a kind of method of utilizing the inherent price difference of relevant financial product to carry out arbitrage according to claim 1, it is characterized in that: step (4) is described sets up that regression analysis model is estimated two groups of historical trading data of selected mistake and then the concrete steps of finding out the correlationship formula comprise the estimation of functional form, the estimation of parameter, and to above the two carry out significance test, evaluation and prediction.
3. a kind of method of utilizing the inherent price difference of relevant financial product to carry out arbitrage according to claim 1, it is characterized in that: after obtaining price differential in the step (7), utilize standardization price and corresponding price differential data to draw relevant financial product standardization price contrast timesharing trend graph, relevant financial product standardization price differential timesharing trend graph and arbitrage K line chart again.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010105349326A CN101986342A (en) | 2010-11-08 | 2010-11-08 | Method for arbitraging by using inherent price discrepancy of relevant finical products |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010105349326A CN101986342A (en) | 2010-11-08 | 2010-11-08 | Method for arbitraging by using inherent price discrepancy of relevant finical products |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN101986342A true CN101986342A (en) | 2011-03-16 |
Family
ID=43710689
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2010105349326A Pending CN101986342A (en) | 2010-11-08 | 2010-11-08 | Method for arbitraging by using inherent price discrepancy of relevant finical products |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101986342A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103383766A (en) * | 2012-05-02 | 2013-11-06 | 上海期货交易所 | Mobility and relative mobility generating method and device |
| CN104112170A (en) * | 2014-06-23 | 2014-10-22 | 德高行(北京)科技有限公司 | Constructing method of patent leading indicator and application |
| CN104463678A (en) * | 2014-12-18 | 2015-03-25 | 上海银天下科技有限公司 | Industrial investment data processing method based on average optimization model |
| CN107845036A (en) * | 2017-11-02 | 2018-03-27 | 浙江富帝科技有限公司 | In a kind of banking software from animation line confirmation method |
| CN111027833A (en) * | 2019-11-29 | 2020-04-17 | 珠海随变科技有限公司 | Commodity conversion index calculation method, commodity conversion index calculation device, commodity conversion index calculation equipment and storage medium |
| CN111709838A (en) * | 2020-06-05 | 2020-09-25 | 海南国际知识产权交易中心有限公司 | Transaction method and related equipment based on intellectual property standardized digital assets |
-
2010
- 2010-11-08 CN CN2010105349326A patent/CN101986342A/en active Pending
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103383766A (en) * | 2012-05-02 | 2013-11-06 | 上海期货交易所 | Mobility and relative mobility generating method and device |
| CN104112170A (en) * | 2014-06-23 | 2014-10-22 | 德高行(北京)科技有限公司 | Constructing method of patent leading indicator and application |
| CN104112170B (en) * | 2014-06-23 | 2018-08-31 | 深圳德高行知识产权数据技术有限公司 | The constructing method of patent leading indicators and application |
| CN104463678A (en) * | 2014-12-18 | 2015-03-25 | 上海银天下科技有限公司 | Industrial investment data processing method based on average optimization model |
| CN107845036A (en) * | 2017-11-02 | 2018-03-27 | 浙江富帝科技有限公司 | In a kind of banking software from animation line confirmation method |
| CN111027833A (en) * | 2019-11-29 | 2020-04-17 | 珠海随变科技有限公司 | Commodity conversion index calculation method, commodity conversion index calculation device, commodity conversion index calculation equipment and storage medium |
| CN111027833B (en) * | 2019-11-29 | 2020-11-10 | 珠海随变科技有限公司 | Commodity conversion index calculation method, commodity conversion index calculation device, commodity conversion index calculation equipment and storage medium |
| CN111709838A (en) * | 2020-06-05 | 2020-09-25 | 海南国际知识产权交易中心有限公司 | Transaction method and related equipment based on intellectual property standardized digital assets |
| CN111709838B (en) * | 2020-06-05 | 2024-01-30 | 海南国际知识产权交易所有限责任公司 | Transaction method based on intellectual property standardized digital asset and related equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lagoarde-Segot et al. | Efficiency in emerging markets—Evidence from the MENA region | |
| Prather et al. | Mutual fund characteristics, managerial attributes, and fund performance | |
| US8577791B2 (en) | System and computer program for modeling and pricing loan products | |
| Hwang et al. | An index for venture capital, 1987-2003 | |
| WO2006013207A2 (en) | Shareholder value tool | |
| CN101986342A (en) | Method for arbitraging by using inherent price discrepancy of relevant finical products | |
| US20120084295A1 (en) | Method and system for generating an index of securities | |
| US20060095353A1 (en) | Indexed annuity system and method | |
| Reydon et al. | Determination and forecast of agricultural land prices | |
| WO2006013208A2 (en) | Information technology value strategy | |
| Du | Mean–variance portfolio optimization with deep learning based-forecasts for cointegrated stocks | |
| Buncic et al. | Heterogeneous agents, the financial crisis and exchange rate predictability | |
| Hong et al. | The roles of past returns and firm fundamentals in driving US stock price movements | |
| Plakandaras et al. | Gold against the machine | |
| Farshadfar et al. | Improving Stock Return Forecasting by Deep Learning Algorithm | |
| Hsieh et al. | Mutual fund performance: The decision quality and capital magnet efficiencies | |
| Murugan | Creation of a recommendation system to recommend cryptocurrency portfolio using Association rule mining | |
| Wang et al. | A modified reduced-form model with time-varying default and recovery rates and its applications in pricing convertible bonds | |
| Song et al. | Virtual currency trading strategy based on ARIMA and AHP-PSO | |
| Alcock et al. | Forecasting Stock Returns Using Model‐Selection Criteria | |
| Bowe et al. | A SMARTer way to forecast | |
| Dumitrescu et al. | A Method for Statistically Determining Inflation–Calculating the “General Index of Inflation”(GII) | |
| Baldwin et al. | Different Concepts for Measuring Owner Occupied Housing Costs in a CPI: Statistics Canada’s Analytical Series | |
| Mihaljek et al. | Do we understand what drives house prices? | |
| Tamang | A Model For Currency Exchange Rates |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C12 | Rejection of a patent application after its publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20110316 |