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Backtest a portfolio asset allocation and compare historical and realized returns and risk characteristics against various lazy portfolios.
Run regression analysis using Fama-French and Carhart factor models for individual assets or a portfolio to analyze returns against market, size, value and momentum factors.
Find funds based on asset class, style and risk adjusted performance, and analyze asset correlations.
Run Monte Carlo simulations for the specified portfolio based on historical or forecasted returns to test long term expected portfolio growth and survival, and the capability to meet financial goals and liabilities.
Chart the efficient frontier to explore risk vs. return trade-offs based on historical or forecasted returns. Optimize portfolios based on mean-variance, conditional value-at-risk (CVaR), risk-return ratios, or drawdowns. Apply the Black-Litterman model to find the optimal portfolio based on market views.
Compare and test tactical allocation models based on moving averages, momentum, market valuation, and volatility targeting.
Compare historical performance and risk vs. return profile of different asset class allocations:
Portfolio #1 Portfolio #2
Analyze the performance, exposures and dividend income of a portfolio consisting of equities, ETFs and mutual funds:
Test a historical sequence of dynamic portfolio allocations where the portfolio model assets and their weights have changed over time.
| Date | SPY | BND | GLD |
|---|---|---|---|
| 01/01/2022 | 50% | 40% | 10% |
| 04/01/2022 | 60% | 30% | 10% |
| 07/01/2022 | 70% | 20% | 10% |
| 10/01/2022 | 80% | 20% | 0% |
| 01/01/2023 | 90% | 10% | 0% |
| 04/01/2023 | 70% | 20% | 10% |
Analyze the sources of risk and return of manager returns and compare those against the selected benchmark.
Use the Monte Carlo simulation tool to model the probability of different outcomes based on the given portfolio asset allocation and cashflows.
Simulate portfolio performance with forward-looking return and volatility assumptions rather than relying on historical estimates for asset returns.
| Asset Class | Expected Return |
|---|---|
| US Equities | 5.5% |
| International Equities | 5.7% |
| US Bonds | 1.8% |
| REITs | 5.0% |
| Sample assumptions for expected annual returns | |
Use Monte Carlo simulation to test portfolio growth and survival against specified financial goals both during career and retirement.
Asset liability modeling assesses available assets, future contributions, and strategic asset allocation against expected future liabilities. This analysis aids in aligning assets with liabilities, ensuring sufficient funding to meet future financial obligations, and optimizing investment strategies for long-term financial planning.
Visualize the efficient frontier for any asset classes or funds.
What asset mix has provided the best risk adjusted return historically?
How has the efficient frontier changed from decade to decade?
Use resampling to mitigate the impact of input estimate errors in the mean variance optimization and to improve diversification in the efficient frontier portfolios
Use the portfolio optimization tool to optimize portfolios based on risk adjusted performance or other target criteria.
Use optimization to find the risk parity portfolio that equalizes the risk contributions of portfolio assets.
| Asset | Risk Contribution Target |
|---|---|
| Invesco QQQ Trust (QQQ) | 25% |
| Vanguard Total International Stock ETF (VXUS) | 25% |
| Vanguard Real Estate ETF (VNQ) | 25% |
| Vanguard Total Bond Market ETF (BND) | 25% |
Use allocation weight constraints at both asset and asset group level to enforce specific minimum and maximum allocation weights.
| Asset | Group | Min Weight | Max Weight |
|---|---|---|---|
| SPDR S&P 500 ETF | Equity | 5% | 40% |
| iShares S&P Small-Cap 600 Value ETF | Equity | 5% | 20% |
| iShares MSCI EAFE ETF | Equity | 5% | 30% |
| iShares MSCI Emerging Markets ETF | Equity | 5% | 15% |
| iShares 20+ Year Treasury Bond ETF | Fixed Income | 5% | 30% |
| iShares 7-10 Year Treasury Bond ETF | Fixed Income | 5% | 30% |
| iShares iBoxx $ Invmt Grade Corp Bd ETF | Fixed Income | 5% | 20% |
Optimize portfolio using forward-looking capital market expectations.
| Asset Class | Expected Return |
|---|---|
| US Equities | 5.5% |
| International Equities | 5.7% |
| US Bonds | 1.8% |
| REITs | 5.0% |
| Sample assumptions for expected annual returns | |
The Black-Litterman asset allocation model combines ideas from the Capital Asset Pricing Model (CAPM) and the Markowitz’s mean-variance optimization model to provide a method to calculate the optimal portfolio weights based on the given inputs. The model first calculates the implied market equilibrium returns based on the given benchmark asset allocation weights, and then allows the investor to adjust these expected returns based on the investor's views. The opinion adjusted returns are then passed to the mean variance optimizer to derive the optimal asset allocation weights.
| Ticker | Name | Equilibrium Return | Allocation |
|---|---|---|---|
| QQQ | Invesco QQQ Trust (QQQ) | 6.85% | 40.00% |
| VGTSX | Vanguard Total Intl Stock Index Inv (VGTSX) | 7.10% | 20.00% |
| VGSIX | Vanguard Real Estate Index Investor (VGSIX) | 6.93% | 10.00% |
| VBMFX | Vanguard Total Bond Market Index Inv (VBMFX) | 0.50% | 30.00% |
Rolling portfolio optimization performs recurring optimization where at the start of each period the portfolio asset weights are optimized for the given performance goal based on the specified lookback window of past returns.
Optimize portfolio asset allocation based on shifts in targeted risk factor exposures.
| Risk Factor | Existing Exposure | Target Exposure |
|---|---|---|
| Market (Rm-Rf) | 0.61 | 0.61 |
| Size (SMB) | 0.10 | 0.10 |
| Value (HML) | 0.15 | 0.20 |
| Term Risk (TRM) | 0.20 | 0.10 |
| Credit Risk (CDT) | 0.09 | 0.15 |
View asset correlations for selected assets. How has the correlation changed over time?
| Ticker | QQQ | VNQ | GLD | BND |
|---|---|---|---|---|
| QQQ | - | 0.76 | -0.02 | -0.32 |
| VNQ | 0.76 | - | 0.06 | -0.02 |
| GLD | -0.02 | 0.06 | - | 0.27 |
| BND | -0.32 | -0.02 | 0.27 | - |
| Daily correlations from 01/01/2011 to 12/31/2016 | ||||
Analyze the autocorrelation of returns for selected assets over a specified time period and lag. By default, it calculates autocorrelation for multiple lag periods, providing insights into the persistence and patterns of asset returns, aiding in forecasting and risk management.
Cointegrated assets have a financial or economic relationship that prevents divergence, with their price difference tending to revert to the mean. This analysis aids in identifying potential long-term relationships between assets for trading or investment strategies.
The Fund Performance Overview enables users to analyze the performance of selected mutual funds or ETFs. By choosing a benchmark for comparison, investors can assess the fund's relative performance, aiding in investment decision-making and evaluating portfolio performance against relevant benchmarks.
Fund screener allows users to search for ETFs or mutual funds based on specific categories and performance metrics. It facilitates fund selection by providing a convenient tool to identify funds that meet certain criteria, guiding investors in their investment decision-making process.
Fund performance ranking provides ETF or mutual fund rankings within a specified asset class category based on preferred ranking criteria. This analysis aids investors in identifying top-performing funds within their chosen category, helping them make informed investment decisions aligned with their investment objectives and preferences.
Run factor regression (Mkt, HmL, SmB, Mom) to see the factor loadings for the specified assets:
| Ticker | Rm-Rf | SMB | HML | MOM | Annual Alpha | R^2 |
|---|---|---|---|---|---|---|
| IJS | 0.98 | 0.77 | 0.32 | -0.04 | 1.84% | 97.2% |
| IWN | 0.97 | 0.81 | 0.43 | 0.04 | -0.38% | 98.1% |
| RZV | 1.08 | 1.03 | 0.35 | -0.40 | 1.09% | 93.2% |
| VBR | 1.04 | 0.51 | 0.26 | 0.02 | 0.12% | 97.9% |
| Monthly returns regression from 01/01/2011 to 12/31/2016 | ||||||
This analysis allows you to match the factor exposures or performance of the given asset or portfolio using a combination of assets from the given list. The tool finds the combination of the given assets that most closely clones the factor exposures of the target asset or portfolio based on the selected time period and factor model.
Use principal component analysis to analyze asset returns in order to identify the underlying statistical factors. The statistical factors are the independent sources of risk that drive the portfolio variance, and the returns of each corresponding principal portfolio will have zero correlation to one another.
Review the factor regression analysis results for mutual funds and ETFs. You can filter the data set based on factor series, geographic market area, factor model, time period and regression fit.
Review the risk factor attribution results for mutual funds and ETFs. You can filter the data set based on factor series, geographic market area, factor model, time period and regression fit.
Explore factor correlations and risk premia over different time periods.
This model compares a baseline balanced portfolio (60% stocks, 40% treasury notes) with a portfolio that adjusts the stock and bond allocation based on the market valuation. The baseline portfolio is rebalanced annually, and the tactical asset allocation portfolio adjusts its allocation at the start of each year based on the Shiller PE10 ratio in order to avoid overweighting equities during peak market valuations when expected risk premiums are lower.
| Valuation | Equity Allocation | Fixed Income Allocation |
|---|---|---|
| 14 <= PE10 < 22 | 40% | 60% |
| PE10 >= 22 | 60% | 40% |
| PE10 < 14 | 80% | 20% |
Backtest moving average based models for a single asset or for a portfolio of assets. For example, test a tactical asset allocation model based on the S&P 500 index using VFINX with a 10-month simple moving average (SMA) from 1990 onwards.
How well did momentum based asset class rotation work in the past? Backtest asset class ETF momentum strategy rotating across asset classes based on the past 5-month performance:
Compare the results against buy-and-hold portfolios. How would the results change based on different time periods?
Explore dual momentum model combining relative momentum with an absolute momentum based trend-following filter:
Momentum Assets:
Adaptive asset allocation model combining relative strength momentum model with inverse volatility or minimum variance based asset weights.
Hold top two best performing assets with risk parity weighting:
Use target volatility model to keep portfolio within preferred risk tolerance. Compare drawdowns and risk adjusted performance against annually rebalanced buy-and-hold portfolio.
Portfolio assets for 8% annualized volatility target:
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