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FUNDAMENTALS
DEFINING RISK ECONOMICS Risk Economics is a novel branch of Applied Economics, term first coined in 1999 by Mamadou Ly* in his seminal work " Risk Economics: Innovative and Scientific Approaches to the Application of Risk Management", an ouvrage undertaken from private research spanning a decade. The work is the major inspiration for the setting up of Lyscale Riskgrade as a viable professional services entity with a financial engineering firm dimension doubled with an Investment Banking concern providing cutting edge instruments and vehicles for Risk Products and Services. Risk Economics is the Economic Management of Risk. Risk is an undesirable commodity that occurs by virtue of all cardinal states. The objective of Risk Economics oscillates around the reduction and elimination of all types of risks facing businesses and other organizations using scientific approach to the problem of dealing, avoiding, reducing, and transferring risks. The scientific predisposition of Risk Economics stems from and involves the application of the scientific method to the process of managing risks and the use of the decision theory in solving risk management problems. Fundamentally, risk management is a problem in decision making as explicited grandly by Professor Emmett J. Vaughan at the University of Iowa. According to his wise perception, Risk Management is therefore more specifically a problem of decision making under conditions of uncertainty. Once a problem is identified, related information is analyzed and evaluated. Alternative solutions are identified, and the one with the greatest potential for success is selected. So were it not for the advances in scientific decision making, it seems doubtful that risk management would have evolved as a discipline. THE PROBLEMATIC DIMENSION OF RISK ECONOMICS AND ITS MANAGEMENT Risk has a temporal perspective for it has existed throughout immemorial times to this day. Exposure to misfortune and adversity brought enhanced gradualist efforts from humans to deal with risks of all sorts. Our ancestors instinct of survival can be amalgamated to a situation of observed practices tending to prevent the occurence of risky events. most of the time coinciding with extinction. Our continued existence is testimony to the success of our ancestors in managing risks. Today the apprenhension that our hazardous environmental derivatives foster via climate change, hurricanes, drought and other previsible calamities equate to the perils that threatened primitive man shivering in the cold, suffering pangs of hunger, and hunted by savage beasts or stronger tribalistic opponents. Managing risk is corrolary to sound assessment of the environment in which exposure evolves. Time frame consideration is pivotal to sound prediction of adversity. Outside temporal dispositions, risks adopt exponential tendencies most of the time with little influence on tolerance. This obsolescence of risk occurence is reflected in many fields and industries. It does not necessarily imply an actual adversity outside perceptive prejudices. Decision making under Uncertainty dictates that the expected value model cannot be used and a different approach is desirable as a condition sine qua non, sufficient and necessary if all things are to be viewed as equals, ceteris paribus. Using our methodology, the minimum payoff for easch choice is tro maximise the minimum possible profits in the spirit of the maximin strategy. When decision making process is faced with problems such as those in which payoffs en relation to costs are to be minimised, the maximin approach is reversed, in that the decision maker lists the costs associated with each decision. In tuned with the Economics Analytics of our deployment, our recommended decision takes the whole ambivalence-discount of the Risk Spectra clearly isolated within Lyscale Riskgrade Master Layout and then responds to the minimum of the maximum costs, giving rise to the term LYSCALE MINIMAX. The beauty behind Risk Economics as pioneering by Lyscale Riskgrade is that its renderings are solidly substracted from the erratic judgemental approaches exercised since now and by ricochet, we bring the scientific dimension of Risk Measure Precision to astronomic heights. Even though no system based on empirical observation and cartesian argumentations is infallible, we proud ourselves with the infinitisimal margin of errors we normally encounter in assessing identitied and isolated risks of all natures. An element of nature is always at hand to disrupt our forecasting and predictions but such element is effectively accounted for in the classes of Systemic Risk in Interactive Markets (SRIM) and in our Inherent Risk Suite. EVOLUTION OF ORGANIZATIONAL RISKS Organizational Risks evolve like any other phenomena characterized by its dynamic scope and stance. Like such phenomena, risks are no different. Organizational risks follow a well-defined pattern of occurence and tend to be jugulated by the sheer response of man-made solutions in a temporal and spacial contexts. When risks occur, they are addressed and solutions are put in place to prevent them from happening in the future. This approach of self-defence induces a dynamic effect that in turn triggers dimensional moral hazards and assymetric information flaws exploited by organizational agents. Lyscale Riskgrade has put in place a mechanism addressing this aspect of recurrence in a scientific setting. Evolution imperatives apply a set of tools designed specifically in order to fine-tune actions of adversity induced by detrimental behaviour of organizational agents. This takes into account realms of deployment unforeseen in the past. RISKS IN A GLOBAL GROWTH CONTEXT Our theory of Global Growth is comprehensively dealt with within the GLOBAL MACROECONOMIC OBSERVATION SYSTEM (GMOS) PLATFORM with pioneering deployment of a set of tools acting like Indexes of measures aiming to eliminate dysfunctional and discrepancies observed in the assessment and prevention of risk occurence. The set, although, globalist in outlook, embeds sophisticated financial engineering rationales disseminated at precise micro-levels. RISK IN THE MODERN BUSINESS ENVIRONMENT In the business world, risks are sub-divised into categories ranging from financial, operational, computational and credit compartments with specialised fields of applications and a varying degree of shortcomings. Many Experts in Risk tend to assimulate Risk as a concept to virtually everything they do not comprehend. This stance is far from the reality. Risk in the Business Environment is a science with real variables and specifics, properly defined, isolated and dealt with. It involves most of the times, the measurement and management of these risks, the valuation and hedging of products related to risk instruments, and the promotion of greater understanding in the area of risk theory and practice. Research in the field of business related risks is extensive, comprehensive with more or less headways made in recent years. Lyscale Riskgrade is working hard to provide a greater access to these highly technical expertise, not widely understood and accessible to the wider audiences, by providing the layman-connectivity gap filling approach with technical jargon translated into simple expressions. RISK AS A CONCEPT The abundance of definitions affiliated to the concept of risk provides proof if any that the matter is of pivotal importance in today's society. Virtually, every field of knowledge has its own specialiazed terminology, and terms that have very simple meanings in everyday usage. Economists, statiticians, decision-theorists, insurance theorists and many other self-proclaimed experts use different definitions when it comes to Risk. Our definition of risk is simple: "a condition of the real world in which there is an exposure to adversity. A condition in which there is a possibility of an adverse deviation from a desired outcome that is expected or hoped for." This definition encompasses various terms and notions including (1) the chance of loss; (2) the possibility of loss; (3) uncertainty; (4) the dispersion of actual from expected results; and (5) the probability of any outcome different from the one expected. Risk has two components:
1. uncertainty, and
2. Exposure.
If either is not present, there is no risk. The development of Lyscale Riskgrade's scientific measure of risk in virtually every asset and liability class conceivable, one needs to explore, in turn, some fundamental mathematical notions and definitions. Although, we endeavour to bring our mathematical formulation to a strict minimum so as to enable everyone to grasp our demonstrative reasonings, it is also true that a minimum mastery in pure and applied mathematics is essential for a comprehensive understanding of what it is all about. First, we explore the notion of a hypothetical Risk Exposure Line (REL) in a Cartesian setting. We denote Pn(S/R) as the Gradual Evolutive Level of Magnitudes by which the Exposure Element of Risk (S/R) as a combination of Exposure and Uncertainty evolves. S/R, on the ordinate level, expresses the cumulative sums of Economic Exposure (Money) of Risk without the Uncertainty element in its composition. The scientific premises by which we reached the rationale in isolating the Uncertainty element of Risk for the first time in the history of the subject rely suprisingly on obvious logical drawings: in general and empirically proven is the fact that pairwise loss given default (LGD) correlation is found to be small (5.9% for large corporate obligors that we comprehensively researched over the past ten years with respect to the Lyscale Riskgrade's Risk Audit for Entities, Global 500 section). Another observation arises from the following perspective: since pairwise correlation is small between the Exposure Element and the Uncertainty element of Risk, economic capital is found to be more sensitive to changes in idiosyncratic probability default/loss given default (PD/LDG) correlation than those in systematic PD/LGD. As shown on the figure below, it is clear that at Unity (i.e point 1), the Uncertainty Element of Risk adopts a declining tendency while the Exposure Element of Risk increases and starts declining with Risk Magnitude increasing. By the time Risk Magnitude approximates the level 2 on the Lyscale Riskgrade System, Risk Uncertainty declines precedence over the Exposure Element and both start evolving at very negligible level, nearing zero. Risk Exposure Line Picking as shown by the graph below. UNCERTAINTY AT THE PERIPHERY OF RISK Uncertainty is a state of mind characterized by doubt, based on the lack of knowledge about what will or will not happen in the future. This amounts to a psychological reaction to the absence of knowledge about the future in relation to a given problem. So, the existence of Risk, which is a condition or combination of circumstances in which there is a possibility of loss, creates uncertainty on the part of individuals when that risk is recognised. Therefore, uncertainty is a peripheric perception inducing negation over the positive outcome of something contingent to fluctuation or to change. Uncertainty exists only when risk exists. However, risk can be isolated and identified without the prevalence of uncertainty. Second, we explore the notion of the Alpha Function as explicited in the graph below.  Alpha Function In finance, alpha is a financial measure giving the difference between a fund's actual return and its expected level of performance, given its level of risk (as measured by beta). A positive alpha indicates that a fund has performed better than expected based on its beta, whereas a negative alpha indicates poorer performance. This notion of an Alpha Function comfirms a solid basis of deploying Financial Risk in that it enables practical approximations in terms of provoding mirroring Betas in the Capital and Money Markets via Special Financial Instrument Computation. The modelling predisposition using Alpha Function provides accuracy in terms of realistic assumptions whereby every conceivable postulate is self-assessed for realistic adoption. The vast morass of Financial Statistics Indicators, most of them meaning approximately nothing and entirely useless, is mind-boggling if one truly wants to make sense of them all or in isolation. Alpha Function rationale, used in a consistent manner, therefore provides a scientific way of making realistic approximations in all field of Risk Economics. It is worth noting that for the purpose of the deployment of Lyscale Riskgrade Methodology, the term "Fund" has been used throughout to represent the numerical value or amount derived from any type of activities, wether latent or explicit. Fund is therefore synonemous to "Return" as well as to notions such as "Profit" in an accounting setting; Revenues in a fiscal setting; "Capital" in an Investment setting; "Land Ristourne" in a tertiary setting and "Labour Output" in an Industrial Relations setting.  Polygamma A special function which is given by the (n+1)st derivative of the logarithm of the gamma function (or, depending on the definition, of the factorial ). This is equivalent to the th normal derivative of the logarithmic derivative of (or ) and, in the former case, to the th normal derivative of the digamma function . The polygamma function can be expressed in terms of Clausen functions for rational arguments and integer indices. Parabola Directrix A parabola (plural "parabolas"; Gray 1997, p. 45) is the set of all points in the plane equidistant from a given line (the conic section directrix) and a given point not on the line (the focus). The focal parameter (i.e., the distance between the directrix and focus) is therefore given by , where is the distance from the vertex to the directrix or focus. The surface of revolution obtained by rotating a parabola about its axis of symmetry is called a paraboloid. Weierstrass Function  The function was published by Weierstrass but, according to lectures and writings by Kronecker and Weierstrass, Riemann seems to have claimed already in 1861 that the function is not differentiable on a set dense in the reals. However, Ullrich (1997) indicates that there is insufficient evidence to decide whether Riemann actually bothered to give a detailed proof for this claim. du Bois-Reymond (1875) stated without proof that every interval of contains points at which does not have a finite derivative, and Hardy (1916) proved that it does not have a finite derivative at any irrational and some of the rational points. Gerver (1970) and Smith (1972) subsequently proved that has a finite derivative (namely, 1/2) at the set of points where and are integers. Gerver (1971) then proved that is not differentiable at any point of the form or . Together with the result of Hardy that is not differentiable at any irrational value, this completely solved the problem of the differentiability .
Amazingly, the value of the Weirerstrass Function can be computed exactly for rational numbers. RISK MAGNITUDE SPREAD
RISK MAGNITUDE SPECTRA SHOWING TRADING-OFF RISK EXPOSURE AND RISK UNCERTAINTY  RISK OPTIMAL LINE ALONG GRADUAL RISK MAGNITUDES WITH LOGARITHM AND EXPONENTIAL CURVES BEHAVIOUR  DATA TABLE EXHIBIT WITH EQUIVALENT MAGNITUDE SPREAD, SPECTRA AND BEHAVIOURAL LOG AND EXPONETIAL CURVES  THE MAGNITUDES OF RISK Under the Lyscale Riskgrade Methodology, the magnitudes of risk spread RM1 to RM20 with gradual increments noth in terms of Risk Spectra and Risk Magnitude alongside the Optimal Risk Line on an cartesian coordinates.  RISK, PERIL, SINISTROSE AND HAZARD  CLASSIFICATION OF RISK AND EQUIVALENT BURDEN Classifying risks is a subjective notion where the rules applied are as many as the rulers themselves. Most of the time, risk is classified with a vested interest perspective enabling many practitioners renegating on potential claims especially in the fields of the Insurance Industry. LYSCALE RISKGRADE believes that it is imperative to set a global classification of risks according to scientific precision so as to enable practitioners and other stakeholders to resolve many point of contention and discorde within the galaxies Risk Economics, Risk Analytics and Risk Management. in doing so, Lyscale Riskgrade has pioneered this groundbreaking risk classification enacted within the Five Platforms contained within LYSCALE RISKGRADE SYSTEM(R). Regardless how risk is defined, the greatest burden in connection with risk is that some losses will actually occur. Losses are therefore the primary burden of risk and the primary reason why firms and individuals attempt to avoid risk or alleviate its impact. In addtion to the losses themselves, risk had other detrimental aspects. The uncertainty as to whether the loss will occur requires the prudent firm or individual to prepare for its possible occurrence. In the absence of insurance, one way this could be done is to accumulate a reserve fund to meet the losses if they do occur. Accumulation of such a reserve fund carries an opportunity cost, for funds must be available at the time of loss and must therefore be held in a highly liquid state. The return of such funds will presumably be less than if they were put to alternate uses. Moreover, the existence of risk may also have a deterrent effect on economic growth and capital accumulation. Progress in the economy is determined to a large extent by the rate of capital accumulation, but the investment of capital involves risk that is distateful. Investors as a class will incur the risks of a new undertaking only if the return on the investment is suffciently high to compensate for both the dynamic and static risks. The cost of capital is higher in those situations where the risk is greater, and the consumer must pay the resulting higher cost of the goods and services or they will be forthcoming. RISK PROTOCOL SETTING Setting the Lyscale Riskgrade System protocols encompasses the deployment of the whole spectrum of all five platforms with differing levels of data capture and the definition of each class of Risk Treatment according to the stance of sources available on the Global Market Place. The idea is to amalgamate sources of information and data provision into one single protocol deployment so as to enable an effective streamlining of provenance and destinations. The reliability of the data provision market is then put on stringent testing exercises in order to provide an accurate aggregation of data veracity using sophisticated aggregator engines. Our aggregator engines, all conceptualised, designed and implemented by Lyscale Riskgrade enable us to fine-tune diversified data provision sources and eliminate discrepancies in those data by replacing them with specific aggregated industry, sector or government-driven scientific approximations to reflect reality. One must stress that many National Statistics Organisations provide biased and sometimes utterly dishonest data about their economies, chiefly among developing countries especially in Africa and Asia. When confronted with such unrealiable data provision, Lyscale Riskgrade applies them to the series of Aggretator Engines so as to determine the level consistent with Aggregated Data Veracity (ADV). The sources of Data Provision include: Banking sector indicators
The main data sources for the banking sector indicators are BankScope, Datastream, Worldscope, International Financial Statistics and Global Financial Stability Report. The coverage varies across countries and over time.
Equity markets indicators
The main data sources for the equity market indicators are Datastream, Emerging Markets Database (EMDB), and Worldscope. For the size indicators we use the World Development Indicators (WDI) which are obtained from the S&P Emerging Market Handbook. Coverage depends on the extent of missing observations for each indicator, as well as the presence of extreme outliers. Notably, we exclude some countries due to the extremely limited number of listed firm observations per year.
Bond markets indicators
The main data sources for the bond market indicators are Datastream, World Exchanges Federarion (WFE), Bank of International Settlements (BIS), and Citicorp. Coverage depends on the extent of missing observations for each indicator. For emerging markets the bond return indices compiled by Citicorp are used directly. For developed markets, long-term bond yields (%) are obtained from Datastream and manipulated with the following methodology: yields (%) are naively transformed into "return index" by the following formula: return index = (100/ (100+Yield))*100. Admittedly, this is not exactly a true return index, but the annualized standard deviation of daily difference of this "return index" should be able to roughly proxy for the standard deviation of the "true return index," as the "true return index" is roughly this naïve index plus a slope, and the slope would have impacts on only the mean not the standard deviation of the daily returns.
Corporate sector indicators
All corporate sector indicators are based on Worldscope. The coverage varies across countries and over time. Moreover, coverage depends on the extent of missing observations for each indicator, as well as the presence of extreme outliers.
Outliers are values that are extremely large or small compared to the rest of the sample data and are suspected of misrepresenting the population from which they were collected. These outliers are either statistic or economic. Economic outliers represent special cases which originate either from unusual circumstances or incorrect data-entry. Statistical outliers are those that lie at the extreme tails of the distribution of firm-level observations, and can adversely affect the representativeness of central tendency measures. Methodologically, we first drop the economic outliers, and then proceed with trimming the two tails at the 1 percent level for each country-year. The region North America consists of the following countries: Bermuda, Canada, United States.
The income level High income: OECD consists of the following countries: Australia, Japan, Korea, Rep., New Zealand, Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, Canada, United States.
Banking Sector - Size Index Average of the scaled indicators within the dimension of size [Deposit money bank assets to GDP, Central bank assets to GDP, M2 to GDP, Total System Deposits to GDP, Private Credit to GDP, Private Credit to Total Domestic GDP, and Private credit to Total Funding]. The average is calculated with at least one indicator.
Central bank assets to GDP
Claims on domestic real nonfinancial sector by the Central Bank as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at]*100 where F is Central bank claims, P_e is end-of period CPI, and P_a is average annual CPI. Source: International Financial Statistics
Deposit money banks assets to GDP
Claims on domestic real nonfinancial sector by deposit money banks as a share of GDP, calculated using the following deflation method:{(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at]*100 where F is deposit money bank claims, P_e is end-of period CPI, and P_a is average annual CPI. Source: International Financial Statistics
M2 to GDP
Money and Quasi-Money to GDP, calculated using the following deflation method:{(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at]*100 where F is money and quasi-money, P_e is end-of period CPI, and P_a is average annual CPI. Source: International Financial Statistics
Private credit to GDP
Private credit (claims on the private sector) by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at]*100 where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Source: International Financial Statistics
Private credit to total domestic credit
Private credit by deposit money banks and other financial institutions to Total Domestic Credit, in % Source: International Financial Statistics
Private credit to total funding
Private credit by deposit money banks and other financial institutions to Total Financial System Deposits, Foreign Liabilities, Bonds and Money Market Instruments by deposit money banks and other financial institutions, in % Source: International Financial Statistics
Financial system deposits to GDP
Demand, time and saving deposits in deposit money banks and other financial institutions as a share of GDP, calculated using the following deflation method:{(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at]*100 where F is demand and time and saving deposits, P_e is end-of period CPI, and P_a is average annual CPI Source: International Financial Statistics
Banking Sector - Efficiency Index
Average of the sub-dimension indexes within the dimension of efficiency [Profitability, Efficiency, and Competitiveness]. The average is calculated with at least one sub-dimension index.
Three-bank concentration ratio (assets)
Assets of the three largest banks within a country as a share of assets of all commercial banks in the system.Source: Bankscope
Three-bank concentration ratio (deposits)
Deposits of three largest banks within a country as a share of deposits of all commercial banks in the system.Source: Bankscope
Lending-Deposit rates spread
Calculated as : [(1+lending rate)/(1+deposit rate) - 1]*100. Source International Financial Statistics
Net interest margin
Weighted average, by total bank assets, of the ratio of net interest revenue to total assets.Source: Bankscope
Operating costs to total assets
Weighted average, by total bank assets, of the ratio of operating cost to total assets.Source: Bankscope
Return on Assets (adjusted)
Return on assets adjusted for cross-country risk differences.Source: Bankscope
Return on Assets (median)
Banking Sector - Stability Index
Average of the sub-dimension indexes within the dimension of stability [Capital Adequacy, Asset quality (borrowers), Asset quality (Lenders), Liquidity, and Sensitivity to market risk]. The average is calculated with at least one sub-dimension index.
Market to book value of equity ratio, median (corporate sector)
Median of Market to book value of equity ratio (corporate sector) Source: Worldscope
Capital adequacy ratio
Total capital adequacy ratio under the Basle rules. It measures Tier 1 + Tier 2 capital which includes subordinated debt, hybrid capital, loan loss reserves and the valuation reserves as a percentage of risk weighted assets and off balance sheet risks. This ratio should be at least 8% Source: International Financial Statistics
Debt to book value of equity ratio, median (corporate sector)
Median of Debt to book value of equity ratio (corporate sector) Source: Worldscope
Interest coverage ratio, median (corporate sector)
Median of the Interest coverage ratio (corporate sector) Source: Worldscope
Liquid assets to total deposits and short-term borrowing
Liquid assets to Total Deposits and Short-Term Borrowing, (%).Source: Bankscope
Liquid assets to total assets
Liquid assets to Total Assets, (%). Source: Bankscope
Market to book value of equity ratio, mean (banks)
Market to book value of equity, mean (banks) Source: Datastream
Non-performing loans ratio
This is a measure of the amount of total loans which are doubtful.
Real credit growth (adjusted) (%)
Accumulated five-year growth rate of real credit to the private sector by deposit money banks and other financial institutions minus the accumulated five-year growth rate of real GDP.
Total return index volatility
Weighted average annualized volatility of the total return index, which include the price and dividend of the listed bank share.Source: Datastream
Equity Market - Size Index
A composite index on equity market size is created on the basis of (1) mkt. cap. to GDP, (2) value traded to GDP and (3) turnover ratio. Each of the above component indicators are standardized by subtracting the median of the distribution of the variable and scaling by the standard deviation of the variable
Equity Mkt. Turnover Ratio (%)
The indicator is defined as the total value of shares traded during the period divided by the average market capitalization for the period. Average market capitalization is calculated as the average of the end-of-period values for the current period and the previous period (Source World Development Indicators)
Number of Listed Firms
The indicator is defined as the number of the domestically incorporated companies listed on the country\'s stock exchanges at the end of the year (Source: World Development Indicators)
Market Cap to GDP Ratio (%)
The indicator is defined as the ratio of market capitalization to GDP (Source: World Development Indicators)
New Capital Raised to Mkt. Cap. Ratio (%)
The indicator is defined as the total volume of new issues (IPOs and SEOs) divided by the market capitalization. (Source: Thomson Financial and World Bank staff estimation)
Trade Volume to GDP ratio (%)
Stock Traded to GDP is the total value traded divided by GDP. Value traded is the total value of shares traded during the period ( Source: World Development Indicators)
Equity Market - Efficiency Index
Two composite indices on equity market efficiency are created. This recommended one is on the basis of (1) number of zero return weeks and (2) stock return synchronicity . Each of the above component indicators are standardized by subtracting the median of the distribution of the variable and scaling by the standard deviation of the variable
Mkt. Cap. Concentration in top 10 firms (%)
The indicator is defined as the share of the market capitalization of the top 10 firms in the total market capitalization of the market. It measures the degree of concentration of the top 10 firms in the market. (Source : Datastream, Emerging Market Database, and World Bank staff estimation)
Percentage of Private Information Trading
Private information trading is a measure that is used to detect the private information trading based on certain price-volume patterns. The indicator is defined as the percentage of firms in a country that exhbit significant pattern of private information trading ( Source: Datastream, and World Bank staff estimation)
Stock Return Synchronicity
Synchronicity measures the degree of comovement of individual stocks with the market. ( Source: Datastream, Emerging Market Database, World Bank staff estimation)
Transaction Costs (%)
Transactions cost measures the illiquidity or transaction cost to the market.A security with high transaction costs will have less frequent price movements, and more zero returns than a security with low transaction costs. Investors will only trade when the gains exceed transaction costs.To estimate the parameters of the model we create a equally weighted market index based on the Datastream market indices. A limited dependent variable model is developed to create the measure. ( Source: Datastream, and World Bank staff estimation)
Percentage of Zero Return Weeks
Percentage of zero return weeks is a simple measure of illiquidity which is the median number of zero return weeks over a particular year for companies in a market. This is an alternative measure of illiquidity since investors will not trade when the transaction cost is too high and hence will result in more zero return weeks.( Source: Datastream, Emerging Market Database, and World Bank staff estimation)
Equity Market - Stability Index
A composite index on equity market stability is created on the basis of (1) skewness, (2) volatility of market returns.Each of the above component indicators are standardized by subtracting the median of the distribution of the variable and scaling by the standard deviation of the variable
Equity Return Skewness
The indicator is defined as the skewness of market index return. A larger positive value for skewness is associated with a more right skewed return distribution. Academic research indicates that emerging markets tend to have more positively skewed returns as compared to developed markets.This measure is the conditional skewness of market returns. This is computed by taking the samples third moment of daily returns and dividing it by the return index sample variation raised to the power of 3/2. A large value of skew is associated with a more right-skewed return distribution.A more detailed discussion on this measure can be found in Bae et al (2005) ( Source : Datastream and World Bank Staff estimation)
Equity Return Volatility (%)
Volatility is the standard deviation of the market index returns. This measure is annualized to give a measure of the annual volatility. Volatility is reported as three year moving averages. (Source: Datastream and Emerging Market Database)
Probability of Earnings Manipulation (%)
This measure determines the likelihood of a firm in a country to demonstrate fraudulent behavior or in other words to manipulate its earnings.To create this measure we use Worldscope since we need extensive accounting data to calculate the probability of earnings manipulation. We take the universe of firms in Worldscope and delete out financials. The data is winsorized at the 1% and 99% level to remove the outliers. We then calculate the 'M-score' for each firm as developed by Beniesh in 1999. The indicator is defined as the percentage of listed firms in a country that exhibit significant pattern of earning manipulation. (Source: WorldScope, and World Bank staff estimation)
Bond Market - Size Index
The index is based on three sub-indicators: (1) private bond to GDP ratio (2) public bond to GDP ratio (3) international bond to GDP ratio. Every component carries equal weight. An index will not be created if (1) or (2) is missing. Range: (1-10)
Domestic debt_All (bil. USD)
The indicator is defined as the total amount of domestic debt securities outstanding, in billion of USD. Sources: BIS
Domestic debt_Government (bil. USD)
The indicator is the total amount of domestic government debt securities, in billion of USD. Source: BIS
International bonds to GDP (%)
The indicator is defined as the ratio of international bond to GDP. Sources: BIS, WDI, and World Bank Staff calculation
International bonds (bil. USD)
The indicator is defined as the amount of outstanding debt securities issued to international market, in billion of USD. Sources: BIS
Value of corporate bonds newly issued to GDP (%)
The indicator is defined as the ratio of newly issued corporate bond volume to GDP. Sources: Thomson Financial, WDI, and World Bank Staff calculation
Private sector bond to GDP ratio (%)
The indicator is defined as the total amount of outstanding domestic debt securities by private domestic entities divided by GDP. Both the numerator and denominator are deflated appropriately. Domestic debt is deflated by the average of the end of year value for year t and year t-1 both deflated by the end of year CPI and the GDP deflated by the annual value of the CPI. Sources: World Bank Finanical Structure Database
Public sector bond to GDP ratio (%)
The indicator is defined as the total amount of outstanding domestic debt securities by public domestic entities divided by GDP. Both the numerator and denominator are deflated appropriately. Domestic debt is deflated by the average of the end of year value for year t and year t-1 both deflated by the end of year CPI and the GDP deflated by the annual value of the CPI. Sources: World Bank Finanical Structure Database
Bond Market - Efficiency Index
The index is based on three sub-indicators: (1) J.P.Morgan Chase settelment efficiency index (2) bid-ask spread (3) turnover ratio in private bond market; (4) turnover in public bond market. Component (1) and (2) carries 40% weight each while (3) and (4) carries 10% each. Range: (1-10)
Turnover ratio (Private sector bond market) %
The indicator is defined as the value of private sector bonds traded (at time t) on the market scaled by average value outstanding at time t and (t-1). Sources: World Federation of Exchanges (WFE)
Turnover ratio (Public sector bond market) %
The indicator is defined as the value of public sector bonds traded (at time t) on the market scaled by average value outstanding at time t and (t-1). Sources: World Federation of Exchanges (WFE)
Bond Market - Stability Index
The index is based on six sub-indicators: (1) volatility of government bonds (2) skewness of government bonds (3) short-term domestic debt ratio (4) short term international debt ratio (5) correlation with U.S. bond returns (6) correlation with Germany bond returns. Each component carries 16.77% weight. An index will not be created if either (1) or (2) are missing. Range: (1-10)
Bond return correlation with German bonds
The indicator is defined as correlation of bond returns with Germany. Bond return indices for developed countries are obtained from J.P.Morgan Chase, while those for emerging markets are obtained from Citigroup. Sources: Datastream and World Bank staff estimation
Bond return correlation with US bonds
The indicator is defined as the correlation of bond returns with the United States .Bond return indices for developed countries are obtained from J.P.Morgan Chase, while those for emerging markets are obtained from Citigroup. Sources: Datastream and World Bank staff estimation
Short-term domestic bond ratio (%)
The indicator is defined as the ratio of short term debt to total domestic debt ratio. Sources: BIS and World Bank staff calculation
Short-term international bond ratio (%)
The indicator is defined as the ratio of short term debt to total international debt ratio.Sources: BIS and World Bank staff calculation
Skewness of government bonds
The indicator is defined as the skewness of daily returns on government bonds. Bond return indices for developed countries are obtained from J.P.Morgan Chase, while those for emerging markets are obtained from Citigroup. Sources: Datastream and World Bank staff estimation
Volatility of government bonds (%)
The indicator is defined as the annualized standard deviation of daily returns on government bonds. Bond return indices for developed countries are obtained from J.P.Morgan Chase, while those for emerging markets are obtained from Citigroup. Sources: Datastream and World Bank staff estimation
RISK PROCEDURES SETTING Lyscale Riskgrade adopts stringent risk procedures setting in taking into account the overall functionality of its deployment. Procedural outlays follow logical embedded deployment stance coupled with a practical solution provision at series of clientele targets. For instance, client cannot access SLA2 without subscribing to SLA1 not merely on the grounds of pricing strategy but only and only because SLA1 embeds indispensable Compartmental and Conceptual Framework that provide the very bloodline of SLA2. It is as simple as that. None other explanation exists to perpetuate any fallacious notion, otherwise. The gradual demarcation of Procedural Setting is also tribute to the logic contained in the whole system. We bear in mind different needs of clientele and targeted specific professions and practices in order to define and implement a system consistent with clientele tradiotional preferences. As a matter of fact, although an Investment Bank, a Multilateral Agency or a Government Regulator may be well off opting for the entire system, it is also true that a small Independent Financial Adviser will tend to stick to the platform best relevant to its professional remit. Once the procedural needs are defined, the system takes care of the deployment, seamlessly. RISK MAGNITUDES SETTING The setting of Risk Magnitudes is made scientifically, without human interference and without human external influence. The intvariable Data Skeleton is built in a manner consistent with fixed inputs defined by geography, connectivity-gaps identified, the level of economic organization, the stance of the regulated industry in question, the enforcement of compliance as dictated by International Standards (if any) and the technical rationales encompassing scientific truths as defined in Mathematics, Statistics, Economics, Accounting and Finance and Cybernetics where elevant. Some financial centres are scientifically proven to be evolving within parameterized specific Risk Magnitudes while others are subjected to wider fluctuations. Despite this fact, Lyscale Riskgrade leaves the system compute the most scientifically-fit magnitude without interference nor external influence. RISK INTERACTIVE REPORT GATHERINGS (IRGs) IRGs provide the essential function of transforming queries generated within each platform in an automatic fashion so as to deliver the required outcomes. Modular deployment is enacted within the Internal System of the innovation consistent with speed (miliseconds in general), stance (evolving along different clusters and platforms), and remit (embedding other functional queries and choosing to highlight rapport de force induced by the explicited factors). INDUSTRIES PROFILING REPORTS (IPRs) IPRs concerns specific reporting exercises within Risk Audit for Industries (RAFI-SLA2), Sector Analysis (SA-SLA2), Market Overview (MO-SLA2) and Underwriting Propositions (UP-SLA2). At a Bespoke deployment, it gathers more intelligence within a cross-section of platforms and modules in an automated fashion. INTERACTIVE DAILY INPUTS (IDIs) Lyscale Riskgrade Interactive Daily Inputs (IDIs) stems from various reliable sources and chiefly from the World Bank first class provenance as appeared in FINANCING GROWTH as well as from various study references. An alphabetical list of indicators below is streamlined daily via Lyscale Riskgrade Aggregator Engines. The list includes sources and methodologies as follows:
Alphabetical list of indicators embedded within Lyscale Riskgrade Aggregator Engines
Capital Adequacy Ratio
Banking Sector - Stability - Capital Adequacy
The data are from the Global Financial Stability Report (IMF).
Central Bank Assets to GDP
Banking Sector - Size - Size
Claims on domestic real non-financial sector by the Central Bank as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/Pet + Ft-1/P_et-1]}/[GDPt/Pat]*100 where F is Central Bank claims, Pe is end-of period CPI, and Pa is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Central Bank claims (IFS lines 12, a-d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF).
Debt to book value of equity ratio
Banking Sector - Stability - Asset Quality - Borrowers
(see Corporate sector component)
Deposit Money Banks Assets to GDP
Banking Sector - Size - Size
Claims on domestic real non-financial sector by deposit money banks as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/Pet + Ft-1/P_et-1]}/[GDPt/Pat]*100 where F is deposit money bank claims, Pe is end-of period CPI, and Pa is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Deposit money bank assets (IFS lines 22, a-d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF).
Financial System Deposits to GDP
Banking Sector - Size - Size
Demand, time and saving deposits in deposit money banks and other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/Pet + Ft-1/P_et-1]}/[GDPt/Pat]*100 where F is demand and time and saving deposits, Pe is end-of period CPI, and Pa is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Financial system deposits (IFS lines 24, 25, and 45); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF).
Interest coverage ratio (corporate sector)
Banking Sector - Stability - Asset Quality - Borrowers
(see Corporate sector component)
Lending-Deposit rates spread
Banking Sector - Efficiency - Efficiency
Percentage difference between the lending rate and the deposit rate. The spread is calculated as: [(1+lending rate) /(1+deposit rate) - 1]. Raw data are from the electronic version of the IMF's International Financial Statistics. Interest lending rate, period average (IFS, line 60p), and interest deposit rate, period average (IFS, line 60l).
Liquid Assets to Total Assets
Banking Sector - Stability - Liquidity
The raw data is taken from the Fitch's BankScope database.
BankScope lines: 2075/2025*100.
To calculate the country average, we weight individual bank liquidity ratios within the country by the share of bank's total assets. TA: total assets, n = total number of banks in country c given by the BankScope sample.
Liquid Assets to Total Deposits and Short-Term Borrowing
Banking Sector - Stability - Liquidity
The raw data is taken from the Fitch's BankScope database.
BankScope lines: 2075/(2030 + 2035 - 2160 -2165)*100.
To calculate the country average, we weight individual bank liquidity ratios within the country by the share of bank's total assets. TA: total assets, n = total number of banks in country c given by the BankScope sample.
Market to book value of equity (banks)
Banking Sector - Stability - Sensitivity to Market Risk
Data is taken from Lyscale Riskgrade DataCentre Aggregator.
Market to book value of equity ratio (corporate sector)
Banking Sector - Stability - Asset Quality - Borrowers
(see Corporate sector component)
M2 to GDP
Banking Sector - Size - Size
Ratio of money plus quasi-money to GDP, calculated using the following deflation method: {(0.5)*[Ft/Pet + Ft-1/P_et-1]}/[GDPt/Pat]*100 where F is liquid liabilities, Pe is end-of period CPI, and Pa is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Money and quasi-money (IFS line 35..ZF); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF).
Net Interest Margin
Banking Sector - Efficiency - Profitability
The raw data is taken from the Fitch's BankScope database.
BankScope lines: 2080/2025*100. The ratio is the net interest income expressed as a percentage of total assets. We present the weighted average of the net interest margin (NIR), by bank assets, at the country level.
TA: total assets, n = total number of banks in country c given by the BankScope sample.
Non-Performing Loans ratio
Banking Sector - Stability - Asset Quality-Lenders
The data are from the Global Financial Stability Report (IMF).
Operating costs to total assets
Banking Sector - Efficiency - Efficiency
The raw data is taken from the Fitch's BankScope database.
BankScope lines: 2090/2025*100. The ratio is presented as the weighted average of the operating cost to total assets ratio, by total bank assets, at the country level. TA: total assets, n = total number of banks in country c given by the BankScope sample.
Private Credit to Total Funding
Banking Sector - Size - Intermediation
Private credit by deposit money banks and other financial institutions to demand, time and saving deposits in deposit money banks and other financial institutions, foreign liabilities, money market instruments, and bonds. Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d), and financial system deposits (IFS lines 24, 25, 26(a,b,c,m,n), 45, and 46 (a,b,c,m,n)).
Private Credit to GDP
Banking Sector - Size - Intermediation
Private credit by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/Pet + Ft-1/P_et-1]}/[GDPt/Pat]*100 where F is credit to the private sector, Pe is end-of period CPI, and Pa is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF).
Private Credit to Total Domestic Credit
Banking Sector - Size - Intermediation
Private credit by deposit money banks and other financial institutions to domestic credit. Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d), and domestic credit (IFS line 52 or if not available line 32).
Real Credit Growth (adjusted)
Banking Sector - Stability - Asset Quality - Lenders
Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF).
Return on Assets (adjusted)
Banking Sector - Efficiency - Profitability
The raw data is taken from the Fitch's BankScope database.
BankScope lines: 2115/2025AVG*100. In order to account for country-risk differences, the variable is standardized in the following way:
First, we calculate the weighted average of the return on assets (ROA) at the country level.
ROAi,c,t = Return on Assets. i, c, and t indices for bank, country and time respectively.
WROAc,t is the weighted average of ROAi,c,t at the country level, where ?i is the bank's share in total assets of the country's banking system. TA: total assets, n = total number of banks in country c given by the BankScope sample.
Second, we calculate the country four-period average.
Third, we standardized the country four-period average (AWROA) by subtracting the cross-country average of WROA at period t (MAWROA). The difference is divided by the cross-country standard deviation of WROA at period t.
Return on Assets (median)
Banking Sector - Efficiency - Profitability
The raw data is taken from the Lyscale Riskgrade DataCentre database.
Lyscale DataCentre lines: 2115/2025AVG*100. This measure is the median of return on assets of the banks in each country, without any adjustments.
Three-bank concentration ratio (assets)
Banking Sector - Efficiency - Competitiveness
The raw data is taken from the Lyscale database.
A country's banks are ranked according to assets, and the assets of the top three banks are divided by the sum of the assets of all banks.
Three-bank concentration ratio (deposits)
Banking Sector - Efficiency - Competitiveness
The raw data is taken from the Lyscale Riskgrade DataCentre database.
A country's banks are ranked according to deposits, and the deposits of the top three banks are divided by the sum of the deposits of all banks.
Total Return Index Volatility
Banking Sector - Stability - Sensitivity to Market Risk
Daily raw data of the total return index for listed banks is taken from Datastream. The daily variability of banks' shares is annualized according to:
Annual Volatility (AVOL) = SQRT(n)* Daily Volatility, where n is the number of trading days during the year.
Then, we calculated the country average of the annual volatility. The annual volatility is weighted within the country by the market value share of the listed bank. i, c, and t indices for bank, country and time respectively.
WAVOLc,t is the weighted average of AVOLi,c,t at the country level, where ?i is the market value share of the listed bank in country c.
Lyscale DataCentre Aggregator constructs the return index (RI) using an annualized dividend yield, using:
RIt = return index on day t
RIt-1 = return index on previous day
PIt = price index on day t
PIt-1 = price index on previous day
DYt = dividend yield % on day t
N = number of working days in the year (taken to be 260)
Bond Return correlation with German Bonds
Capital Markets - Bonds - Stability
Equals: Correlation of government bond returns in a country with respective bond returns in Germany
Bond Return correlation with US Bonds
Capital Markets - Bonds - Stability
Equals: Correlation of government bond returns in a country with respective bond returns in the US
Domestic Debt - All
Capital Markets - Bonds - Size
Bank of International Settlements (BIS)
Domestic Debt - Government
Capital Markets - Bonds - Size
Bank of International Settlements (BIS)
Domestic Debt Ratio
Capital Markets - Bonds
Equals: Domestic Debt/Total Debt
Bank of International Settlements (BIS)
Equity Return Skewness
Capital Markets - Equity - Stability
This measure is the conditional skewness of market returns.
Equity Return Volatility
Capital Markets - Equity - Stability
Volatility is the standard deviation of the market returns. This measure is annualized to give a measure of the annual volatility. Volatility is reported as three year moving averages.
Equals: for daily data= sqrt (252)* std (market returns)
Equity Market Turnover Ratio (%)
Capital Markets - Equity - Size
Turnover is the total value of shares traded during the period divided by the average market capitalization for the period. Average market capitalization is calculated as the average of the end-of-period values for the current period and the previous peri
Financial derivatives traded
Capital Markets - Bonds - Size
A dummy that equals 1 if there is financial derivatives are traded in the country, 0 otherwise.
Compaq Handbook of World Stock, Derivative and Commodity Exchanges
Government Bond Yield (3 month and 10 year)
Capital Markets - Bonds
World Federation of Exchanges
International Bonds
Capital Markets - Bonds - Size
Total amount of international debt securities outstanding (in billion USD).
Bank of International Settlements (BIS)
International Bonds to GDP ratio
Capital Markets - Bonds - Size
Total amount of international debt securities divided by the GDP.
Bank of International Settlements (BIS) and World Development Indicators (WDI)
IPO to Market Capitalization ratio
Capital Markets - Equity
Total volume of IPOs divided by the market capitalization.
Thomson Financial League Tables and World Development Indicators (WDI)
Market Capitalization Concentration in Top 10 Firms
Capital Markets - Equity - Efficiency
Equals: (sum of Market capitalization of top 10 firm)/ (sum of Market capitalization of all firms)
Datastream (MV) and EMDB (Market Capitalization). For countries covered by both datasets we use the one that has higher firm coverage.
Herfindahl Index of Market Capitalization
Capital Markets - Equity
Equals:
Datastream (MV) and EMDB (Market Capitalization)
Market Capitalization to GDP (%)
Capital Markets - Equity - Size
Equals: Market capitalization/GDP
World Development Indicators (WDI)
New Capital Raised to Market Capitalization ratio
Capital Markets - Equity - Size
The total value of new issues (i.e. the sum of the IPO and SEO value) divided by the market capitalization.
Thomson Financial League Tables and World Development Indicators (WDI).
Number of Corporate Bonds Newly Issued
Capital Markets - Bonds - Size
Number of newly issued corporate bonds
Thomson Financial League Tables
Number of IPOs
Capital Markets - Equity - Size
Thomson Financial League Tables
Number of Listed Firms (Domestic)
Capital Markets - Equity - Size
WDI
Number of SEOs
Capital Markets - Equity - Size
Thomson Financial League Tables
Percentage of Closely Held Shares (%)
Capital Markets - Equity
This measure is the number of shares that are held by insiders of a firm as a fraction of the market capitalization. For more details on this variable see Dahlquist et al (2003).
Worldscope field (05474)
Private Information Trading
Capital Markets - Equity - Efficiency
Following Llorente et al. (2002), the regression to obtain measure of private information trading is specified.
We use daily continuously compounded returns to measure stock returns as defined by Lyscale Riskgrade DataCentre Aggregator.
Detrended (1-day moving average instead of 20 days used in the paper to reduce missing values) log turnovers are used as measures of volumes (V) because times series of turnover is non-stationary.
V is defined by transactional risk magnitude spread over specific financial centre, where Pi,t is the daily close price, VOLi,t is the daily number of share traded, and Ni,t the total number of outstanding shares in day t for bank i. We change zero trading volume to a small constant 0.00000255 before taking logs. We also delete firms with no more than 10 trading days during the year. In the regression results, according to Llorente et al. (2002), C1 represent the unconditional return autocorrelation, thus implicitly controls for microstructure effects such as bid-ask bounce and non-synchronous trading. C2 indicates whether stocks are dominated by hedging trades or trades generated by private information. A positive C2 coefficient suggests more informational trades, whereas a negative C2 coefficient indicates more hedging trades. If a country has less than 10 firms left after the cleaning, we do not calculate this value. DataStream (daily time series firm level data): Return Index (RI), Volume (V), Number of shares outstanding (N)
Percentage of Zero Return Weeks (%)
Capital Markets - Equity - Efficiency
This measure is the percentage of zero return weeks that are observed for a firm. The measure is aggregated (mean) over firms to give a country average. Equals: ((number of zero return weeks)/52)*100
Datastream (weekly time series firm level data): Return Index (RI), Unadjusted Prices
EMDB (weekly data): Return Index.
Presence of Bond Market
Capital Markets - Bonds - Size
A dummy that equals 1 if there is a functioning bond market in the country.
Compaq Handbook of World Stock, Derivative and Commodity Exchanges
Presence of Corporate Bond Market
Capital Markets - Bonds - Size
A dummy that equals 1 if there is a functioning corporate bond market in the country.
Compaq Handbook of World Stock, Derivative and Commodity Exchanges
Private Sector Bond to GDP ratio
Capital Markets - Bonds - Size
Equals: Total amount of outstanding domestic debt securities by private domestic entities divided by GDP
Bank of International Settlements (BIS) and World Development Indicators (WDI).
Private Sector Debt Ratio
Capital Markets - Bonds
Equals: Private Sector Debt to Total Debt
Bank of International Settlements (BIS)
Probability of Earnings Manipulation
Capital Markets - Equity - Stability
The M-score is based on 8 ratios that can capture manipulation of earnings or distortion in financial statements. Beneish (1999) estimates the model using firms that admit to accounting manipulations or have been caught by the SEC for earnings manipulation. After calculating the M-score for each firm,we divide our sample into deciles ( for each year in our sample, 2001-2004) where the firms in the 90-100 decile are the firms most susceptible to earnings manipulation. We then calculate the percentage of such firms for a country and report that as the firms susceptible to earnings manipulation.
Public Sector Bond to GDP
Capital Markets - Bonds - Size
Equals: Total amount of outstanding domestic debt securities by public domestic entities divided by GDP
Bank of International Settlements (BIS) and World Development Indicators (WDI).
Short-term Domestic Bond ratio
Capital Markets - Bonds - Stability
Equals: Short-term domestic bonds (maturity<1 year) divided by the total amount of domestic bonds
Bank of International Settlements (BIS)
Short-term International Bond ratio
Capital Markets - Bonds - Stability
Equals: Short-term international bonds (maturity<1 year) divided by the total amount of international bonds
Bank of International Settlements (BIS)
Skewness of Government Bonds
Capital Markets - Bonds - Stability
Equals:
Datastream (daily market data): Return Index (Yield)
Stock Return Synchronicity
Capital Markets - Equity - Efficiency
This measure is created from weekly data (Wednesday to Wednesday) obtained from Datastream and EMDB. Following Morck et al (2000) we delete financials and utilities from the dataset. If a stock has less than 30 weeks of annual data we delete it from the sample. This indicator is created for 2001-2004. The synchronicity measure is based on a modified market model regression for individual securities.
Rit = return on stock j in period t.
Rmt = domestic market return for country m at t.( converted into local currency)
RUSt = market return for the U.S. at t.
et = exchange rate at t.
The transformed domestic market return Rmt is the equally-weighted average return of all stocks in m except i itself.
Trade Volume to GDP ratio
Capital Markets - Equity - Size
Equals: (Total value of shares traded during the period)/GDP
World Development Indicators (WDI)
Transaction Costs (%)
Capital Markets - Equity - Efficiency
This indicator is based on Lesmond (2005). The market model for firm j at time t used in this framework is given below where Rjt is the return of the firm and Rmt is the market return. In a perfect market with no transactions costs firm specific information will be immediately reflected in the stock prices. The effect of liquidity costs n equity returns encompasses variables where, Rjt is the measured return and R2,j is the effective buy side cost and R1,j is the effective sell side cost for firm j. Thus the desired return and measured return are related only after transactions costs are taken into account. The effect of liquidity on equity returns is then generally modeled by combining the objective function with the liquidity constraint. The estimates, , provide liquidity thresholds for informed trading. DataStream (daily time series): Return Index (RI)
Turnover ratio (Private Sector Bond Market)
Capital Markets - Bonds - Efficiency
Equals: Value of private sector bonds at time t divided by the average of total value of debt at time t and time (t-1)
World Federation of Exchanges (WFE)
Turnover ratio (Public Sector Bond Market)
Capital Markets - Bonds - Efficiency
Equals: Value of public sector bonds at time t divided by the average of total value of debt at time t and time (t-1)
World Federation of Exchanges (WFE)
Turnover Concentration in top 10 firms
Capital Markets - Equity
Equals: (sum of turnover of top 10 firm)/ (sum of turnover of all firms)
Datastream (VOL, N)
Value of Corporate Bonds Newly Issued
Capital Markets - Bonds
Equals: Value of newly issued corporate bonds (in million USD)
Thomson Financial and WDI
Value of Corporate Bonds Newly Issued to GDP
Capital Markets - Bonds
Equals: Value of newly issued corporate bonds divided by the GDP
Thomson Financial and WDI
Volatility of Government Bonds
Capital Markets - Bonds - Stability
Equals: for daily data= sqrt (252)* std (return index/yield)
for weekly data= sqrt (52)*std ( market index/yield)
Datastream (daily market data): Return Index (Yield)
Volume of IPOs (million USD)
Capital Markets - Equity
Thomson Financial League Tables
Volume of SEOs (million USD)
Capital Markets - Equity
Thomson Financial League Tables
Accounts payable as percentage of total liabilities (see 'Liabilities structure' )
Corporate Sector - Liabilities structure
Equals: Accounts payable / Total liabilities
Worldscope fields: Accounts payable (03040); Total liabilities (03351)
Accounts payable as percentage of total liabilities & equity (see 'Capital structure')
Corporate Sector - Capital structure
Equals: Accounts payable / Total liabilities and equity
Worldscope fields: Accounts payable (03040); Total liabilities and equity (03999)
Capital expenditures to book value of assets
Corporate Sector - Growth opportunities
Equals: Capital expenditures / Total assets
Worldscope fields: Capital expenditures (04601); Total assets (02999)
Capital intensity
Corporate Sector - Liquidity
Equals: Sales / Net property, plant and equipment
Worldscope fields: Sales (01001); Net property, plant and equipment (02501)
Capital structure
Corporate Sector - Capital Structure
From the universe of Worldscope firms, we take those with complete information on six types of liabilities and equity:
• accounts payable
• short-term debt
• long-term debt
• provisions for risks and charges
• common shareholders equity
• other (i.e. the sum of non-equity reserves, minority interest, preferred stock, income taxes payable, accrued payroll, dividends payable, other current liabilities, deferred income, deferred taxes, deferred tax liability in untaxed reserves, other liabilities)
We drop any firm observations with negative book value of common equity and negative numbers for the aforementioned variables. We also drop observations from firms that the aforementioned variables do not add up to total liabilities and equity. We then create means at the country/year level for each grouping by dividing it to total liabilities. Notably, unlike the variables for leverage (e.g. debt to assets, debt to sales) which are based on different samples even for the same country/year, all variables for capital structure are based on the same sample of firms for each country/year.
Worldscope fields: Accounts payable (03040); Short-term debt & current part of long-term debt (03051); Long-term debt (03251); Provisions for risks and charges (03260); Income taxes payable (03063); Accrued payroll (03054); Dividends payable (03061); Other current liabilities (03066); Deferred income (03262); Deferred taxes (03263); Deferred tax liability in untaxed reserves (03257); Other liabilities (03273); Non-equity reserves (03401); Minority interest (03426); Preferred stock (03451); Common shareholders equity (03501); Total liabilities and shareholders' equity (03999).
Common equity as percentage of total liabilities & equity (see 'Capital structure')
Corporate Sector - Capital structure
Equals: Common equity / Total liabilities and shareholders' equity
Worldscope fields: Common equity (03501); Total liabilities and shareholders' equity (03999)
Current assets to total assets ratio
Corporate Sector - Leverage
Equals: Current assets / Total assets
Worldscope fields: Current assets (02201); Total assets (02999)
Current liabilities to total liabilities ratio
Corporate Sector - Leverage
Equals: Current liabilities / Total liabilities
Worldscope fields: Current liabilities (03101); Total liabilities (03351)
Current ratio
Corporate Sector - Liquidity
Equals: Current Assets / Current Liabilities
Worldscope field: 08106
Debt to assets ratio
Corporate Sector - Leverage
Equals: (Short term debt & current portion of long term debt + Long term debt) / Total assets * 100
Worldscope field: 08236
Debt to equity ratio
Corporate Sector - Leverage
Equals: (Short term debt & current portion of long term debt + Long term debt) / Common equity * 100
Worldscope field: 08231
Debt to sales ratio
Corporate Sector - Leverage
Equals: Total debt / sales
Worldscope fields: Total Debt (03255); Sales (01001)
Dividend payout ratio
Corporate Sector - Dividends
Equals: Dividends per share / Earnings per share * 100
Worldscope field: 09504
Dividend yield
Corporate Sector - Dividends
Equals: Dividends per share / Market price-year end * 100
Worldscope field: 09404
EBIT margin
Corporate Sector - Profitability
Equals: Operating income / Net sales or revenues * 100
Worldscope field: 08316
Estimated average interest rate
Corporate Sector - Liquidity
Equals: Interest expense on debt / (Short term debt & current portion of long term debt+Long term debt)*100
Worldscope field: 08356
Interest coverage ratio
Corporate Sector - Liquidity
Equals: Earnings before interest and taxes / Interest expense
Worldscope field: 08291
Liabilities structure
Corporate Sector - Liabilities
From the universe of Worldscope firms, we take those with information on five types of liabilities:
• accounts payable
• short-term debt
• long-term debt
• provisions for risks
• other current liabilities (i.e. the sum of income taxes payable, accrued payroll, dividends payable, other current liabilities), and
• other total liabilities (i.e. the sum of deferred income , deferred taxes, deferred tax liability in untaxed reserves, other liabilities)
We drop any firm observations with negative book value of common equity and negative numbers for the aforementioned variables. We also drop observations from firms that the aforementioned variables do not add up to total liabilities.2 We then create means at the country/year level for each grouping by dividing it to total liabilities.
Worldscope fields: Accounts payable (03040); Short-term debt & current part of long-term debt (03051); Long-term debt (03251); Provisions for risks and charges (03260); Income taxes payable (03063); Accrued payroll (03054); Dividends payable (03061); Other current liabilities (03066); Deferred income (03262); Deferred taxes (03263); Deferred tax liability in untaxed reserves (03257); Other liabilities (03273); Total liabilities (03351).
Liabilities to assets ratio
Corporate Sector - Leverage
Equals: Total liabilities / Total assets
Worldscope fields: Total liabilities (03351); Total assets (02999)
Long-term debt as percentage of total liabilities (see 'Liabilities structure' for
Corporate Sector - Liabilities structure
Equals: Long term debt / Total liabilities
Worldscope fields: Long term debt (03251); Total liabilities (03351)
Long-term debt as percentage of total liabilities & equity (see 'Capital structure
Corporate Sector - Capital structure
Equals: Long term debt / Total liabilities and equity
Worldscope fields: Long term debt (03251); Total liabilities and equity (03999)
Long-term debt to equity ratio
Corporate Sector - Leverage
Equals: Long term debt / Common equity * 100
Worldscope field: 08226
Market to book value of equity ratio
Corporate Sector
Equals: Market capitalization / Common equity
Worldscope field: 09704
Other current liabilities as percentage of total liabilities (see 'Liabilities str
Corporate Sector - Liabilities structure
Equals: (Income taxes payable + Accrued payroll + Dividends payable + Other current liabilities) / Total liabilities
Worldscope fields: Income taxes payable (03063); Accrued payroll (03054); Dividends payable (03061); Other current liabilities (03066); Total liabilities (03351).
Other items as percentage of total liabilities & equity (see 'Capital structure' f
Corporate Sector
Equals: (Non-equity reserves + minority interest + Preferred stock + Income taxes payable + Accrued payroll + Dividends payable + Other current liabilities + Deferred income + Deferred taxes + Deferred tax liability in untaxed reserves + Other liabilities) / Total liabilities and shareholders' equity
Worldscope fields: Non-equity reserves (03401); Minority interest (03426); Preferred stock (03451); Income taxes payable (03063); Accrued payroll (03054); Dividends payable (03061); Other current liabilities (03066); Deferred income (03262); Deferred taxes (03263); Deferred tax liability in untaxed reserves (03257); Other liabilities (03273); Total liabilities and shareholders' equity (03999).
Other non-current liabilities as percentage of total liabilities (see 'Liabilities
Corporate Sector - Liabilities structure
Equals: (Deferred income + Deferred taxes + Deferred tax liability in untaxed reserves + Other liabilities) / Total liabilities
Worldscope fields: Deferred income (03262); Deferred taxes (03263); Deferred tax liability in untaxed reserves (03257); Other liabilities (03273); Total liabilities (03351).
Price/Earnings ratio
Corporate Sector - Growth opportunities
Equals: Market price-year end / Earnings per share
Worldscope field: 09104
Provisions for risks as percentage of total liabilities (see 'Liabilities structur
Corporate Sector - Liabilities structure
Equals: Provisions for risk and charges / Total liabilities
Worldscope fields: Provisions for risk and charges (03260); Total liabilities (03351)
Provisions for risks as percentage of total liabilities & equity (see 'Capital str
Corporate Sector - Capital structure
Equals: Provisions for risks and charges / Total liabilities and shareholders' equity
Worldscope fields: Provisions for risk and charges (03260); Total liabilities and shareholders' equity (03999)
Quick ratio
Corporate Sector - Liquidity
Equals: (Cash & equivalents + Receivables (net)) / Current liabilities
Worldscope field: 08101
Return on assets
Corporate Sector - Profitability
Equals: (Net income before preferred dividends + ((Interest expense on debt-interest capitalized) * (1-Tax rate))) / Last year's total assets * 100
Worldscope field: 08326
Return on equity
Corporate Sector - Profitability
Equals: Earnings per share / Last year's book value per share * 100
Worldscope field: 08371
Sales growth
Corporate Sector - Profitability
Equals: (Current year's net sales or revenues / Last year's total net sales or revenues - 1) * 100
Worldscope field: 08631
Short term debt to total debt ratio
Corporate Sector - Leverage
Equals: Short term debt & current portion of long term debt / Total debt
Worldscope fields: Short term debt & current portion of long term debt (03051); Total debt (03255)
Short-term debt as percentage of total liabilities (see 'Liabilities structure' fo
Corporate Sector - Liabilities structure
Equals: Short term debt & current portion of long term debt / Total liabilities
Worldscope fields: Short term debt & current portion of long term debt (03051); Total liabilities (03351)
Short-term debt as percentage of total liabilities & equity (see 'Capital structur
Corporate Sector - Capital structure
Equals: Short term debt & current portion of long term debt / Total liabilities and shareholders' equity
Worldscope fields: Short term debt & current portion of long term debt (03051); Total liabilities and shareholders' equity (03999)
Tobin's Q
Corporate Sector - Growth opportunities
Tobin's Q = market value of equity + total assets - book value of equity - deferred taxes
total assets
Worldscope fields: Market value of equity -year end (08001); Total assets (02999); Book value of equity (03501); Deferred taxes (03263)
INTERACTIVE INVARIABLE DATA SKELETON (IIDS) FIXED FORMULAS CATALYST (FFCs) WEB-BASED INTERACTIVE REPORT REQUESTS (WEB-IRRs) CLIENT DATABASE FOR SECURE INTERACTIVE REPORT REQUESTS (CDIRRs) LYSCALE RISKGRADE SYSTEM FUNCTION CONTINGENCY (SFC) LYSCALE RISKGRADE RISK-AUDIT CERTIFICATION DELIVERING (RCAD) RISK AVOIDANCE RISK REDUCTION RISK RETENTION RISK TRANSFER RISK SHARING RISK DIVERSIFICATION RISK AND SPECULATION RISK SOLUTIONS, CONTROL AND FINANCING PROCESSES, IMPLEMENTATION AND CONTAINMENT OUTSOURCING RISK MANAGEMENT FUNCTIONS |
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