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As our investor community grows we adjust the calculation
of the relative score to account for individual time horizons, different tolerance
for risk, sector and industry allocations, as well as number of securities and types of financial
instruments.

## Macroaxis grades portfolios and equities in 2 main categories

**1. Risk-Adjusted Performance Score**within the context of mean-variance optimization

**2. Chance of Financial Distress**within the context of traditional fundamental data analysis

## 1. Risk-Adjusted Performance Score

The performance score is an integer between 0 and 100 that represents market performance of a financial instrument from risk-adjusted return prospective. Generally speaking, the higher the score the better is overall performance as compared to other investors. The score is normalized against average position of the entire investing universe (the best we can interpret from the data available) and can be applied to a single equity instrument, portfolio of equities, or a group of portfolios. To calculate the grade we take the best performance scores of all individual equity instruments and portfolios and remove everything that falls beyond 3 standard deviations from the mean. The score is then calculated as a percentage of averaged/normalized best score. Within this methodology scores of individual equity instruments will always be inferior to the scores of portfolios of equities as portfolios typically diversify a lot of unsystematic risk away. This is why scores of 'good' stocks may seem lower then would be otherwise expected by most investors. Simply put, we do not sugar code investor positions. Since most investors use various time horizons, have different tolerance for risk, and hold different number of securities from different industries, you have to be careful when interpreting the relative score of your position. Think of your score as a naive interpretation of where your position stays in relation to the positions of other investors. The formula to derive Macroaxis score is based on multiple factors that are weighted according to Macroaxis proprietary algorithms. It utilizes Modern Portfolio Theory (MPT) as well as other fundamental diversification methodologies. Among other things, these algorithms take into account portfolio expected return and risk, number of assets, market volatility, industry and country specifications as well as types of securities that compose user portfolios.## 2. Chance of Distress

Unlike evaluating score of a single portfolio or a group of portfolios, looking at performance score of an individual equity instrument may not be enough because performance scores do not take into account any of the fundamental data available for a given equity instrument. Macroaxis provides a single grade for evaluating probability of bankruptcy of an equity instrument that have enough relevant information on their current balance sheets. Macroaxis probability of bankruptcy score is a single number between 1 and 99 that represents a chance of a company going through financial distress in the next 2 years. Probability of Bankruptcy compliments equity performance score by supplying investors with insight into company financials without requiring them to know too much about all of the complex accounting and financial ratios. In a nutshell, companies with Probability of Bankruptcy above 90% are generally considered to be high risk with a good chance of bankruptcy in the next 2 years. On the other hand companies with Probability of Bankruptcy of less than 15% will most likely experience some growth in the next 2 years. Investors can think about Probability Of Bankruptcy as a normalized value of Altman Z-score. The score is used to predict probability of a firm going into bankruptcy within next 24 months and is a very simple linear, multi-factor model that measures the financial health and economic stability of a company. The score is used to predict probability of a firm going into bankruptcy within next 24 months or two fiscal years from the day stated on the available accounting statements. The model uses five fundamental business ratios that are weighted according to algorithm of Professor Edward Altman who developed it in late 1960s at New York University.## Diversification Score

The portolio diversification score ranges from 0 to 100. The score of 100 means that portfolio positions are perfectly diversified against each other and the market. If the diversification score is equal to 0, the portfolio has no diversification. There is only one way to get diversification score of 0 and that is when the entire portfolio has only one security that historically generates negative risk-adjusted returns. In all other cases diversification score will not be zero. If the diversification score is equal to 100, the portfolio has maximum diversification potential proven by positive long-term expected returns. It is virtually imposible for a portfolio to have diversification score of 100. The main idea behind diversification score is to grade a portfolio based on its potential to diversify away both market-driven and non-market volatility and different sources of correlations that portfolio inherits after its origination. It can also be used as a tool to ensure that investors put their money into a range of asset classes at various allocations as opposed to putting it all in one bucket. Macroaxis diversification score is an adjusted value of portfolio risk-adjusted performamce score. The value is adjusted based on the following factors: 1. Number of positions in the portfolio 2. Position weight distribution 3. Amount of cash not invested in the market 4. Portfolio Sharpe Ratio### Performance Computation Methodology

At this point to calculate Macroaxis scores of portfolios users have to run one of our analytical modules from Wealth Management Toolset. The updates to the score will be scheduled within a short period of time and will be performed weekly, unless user runs one of the provided analytics. Register to optimize your portfolios## Portfolio RebalancingAnalyze risk-adjusted returns against different time horizons to find asset-allocation targets |

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