Multi-Stock Basket

We recommend A$50,000 minimum for a properly diversified basket of 10+ stocks. With less capital, position sizes become too small, and brokerage costs eat into returns. If starting with less, consider fewer stocks (8-10) or use CFDs for smaller position sizes.

Quarterly rebalancing is standard for most baskets. This captures drift while keeping transaction costs reasonable. For income baskets, semi-annual may suffice. For momentum baskets, monthly may be better. Balance the benefit of rebalancing against transaction costs.

For beginners, equal weight is simpler and historically has outperformed. Give each stock the same percentage. Market cap weight is appropriate if you want to match index characteristics. More sophisticated approaches like risk parity can be used as you gain experience.

If a stock is acquired, you will receive cash or acquirer shares. Reinvest the proceeds in remaining basket stocks or add a replacement. If delisted, sell before delisting and replace. Review basket quarterly to handle corporate actions.

Basket beta is the weighted average of individual stock betas. If stock A has beta 1.2 at 20% weight and stock B has beta 0.8 at 80% weight, basket beta = (1.2 × 20%) + (0.8 × 80%) = 0.24 + 0.64 = 0.88. Use 2-year weekly returns against XJO for individual betas.

Calculate volatility (standard deviation of returns) for each stock. Weight inversely to volatility: if stock A has 20% volatility and stock B has 10% volatility, B gets twice the weight of A. Then normalize so weights sum to 100%. Recalculate quarterly as volatilities change.

Yes, but clearly define the blend. A dividend-plus-growth basket might screen for yield above 3% AND earnings growth above 5%. Weight toward your priority: if income is primary, tilt weight toward higher yielders. Document your rules for consistent application.

Exit stocks that cut dividends within one month of the announcement. Dividend cuts often signal fundamental problems and usually lead to further underperformance. Replace with the next highest-yielding qualifying stock from your screening universe.

Identify cointegrated pairs or groups using Engle-Granger or Johansen tests. Calculate hedge ratios from cointegrating relationships. Go long undervalued stocks, short overvalued stocks with equal dollar exposure (dollar neutral). Rebalance daily or when spreads reach threshold. Monitor for cointegration breakdown.

Hierarchical clustering identifies diversifying groups. LSTM networks can predict covariance matrices. Gradient boosting predicts relative stock returns. Due to limited Australian data, use regularization heavily and validate with walk-forward testing. Consider pooling with similar markets (NZ, Singapore) for more training data.

Sell 30-delta covered calls on liquid stocks 30-45 DTE. Roll when options reach 21 DTE or 80% of max profit. Only overlay the 5-6 most liquid stocks to maintain manageable positions. Target 2-3% annual income with 80% delta coverage. Adjust strikes based on volatility regime.

Track returns from: market (beta × benchmark return), sector allocation, stock selection within sectors, factor tilts, and residual. Use Brinson-Fachler for sector attribution and factor regression for factor attribution. This identifies whether alpha comes from sector calls, stock picks, or factor timing.