METHOD AND COMPUTING APPARATUS FOR DETERMINING A PROBABILISTIC THRESHOLD EVENT

A method and computing apparatus for determining a probabilistic threshold event is described. The method and computing apparatus obtains shareholder records, reference records, and custodian records, processes the records, and uses a classifier with a threshold to determine when a probabilistic event has occurred.

BACKGROUND

The stock market has both a primary and secondary marketplace where numerous financial instruments (e.g., warrants, discounts, puts, calls, convertible debt, etc.) are available to short term traders and long term investors to utilize as a platform for investing and forecasting risks to maximize profits. The underpinnings of these financial instruments are the stocks of a publicly traded company.

One of the primary reasons for companies to offer shares to the public is to raise funds from outside investors. In return, the company's founders and/or current owners relinquish part of their ownership to these new investors. Generally, the executives of a publicly listed company should ensure the company has access to capital but in the most equitable terms for all shareholder constituencies, including both short term traders and long-term investors.

The CEO/CFO must understand how their stock value is being leveraged and impacted by its participation in these capital raising instruments. That is, knowing specific investors and their trading history is useful to the company. However, this understanding has many challenges due to the complexities of trading strategies and the potential anonymity of the shareholder constituencies. For example, investor identity is generally known to privately held companies by virtue of not being traded on a public market. Additionally, companies know investor identities when issuing securities in a primary marketplace, e.g., through a private investment in public equity offering or PIPE.

However, once stock is traded on the secondary market, investor identity data associated with Non-Objecting Beneficial Owners or (NOBOs) can be obtained publicly. In particular, a NOBO elects that an intermediary can release their private personal information such as their name, address, and number of shares owned to the issuer. By contrast, at least half of all investors participating in primary security offerings, are either exempt from reporting regulations or choose to opt-out of such disclosures. For example, investors may elect Objecting Beneficial Owner (OBO) status to keep their financial holdings private, by instructing the financial intermediary not to provide their personal information to the securities issuer. Methods that track investor trading activity to determine potential investors impacting company's stock price are needed.

SUMMARY

In accordance with one or more embodiments, various features and functionality are provided to enable investor-impact forecasting by identifying likely investors affecting a company's stock price based on monitoring trading activity.

DETAILED DESCRIPTION

Described herein are systems and methods for improving investor impact forecasting by identifying likely investors affecting a company's stock price based on monitoring trading activity. The details of some example embodiments of the systems and methods of the present disclosure are set forth in the description below. Other features, objects, and advantages of the disclosure will be apparent to one of skill in the art upon examination of the following description, drawings, examples and claims. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the present disclosure, and be protected by the accompanying claims.

Not all investors choose to disclose their identity after acquiring stock through participating in a primary securities offering or other acquisition event that requires the company to know investor identities. Often, more influential anonymous investors are the ones who participate in the primary securities offerings and while their complex investment strategies are not disclosed during the transaction, it is known that their investment strategies and financial instruments are often related and there exists informative signals in the relationships between an investor and the financial terms of their negotiations. Moreover, the buying and selling behavior of each investor may be used to identify connections between investors and their deal terms, e.g., when the stock price reaches a warrant strike price and the investor executes the warrant.

Unfortunately, investor buying and selling behaviors may be difficult to capture because of limited data availability. Moreover, even when the data is available the rate with which trading data should be sampled is unknown. Currently available software tools simply aggregate shareholder data from multiple sources (e.g., NOBO, Securities Position Reporting (SPR), share range analysis and similar public data sources). Existing solutions fail to track investor activity when an investor requests to keep its information private.

In accordance with various embodiments, a system and method for assisting in determining investor identity based on monitoring trading activity of the most influential investors is disclosed. In one embodiment, the method is configured to perform an initial “set-up” process, during which investor profiles of known and unknown investors are obtained and analyzed to establish investor (shareholder) baseline, iteratively monitor features associated with the profiles by utilizing a Temporal Attention Mechanism (TAM), and, finally, determine a set of likely investor candidates whose holdings have likely changed along with relevant investment, market, and economic data. Further still, the method is configured to present the set of investor candidates to an operator user (e.g., a financial analyst, CEO/CFO) who then may finalize investor candidacy by attributing buying/selling behavior.

As will be described in detail below, the method addresses issues related to limited data (e.g., due to investor anonymity and lack of intraday trading activity) and unknown sample rate. In particular, the sampling rate issue is the rate with which the system requests shareholder and custodian time-series data to capture significant events related to share count changes. For example, if the frequency with which the time-series data is requested is too low, then shareholder ownership tracking becomes impossible. By contrast, if the frequency is too high, then the system is inundated with data which increases storage and processing costs and reporting fees paid to third parties or custodians to receive various reports. The present embodiments resolve the sampling rate problem by determining likely significant changes to custodian share range by analyzing one or more trends related to custodian share count, share range trends, and/or other similar time-series data.

FIG.1illustrates a custodian identifier system100, in accordance with the embodiments disclosed herein. This diagram illustrates an example system100that may include a computing component102in communication with a network140. The system100may also include one or more external resources130and a client computing device120that are in communication with network140. External resources130may be located in a different physical or geographical location from the computing component102.

As illustrated inFIG.1, computing component or device102may be, for example, a server computer, a controller, or any other similar computing component capable of processing data. In the example implementation ofFIG.1, computing component102includes a hardware processor104configured to execute one or more instructions residing in a machine-readable storage medium105comprising one or more computer program components.

Hardware processor104may be one or more central processing units (CPUs), semiconductor-based microprocessors, and/or other hardware devices suitable for retrieval and execution of instructions stored in computer readable medium105. Processor104may fetch, decode, and execute instructions106-112, to control processes or operations for determining investor identity. As an alternative or in addition to retrieving and executing instructions, hardware processor104may include one or more electronic circuits that include electronic components for performing the functionality of one or more instructions, such as a field programmable gate array (FPGA), application specific integrated circuit (ASIC), or other electronic circuits.

A computer readable storage medium, such as machine-readable storage medium105may be any electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. Thus, computer readable storage medium105may be, for example, Random Access Memory (RAM), non-volatile RAM (NVRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a storage device, an optical disc, and the like. In some embodiments, machine-readable storage medium105may be a non-transitory storage medium, where the term “non-transitory” does not encompass transitory propagating signals. As described in detail below, machine-readable storage medium105may be encoded with executable instructions, for example, instructions106-112.

As noted above, hardware processor104may control processes/operations for determining investor identity by executing instructions106-112. Hardware processor104may execute instruction106to perform the set-up process. The set-up process may begin by acquiring shareholder profiles of known and unknown shareholders and through a series of protocols and identifies the custodian for each shareholder. The set-up process may end when the system100has determined a breakdown of existing shareholders and determined one or more patterns of fluctuation of shares held by each custodian.

Hardware processor104may execute instruction108to perform the monitoring process. The monitoring process may begin upon completing the set-up process. During the monitoring process, features of shareholder and custodian profiles may be actively monitored utilizing a Temporal Attention Mechanism (TAM). The monitoring process may be iterative and continue until an event determined by TAM (e.g., a TAM Event) is detected upon executing instruction110by hardware processor104. Hardware processor104may execute instruction112to perform the attribution process upon detecting the TAM event. The attribution process may generate and present the user with a set of likely investor candidates that the user will then finalize by making a final investor candidacy determination by attributing each investor candidate's buying/selling behavior.

In some embodiments, the set-up process may be triggered upon creating a profile for a company that recently went through a primary security offering within the system100. For example, information related to a new company may be entered via a graphical user interface of a deal center client application127running on the client computing device120and communicating with computing component102via network140.

In a primary security offering, such as a private investment in a public equity (PIPE), a company sells its shares directly to investors rather than through intermediaries such as a stock exchange or a wholesaler. Because the shares are sold to investors directly, the company (i.e., the issuer of the shares) must be able to verify the identity of investors to ensure regulatory investor accreditation and other requirements are met. Thus, in a PIPE transaction, the company is required to receive the identity information associated with each investor who obtained shares. Once the shares are sold, the company transfers the shares to the custodian specified by the investor. Accordingly, in a PIPE transaction, the investor, the shares, and the custodian are all known to the company at the time the securities are issued. Of course, subsequently, a shareholder may object to disclosure of its identity and request OBO status. This designation by an investor to OBO status makes it difficult for the company to track individual investor trading activity using publicly available trading information, such as a Securities Position Reporting (SPR). This is especially true for an OBO investor that changes its original holding position (e.g., buying and selling) and/or moves shares to one or more different custodians. It may be possible to determine a potential investor candidate by simply “matching” an influential OBO investor with a PIPE investor, having the same share count and custodian information, by virtue of having a relatively small number of such influential investors. However, once the original PIPE investors who have elected OBO status, start participating in the public secondary market through buying and/or selling and transferring shares across multiple custodians, relying on matching is not effective. To further complicate the matter, additional investors who were not part of the PIPE or other offering may buy shares on the secondary market, whose identity may be unknown to the company.

Creating a company profile and obtaining the initial shareholder, share number, and custodian information, to which the company is privy, as described above, may initiate the setup process which itself may comprise a number of sub-processes. For example,FIG.2illustrates the operations of the set-up process. In this particular example, the set-up process (performed by executing instructions106inFIG.1) may include a shareholder baseline process203, a trend analysis process205, and a custodian identifier process207.

The shareholder baseline process203may be configured to obtain shareholder data from one or more sources during one or more time periods. For example, shareholder baseline process203may obtain publicly available shareholder data (e.g., financial data, corporate entity information that is filed with the Securities and Exchange Commission (SEC), NOBO Report, OBO Report, SPR, etc.) from an external resource (e.g., external resource130inFIG.1).

Each time shareholder data is obtained it represents a snapshot of the company's share ownership at a particular time (time-series). Shareholder data associated with a PIPE transaction will always include the shareholder identity, share count, and custodian information, while shareholder data obtained any time after the initial PIPE transaction may not reveal identity information (e.g., by virtue of investors electing OBO status).

The shareholder baseline process203may be configured to generate a baseline shareholder trend report using the known PIPE data and subsequent time-series ownership data. An example baseline report process is illustrated inFIGS.3A-3B. As illustrated inFIG.3A, in block303, the baseline report is populated with information from the PIPE transaction, for example stored in the Deal Center application database. If all PIPE records have been added to the baseline report, as determined in305, then records from a NOBO report are obtained in307. As explained above, a NOBO report will identify investors. Upon determining that a shareholder on the NOBO report corresponds to a shareholder already added to the baseline report in309, NOBO data is used to update existing investor records with more recent NOBO data in steps313and315. Alternatively, new NOBO records are added to the baseline report in311. These shareholder records are known as identified NOBO investors.

If all NOBO records have been processed, as determined in317, then records from OBO report are obtained in319, as illustrated inFIG.3B. In block319, share range records are obtained from the OBO report. As explained above, an OBO report will not identify specific investors, rather, it will report ownership within a particular share range (e.g., 500,000-1,000,000 share range). In block321, all shareholders in the baseline report that meet the share range of the OBO records are obtained. Upon determining that the OBO share-range records exceed existing baseline report records in block323, alias records are added to the baseline report to represent these anonymous owners in block325. These shareholder records are known as unidentified anonymous OBOs. Conversely, if no OBO share-range records exceed existing baseline report records in block323, a determination whether the OBO has remaining unprocessed ranges is made in block327. If all records have been processed, the shareholder baseline process ends. Alternatively, share range records are obtained from the OBO report are obtained for any unprocessed records processed are taken (back to block319), as discussed above.

FIG.3Cillustrates the shareholder baseline report generated by the shareholder baseline process illustrated inFIGS.3A-3Band described above. In this particular example, records331,332,333,334may correspond to shareholders categorized as unidentified anonymous, records341,345as identified-anonymous; and records351,352,353,354as identified. Unidentified anonymous shareholders331,332,333, and334are identified by an alias and have been added to the baseline report by analyzing share-range records and determining that these share-ranges are not associated with a previously identified investor, as described above. Identified anonymous shareholders341,345are identified using their previously known identity but are tagged with “Y” in the OBO field360signifying objection to disclosure. Identified shareholders351,352,353,354are non-objecting and thus are identified using information obtained from the NOBO report, which is known. In particular, because the NOBO report is usually requested less frequently (e.g., monthly or even annually) due to its costs, any tracking of shareholder activity by only relying on the NOBO report is generally meaningless by virtue of its low frequency. That is, the company would miss a number of buying and selling events if it only looked at the trading activity once a year. Additionally, requesting the NOBO report is expensive. In accordance with various embodiments, the method described further below will provide company's management with a way to track trading activities of these investors without reliance on the NOBO.

Referring back toFIG.2, the next step in the set-up process (illustrated inFIG.1) is the trend analysis process205. The trend analysis process205may be configured to track one or more trends related to custodian share count, share range trends, and/or other similar time-series data. By determining a baseline signal first, the present system may detect future TAM Events, as described with respect toFIGS.5,7, and8. In essence, the trend analysis process determines a likelihood that a TAM Event is going to occur thereby providing a solution to the sampling frequency.

The custodian share count refers to the share number a particular custodian may hold at any given time. The number of shares a particular custodian holds at a particular time tends to strongly correlate with the future number of shares. That is to say, the total number of shares held by a custodian tends to stay the same over time. A linear regression analysis applied to custodian share count values in a particular time period is associated with having a high R2value. For example, as illustrated inFIG.4A, the share counts collected over a week have R2value of 0.843. Referring back toFIG.2, the trend analysis process205exploits this characteristic of custodian share count (e.g., a high R2value) and tracks share count at each custodian that holds the company's shares to determine if any statistically significant events have occurred. For example, a custodian having a significant reduction or increase in shares may be such an event. In some embodiments, the trend analysis process205may use TAM to determine a specific activity in the time-series data. The TAM learns to weigh critical days that impact the future share count prediction resulting in an optimization of the data acquisition cost. The optimization is achieved by considering several statistical features of the time-series data for share count in the custodian, such as the statistical outlier or confidence levels. For example, high pass filtering, numerical differentiation using various finite differences methods such as two-point estimation, symmetric difference, and other higher order methods could be used. In addition, if higher order derivatives are needed, these can be approximated as well using standard numerical techniques. In some embodiments TAM applies high-pass filtering on the custodian data in order to clean up the noise in the low value trends from the time-series data, and produces a local approximation of that to optimize the outcomes. To estimate a population mean μ for each custodian, in some embodiments a collection of random samples of data c1, c2, c3, . . . from the historical custodian time-series data may be used. One possible estimate of μ, which is the sample mean or first moment is:

In some embodiments, μ can then be used to analyze the linear regression and generate a signal indicative of statistically significant events that may have occurred over a particular index of the time-series data. Depending on the initial sampling frequency, if a TAM event has occurred, in some embodiments, additional SPR reports may be ordered to fill-in the missing time-series data.

Similarly, the trend analysis process205may track the changes in the share range analysis report to determine occurrence of statistically significant changes in share ownership. The share range analysis report is available to publicly traded companies on a weekly basis. The share range analysis report provides a breakdown of shareholders by share range. An example share range report is illustrated inFIG.4B. There are twenty-three share ranges. The first share range includes shareholder accounts that each own between 1 and 24 shares. Conversely, the twenty-third (i.e., the last share range) share range includes accounts that own over 1,000,000 shares. In this particular example, each bar represents shareholder counts per share range for a particular week (in this case Jul. 14, 2021). As shown in the share range report, shareholders holding relatively small counts are much more common than those holding large counts of shares. Further, time-series data associated with weekly share ranges indicates that fluctuations in shareholder counts are much more common in the smaller ranges, while the shareholder count for the upper ranges tends to remain steady. For example, as illustrated inFIG.4C, the share ranges collected over a 6-month period corresponding to higher share counts (e.g., share counts over 250,000 shares) have lower fluctuations. Referring back toFIG.2, the trend analysis process205exploits this characteristic of share range analysis (e.g., low fluctuation and low probability of fluctuation at high shareholder range) and tracks share count in the upper ranges to determine if any statistically significant events have occurred.

Referring back toFIG.2, the last step in the set-up process (illustrated in inFIG.1) is the custodian identifier process207. This process is configured to determine the likelihood the custodian provided by the shareholder is the actual custodian. Because investors, especially those that are sophisticated and influential, tend to “spread” their shares across a number of custodians, identifying the custodian(s) associated with a particular investor allows the system to track investor trading activity with more precision.

In one embodiment, custodian identifier process207uses an approach that leverages data captured in a primary security offering. As discussed above, in a PIPE transaction, the investor, the shares, and the custodian are all known to the issuing company. The custodian identifier process207is configured to determine a likelihood a custodian identified in the primary security offering is the actual custodian of the shareholder.

As illustrated inFIG.5, custodian identification process begins by retrieving the primary security offering transactional details in501. This data is used to obtain the name of the custodian and custodian statistical features (e.g., custodian share count statistic, such as those illustrated inFIGS.4A-4C) in503. Using custodian statistical features, the custodian identification process determines whether the Temporal Attention Mechanism (TAM) can detect the occurrence of additional shares at the custodian in505. In other words, the custodian identification process confirms whether the custodian indicated in the primary security offering data is the actual custodian.

If TAM cannot detect the occurrence of additional shares at the custodian in505, the primary security offering data stored in the Deal Center database is updated to reflect that the custodian identified by the shareholder has a medium likelihood of being the actual custodian. Alternatively, in the affirmative case, the TAM test is applied to the available custodian time-series data in507. Upon the TAM test identifying a TAM Event, the primary security offering data stored in the Deal Center database is updated to reflect that the custodian identified by the shareholder is highly likely to be the actual custodian.

However, if the TAM test fails to identify a TAM Event, the process attempts to determine whether the Owner immediately transferred shares to another custodian. Often, a more sophisticated investor will request the shares to be delivered to a custodian defined in the transaction, only to immediately transfer the shares to one or more different custodians. Upon determining that the custodian should have experienced a TAM Event (by virtue of expected share transfer) but failed to register the shares, the process determines whether a custodian change has occurred at509. Upon determining that the custodian change occurred by reviewing all custodians for a correlated TAM Event at511, the primary security offering data stored in the Deal Center database is updated to reflect that the custodian identified by the shareholder is highly likely to be the actual custodian. Alternatively, the process determines whether a splitting of shares over multiple custodians took place at513.

Upon determining that the shares were likely split across multiple custodians by determining that one or more share range histogram statistics experienced a TAM Event at515, all custodians with histogram outlier events are obtained at517. Each of the custodians identified is then examined for a correlated share count TAM Event at519. Each custodian having a determination of a share count TAM Event (e.g., a correlation between share count and TAM event is determined) is identified as the likely custodian of record for the shareholder and a high confidence score is assigned at521. By contrast, upon determining that no share count TAM Event is associated with a custodian, that custodian is assigned a medium confidence score at523.

In another embodiment, custodian identifier process207uses an approach leveraging data from the NOBO report. As explained earlier, the NOBO report provides limited information about the relationship between the NOBOs and the custodians by virtue of being obtained at irregular intervals. By using the NOBO approach, the custodian identifier process207is configured to determine a likelihood a NOBO is associated with a particular custodian.

The process obtains the NOBO report. The data obtained in the NOBO may include: a number of shares owned by each NOBO, identified as matrix A1, a total number of shares in each custodian, identified as matrix B1, and a number of NOBOs in each custodian, identified as matrix B2. Based on these identifications, a system of linear simultaneous equations can be formed, where A2is a 1 by n matrix, and x is a map that needs to be solved:

The x matrix contains only 0s and 1s. In addition, the following four constraints have to be taken into consideration: (1) each NOBO can only appear in at most one custodian, (2) each NOBO needs to be on at least one custodian, (3) the number of NOBO shares is an integer and a non-negative number, and (4) this system always has at least one solution.

This approach will not generate a unique solution. Any number of NOBOs with the same number of shares can be exchange between any two custodians, thus creating another solution. For example, if there are more than one NOBO with the same number of shares, then we know that we can exchange the position of these two NOBOs between custodians to create another solution. For example:

If the shares of NOBO1and NOBO2are equal to the share of NOBO3+NOBO4, then:

custodian A=NOBO3+NOBO4, and

Accordingly, more than solution may be generated.

In one embodiment, the problem may be solved using the knapsack-based decomposition approach. This algorithm decomposes the problem into a series of knapsack problems, which requires all the knapsacks to be filled instead of just a single knapsack.

Here, the knapsack problem is defined by N and it contains i items, where N={1, 2, 3, . . . . N} as a set of items. Each item has its associated weight Wi, and value Viand the knapsack can only take a total of weight of W. The goal is to maximize the total value from the items that can be put into the knapsack. For example, the following knapsack problem formula may be applied:

maxΣ1nViYi, so that ΣinWiYi≤W, where Yn∈{0,1}

In some embodiments, the following pseudocode for the traditional Knapsack problem may be used:

In some embodiments, a linear optimization solver may be used to estimate the optimal solution for the multiple knapsack problem when determining a likelihood that a NOBO is associated with a particular custodian. By using linear optimization, the solution may be obtained faster and without incurring significant processing costs traditionally associated with processing a knapsack problem, a np-complete problem, despite the extra contractions to narrow down the number of solutions.

As illustrated inFIG.6A, an example report demonstrates the results of using the NOBO approach to determine which NOBO is associated with which custodian. In this particular example, 3,300 NOBOs (represented by the bar graphs) are assigned to one of fifty custodians (identified by a label on the x-axis). The same results can be represented as a NOBO distribution report illustrationFIG.6B. Differently shaded sections within the same bar identify distinct NOBOs.

By using this approach, the method may determine a likelihood a NOBO is associated with a particular custodian. The NOBOs with higher likelihood ranges may be assigned a custodian based on this analysis. The primary security offering data stored in the Deal Center database may be updated to reflect this new custodian determination.

Referring back toFIG.1, instruction108executed by hardware processor104may perform the monitoring process. The monitoring process may begin upon completing the set-up process. During the monitoring process, features of shareholder and custodian profiles may be actively monitored utilizing a Temporal Attention Mechanism (TAM). The monitoring process may be iterative and continue until an event determined by TAM (e.g., a TAM Event) is detected (e.g., upon executing instruction110). In some embodiments, the monitoring process may be triggered each time new custodian data (e.g., an SPR report) is available to the system. Alternatively, the monitoring process may be executed periodically at a particular frequency.

An example monitoring process is illustrated inFIG.7. In this example, custodian share count data may be obtained in703. Upon determining that no custodians experienced a TAM Event in705, the monitoring process may end. Alternatively, upon determining that custodians experienced a TAM Event in705, for example as described above with reference toFIG.5, the attribution process707may be initiated. Additionally, based on the detection that TAM Event occurred, based on the frequency of the SPRs reports as obtained and potentially under-sampled data, SPR reports may be ordered for the missing time-series data values to more accurately determine the date of when the TAM Event occurred.

Referring back toFIG.1, instruction112executed by hardware processor104may perform the attribution process upon detecting the TAM Event. The attribution process may generate and present the user with a set of likely investor candidates that the user will then finalize by making a final investor candidacy determination by attributing each investor candidate's buying/selling behavior. An example attribution process is illustrated inFIG.8. At803, the attribution process retrieves all shareholders assigned to a custodian experiencing a TAM Event (e.g., statistically significant activity). At step805, the operator uses and inductive reasoning process to determine which shareholder to assign the TAM event. To assist the operator in the inductive reasoning process, as described below, in some embodiments shareholder candidates along with the information known and/or attributed to that candidate is presented to the operator. At807, the operator makes a selection of which shareholder to assign the buying or selling event that led to the TAM event. At809, the process appends micro- and macro- market and economic data to shareholder's profile. Next, at811, the process reviews existing attribution and confidence score data. Further, at812, the process determines whether the SEC quarterly filings warrant the audit. Finally, at813, upon determining that the SEC quarterly filings warrant the audit, the operator audits attribution and confidence scores are determined by the process.

FIG.9Aillustrates investor/shareholder candidates with a summary of known estimates generated by the attribution process. Investor/shareholder candidates901,903,905, and907are presented to the operator to conduct their inductive analysis. Note, candidates903,905, and907are identified non-anonymous OBOs while candidate901is unidentified anonymous identified using an ‘Alias’ name.

Next, to assist the operator in the inductive analysis, information for each shareholder under consideration is provided. For example, as illustrated inFIG.9B, data in primary equity offering, all historic transactions previously attributed to the shareholder, all publicly available data in the form of current and past investments (e.g., from SEC Forms 13G and 13F), data indicating whether the shareholder is an institutional investor, shareholder's investment portfolio, and their investment thesis (e.g., buy & hold, short-squeezer, day-trader, etc.) is presented.

Further, an “investor snapshot” of all known buying and selling statistics as well as ownership behavior is presented to the user, as illustrated inFIGS.9B-9C, for example.

InFIG.9D, the operator uses inductive reasoning to determine which investor should be attributed to the buying or selling activity. Due to the nature of the TAM, the exact number of shares in a transaction is not precisely known and thus the operator can suggest the level of confidence in the attribution assignment. Additionally, in some embodiments the operator makes an assessment as to the driver leading to the investor's selling/buying decision. These drivers include micro and macro factors such as the investor's warrants are expiring or the performance of the company, the industry or the economic sector, and it is important to understand the driver in order to classify the type of investor. Finally,FIG.9Dillustrates an example electronic form present in some embodiments that is presented to the operator to assign the attribution of a sale based on the information presented to the user inFIGS.9A-9C.

In some embodiments, the system may periodically retrieve SEC filings, such as Form 13G, or present any applicable NOBO reports which represent an investor's reported share ownership, for a particular investor. While these sources are still vulnerable to the sampling issue, the data therein can be used to collaborate on going trends. The system examines the SEC filings and upon determining that the company has filed a 13G, alerts the user to audit entered attributions of said investor.

In some embodiments, the attribution process may be configured to automatically assign trading signal data to a shareholder. For example, the attribution process may be trained using the manual shareholder assignment by periodically randomly splitting the data into training and predicted values. In some embodiments, the attribution process may provide the securities issuer a warning that the outcome of the delta between learned and predicted is high. For example, this data may be used as additional training data to attribute the buying / selling decision.

The following is an example of the custodian identifier system implemented using real life custodian share counts. For example,FIG.10Aillustrates share count statistics for an example custodian (TD Ameritrade). In this instance, both the inner fence and the outer fences of an example custodian for a given company are displayed. These values are derived from the last several days of trading. Notably, the share counts on July 9 and July 12 appear as statistical outliers. By virtue of being statistical outliers, these days represent a TAM Event. In this specific case, for example, an investor sold off a large number of shares. While TAM only detects unusual activity, it is possible, through inductive reasoning, to determine the correct shareholder that corresponds to the underlying TAM event. For example, a probability density function for a given security, as illustrated inFIG.10B, suggests that very few shareholders have large quantities of shares. When the share count exceeds 300K, it can be easily observed that the probability of a shareholder having more than 100K is nearly zero. Moreover, it is typical that the top five percent of shareholders leverage the primary equity offerings, thus the shareholder and its name is already in the database118.

FIG.10Cillustrates an example histogram share range counts for an underlying security. The data indicates that there are only eleven shareholders (bottom three rows) that have in excess of 250,000 shares. By providing the operator (i.e., securities issuer) with a histogram, an embodiment enables and assists the securities issuer in conducting its analysis when determining the shareholder.

Where components, logical circuits, or engines of the technology are implemented in whole or in part using software, in one embodiment, these software elements can be implemented to operate with a computing or logical circuit capable of carrying out the functionality described with respect thereto. One such example computing module is shown inFIG.11. Various embodiments are described in terms of this example computing module1100. After reading this description, it will become apparent to a person skilled in the relevant art how to implement the technology using other logical circuits or architectures.

FIG.11illustrates an example computing module1100, an example of which may be a processor/controller resident on a mobile device, or a processor/controller used to operate a payment transaction device, that may be used to implement various features and/or functionality of the systems and methods disclosed in the present disclosure.

Computing module1100might include, for example, one or more processors, controllers, control modules, or other processing devices, such as a processor1104. Processor1104might be implemented using a general-purpose or special-purpose processing engine such as, for example, a microprocessor, controller, or other control logic. In the illustrated example, processor1104is connected to a bus1102, although any communication medium can be used to facilitate interaction with other components of computing module1100or to communicate externally. The bus1102may also be connected to other components such as a display1112, input devices1114, or cursor control1116to help facilitate interaction and communications between the processor and/or other components of the computing module1100.

Computing module1100might also include one or more memory modules, simply referred to herein as main memory1106. For example, preferably random-access memory (RAM) or other dynamic memory might be used for storing information and instructions to be executed by processor1104. Main memory1106might also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor1104. Computing module1100might likewise include a read only memory (“ROM”)1108or other static storage device1110coupled to bus1102for storing static information and instructions for processor1104.

Computing module1100might also include one or more various forms of information storage devices1110, which might include, for example, a media drive and a storage unit interface. The media drive might include a drive or other mechanism to support fixed or removable storage media. For example, a hard disk drive, a floppy disk drive, a magnetic tape drive, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive might be provided. Accordingly, storage media might include, for example, a hard disk, a floppy disk, magnetic tape, cartridge, optical disk, a CD or DVD, or other fixed or removable medium that is read by, written to or accessed by media drive. As these examples illustrate, the storage media can include a computer usable storage medium having stored therein computer software or data.

In alternative embodiments, information storage devices1110might include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into computing module1100. Such instrumentalities might include, for example, a fixed or removable storage unit and a storage unit interface. Examples of such storage units and storage unit interfaces can include a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, a PCMCIA slot and card, and other fixed or removable storage units and interfaces that allow software and data to be transferred from the storage unit to computing module1100.

Computing module1100might also include a communications interface or network interface(s)1118. Communications or network interface(s) interface1118might be used to allow software and data to be transferred between computing module1100and external devices. Examples of communications interface or network interface(s)1118might include a modem or softmodem, a network interface (such as an Ethernet, network interface card, WiMedia, IEEE 802.XX or other interface), a communications port (such as for example, a USB port, IR port, RS232 port Bluetooth® interface, or other port), or other communications interface. Software and data transferred via communications or network interface(s)1118might typically be carried on signals, which can be electronic, electromagnetic (which includes optical) or other signals capable of being exchanged by a given communications interface. These signals might be provided to communications interface1118via a channel. This channel might carry signals and might be implemented using a wired or wireless communication medium. Some examples of a channel might include a phone line, a cellular link, an RF link, an optical link, a network interface, a local or wide area network, and other wired or wireless communications channels.