Quantpedia Pro Reports



Quantpedia Pro platform gives you the opportunity to investigate your model multi-factor multi-strategy portfolios in hundreds of different charts and tables in over 20 quantitative reports in our Portfolio Analysis section.

Basic Overview – review equity curve of your custom model portfolio, see it’s performance, drawdowns, volatility, risk-return characteristics, monthly and yearly performance charts and rolling beta and correlation.

Multi-Factor Analysis – decompose you custom portfolio into elementary investment factors by using our state-of-the-art multi-factor regression analysis with over 80 underlying factors. Visit our introduction into the multi-factor analysis methodology to learn more.

Factor Analysis Models – similar to the report above, decompose your portfolio into elementary factors by using traditional factor models (like Fama&French etc.).

Closest Neighbours –  inspect recommended Quantpedia’s ideas for trading strategies which are the most similar to the significant factors out of the multi-factor regression analysis. 

Complementary Strategies – review recommendations for possible additions/enhancements of your model multi-factor multi-strategy portfolio. 

Seasonality Analysis – analyze your model portfolio’s performance during the significant market-action periods like – days of economic announcements, FOMC meeting days, etc. Visit seasonality analysis case study subpage to learn more.

Trend/Reversal Analysis – figure out if you can improve your model portfolio by using the momentum, trend-following or reversal overlay. 

Correlation Analysis – review your portfolio’s correlation structure in connection with the most common market and systematic equity, fixed income, currency, commodity, and alternative factors. Visit correlation analysis case study subpage to learn more.

Equity Crisis Analysis – is a risk-management report that allows you to review your portfolio’s performance during 15 significant crisis periods over the last 20+ years. Visit crisis analysis case study subpage to learn more.

Commodity Crisis – is a risk-management report that allows you to review your portfolio’s performance during 12 significant crisis periods over the last 20+ years. The report contains six strongly positive and six negative periods with significant commodity market’s drawdowns.

Fixed Income Crisis – is a risk-management report that allows you to review your portfolio’s performance during 8 significant crisis periods over the last 20+ years. The report contains four strongly positive and four negative periods with significant fixed income market’s drawdowns.

Crisis Hedge –  helps you find a trading strategy that works as a hedge during negative months or bear markets. It shows performance distribution in negative periods and suggests five trading strategies with the lowest downside correlation. Visit our announcement blog for the Crisis Hedge report to learn more.

Market Phases Analysis – offers the possibility to investigate past and future average performance and correlation of benchmark SPY ETF and model portfolio in each of the four baseline market phases (bear market, recovery, bull market, correction). Visit market phases analysis case study to learn more.

Inflation/Commodity Phases Analysis – offers you a glimpse into the behavior of your portfolio in different inflationary/dis-inflationary phases defined through the lenses of commodity market movements.

Value-at-Risk –  is the maximum loss with a given probability, in an established period, with an assumed probability distribution, and under standard market conditions. In this report, you can review VaR and CVaR magnitudes during the last two years period and the longest available period. Additionally, you can examine their statistical distribution. Thus, it helps to understand what level of risk we can expect during a crisis to better prepare for it.

Portfolio Factor Cycles – shows your portfolio’s components current state (Bear Market, Recovery, Bull Market, Correction) and historical changes in one table and two interactive charts.

Volatility Targeting – amend your model portfolio by using one of the proposed volatility targeting methods (simple volatility, EWMA volatility, momentum-based volatility targeting). Visit our introduction into the volatility targeting methodology to learn more.

Markowitz Portfolio Optimization – aims to create the most return-to-risk efficient model portfolio by analyzing several portfolio combinations based on expected returns (mean) and standard deviations (variance) of the assets or trading strategies. Visit markowitz portfolio optimization case study subpage to learn more.

Portfolio Risk Parity – finds weights of assets selected in the Portfolio Manager that ensure an equal level of risk, most frequently measured by volatility of the individual components of the portfolio. Visit portfolio risk parity case study subpage to learn more.

CPPI (Constant Proportion Portfolio Insurance) – is a report that helps an investor test the CPPI methodology – a position sizing model that maintains exposure to a model portfolio’s upside potential while providing a guarantee against the downside risk by dynamically scaling the weight of the model portfolio. Visit our introduction into the CPPI methodology to learn more.

Portfolio Clustering –  is based on the most popular clustering methods such as Partitioning Around Medoids (PAM), Hierarchical Clustering, and Gaussian Mixture Model. You can examine clusters for various periods and compare how many clusters are identified in your model portfolio by all three clustering methods. Complete methodology for all three methods is available in the following article and its 2nd and 3rd continuation.

Dollar-Cost Averaging – allows our clients to test and analyze different approaches to investment and divestment of model portfolio. As a result, investors decide between making regular, piecewise investments, regardless of market conditions (Dollar-Cost Averaging), and the riskier way – investing all at once (Lump-Sum Investment).

The Monte Carlo method –  is a category of algorithm that relies on repeated random sampling to obtain different scenario results. Monte Carlo simulations are used to predict the probability of different outcomes when it would be difficult to use other approaches such as optimization.

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