IES84184Y1 - Systemic investment data analysis - Google Patents
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- IES84184Y1 IES84184Y1 IE2005/0067A IE20050067A IES84184Y1 IE S84184 Y1 IES84184 Y1 IE S84184Y1 IE 2005/0067 A IE2005/0067 A IE 2005/0067A IE 20050067 A IE20050067 A IE 20050067A IE S84184 Y1 IES84184 Y1 IE S84184Y1
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Abstract
ABSTRACT A system and method for outputting hypothetical investment defining data (411) are provided by the present invention. The system comprises a plurality of networked terminals (101 to 104), each of which is configured with at least processing means (208), memory means (209), networking means (211,212) and visual display means (202). The memory means (209) stores at least a local instantiation (409) of a network—distributable, updateable data structure and instructions (403), which configure the processing means of at least one terminal to obtain (304) financial data (410) from at least another one of the networked terminals (103) by means of the networking means (212). The local data structure instantiation (409) is updated (501) with the financial data (410) obtained. Data in the data structure instantiation (409) is processed with a plurality of data processing functions (404 to 407), wherein the data processing functions define a systemic financial data process (502 to 507). Processed systemic data (411) is output (306 to the visual display means (202) or to another of the networked terminals (104), as hypothetical investment defining data and the local instantiation (409)is either removed from the memory means or further remote financial data (410) is requested.
Description
Field of the Invention
This invention relates to systems for analysing financial data and, more particularly, to a
system and method for outputting hypothetical systemic investment data from a plurality of
financial data processing functions.
Background to the Invention
Numerous systems exist with which to attempt to forecast investment variables. Research and
developrnent is particularly extensive in this field, which aims to detennine an optimum
process for constructing and managing investment portfolios, because investing is a
complicated process, the dynamic of which is generated by the interaction of institutions,
companies and people who produce, distribute and apply financial knowledge. In other words,
it is a systemic process. One element by itself — such as currency price~ will not provide
judicious investment data. On the contrary, the investment process requires a large variety of
other supporting elements such as price, interest rate, implied option volatility, and financial
futures data etc. in order to accurately forecast investment data, including for instance price
movements, market risk, market correlation and investment portfolio allocation.
In the particular context of managing currency hedge funds, the investments of which are
independent from more traditional investments such as stocks, bonds and property markets for
which numerous portfolio construction processes are know, a major problem is the volatility
of the investment markets in which currency purchases and sales, i.e. trades, are placed, best
expressed as a fluctuation of currency prices and interest rate yields over a variety of time
frames. Such a volatility is usually more severe in currency markets than in said other
traditional markets, making the portfolio construction process more difficult in terms of
detennining an optimal allocation of trades for any given investment portfolio, and also
making the timing of trades more significant.
Currency investment portfolios are known to range between $5,000 and $30m or more in
value, and a second problem is that owners of smaller portfolios may be disadvantaged with a
lower trading priority because purchasing smaller amounts of currencies, which is
compounded if the mix of portfolios of their currency hedge fund management company
comprises a disproportionately high number of large portfolios.
Moreover, another problem stems from the inherent difficulty in modeling trades and market
conditions, cg. generating hypothetical investment data, for formulating investment strategies
in the highly-volatile and time—sensitive context described above.
Object of the Invention
It is therefore desirable to improve financial data processing for outputting hypothetical
investment data, wherein said investment data may be output as a sequence of test trades or
may define said sequence of test trades independently of the trading’ volume defined by said
investment data. It is also preferable that said hypothetical investment data be systemic, i.e.
that each such test trade may be output as an optimum construct of a portfolio in respect of a
plurality of factors including market forecast, market risk and investor risk.
Summary of the Invention
According to an aspect of the present invention, a system is provided for outputting
hypothetical investment defining data, comprising a plurality of networked terminals. each of
which is configured with at least processing means, memory means, networking tneans and
visual display means, said memory means storing at least a local instantiation of a network-
distributable, updatcable data structure and instructions which configure said processing
means of at least one of said temiinals to obtain financial data from at least another one of
said networked terminals by means of said networking means; update said local data structure
instantiation with said obtained financial data; process said data in said data structure
instantiation with a plurality of data processing functions, wherein said data processing
functions collectively define a systemic financial data processing application; output said
systemic data to said visual display means as hypothetical investment defining data and either
remove said local instantiation from said memory means or request further remote financial
data.
According to another aspect of the present invention, A method for outputting hypothetical
investment defining data is provided, said method comprising the steps of obtaining financial
data from a networked terminal; locally updating a data structure instantiation with said
obtained financial data; locally processing said data in said data structure instantiation with a
plurality of data processing functions, wherein said data processing functions collectively
define a systemic financial data processing application; outputting said systemic data as
liypotlietical investment defining data and either removing deleting said systemic data or
requesting further financial data.
According to yet another aspect of the present invention, a computer programmed to output
hypothetical investment defining data is provided, which comprises processing means,
memory means, networking means and visual display means, said memory means storing at
least a local instantiation of a remote data structure and instructions which configure said
processing means to obtain financial data from at least another networked terminal by means
of said networking means; update said local data structure instantiation with said obtained
financial data; process said data in said data structure instantiation with a plurality of data
processing functions, wherein said plurality of data processing functions collectively define a
systemic financial data processing application; output said systemic data to said visual display
means as hypothetical investment defining data and either remove said local instantiation
from said memory means or request further remote financial data.
The financial data preferably includes at least one currency price and at least one interest rate
yield and the data structure is preferably a local instantiation of a remote database storing data
therein as historical series.
The data processing functions preferably include a market forecasting function, a market risk
forecasting function, a portfolio risk forecasting function and a broadcasting function. The
market forecasting function advantageously outputs an optimal combination of expected
currency price, expected interest rate yield and time fame. Alternatively, the market risk
forecasting outputs volatility data.
The portfolio risk forecasting function preferably outputs hypothetical investment—defining
data when the correlation between pairs of currencies is negative.
Brief Description of the Drawings
The features and advantages of the invention will be presented in conjunction with the
following illustrations listed below:
Figure 1 shows a preferred embodiment of the present invention in an environment, including
distributed financial data sources and at least one networked terminal;
Figure 2 provides an example of the networked terminal shown in Figure l, which includes
processing means, memory means and networking means;
Figure 3 details the operational steps according to which the networked terminal shown
Figures 1 and 2 processes data, including a step of loading a set of instructions and a step of
processing financial data;
Figure 4 illustrates the contents of the memory means shown in Figure 2 funher to completing
the loading step shown in Figure 3, including a local copy of a data structure and a plurality of
modules;
Figure 5 further details the step of processing financial data shown in Figures 1, 3 and 4,
including steps of processing financial data with a plurality of modules shown in Figure 4;
Figure 6 further details the step of outputting market forecast data shown in Figure 5;
Figure 7 further details the step of outputting market risk data shown in Figure 5;
Figure 8 further details the step of outputting trading risk data shown in Figure 5; and
Figure 9 further details the step of outputting hypothetical investment data shown in Figure 5.
Detailed Description of the Drawings
A preferred embodiment of the present invention is shown in an environment in Figure 1,
which includes a plurality of network—coimected terminals 101, 102, 103 and 104. Each of
said terminals is configured with data processing means, data storage means, data output
means such as video display units and networking means and data input means such as said
networking means and user input means, respective examples of which will be further
described hereinbelow.
In the example, terminals 101 and 102 are located at a hedge fund management company such
as ACM, based in Dublin, Republic of Ireland. Terminal 103 is remotely located at a rea1~tiine
financial data service provider, such as the REUTERS Trading Systems Division of
REUTERS, based in New York, New Jersey, USA. Terminal 104 is remotely located at a
financial trading company, for instance another branch of ACM located elsewhere.
Each of terminals 101, 103 and 104 are preferably network-connected to a Wide Area
Network (WAN) 105, such as the Internet, via their respective Internet Service Providers
(lSPs) 106, 107 and 108. Terminals 101 and 102 are also network-connected to a Local Area
Network (LAN) 109 located at said hedge fund management company. In the preferred
embodiment of the present invention, terminal 101 receives real-time financial data from
terminal 103 over a first WAN connection, which is preferably secured with using standard
128—bit SSL encryption, which both encrypts and decrypts said financial data exchanged
between said terminals over said network 105. In the preferred embodiment of the present
invention still, terminal 101 broadcasts investment trading data to terminal 104 over a second
WAN connection, which is also preferably secured with using said standard 128-bit SSL
encryption as described above.
It will be readily apparent to those skilled in the art that the above environment is provided by
way of example only, and that the present invention may be embodied in any network
comprising devices connected thereto, exchanging data processed as further described
hereinbelow
An example ofthe terminal 101 shown in Figure l is provided in Figure 2. Terminal 101 is a
computer terminal configured with a data processing unit 201, data outputting means such as
video display unit (VDU) 202, data inputting means such as a keyboard 203 and a pointing
device (mouse) 204 and data inputting/outputting means such as a network connection 205,
magnetic data—carrying medium reader/writer 206 and optical data—earrying medium
reader/writer 207.
Within data processing unit 201, a central processing unit (CPU) 208, such as an Intel
Pentium 4 manufactured by the Intel Corporation, provides task co-ordination and data
processing functionality. Instructions and data for the CPU 208 are stored in main memory
209 and a hard disk storage unit 210 facilitates non—volatile storage of data and data
processing instructions. A modem 211 provides a wired connection 205 to the ISP 106 or said
wired connection 205 to the Intemet may be perfonned through Local Area Network (LAN)
via a network card 212, particularly if said connection is ofthe type known as broadband. In a
preferred embodiment of the present invention, said network card 212 is further configured
with wireless data distributing means, for instance to establish a wireless network connection
of the type known as Wi-Fi (IEEE 802.11 protocol) with other proximate devices having
similar networking means, for instance temiinal 102. A universal serial bus (USB)
input/output interface 213 facilitates connection to the keyboard and pointing device 203, 204.
All of the above devices are connected to a data input/output bus 214, to which said magnetic
data—carrying medium reader/writer 206 and optical data—carrying medium reader/writer 207
are also connected. A video adapter 215 receives CPU instructions over said bus 214 for
outputting processed data to VDU 202 and/or to further output data display means.
In the preferred embodiment, data processing unit 201 is of the type generally known as a
compatible Personal Computer (‘PC’), but may equally be any device configured with data
inputting, data processing, data outputting and networking means providing at least the
functionality described above. Any such device may include, but is not limited to,
PowerMac® or iMac® computers manufactured by the Apple® Corporation of Cupertino,
California, USA; a Portable Digital Assistant (PDA) such as a Palm m505® manufactured by
PalniOne® lnc. ofMilpitas, California, USA; a Portable Digital Computer (PDC) such as an
IP/\Q® manufactured by the Hewlett—Packard® Company of Palo Alto, California, USA; or
even a mobile phone such as a Nokia 9500 manufactured by the Nokia® Group in Finland, all
of which are generally configured with processing means, output data display means, memory
means, input means and wired or wireless network connectivity.
Processing steps are described in Figure 3 according to which terminal 101 operates. Temiinal
101 is first switched on at step 301. At step 302, the operating system is loaded which
provides said temiinal 101 with basic functionality, such as initialisation of data input and/or
output devices, data file browsing, keyboard and/or mouse input processing, video data
outputting, network connectivity and network data processing. At step 303, a set of financial
data processing instructions is loaded into memory 209, which is a set of instructions for
configuring CPU 208 to process financial data according to rules described hereafter.
At step 304, terminal 101 connects to both terminal 103 and terminal 104 over WAN 105 by
way of network card 212 and accesses data respectively stored thereat in real—time. In the
example, terminal 103 is configured according to the TRIARCHT“ application and data
distribution structure of REUTERS, which is well known to those skilled in the art and
provides financial data. In the example still, terminal 104 is configured substantially like
terminal 101 and stores a database ofthe financial data of terminal 103 organised in historical
series. Said financial data and a copy of the database thereof are preferably downloaded
locally and stored in memory 209. Said data is subsequently processed at step 305 according
to said set of instructions loaded at said step 303, such that hypothetical investment data may
be output by said set of instructions according to the present invention at step 306.
Said hypothetical investment data is output at said step 306 to VDU 202 for consideration by
the user of terminal 101. A question is asked at step 307, as to whether additional input
financial data is required, for instance if hypothetical data output at step 306 does not satisfy
conditions set by the user in terms of return on investment, risk position and/or the like, or if
hypothetical data output at step 306 is insufficient to formulate an optimum investment
strategy, or if local financial data previously downloaded according to step 304 and stored in
memory 209 is out—of—date to re-formulate an optimum investment strategy. If the question of
step 307 is answered positively, control returns to step 304, where financial data having been
updated in real—time during the processing of steps 305 and 306 may therefore be
downloaded, to ensure the processing of step 305 at terminal 101 uses up—to—date financial
data parameters to output optimum hypothetical investment data, e.g. reduces the hypothetical
character of the investment data output after downloading the updated financial data further to
answering question 307 positively. The local copy of the data structure is not however
downloaded again at said second iteration of step 304, in order to avoid discrepancies in the
hypothetical investment data arising from data locally input by the user of terminal l0l. In an
alternative embodiment of the present invention, the question of step 307 is periodically
answered positively regardless of the example conditions described above, to ensure up—to—
date financial data is processed at terminal 101 to output optimum hypothetical investment
data at any particular point in time whilst the systemic application 403 is processed by CPU
208.
Alternatively, the question of steps 307 may be answered negatively, and the functionality of
the set of instructions loaded at the previous step 303 may not be required anymore, whereby
said set of instructions is unloaded from memory 209 at step 308, along with the copy of the
database downloaded from tenninal 104. T enninal 101 may eventually be switched off at
final step 309.
The contents of the memory 209 are illustrated in Figure 4, further to completing the
instructions loading step 303. An operating system is shown at 401 which, in the example, is
Windows XP Corporate Edition manufactured and distributed by the Microsoft Corporation
of Redmond, California, USA. Said operating system configures CPU 208 to address data
input from and broadcast to local devices connected to bus 2l4 according to a data file and
memory addressing system, as well as provide processing instructions to said devices
according to device drivers. Accordingly, communication instructions are shown at 402,
which represent a portion of the functionality of OS 401 dedicated to receiving and
broadcasting data respectively from and to remote data processing systems such as terminals
102 to 104 via modem 211 or NIC 212, including a Secure Socket Layer (SSL) encryption
module (not shown) to secure any such data distribution, and according to particular data
exchange protocols such as TCP/IP. It will be readily apparent to those skilled in the art that
the above OS is provided by way of example only, and that the present invention may be
embodied with any of the previously—enumerated data processing devices, having respective
operating systems that may differ from the example above, such as Mac OS-X in the case of a
computer manufactured by Apple®, or Windows Mobile 2003 in the case of a PDC
manufactured by Hewlett—Packard® or a mobile phone manufactured by Nokia®.
In the preferred embodiment of the present invention, the set of financial data processing
instructions loaded at step 303 is shown at 403 and includes a plurality of respective financial
data processing functions, which collectively define a systemic financial data processing
application 403. Said functions include a market forecasting module 404, a market risk
assessing module 405, a portfolio risk assessing module 406 and a hypothetical investment
data outputting module 407, each of \vhich will be described in further details hereinbelow.
Financial data processed by application 403 includes downloaded data stored in a copy 409 of
the data structure stored at terminal 104. Financial data processed by application 403 also
includes remote real-time financial data 410 downloaded from TRIARCHW terminal 103 and
local hypothetical investment data 41 1 output by application 403. Memory 209 also includes
user input data 412, which may be alphanumerical data input by means of keystrokes on
keyboard 203, user interrupts input by means of clicking user—operable switches of mouse 204
and graphical user interface updates input by means of said user translating mouse 204.
The step 305 of processing financial data 409, 410 with a plurality of modules 404 to 407 is
fuither detailed in Figure 5. At step 501, application 403 firstly reads the remote data 410
input from remote terminals 103 and/or 104, which includes the database copy 409 and up-to-
date financial data 410 at the first iteration of step 304, then update financial data 410 at
subsequent iterations of step 304. Database copy 409 is a copy of the data structure storing
and referencing financial data 410 downloaded over time at terminal 104 as historical series
thereof, wherein said financial data 410 respectively includes prices of currencies and yields
of interest rates.
At step 502, a question is asked as to whether the data within the database copy 409 should be
updated, i.e. when input network data 410 only comprises update financial data. In the
preferred embodiment of the present invention, the question of step 502 is only ever answered
negatively once, that is when the question of step 502 is asked for the first time subsequently
to loading and starting the processing of application 403 according to step 303. When the
question of step 502 is answered positively, whether upon user request according to the
preferred embodiment of the present invention or automatically in an altemative embodiment,
application 403 replaces any financial data stored in memory 209 with corresponding, updated
financial data 410 at step 503, to the exclusion of any other data stored in local database copy
209.
At step 504, application 403 invokes the functionality of market forecasting module 404 to
process historical currency and yield data in database copy 409 in order to output market
forecast data defining price movements in the currency and bond markets, under the form of
specific values at a specific date. Said market forecast data output at step 504 forms first input
data for the processing of systemic investment data. At step 505, application 403 invokes the
functionality of market risk assessing module 405 to process historical currency and yield
volatility data in database copy 409 in order to output market risk data for each individual
market, under the form of a confidence interval. Said confidence interval defines the
likelihood ofa one standard deviation move (see graph 1 below).
CO(.DV1’C\l‘.OK>(D
VVTIOLO CDO’)
Graph 1
Using the Normal Distribution, the first confidence interval is at 68%, the second at 95%.
Treasury operations in banking, for example, use this curve to assess how susceptible to a loss
they are with the current open positions. Said market risk data output at step 505 therefore
forms second input data for the processing of systemic investment data. At step 506,
application 403 invokes the functionality of portfolio risk assessing module 406 to process
database copy 409 and first and second input data respectively output from steps 504 and 505
in order to output trading risk data for each individual test investment or test trade of a
portfolio or a plurality thereof, and adjust said hypothetical portfolio investments accordingly.
Said trading risk data output at step 506 forms third input data for the processing of systemic
investment data. Application 403 next invokes the functionality of outputting module 407 at
step 507 to process database copy 409 and first, second and third input data respectively
output from steps 504, 505 and 506 in order to output optimal hypothetical investment data
defining trades that may be effected in one or a plurality of markets at particular points in time
or price conditions.
The step 504 of processing financial data 409 by Market Forecasting Module (MFM) 404 to
output market forecast data is further detailed in Figure 6. The inputs to the MFM 404 are
stored in database copy 409 by downloading live real—time data 410 from Reuters via a
TRIARCHTM structure 103. Once stored in the database copy 409, the module 404 uses this
historical data 409 to forecast future price movements. There are two data components 4
stored (501), price and interest rate yields. The data inputs are periodically and/or manually
downloaded and the forecast will be calculated independently from previous days. Markets
are preferably first segregated into certain types of behavioral groups at step 601, which
processes a correlation (CR) and divides markets into three distinct groups: Persistent,
Random and Anti—persistent. Persistent markets exhibit serial correlation with the memory
function positively correlated to historical values in the time series, whereby a question at step
602 is answered positively and any such Persistent market is declared as such at step 603 for
further data processing. Conversely, Random markets display no memory feature of any
nature and Anti-persistent markets exhibit serial correlation with the memory function
negatively correlated to historical values in the time series, neither of Random or Anti-
persistent markets warranting further processing for outputting investment data therein. The
MFM 404 thus determines (602, 603, 604) the behavioral characteristics of each market to
determine which markets can be defined as persistent at said step 603. In the preferred
embodiment of the present invention, application 403 replaces steps 601 to 604 in daily use
with a step (not shown) of looking up previously—detemiined persistent markets from database
copy 409, thereby further accelerating the processing of step 305.
Within these persistent markets, an exponential weighed average of the historical price data is
processed at step 605 to determine the expected momentum (C) of each individual currency
market. The expected interest rate yield (Y) in each market is then forecasted at step 606 by
extrapolating yields over multiple time frames. At the final step 607, the forecasting period is
detemiined: using historical data 409, the MFM 404 determines the current market outlook
and the cost associated with each time frame. The MFM 404 assesses relative strengths of
each component to detemiine the optimal combination between momentum, yield and
timeframes, whereby market forecast data (C; Y) is output at said step 607.
The step 505 of processing financial data 409 by Risk Assessing Module (RAM) 405 to
output market risk data is further detailed in Figure 7. The systemic investment process of the
present invention defines how much risk a portfolio will theoretically take prior to placing any
trades in the market. This requires a forecast of what risk is to be expected both at a portfolio
level and at within each individual market. Risk in the financial markets is often defined by
the Value At Risk (VAR) of a portfolio. This defines how much money is at a risk of loss at
any point in time in terms of confidence intervals, as previously described. In order to
measure the VAR, a forecast of the expected volatility of each component of the portfolio is
required. Such forecasts typically rely on implied volatilities used in the Option Markets to
forecast volatility. This can lead to problems in complex cross—currency pairs, where the
relationship between volatility and correlation breaks down. The correlation matrix between
currency pairs is often not positive definite. This can lead to expected correlations being
outside the correlation range -1 to +1. Thus, it is difficult or even impossible to use implied
volatilities to accurately forecast risk for each currency pair.
The RAM 405 forecasts the risk within each individual market while the Optimising Module
406 further described hereinbelow manages portfolio risk. At step 701, RAM 405 selects the
respective price data (P1, P2) in database copy 409 of a first currency pair (CPD). RAM 405
processes forecast price volatility data (Vg) for said pair (CPU) at step 702. RAM 405
processes historical price volatility data (V11) over a time interval or frame (T) for said pair
(CPH) at step 703.
A comparison of forecast and historical volatility data sets (V5) and (VH) is subsequently
processed at step 704, whereby a question is asked at step 705, as to whether the forecast price
volatility data (Vg) exceeds historical price volatility data (VH) by an empirical safety margin,
signifying that there may be artifacts in the most recent set of downloaded data 410, whether
as a result of unexpected or transitional market trades, or as a result of data error. If the
question of step 705 is answered positively, RAM 405 elects to wait for the next database
update at step 706, e.g. wait to process the next set of downloaded data 410, which may erase
some or most ofthe statistical ‘noise’ caused by said unexpected or transitional market trades,
or data error. At said step 706, the currently—selected currency pair (C P“) is therefore marked
for subsequent re—processing according to steps 701 to 705 and control proceeds to returns to
step 701, at which a next currency pair (CPM) may be selected and risk~assessed, and so on
and so forth until said first—selected currency pair (CPU) is eventually processed again.
Alternatively, if the question of step 705 is answered negatively, RAM 405 outputs market
risk data (Vp) for the selected currency pair (CP,,) at step 707 and a second question is asked
at step 708, which is also asked after step 706, as to whether another currency pair exists in
the database copy 409, which should be processed according to steps 701 to 708. If the
question of step 708 is answered positively, control return to step 701, at which said next
currency pair (CPM) may be selected and risk-assessed, and so on and so forth. Preferably,
the processing loop defined by steps 701 to 708 of module 405 is continuous and only ever
interrupted with answering the question of step 708 negatively if the user of terminal 101
wants to terminate the application 403 according to steps 308 and 309.
The step 506 of processing financial data 409 and data respectively output by modules 404
and 405 by portfolio risk assessing module (OM) 406 to output trading risk data is further
detailed in Figure 8. The OM 406 systematically reconstructs each portfolio (P) of
investments (I) stored in database copy 409 on a daily basis or more frequently if required. A
first portfolio (PN) is therefore selected at step 801 and a first investment (IN) thereofselected
at step 802. OM 406 processes the Value-At—Risk (VARN) of said selected investment (IN) at
step 803 and a question is asked at step 804, as to whether portfolio (PN) includes another
investment (INN). If the question of step 804 is answered positively, control returns to step
802 and said next investment (INN) is selected for processing of its respective Value—At—Risk
(VARNN), and so and so forth until such time as all investments (IN) of portfolio (PN) have
been processed and OM 406 may calculate the total VaIue—At—Risk (PVARN) of said portfolio
(PN) prior to establishing any new investment positions at step 805.
OM 406 then establishes the optimal set of hypothetical investment trades (ID) for the day
using the inputs from the MFM and RAM modules 404 and 405 and adjusts the investments
within the portfolio to achieve the desired asset allocation and position size. For each test
investment (IN) of the currently—selected portfolio (PN), OM 406 identifies markets (M1, M2)
respectively corresponding to the investment currency pair (CP) at step 806. To output
hypothetical investment data (ID) for a portfolio which optimizes the expected return using
the volatility forecast (VC) for each market requires examining the correlation between each
of said markets (Ml, M2). Negatively correlated markets will reduce the overall risk of the
investments while positively correlated markets will increase the overall risk. A relationship
between the joint risk and the two markets is therefore processed as a factor (JR) at step 807.
As the joint risk is reduced if the correlation between the currency pairs is negative, it is
preferable to construct a portfolio that takes advantage of any negatively correlated trades,
whereby (IR) is subsequently compared against an empirical threshold, shown as a question at
step 808.
If the question of step 808 is answered positively, signifying that (IR) is below said empirical
threshold and that the correlation is negative, OM 406 preferably recalculates the risk of
investment (IN) at minimum and the (VARN) of said investment (IN) is recalculated at step
809, whereby a question is asked at step 810, as to whether said recalculated (VARN)
compares favorably with the (VARN) of said investment (IN) output at the previous step 803.
If the question of step 810 is answered positively, OM 406 outputs data (ID) defining an
investment or trade at step 811 and control proceeds to a final question at step 812, as to
whether another portfolio (PNH) of investments (IN) stored in database copy 409 should be
processed according to steps 801 to 812. Incidentally, if either of the question of step 808 or
the question of step 810 are respectively answered negatively, control proceeds directly to the
question of step 812. Preferably, the processing loop defined by steps 801 to 812 of module
is continuous and only ever intemipted with answering question 812 positively if the user
ofterminal 101 wants to terminate the application 403 according to steps 308 and 309. Risk-
assessed and forecast investment data (ID) is therefore output by portfolio risk assessing
module 406 to maintain one or a plurality of portfolios of investments, wherein any
investment positions that exceed the expected volatility forecast (Vc) are continually adjusted
to reflect the new market conditions.
The step 507 of outputting hypothetical investment data 411 by broadcasting module (BM)
407 is further detailed in Figure 9. BM 407 allows the test trades (ID) to be mock—placcd in
one or a plurality of markets in an efficient manner. The speed at which the test trades can be
placed is essential to the performance of the hypothetical investments in either of the short
and long terms. BM 407 also ensures that no customer is disadvantaged because of portfolio
size.
Like the OM 406, the BM 407 systematically reconstructs each portfolio (P) of investments
(1) stored in database copy 409 on a daily basis or more frequently if required. A first
portfolio (PN) is therefore selected at step 901 and a first investment (IN) thereof selected at
step 902. BM 407 processes the Net Asset Value (NAVN) of said selected investment (IN) at
step 903 and a question is asked at step 904, as to whether portfolio (PN) includes another
investment (INN). If the question of step 904 is answered positively, control returns to step
902 and said next investment (IN+i) is selected for processing of its respective Net Asset
Value (NAVNN), and so and so forth until such time as all investments (IN) of portfolio (PN)
have been processed and BM 407 may calculate the total Net Asset Value (NAVN) of said
portfolio (PN) at step 905.
The BM 407 calculates the net asset value (NAV) of each account prior to trading and based
on that determines what size position to enter for each account. A question is therefore asked
at step 906, as to whether database copy 409 includes another portfolio (PNN). If the question
ofstep 906 is answered positively, control returns to step 901 and said next portfolio (PN+1) is
selected for processing of its respective Net Asset Value (NAVNN) at step 905, and so and so
forth until such time as all portfolios (PN) of database copy 409 have been processed. A
position size (S) is subsequently assigned to each account (PN) based upon its respective
(NAVN) at step 907. The BM 407 then summarizes all of the investments (IN) of each
portfolio (PN) into a table at step 908 according to (S), wherein each hypothetical investment-
defining data (ID) output by OM 406 is assigned a corresponding position size reference (S).
Said table therefore defines a mock—broadcasting sequence, according to which each (ID)
entry of said table (which would be sent to the communication instructions 402 for broadcast
to the remote terminal of a trading organization, such as Deutsche Bank based in London, in
the case of actual investment—defining data) is time-logged, thus allowing one trade (ID) to be
executed with its particular market at one price (S) and, further, allowing the user ofterminal
l0l to observe the results the hypothetical investment data output using actual financial data
would have upon portfolio data within database copy 409, if said hypothetical investment data
were to be used for actual portfolio investment data.
Claims (5)
1. A system for outputting hypothetical investment defining data, comprising a plurality of networked terminals, each of which is configured with at least processing means, memory means, networking means and visual display means, said memory means storing at least a local copy of a network-distributable, updateable data structure and instructions which configure said processing means of at least one of said terminals to obtain financial data from at least another one of said networked terminals by means ofsaid networking means; update said local data structure with said obtained financial data; process said data in said data structure with a plurality of data processing functions, wherein said data processing functions collectively define a systemic financial data processing application; output said processed data to said visual display means; and either remove said local data structure from said memory means or request further remote financial data ; wherein said processed data in said local data structure is hypothetical systemic investment defining data and requesting further remote financial data reduces the hypothetical character of said hypothetical systemic investment defining data.
2. A method for outputting hypothetical investment defining data, said method comprising the steps of obtaining financial data from a networked terminal; updating a local copy of a data structure with said obtained financial data; processing said data in said local data structure with a plurality of data processing functions, wherein said data processing functions collectively define a systemic financial data processing application; outputting said systemic data as hypothetical investment defining data; and either removing deleting said systemic data or requesting further financial data, wherein said processed data in said local data structure is hypothetical systemic investment defining data and requesting further remote financial data reduces the hypothetical character of said hypothetical systemic investment defining data
3. A computer programmed to output hypothetical investment defining data, comprising processing means, memory means, networking means and visual display means, said memory means storing at least a local copy of a remote data structure and instructions which configure said processing means to obtain financial data from at least another networked terminal by means of said networking means; update said local data structure with said obtained financial data; process said data in said data structure with a plurality of data processing functions, wherein said plurality of data processing functions collectively define a systemic financial data processing application; output said systemic data to said visual display means as hypothetical investment defining data; and either remove said local instantiation from said memory means or request further remote financial data, wherein said processed data in said local data structure is hypothetical systemic investment defining data and requesting further remote financial data reduces the hypothetical character of said hypothetical systemic investment defining data.
4. The system for outputting hypothetical investment defining data according to claim 1, wherein said data processing functions include a market forecasting function, a market risk forecasting function, a portfolio risk forecasting function and a broadcasting function.
5. A system substantially as herein described in relation to the accompanying drawings.
Publications (2)
Publication Number | Publication Date |
---|---|
IES84184Y1 true IES84184Y1 (en) | 2006-04-19 |
IE20050067U1 IE20050067U1 (en) | 2006-04-19 |
Family
ID=
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