For books on quantitative forecasting models and trading approaches, all with equity orientation, see Grinold—Kahn , Qian—Hua—Sorensen , and Fabozzi—Focardi—Kolm For examples of similar business cycle analysis, see Naik—Devarajan and Lustig—Verdelhan Ilmanen, Anti.
ISBN: Skip to content. Star Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats commits. Failed to load latest commit information. View code. GSoC-Expected-Returns-Ilmanen Background A lack of portfolio diversification can severely impact the financial goals and long term plans for individual retirement accounts, University Endowment funds, and Municipal Pension funds alike.
From the Description; This comprehensive reference delivers a toolkit for harvesting market rewards from a wide range of investments. Areas of Interest We'll focus on three broad sections with specific subsection to explore Approaches to Dynamic asset weighting Value-oriented equity selection Currency Carry Commodity Momentum and Trend following Return Factors and their risk premia Inflation factor and inflation premium Liquidity factor and illiquidity premium Tail risks volatility, correlation, skewness Time-Varying Expected Returns Endogenous return and risk: Feedback effects on expected returns Tactical return forecasting models Cyclical variation in asset returns Approaches to Dynamic asset weighting Value-oriented equity selection , chapter Currency carry strategies , Chapter Among practitioner work, see Bilson and Nordvig Commodity Momentum and trend following , Chapter For a broad survey, see Schneeweis—Kazemi—Spurgin Return Factors and their risk premia Inflation factor and inflation premium , Chapter 17 For a historical perspective on the inflation factor, see Ferguson , Greenspan , and Reinhart—Rogoff , Fixed percentage based withdrawals can be smoothed with spending rules.
Rolling average spending rule calculates the withdrawal amount as a percentage of the rolling average portfolio balance, and geometric spending rule calculates a weighted sum of the prior period's spending level adjusted for inflation and the percentage of the current portfolio balance where the former is multiplied by the smoothing rate and the latter by one minus the smoothing rate.
For life expectancy based withdrawals the current age is a required parameter, and the age is assumed to increase by 1 before the end of the first simulated year, i. If the withdrawal frequency is not annual, then the percentage is adjusted automatically, e. For percentage based withdrawals the tool displays the withdrawals both in nominal dollars and in present dollars per the simulated inflation model.
The cashflows for withdrawals and contributions can also be imported from a file. Positive amounts are contributions, negative amounts are withdrawals. The frequency of the cashflows is specified in the user interface, i. If there are periods without cashflows, the cashflow amount would be zero for those periods.
The simulation model supports testing for the sequence of returns risk. The sequence of returns risk is the risk of receiving poor returns early in a period when withdrawals are taken from a portfolio, e. These poor early returns may cause the portfolio to be depleted much faster than expected based on historical averages. The stress testing model allows the user to specify the number of years of poor early returns, and the simulated return series is adjusted so that the worst returns occur upfront.
The efficient frontier is a concept in modern portfolio theory introduced by Harry Markowitz in The efficient frontier tool shows the return and risk curve for the mix of the selected assets that minimizes the portfolio risk measured by volatility for the given expected return. The tool also supports rendering the geometric mean efficient frontier, which addresses the single period nature of traditional mean variance optimization by displaying the expected portfolio growth rate in a multi-period context, where the return is given by the geometric rather than the arithmetic mean.
Since the arithmetic mean of any return series is always greater than the geometric mean, the return predicted by the Markowitz efficient frontier is always greater than the true long term return that would have been obtained by using the actual rebalanced allocation. Efficient frontiers can be constructed based on historical returns, or forecasted returns for and volatilities. Forward-looking efficient frontier portfolios are based on the user-specified expected returns and volatility combined with historical asset correlations.
Monte Carlo method can be used for more robust optimization that resamples the optimization inputs to mitigate the impact of estimation error in the mean variance optimization inputs and improve diversification. The asset correlation tool computes the Pearson correlation for the selected assets based on daily, monthly or annual asset returns. The tool also shows the annualized return for the selected assets based on the compound annual growth rate formula and the selected asset return series.
Monthly standard deviation is calculated based on full calendar months within the time period for the selected tickers. The factor regression analysis tool enables risk factor exposure analysis of mutual funds, ETFs, and portfolios.
The supported equity models include:. The multiple linear regression shows how well the returns of the given assets or a portfolio are explained by the risk factor exposures. The analysis is based on monthly asset returns total return and monthly factor returns. Term and credit risk factors are also supported to enable analysis of fixed income funds and balanced funds.
The supported fixed income factor models include:. The fixed income factor premiums are calculated using the following funds:. The factor regression tool supports the use of robust standard errors based on the Newey—West estimator. The estimator can be used to try to overcome autocorrelation and heteroscedasticity of the residuals, which can impact the standard errors and thus the calculated t-statistics and p-values.
Note that historical equity factor returns are sometimes revised based on changes to CRSP database. Examples include updating and correcting the number of shares outstanding in past periods and changes in the treatment of deferred taxes described in FASB , i.
The factor exposure matching tool enables testing whether the factor exposures and performance of the given asset can be replicated using other available assets, e.
The tool computes the factor exposures of the target asset using the specified factor model and time period, and then explores the combinations of the given alternative assets to find the closest match.
The factor matching can be weighted equally, in which case the tool tries to minimize the sum square of factor loading differences for statistically significant factors, or based on t-stat, in which case the absolute t-stat value of the factor loading is used to weight the squared differences to prioritize matching based on the most significant factors.
The tool also displays the closest match based on straight performance matching for comparison purposes. The straight performance matching is based on a combination of alternative assets that minimizes the sum square of monthly return differences. The baseline portfolio is rebalanced annually, and the timing portfolio adjusts its allocation at the start of each year based on the Shiller PE10 ratio. The allocation changes to the timing portfolio are based on market valuation differing significantly from PE10 value of 18 PE10 average since Moving average model uses the crossover of the moving average with the price or another moving average to decide whether to invest in the given asset.
If the end-of-period price is greater than or equal to the moving average, the model will invest in the selected asset risk on. If the end-of-period price is less than the moving average, the model will invest in an alternative safe asset, e.
Alternatively the crossover of price and moving average can be replaced with the crossover of two moving averages, typically using a shorter term moving average being higher than the longer term moving average as the buy signal. The moving average is calculated from adjusted close prices either based on end-of-month prices for monthly moving averages or daily prices when the moving average is specified in trading days. The start year of the timing model backtest is adjusted forward if necessary, so that there is enough historical data to trigger the timing signal from the start of the timing period.
Trades are performed at either the end-of-period close price, or at next trading day's close based on defined trading policy. The delayed trading accounts for the fact that typically in practice one would not be able to execute the trade at the point in time when the signal becomes available.
The relative strength model uses the relative strength of an asset compared to other assets to decide which assets to invest in. The model favors assets with the best recent performance momentum , and invests in one or more assets based on a performance ranked list of assets. The returns based momentum can be calculated over the specified time period directly, or by excluding the most recent month.
The latter option addresses the short-term reversal effect, which is why academic momentum definition generally uses momentum last month returns excluding the previous month. Two options for relative strength timing windows are provided:. Both models above also support moving average based risk controls, which allows the relative strength model selections to be overridden so that the model invests in cash instead of the asset if the price of the asset is below its moving average.
The moving average can also be based on an external asset, e. The moving average is calculated as described above. The months in the timing period are calendar months, and monthly changes are based on the end-of-month adjusted close price. Stop-loss can also be used as an additional risk control measure and be applied either at individual asset or at the portfolio level. The stop-loss trigger is specific to each trade period and the loss is measured against the balance at the start of the timing period.
If a stop-loss is triggered the model will stay out of the market until a new trade for the following timing period occurs. The dual momentum model combines relative momentum and absolute momentum based timing.
Relative strength is used to select the best performing model asset s and absolute momentum is then applied as a trend-following filter to only invest in the selected asset s if the excess return over the risk free rate has been positive.
If the excess return is negative, then the model invests in short- to intermediate-term fixed income instruments the out-of-market asset until the trend turns positive. The relative momentum performance is calculated as the asset's total return over the timing period, and the return of 3-month treasury bills is used as the risk free rate for the absolute momentum filter. Trades are performed at either the end-of-month close price, or at next trading day's close based on defined trading policy.
Rapach and Strauss and Zhou find that the US stock market leads the world markets even at the monthly frequency, so the supported options include specifying a single asset to be used for absolute momentum. As discussed in Gary Antonacci's Dual Momentum book, we can first apply absolute momentum based on the US stock market e. If the absolute momentum excess return is negative, the model is invested in the selected out-of-market asset, e.
Aggregate Bond Index. The adaptive asset allocation model combines relative strength momentum model with different asset weighting.
The relative strength model uses an equal weight allocation for the model selected assets, whereas the adaptive asset allocation uses either risk parity allocation or minimum variance allocation for the model assets, i. Both the relative strength momentum timing period and the period for daily volatility and return calculations can be specified. The default model uses 6-month relative momentum with day volatility window.
The target volatility model uses dynamic asset allocation to achieve a stable level of volatility. The model manages volatility by forecasting future equity volatility based on historic realized volatility and then dynamically adjusts the market exposure to target a set level of volatility. A rated it it was amazing May 28, Empirical evidence cannot distinguish between rational and irrational explanations. His approach isboth scientific and practical, based on decades of studies and hisown trading experience.
Sporting a safety jacket in a market of weak protections The lending environment is undergoing a worrying change. The ending advice are amongst other to pursue several strategies in parallel to harvest diverse sources of expected returns as long as they are not overvalued, that investors should diversify more than they do and use leverage to leverage up low volatility assets and the low volatility parts within different assets.
Rethinking auto-enrolment Thu, 1 Nov Benchmarking: Just a moment while we sign you in to your Goodreads account. Entropy cancels exppected and bigger temporary alpha increases entropy levels. It seems some people have nothing to do. Reporting on natural capital. Applying economics models to understand politics is like trying to use a trowel to saw a piece of wood in half. This website uses cookies to improve your experience while you navigate through the website.
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