Financial Analysis - Complete Information

The Evolution of Financial Analysis

The financial industry is in a bad shape from an analytical perspective, dominated by analytical silos and lacking a unified approach.

How did this happen?

Just a few decades ago, financial analysis was generally synonymous with bookkeeping. This situation has changed with the advent of the modern finance, accelerated by increasing regulation. A brief and comprehensive history of economic analysis is then given, explaining its evolution to its present state and focusing only on developments relevant to the objective. The following article outlines what the solutions to these problems should be.

Bookkeeping

Many of the earliest cuneiform clay tablets found in Mesopotamia were records related to economic activities that recorded transactions, loans, etc., suggesting that the invention of writing was closely linked to bookkeeping. Bookkeeping involves keeping records of transactions and cash flows. It is the process of recording the financial transactions of a company in organized accounts. Bookkeeping can also refer to the different recording techniques a business uses. Bookkeeping is an important part of the accounting process in life for many reasons. At a time when bookkeeping systems focused on cash flows, there was no real idea of assets, liabilities, expenses, and revenues other than in memorandum form. Any investment or even a loan has to be registered as a stress on the bond, thus negatively impacting these activities.

Given the constant scarcity of cash before the advent of paper money, the extent of the cash flow is not surprising. Even today, many people think of cash when they think of wealth. Another reason for fixing it on cash is its tangibility, which is the only observable fact of financing.

In the banking sector it first became apparent that the simple recording of cash was not enough. A pure cash flow view made it impossible to measure value. To someone, (for example-a loan of 1000 dinars for two years registered a withdrawal of 1000 dinars from the cash box). Against this outflow the banker had a paper in his hand reminding him of the fact that he was entitled to get back 1000 dinars with possible periodic interest. But this was not recorded in the book.

It was not possible to calculate continuous income by the same token. If, (Example - 1000 dinars payable at 12% per annum, cash payment of 120 dinars would have been recorded after first and second year only). Nothing was visible in the middle month.

When the double-entry bookkeeping system was invented in Florence in the 13th or 14th century, it was probably the Medici family who made the breakthrough. The system was formalized in 1494 by the monk Luca Pacioli, an associate of Leonardo da Vinci. Although Pacioli only formalized the system, he is generally considered the father of accounting. Pacioli described the use of journals and ledgers, his ledgers had accounts of assets (including receivables and inventory), liabilities, capital, income and expenses. Pacioli warned every man not to go to bed at night until the debits equaled the credits.

Following the above example, a credit entry of 1000 dinars in the loan account can now be registered and balanced by a debit entry in the cash account without changing the equity position. However the equity position will increase over time through the income statement. If sub-annual income statements are prepared, it is now possible to attribute income of 10 dinars each month which represents accrued interest income.

Thanks to Pacioli, accounting became a generally accepted and known art that spread throughout Europe and eventually conquered the whole world. Accounting made it possible to think in investment terms that shifted delayed but highly profitable revenue streams toward value and away from the pure cash register view. It has been convincingly argued that bookkeeping was an essential innovation leading to the European take off. Focusing on value and income or expenses that would create net worth was new. As a side effect, the preoccupation with value meant that cash became debased. This position applies widely to today's bookkeeping. Most students of economics and finance are introduced to business through the balance sheet and P&L statement. Although mathematical finance is taught, it focuses entirely on value concepts.

The focus is on value. The evolution of the cash flow position in the system should be noted with interest. Given the importance of liquidity and liquidity risk in banks, this is particularly striking for early stage banks. Finally, liquidity risk is the primary risk of banking after credit risk because liabilities must exceed available cash to be profitable.

Liquidity risk can only be properly managed if it is represented as a flow. However, instead of representing it in this way, liquidity was treated like a simple investment account and liquidity risk was approximated by the liquidity ratio. Was the determination of cash flows still considered primitive, or was it because flows were more difficult to register than stocks? Be that as it may, the liquidity ratio remained sophisticated for a long time. The initial regulation demanded that cash holdings could not be less than a certain percentage of short-term liabilities. It was therefore managed in the same way as credit risk. Another important risk faced by banks is where equity ratios are introduced. Equity ratios describe the relationship between certain types of debt and the amount of available equity. For example-a bank's largest single borrower cannot be greater than x% of the bank's equity.

Another related effort to improve cash flow measurement is the introduction of the cash flow statement. Bookkeepers derive liquidity from value determination and cash versus opposing balance sheets. The remarkable fact here is that bookkeepers derive cash flow from the balance sheet and P&L, which itself derives from cash flow, a classical tail biter! Is this an inherent contradiction that makes teaching the cash flow statement so difficult in finance classes? Who doesn't remember confusing classes where a frustrated teacher tries to teach cash flow statements! Marx would have said that bookkeeping stands on its head from where it has to be put back on its feet.

This was the state of financial analysis regulation before FASB 1335 and the Basel II rules and before the advent of modern finance. This change was caused by the US savings and loan crises of the 1970s and 1980s. These institutions have been strictly regulated since the 1930s. They can offer long-term mortgages (up to 30 years) and are financed by short-term deposits (about six months). As a joke, the savings and loan manager only needed to know the 3-6-3 rule: pay 3% for deposits, get 6% for mortgages, and be on the golf course at 3 p.m.

During the 1970s the 3-6-3 rule was broken. The US government had to finance the unpopular Vietnam War through the money press. The ensuing inflation may first be exported to other countries through the Bretton Woods system. International tensions brought down Bretton Woods and inflation hit home. Short-term rates had to rise by 20% and more to contain inflation. In such an environment no one would save in deposits paying a rate of 3% and lost responsibility for savings and loans, creating a severe liquidity crisis. The crisis was overcome by legislation allowing savings and loans to refinance themselves in the money market. At the same time that savings and loan conditions were already known to the public, government guarantees for savings and loans had to be increased. Although the refinancing has now settled down, the income outlook was disastrous. The liability was huge at somewhere close to 20% and the long term and fixed rate nature of the existing business meant that assets could be adjusted from a mere 6% level to a higher environment. As a result, many banks went bankrupt. The government was eventually given an uncovered guarantee of $500 billion, an incredible amount that negatively impacted the economy for years.

This phenomenon brought market risk, especially interest rate risk, into the picture. The idea of interest rate risk for a bank did not exist earlier. As mentioned above the focus was on liquidity and credit risk. Regulation was triggered by the enormous cost to the US taxpayer, and Thrift Bulletin 136 was the first reaction.

Thrift Bulletin 13 requires an interest rate gap analysis showing revaluation discrepancies between assets and liabilities. Interest rate risk arises from the mismatch of the interest rate adjustment cycle. The discrepancy arose from the 30-year fixed mortgages financed by short-term deposits in the savings and loan industry, which became a problem during the interest rate hikes of the 1980s. Short-term liabilities adjusted rapidly in the high-rate environment, thereby increasing costs, while fixed-term mortgages on the asset side did not allow for significant adjustment.

Gap analysis introduced future timelines in day-to-day bank management. The introduction of timelines led to renewed interest in the flow nature of business. New techniques allow not only a fairer representation of interest rate risk but also of liquidity risk. However, getting introduced to timeline bookkeeping was not easy. The idea of value is almost the opposite of timeline. Value is the aggregation of all future cash flows at once. Valuation of finance was invented to overcome the time aspect. The concept of net present value, for example-was introduced to overcome the problem of irregularly spread cash flows over time. This allowed two completely different cash flow patterns to be compared on a value basis. In other words, bookkeeping was not suitable for the job. Disregard for cash and cash began to hurt.

Asset and liability management (ALM) was introduced for the time line model. Although ALM is not the neat term it is today, at the time it meant the management of interest rate risk in the banking book. Why just the banking book? Due to the growing conflict between trading people who managed trading books on mark to market terms and bookkeepers who stuck to old-fashioned bookkeeping.

ALM stands for Practice Gap Analysis and Net Interest Income Simulation (NII). The gap analysis is further divided into interest rate gap and liquidity gap. Another development was the introduction of the duration concept for managing interest rate risk.

At this point it is only intended to show the interest rate gap and the net interest income report as it introduces the timeline. A classical representation of net cash flow shows a small outflow in the first period, a large inflow in the second period. Net interest income also demanded dynamic simulation techniques. In short it is possible to predict expected future market conditions and future planned strategies (what type of business is planned) and see the cumulative effect on value and income. For example-Evolution of projected returns under different scenarios/strategy mixes.

Such reports are used to judge the risk of strategies and help in choosing the optimal strategy.

The introduction of the future timeline was a major step forward in financial management. The problem is not the evolution of time, but that time in finance appears twice:

Natural Time:- We experience this time day by day.

Investment horizon:- It represents the terms of the contract from day to day. For example-If investing in 10 year bond then we have 10 year investment horizon. Life Insurance – If insuring a young person, the investment is up to 80 years.

Because a financial contract is a sequence of cash flows that change over time, a bank or insurer invests on a natural timeline that is continuous over the investment horizon.

Such information cannot be managed through traditional bookkeeping. Bookkeeping allows natural time management. It does so in the P&L statement but usually from a backward-looking perspective. The exception is during the budgeting process, where the investment horizon itself poses difficulties as shown above. A breakdown of each asset and liability account for each day will be called for when the business is in the future. This is partially done where banks, for example-divide interbank accounts into three months, up to one year and more than one year. However, this is not sufficient for analytical needs. To make it even more complex, time (the march of natural time) constantly slows maturities, and new deals with new horizons can appear every day. It certainly cannot be managed with a handwritten general ledger but it is quite impossible with the help of a computer. Even if it were possible, it would be unsuitable for analysis as it would produce a largely arbitrary and most inaccurate balance sheet.

The appearance of new ALM systems helped. ALM systems tried to correct the timeline problem, but many of them were still too bookkeeping conditioned and did not properly take into account this dual existence of time. They focused more on natural time horizons than investment horizons. Many systems were more or less Excel spreadsheets that used the x axis as the natural time dimension and the y axis for charts of accounts. Spreadsheets do not have room for a third dimension that reflects the investment horizon.

At this point the miscalculation was really in trouble. So better solutions were needed.

Modern Finance

Modern finance arose partly from the natural evolution of science. The first paper on option pricing by Merton, Black and Scholes was published in 1972. The Nobel Prize was awarded in 1997 to Merton and Scholes (a black prior). This Nobel Prize coincided with the advent of exchange traded options in 1973. It completely revolutionized the finance sector in a short span of time.

However, scientific progress was not the only factor at work. The savings and loan (S&L) crisis of the 1970s made it clear that traditional bookkeeping methods were not enough. Bookkeeping, with its smooth techniques, tends to hide rather than reveal risk. The savings and loan crisis made it clear that new tools such as swaps, options, and futures were needed to manage risk, but these newly created contracts could not be valued with traditional accounting methods. Furthermore, with inadequate controls, these devices may increase rather than decrease risk. This led to further theoretical advances and at the same time calls for better regulation such as the Financial Accounting Standards Board (FASB 133).

Modern finance generally tries to incorporate uncertainty into the valuation of financial instruments and does so in a theoretically sound way. The starting point for valuing an instrument is to discount its cash flows to the present date. However accounting for uncertainty requires consideration of expected cash flows that imply a probability distribution. Calculating expected cash flows in a risk neutral world is a key approach in modern finance.

For example-Alternatives are evaluated by solving the Black-Scholes-Merton differential equation. An important assumption in the derivation of this equation is that investors are risk neutral. In a risk neutral world only the expected return from a portfolio or investment strategy is relevant to investors, not its relative risk. Risk neutrality can be built into the option pricing framework through the hedge argument.

The real world is full of risks and investors care about that. Real people are risk averse, a truth demonstrated in the St. Petersburg Paradox. In the game of fair coin toss, a coin is tossed until a head appears. If the first n − 1 tosses are tails, then the payoff is 2n−1. So, the expected payoff is

which is infinite. The paradox lies in the fact that rational people do not pay infinite sums to participate in this game. In fact, the cost is much lower, in the range of a few ducats depending on the utility assigned to the return. In the real world, people prefer lower and more certain returns to higher and more uncertain returns, even if the expected returns are the same.

Limitations of the risk neutrality assumption in option pricing returns are revealed by the well-known volatility smile. Out-of-the-money or risky options have lower prices than those calculated using implied volatility. In effect the expected cash flows from such options are revised to their risk neutral values so that the investor's risk is avoided.

There are two basic approaches to valuation under uncertain market conditions. First calculating the risk-neutral cash flows and discounting them with the risk-free discount factor. The second involves calculating real-world expected cash flows and discounting them with a deflator. Modern finance has generally adopted the first approach with necessary modifications, as in the case of the volatility smile. Traditional bookkeepers prefer another method with their go-to approach. In many cases, where there is an absence of an efficient market, there is only one other option open.

The basic challenge of an integrated analytical methodology is to incorporate bookkeepers and modern finance methods. If one is only interested in valuation and value-related risk, as is the case with many quantitative analysts, then only risk-neutral cash flows are required. However real world analytical needs also include analysis of liquidity and its associated risks. Expected cash flows based on financial expectations cannot be ruled out. Theoretical approaches to this problem are only beginning to develop.

The advent of modern finance opened a gap between its followers and the more traditional bookkeepers. This was partly because bookkeepers did not understand what rocket scientists were doing. The opposite is true. The rocket scientists of modern finance, perhaps out of intellectual arrogance, refuse to understand what bookkeepers are doing. Market value was declared to be the only relevant value, leaving other valuation methods to a mere numbers game. This approach ignores the fact that market valuation is inherently based on a liquidation view of the world and ignores the alarming reality. It also ignores the fact that rocket scientists' formulas only work in efficient markets, while most markets are inefficient.

Moreover, little effort was made to conduct a thorough analysis of the bank or insurance company. A strong focus on a single transaction led to the loss of the vision of the financial institution as a closed cash flow system. Pacioli's advice not to go to sleep before all accounts are balanced was ignored.

At the end of the 20th century the financial system on the one hand was double entry bookkeeping methods considering the entire organization, but had weaknesses in analyzing uncertain cash flows. On the other hand there were methods with powerful evaluation capabilities but these focused on single financial transactions or portfolios, missing the overall balance and ignoring the alarming view. Finance is by nature a flow, viewed even more strongly as a stock.

Modern finance gained the upper hand because it had the power to explain risk, an important question that demanded an immediate solution. The result of this influence is a fixed focus on sub-parts of the organization, such as a single portfolio or division, and a focus only on the existing position. Sectarianism is very common today. In banks, it is seen that the treasurer and bookkeeper do not talk to each other. A similar division can be seen between actuaries and asset managers in insurance. Departmentalism has become an important cost factor. Getting an overview of the entire organization is very difficult and answering new questions, especially at the top level, is very expensive. It will take many years to overcome this problem. To do this requires a clear view of the unity of the underlying structure of all economic problems.

Department, silos and analysis

The organizational structure of typical banks at the beginning of the 21st century follows a rigid silo structure. The following segments require financial analysis and formulation:

Treasure

The repository is the section where all the information flows together. Typical analysis in treasury departments are gap analysis (mainly liquidity gap but also interest rate gap), cash management, sensitivity analysis period, exchange rate sensitivity and risk (value at risk). Since all information needs to be integrated into the treasury, the idea of creating an integrated solution often finds fertile ground in treasury departments.

Controlling

Classical Controlling

This is a watchdog function. Are the figures correct? Often a profit center is also responsible for controlling product and customer profitability. This requires fund transfer pricing (FTP) analyzes on one hand and cost accounting on the other. Also here all data needs to come together, but controllers assume the first role of the silo landscape. They just go to each silo, check if the calculation has been done and report properly.

Risk Controlling

By the mid-1990s, it became necessary to create independent risk control units driven by regulators. Risk control focuses entirely on the risk side of control rather than classical work on classical control. As in classical control, usually no independent calculations are performed but existing results are rechecked.

ALM (Application Lifecycle Management)

ALM can have many meanings. The most traditional definition is the function of managing interest rate risk. Most of the analytical tools in treasury are used but have a strong view on interest rate rather than liquidity risk. Popular analysis tools are interest rate, liquidity gap and sensitivity. Sometimes value at risk (VaR) is used in ALM. In addition to Treasuries, the focus is on Net Interest Income (NII) forecasting to model the worrisome (natural time) view. It strongly depends on the simulation specifications. FTP is also important to separate the conversion income for which ALM is usually responsible from the margin, which is usually associated with the deal making department.

Trading

Trading is like a small bank within a bank. Similar analyzes are used for Treasury and ALM (Application Lifecycle Management), except for NII (Net investment income) forecast and FTP(File Transfer Protocol) analysis.

Budgeting

The budget department is responsible for planning the income of the bank. This has strong overlap with ALM's NII estimate. However apart from NII it brings the cost side into the picture. FTP plays an important role whenever profit center results are estimated.

Bookkeeping

Traditional bookkeeping has little to do with the other functions mentioned here, as book value is always created directly by the transaction system. However, that changed around 2004 with the advent of new IFRS rules IAS 32/39. These rules are strongly market value oriented and demand a more appropriate treatment of impairment (expected credit losses). These methods overlap strongly with market and credit risk techniques. IFRS calculations are often performed in the ALM department.

Risk department

In addition to these sections, one often sees a risk section, which is divided into three categories. Often they are under the same top division level:

Market risk

This again has a strong overlap with Treasury/ALM/Trading. Similar to these sections, analysis is done here as well.

Credit risk

With Basel II comes the need for more market risk analysis. From an analytical point of view they add credit exposure analysis that relies heavily on results also used in market risk, such as net present value (NPV) (for replacement value calculations).

Operational risk

It can be seen as completely independent of the other functions listed above, as operational risk (OR) is more focused on physical activities than financial contracts. The applied methods are loss database, risk assessment and Monte Carlo simulation on OR.

There may be division of other departments and other responsibilities. The problem is not the existence of these sections. At least a good number of them must exist for check and balance reasons, if not all. The problem is that all of these departments, with the exception of operational risk and cost accounting, have a great deal of coordination and analytical needs between them. While it seems logical that departments should coordinate to solve problems, this has not been the case.

IT System Landscape

The evolution of finance parallels the evolution of IT systems used for financial analysis. Financial analysis cannot be separated from IT systems and underlying infrastructure. Much of finance (for example:-Monte Carlo technique) is completely dependent on powerful IT systems.

Early IT systems in the banking industry were transaction systems, general ledgers (GL), and systems for storing market and counterparty data. The transaction system is used to register savings and current accounts but also for bonds, loans, swaps, futures, options, etc. Banks have many such systems, usually four to eight, but in some cases up to 40 systems. For the sake of simplicity, let us leave out the market and counterparty data and focus only on transaction system data in the following discussion. This is justified on cost basis as transactional data is the most expensive to maintain.

Prior to the savings and loan crisis of the early 1980s most all analysis was not based on general ledger data. With Thrift Bulletin 13 (BT 13), increasing innovation leading to new financial instruments and Basel initiatives increased complexity. Partly because of new needs and partly because of differences between bookkeeping and modern finance, banks began to divide analysis work into manageable chunks: treasury analysis, control, profitability, ALM, regulation (Basel II). The sub-functions can be roughly classified into bookkeeping and market value oriented solutions.

This structural change was followed by the development of customized software solutions for the financial sector, developed to meet specific analytical needs. In an effort to accommodate specific needs, software vendors have increased technical separation within financial institutions and strengthened the independence of departments.

Banks now had a wide range of specialized departments or silos with their own tools, created by different software houses, which were often incomparable and difficult to harmonize. Additionally, such systems were extremely expensive due to the interfacing involved. For example:- Suppose four separate analytical systems of a bank extract data from six transactional systems. The entire system of the bank will require 4 × 6 interfaces.

Not only was the separation expensive and created more work through interfacing and difficult reconciliation processes, it was also logistically and functionally artificial because financial analysis uses the same core calculation tools set to produce the same basic results: cash flow, value, and income.

Ultimately, although they set out to make the growing field of financial analysis more manageable by dividing it into smaller, focused areas, banks and solution providers actually created more complexity: now analysts in different analytical fields were using customized systems, variations on the same calculations to obtain essentially the same information. Intricately interfaced with various data sources for use.

In the early 1990s, upper management realized the problems associated with this silo architecture. However, it was mainly perceived as a data problem. The industry tried to overcome the problem by using integrated data warehouses, single locations where all financial data could be stored and shared by various analytical departments.

This involves the transfer of data from multiple transaction systems into a single pool. The theory was that data consistency and accuracy of results would be improved, and when different analytical functions entered the same database, it would be easier to reconcile when working on it. Many organizations still rely on such data warehouses, but is this the optimal solution? The answer is no. I identify two problems with the integration of this article:

There is no real data integration

Data warehousing as described above is essentially a technical integration of data and does not integrate financial data from a logical perspective. Data are moved in bulk from their parent systems to a separate cell in an analytical data warehouse. This is clearly described in the industry as simply building a huge data pile from a bunch of smaller piles. Admittedly, there are fewer interfaces (using the example shown, six transaction systems and four analytics systems require 10 interfaces) and there is some data cleaning, processes such as converting all dates to a common date that increase data quality. Format and use a unique code for each counterparty. While useful, these adjustments are small and technical. In other words they do not create true data integration from a financial analysis point of view.

A simple example is provided by the concept of notional value. Although this basic concept exists in all transaction systems, it is stored under different names such as notional value, nominal value, current principal or balance. When moved to a data warehouse, this data is in most cases not aggregated but instead stored in four separate fields. Since the same logical information is not stored in multiple fields, the interpretation of the actual figure may depend on the source system from which it originates. For example, in one transaction system notional value may be positive for asset contracts and negative for liabilities, while in another transaction system nominal value may be defined as an absolute value or a percentage of the other, depending on the sign convention. Building an analysis or reporting engine on such a data warehouse requires a logical layer that correctly interprets the data according to the source system. Creating and maintaining such logic is expensive and error-prone. As a result early data warehouses did not reduce interface complexity, were cumbersome and expensive to maintain, and produced inconsistent results.

Multiple analytical engines lead to inconsistent results

Let us assume a more ideal (but rarely seen) world where all financial data received from transaction systems is standardized and interface complexity is reduced to data warehouses. To stay with the example above, the four fields mentioned above will map exactly to one field and all data will be defined the same way. A question may be fairly simple now, but financial analysis cannot be handled with simple questions.

why In the old days, when finance was just accounting, it was really possible to generate any report directly from the database. This works because accounts and sub-accounts are always added to mark assets and liabilities. As long as the analysis is only grouping and summarizing, the basic idea of data warehousing is sufficient. With the advent of modern finance, the grouping and summing hypothesis no longer holds. In this article we will see that the data stored in the data warehouse is the basic data about the financial contract (value date, maturity date, principal, interest rate, interest payment schedule and so on) and market conditions, from where the necessary information is obtained. This can be calculated. Of course, there is also history that is first calculated and stored for later retrieval. However, changes in the market require frequent recalculations of most interesting information, as demonstrated by the fair value function described above.

For this reason it is not enough to have a central data warehouse where one can plug-in all the necessary analytical systems or calculation engines that approach analysis from various perspectives like VaR (Value), NPV (Net Present Value). Like Risk, CAD (Capital Adequacy), FTP (Funds Transfer Pricing) reports, there are many more in practice.

Each analysis mentioned relies on the same preliminary calculations to generate expected cash flows and to derive yield, sensitivity and risk from this value. However customized systems developed by different vendors implement different forms of key calculations. In particular, calculating expected cash flows requires an elaborate process that is costly. Programming costs can be reduced by making shortcut assumptions, especially regarding generating expected cash flows. In order to maintain such diverse systems, simplification is necessary and therefore often applied. Consequently, even when based on integrated and consistent data, many differ. A data warehouse solution therefore suffers from serious compatibility issues and cannot overcome the bottlenecks created by analytical isolation.

In the 1990s there was a movement to overcome this problem by calculating expected cash flows and storing them once. Since all results are derived from this cash flow, this information is centralized and made available to all analytical tools of various departments. Ultimately it is the cash flow that is most difficult to calculate and where most of the variance in applications is created. Having the same cash flow for everyone will close most of the gap. After this stage only a little extra calculation is done. Many banks invest large sums of money in what is known as super cash flow. Information is measured once and then made available to all through a data warehouse.

As tempting as the argument sounds, it was, and still is, wrong. The concept of super cash flow ignores the expectation aspect. Cash flow is a fixed value that can be calculated in advance only in very rare cases. This mostly applies to fixed noncallable government bonds of the highest rated governments (with a probability of default of zero). In all other cases cash flows depend on market conditions, behavior and issuer ratings. For example, the cash flows themselves depend on, as well as the real yield curve and the curve can change at any moment. Therefore, the calculation of expected cash flow must be a part of the analysis itself.

New Perspective

The answer to the silo and consistency issues associated with standard data warehouses is by using a core calculation engine that runs on integrated and consistent data as shown in the picture. This analytical engine generates expected cash flow independently of the segment, from which reporting elements derive cash flow, value, yield, sensitivity and risk. The engine must be powerful enough to handle double timing events and perform multiple evaluations.

Creating reports for different analytical needs simply becomes a matter of selecting the right reporting elements, filtering financial events with only minimal post-treatment, and finally reporting the results according to the design of the analysis method. Because these building blocks are consistent, the higher-order analytical results are consistent and comparable. For example, market value reports rely heavily on market values and similarly on the replacement value of credit risk. Computation of fair or market value is same in both the cases but further treatment differs in each case.

The new methodology of integrated economic analysis is an excellent answer to the complexity created by isolation in economic analysis, as it is truly integrated, providing consistent and comparable results. Although the proposed methodology can handle more complexity, it is much simpler than the sum of silo systems in place. The rest will talk about elements in the data and calculation kernel, which will facilitate financial analysis and significantly increase consistency and information content.

This concept came under suspicion in the 1980s and into the 1990s. The common wisdom seems to be that the analytical capabilities of an integrated system can only be built if they are not deep or broad enough. This resistance was overcome in the late 1990s as the complexity and resources to use multiple analytical systems increased. The need for integration, for example, is strongly supported by Basel II and Solvency II regulation. It is now accepted that a system based on an integrated approach makes sense. There is certainly a gap between lip service and reality, but the possibility of a coherent methodology is an accepted fact, which is at least an important first step.

An analogy with the development of writing illustrates that it is possible to create simpler systems that can handle greater complexity. Early scripts were pictorial in nature. However, going beyond the simple stage with pure pictographs is very difficult and - (as can be seen from the Chinese language) - (the system can become quite complex), absorbing a lot of intellectual energy. The script is very beautiful, but the main problem is focusing on the words. In contrast, the invention of phonetic script by the scribes of the Mediterranean city of Ugarit around 1400 BC was an intellectual breakthrough. Focusing on the sound of objects and verbs rather than objects and verbs greatly simplified and improved the art of writing. Only two dozen letters were enough to describe everything that could be said. It was now possible for the majority of the population to read and write without investing more than a few months or years in childhood. Such a system applies not only to words existing at the time of invention, but to all future words. Obviously, some letters may need to be added, especially when applied to new languages where new sounds are used, but the basic system is stable. Explain to an educated writer who has spent a good part of his life learning 10,000 or even 20,000 pictorial characters that any reasonably intelligent child can easily learn to write the same number of words in a short time. If not stupid, perhaps arrogant.

The dangers of a single solution

Let us assume that a single system based on a consistent methodology can be created that can generate all economic analysis results. A valid question is whether relying on a single system is a prudent way to build an analytics infrastructure. After all, a single system can not only give wrong results, but it can do so consistently. Isn't it better to use multiple systems with overlapping coverage?

This concern should be taken seriously. Mitigating factors in practice include:

1. Even with a single unified analytical system, feeder transaction systems are often capable of producing a subset of analytical results. Most transaction systems record values and some generate gap reports. Trading systems can often generate sensitivity and risk figures. Overlapping analytical capabilities make it possible to find consistency and errors in calculation results.
2. The calculation results of an analytical system are used by various departments of the bank, whereas in a traditional setup the calculation results are verified by one or two departments using a particular system. As analytical results are used and verified by a larger audience, software errors are more likely to be discovered.
3. Data quality and consistency are often overlooked in aspects of financial analysis. A large user base, like software quality, means that problems are found and fixed quickly.

However, it appears that all analytical functions are replaced by this one system, so we can recommend creating two systems separately. It is still much cheaper than having numerous parallel segmental systems.

Finding the Elements of Financial Analysis

This topic provides an overview of the basic ideas and concepts discussed in this article. Also of fundamental importance to methodology is the notion of factors or stable basic building blocks from which all things of analytical interest can be derived.

After introducing the input and analysis components, additional important basic concepts used throughout the article will be discussed. First is the concept of financial affairs, which can be considered the bottom line of finance. Financial affairs are the lowest level factor. Let us then discuss the risk factors and risk categories and also learn about the role of time in financial analysis and the dual existence of time. Finally, various categories of economic analysis will be presented, mainly divided into static and dynamic, these concepts determine the main organizational structure of the article.

Concept of elements

Elements and science

The search for principles is at the heart of philosophy and science. One of the early questions asked by the ancient Greek philosophers when looking at the constant changes in nature was: What is there in nature that does not move? He arrived at the concept of atoms or groups of elements which he later considered to be earth, air, fire and water.

While they were wrong about this first set of elements, they were quite right about thinking in terms of atoms or elements. Behind the amazing facts of nature are things that do not change (a kind of seed value), and all visible phenomena are recombination's of basic elements. This is true at least for the hard sciences.

Rene Descartes in his Discourse on Method stated the following principles on which science should be built:

1. Never accept as truth what is not clearly known, avoid extremes and prejudices.
2. Divide each problem in the examination into as many parts as possible and as necessary for its adequate solution.
3. Conduct the thoughts in such an order that, beginning with the simplest and easiest objects to know, gradually ascend, as it were, by stages, to the knowledge of greater complexity, assigning the thoughts to a particular order. Objects which in their own nature do not stand in relation to antecedent and sequence.
4. In each case make the calculations so complete and the reviews so general as to ensure that nothing is omitted.

Based on Plato, Aristotle added ether or quintessence. It was interesting to see that all the great early cultures had a similar set of elements, perhaps a reflection of early Greek influence. Only the fifth factor varied greatly between cultures, reflecting their particular religious perspective.

The influence of Descartes' advice on scientific development is undeniable. Based on the scientific methods described by Descartes, the advances in science since then have been tremendous. Once open to questions, practical scientific work begins by decomposing a problem into its proper components. When this is established, these parts can be reassembled to obtain a model that reflects the true complexity.

Many examples could be enumerated, but a reference to chemistry should suffice to demonstrate the case. In the Middle Ages, the science of ancient Greece remained unchallenged and people continued to think in terms of the four elements and conducted many experiments trying to unify the four elements using various mechanical methods. The main objective was to produce gold, which they never managed to do. Instead, they accidentally found gunpowder and maybe other things. Chemistry did not attain the status of a real science and progress could be made until the actual structure of atoms and molecules was known. Once this construct is known, it becomes possible to formulate new hypotheses that can then be tested empirically.

Similar achievements can be claimed in many other fields such as physics and biology. However, not all sciences are equally successful. (Example:- Taking economics, utility or profit maximization) There are some elementary components from which supply and demand curves can be derived, but the recombination of these components does not reproduce the richness of the actual phenomenon. Predictive power (given a particular initial condition) has been of great concern to date. Psychology, Sociology etc. There are other sciences that fall into this category.

How is it with the subject under scrutiny? This article claims to define a methodology that can generate all financial analysis for any financial institution. Although some limitations of the words all and any will be discussed, the idea should be very complete in terms of all and any. Can Descartes help define this method? Can the component parts (Rule 2) be found and described well and accurately (Rule 3) to achieve the objective of financial analysis (Rule 4) for any financial institution in a fully described method (Rule 4)? It is of course impossible to prove this in this passage. The article should prove it and the reader is asked to follow it. A primary empirical evidence may be provided to prevent the reader from following blindly: everyone has seen the methodology in practice. Different parties and avenues have taken a similar approach with more or less progress. What about Rule 1? Belief that when creating procedures, the mind is open. Is the mind still open? I hope so, and I trust my readers as a check.

How can one be sure that the right or good primary level is found? Its level can be very low or very high. The approach may be completely inappropriate. This can again be demonstrated by chemistry. On the one hand, the approach fails with water, air, etc. On the other hand, the idea of quarks is not necessary to understand matter and its combinations. Chemistry takes place mainly in the outer electron shell which must be the focal point. While the ancient four or five elements are too high level for chemistry to focus on particles, the level may be too low. This idea is also expressed by Albert Einstein's famous quote (Make everything as simple as possible, but no simpler than that).

The invention of gunpowder seems to be an oft-told story but not true. There is always a limit to the openness of the mind. Descartes himself advises keeping the mind open until he knows clearly. It is obvious that there must be moments where we cling to our faith. Descartes also does not apply the model of constant doubt to his Four Laws, as this would invalidate them from the very beginning.

Trust is a good rule of thumb to ensure you find the right level. Just Ask Yourself The method is simple and elegant, yet creates the rich detail needed. The best theories are not only logical, they are also beautiful. It is the application of the law of parsimony or Occam's razor that we have been applying for the past 20 years in developing methodologies.

Analyzing analysis

What is Financial Analysis?

Although the basic question of economic analysis does not enter the picture at the appropriate time, how to price the option or how to model the yield curve is useful. Actual analysis starts with financial analysis. Elements of analysis can be traced in the above sense. It is important to know how to build financial arrangements that range from simple savings accounts to foreign options and structured products. How can an economic system be created? Where an external shock such as a change in interest rates can be applied and then seen to circulate through all financial foreign instruments to bring about a consistent aggregate effect from savings accounts upwards. What are the basic elements of such a system? It is important to know how this structure can be simplified as much as possible without compromising the results.

Academia has yet to make much of an effort to address these questions. On the one hand the focus was on bookkeeping and on the other hand new finance focused on single financial instrument valuation using the liquidation view. Regrettably, the question of “what is economic analysis” seems to have fallen outside the core purview of academia over the past few decades.

A word remains to be said about proper representation. Some systems require a very rigorous approach to achieve elegance and perfection with little or no discretion. A good example is the Copernican system which replaced the Ptolemaic system with its complex hypercycle. However, it turns out that some systems have more than one representation with equal explanatory power. Thus writing systems once again provide a good example. From a purely logical perspective it can be argued that the word is the appropriate element and entry point for writing. Despite the many different underlying languages, choosing this entry has advantages such as the universal use of the Chinese script. (For example - if political unity is important, it may be more appropriate to focus on words). On the other hand, if simplicity of writing in terms of input and output is concerned, phonics clearly has advantages, and phonics is an excellent approach at the elementary level.

Accordingly, it is observed that while many parts of the method, such as the separation of risk factors from financial instruments, seem rigid and cannot be approached from many different angles without losing elegance and simplicity, some parts (for example the classification of financial contracts into contract types) can follow different paths without losing elegance.

Elements of Financial Analysis

By elements of financial analysis or smaller elements of analysis is meant the basic building blocks from which any financial analysis can be assembled. One can use the historical development of financial analysis as a guide to determine the analysis factors.

Liquidity

Money and financial instruments create a countercurrent to the flow of goods. In contrast to highly heterogeneous commodities, money or financial flows are homogeneous in nature. Money is a good against which any other good can be exchanged. This homogeneity is also reflected in financial instruments.

Money is a good thing that is always accepted and understood at value. A currency unit, a euro or a dollar, can be understood as a base unit, just like a meter, second or kilogram. The main feature of the base unit is its finality. This base unit cannot be judged but everything else is determined by it. Everything in the proposed system is expressed by a base unit.

Only a few phenomena can be observed related to this basic concept. First money, or cash flow, is the only visible, tangible, or tangible artifact of finance. In today's abstract world, even these words can be very strong. Most of the money that exists today, or what people think of as money, is not in the form of paper bills or coins, but is simply represented by numbers stored on a disk somewhere. Nevertheless, these terms continue to be used when discussing cash because changing numbers in a particular account, or rather two accounts, is directly visible.

Given the tangibility of cash, it is not surprising that it was the only consistently recorded concept of early (single-entry bookkeeping). Cash, or liquidity, is therefore the first analysis factor. This visibility or tangibility of cash is probably the reason for the late evolution of double-entry bookkeeping and modern finance. Moving beyond cash requires more abstract thinking.

Value and income

Double-entry bookkeeping adds two new concepts to the simple cash statement, namely value and income, which is the change in value over time. Initially value means something like nominal value. Nominal value outstanding reflects the sum of principal cash flows, so it is the most obvious number to record for bookkeeping.

The advent of stocks and bonds, and especially traded stocks and bonds, recognized the need for more sophisticated bookkeeping methods. When the coupon rate of a tradable 10-year fixed bond is below the prevailing market yield, its value should be adjusted by a premium to account for the difference. Similarly, the value of a share changes according to the expectations of market participants. That naturally gave birth to market or fair value, valuation. Moreover the bookkeeping of traded instruments also required more frequent revaluation.

Monetary aggregate M1 includes overnight and short-term deposits, in addition to paper money and accounts with the central bank. Aggregate M2 also includes term deposits of up to two years. In layman's terms, money is close to the total meaning of M1. Arguably more if accounting rules involving foreign exchange are also considered.

Over time, other bookkeeping methods were introduced such as amortized cost (also known as effective income), cost/market, or the minimum value principle, resulting in approximately 10 different valuation methods for financial transactions. IFRS 32/39 reduces the number of allowed rules to four: fair value, amortized cost, historical cost and nominal value (the latter only for savings and current account types). Although the need for multiple assessment methods has been reduced to a few methods, the need for parallel assessment remains.

Risk and sensitivity analysis

The peculiar position of cash in connection with double-entry bookkeeping has already been noted. Value and income, like cash, were booked only with a past orientation. It has been remarked that using traditional bookkeeping was like driving a car using the rearview mirror, telling what had happened but unable to provide any clues and warnings of what was to come.

The savings and loan crisis of the early 1970s and the search for financial alternatives led to the need for more visionary and more efficient analytical methods. As savings and loan losses become apparent, it is highly desirable to predict value and income under given market conditions. Similarly, newly introduced financial instruments such as options and futures were known as financial time bombs at the time which demanded better analysis. This was the birth date of risk management, but sensitivity analysis should be introduced before discussing risk concepts.

Sensitivity analysis attempts to provide numerical answers to questions related to future outcomes. (For example:- How much will the value of a portfolio or an entire financial institution change if market interest rates change in a particular way). The given diagram shows the situation where the prevailing yield Y C1 curve on the left side is shocked to yield Y C2. This shock affects the value of each asset, liability or any other position depending on the sensitivity of interest rates. When these value changes are aggregated over the entire portfolio or financial institution, the value shock shown on the right side of the figure is obtained.

More formally, sensitivity is the first derivative of the value of a financial instrument with respect to changes in the price of the risk factor. Since the derivative is a linear function, the sensitivity of the entire organization with respect to any risk factor is obtained by summing the relative contributions of all the contracts in its portfolio.

When the relationship between the value of the instrument and the underlying risk factor(s) is highly nonlinear, the first derivative is not sufficient and the second derivative must also be calculated. Throughout this article the term sensitivity will mean first and possibly second order derivatives. Third and higher-order derivatives have so far not been useful in practice. However, the definition of sensitivity will cover this as well.

The term aspect of the yield curve is also relevant in terms of interest rate sensitivity because different segments of the curve can move independently. Reducing sensitivity to a single scalar number such as duration can be useful but misses the time aspect of the investment horizon. An elegant representation of this time information is captured in sensitivity interval analysis, which is discussed next.

Sensitivity brings a new level of complexity that is unmatched by the level required for value and income analysis alone. Value Even fair value can usually be derived from expected cash flows by making some minor adjustments, such as by discounting them. However, the derivation is complicated and a whole range of new problems are introduced when the evaluation is not analytical in the underlying risk factors.

After introducing sensitivity, let's discuss risk. By replacing the single risk factor shock considered above with the distribution of changes in this risk factor, a distribution of values will be obtained. Sensitivity, however plays an equal role in both cases. Risk analysis therefore introduces the notion of distribution, which may not always be analytically straightforward. The yield curve is replaced by a single shock distribution as shown in the figure where only one term is shown. Instead of a single value shock, a distribution of values is created.

When discussing risk it is often assumed that the value under scrutiny is expressed in value at risk (VaR). However, this is not always the case. In fact, any of the three analysis factors mentioned above (liquidity, value or yield) can be considered. When value risk is of interest, the relevant analysis is value risk. If the objective is income risk or cash flow, it should be analyzed as Earnings at Risk (EaR) or Liquidity at Risk (LaR) respectively.

Furthermore, care must be taken to clearly define the assessment basis used for each risk measure. While VaR analysis is commonly associated with a mark-to-market view of valuation, this is not always the case for earnings where other valuation methods are more commonly used. The valuation method used for EaR measurement must agree with the method chosen for earnings. When considering earnings and cash flows or VAR on a non-mark-to-market view, there is usually no analytical formula (largely due to the dynamic nature of these concepts). This means more Monte Carlo technique if these solutions are considered.

A set of financial analysis components includes:

1. Liquidity
2. value, using various valuation methods
3. Income using different valuation methods
4. sensitivity
5. Risk is calculated for liquidity, value and yield

Any known financial analysis is a direct representation of these factors or a combination of them. Return on Return Adjusted Capital (RORAC), for example, is the return figure divided by the value of capital figure adjusted for risk of return.

Input Elements

So far the potential outputs of financial analysis have been discussed, but what inputs are needed to calculate them? The required input factors can be obtained by considering the activities of financial institutions.

Financial institutions are in the business of creating financial contracts. A financial contract can be thought of as a set of rules that determine how and when cash flows are to be exchanged between two counterparties to the contract. Many of these contracts contain contingent features such as variable rate instruments or options. It introduces market conditions requirements or more generally risk factors. Although all contractually agreed cash flows can be derived from contract terms and market conditions, these inputs are not sufficient because contracts cannot always be kept. It identifies counterparty or credit risk. Finally, there is a set of rules that cannot be expressed at a single contract level due to their statistical nature. These considerations lead to the identification of the following input factors:

Financial contract

In general, financial contracts represent contractually binding agreements between two counterparties that govern the exchange of cash flows (timing and amount).

Risk factors

Many financial contracts contain market conditions clauses (for example:- variable rate bonds or loans) that define when and to what index the contract price has to be repaid. Alternatives are other examples. There are two subgroups of risk:

• Market Risk: Stock and commodity prices, interest and exchange rates.
• Insurance Risk: Frequency and Severity of Claims, Mortality Rate.

Counterparty

A contract is a promise to exchange cash flows according to certain patterns. Whether promises can be kept depends on the standing or rating of the counterparties. Beyond that, counterparties may hold multiple contracts, which may or may not be collateralized or guaranteed. On top of this, the counterparts can be linked together through a child-parent relationship. All of this affects the expected loss on any given exposure.

Behavioral factors

A contract contains rules relating to the exchange of cash flows. However there are certain rules that affect cash flow that can only be observed statistically. Due to their statistical nature they cannot be encoded at the level of a financial contract or easily represented as a risk factor.

For example:

Contracts with undefined cash flow profile. Some savings accounts limit the amount of withdrawals allowed in a given period. The cash flow pattern generated by such contracts can only be manipulated statistically by replication techniques.

A mortgage where the borrower has a legal right to prepayment. If this option is used rationally, it will be relatively easy to model the contract level by considering the value of the put option. In fact, some borrowers prepay under unfavorable conditions or, conversely, do not pay where it is to their advantage. Such behavior requires statistical modeling.

Life insurance contracts usually have a bonus attached to the life insurance that depends on the return on investment of the insurance company. The algorithm for calculating this bonus varies from contract to contract and changes over time.

In other words a financial contract is a set of promises related to the exchange of cash flows depending on market conditions and behavioral assumptions. Moreover, the final exchange depends on the counterparties' ability to fulfill their promises. These are the truths of finance.

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