A full asset allocation strategy could make use of the following sources of information:
- stochastic representation of asset returns and their future evolution
- asset prices: converting fractional weights to integers
- investment history
- turnover minimization
- historic portfolio performance and risks taken
- taxable gains / losses for tax optimization
- cash-flow data
- initial investment
- deposit in / out
- client data: tax allowance, tax classes
- partial execution
- live trading with feedback loop
- unknown unknowns?
Different strategies require different granularity of overall
information. All strategies are subtypes of abstract type
Fix weights regardless of market data.
Optimizes portfolio with respect to single period. Turnover problematic becomes irrelevant: only asset return model as additional information required.
Multi-period setting: portfolio rebalancing with turnover maximization. Specifies initial investment strategy, rebalancing strategy and turnover filter. Doesn't deal with weight discreteness, cash-flows or taxes.
optimizeWgts(strat::??, univ::UniverseModel, invHistory::Investments)
Repeated application of estimator
Estimate model for each date in order to get insights into variation in asset return distributions. If possible, array of estimated models is simplified to more concise output.
Output: array of mus, sigmas and correlations, with values only for those days with enough data for model estimation.
Repeated application of strategy
Output: investments, expected portfolio properties
- estimator: way of getting concrete specification of asset return model
- model: complete specification of asset return model
- universe: complete specification of asset return model together
with information to redo estimation process (for resampling
techniques to deal with estimation uncertainty)
- strategy: investment rules of different granularity
- investments: series of resulting portfolio weights