How valuation and rules-based investing work

Using Stoxa

• How to value a stock in StoxaA step-by-step walkthrough of the valuation workbench: find a company, check its filings, set assumptions, and read value, margin of safety, sensitivity and Monte Carlo.
• How to screen and backtest in StoxaBuild a stock screen on a universe, read the funnel counts, run it on any past date, then backtest it with dividends, trading costs and a benchmark, and read the statistics, drawdown and rolling results.
• How to build a ranking system in StoxaBuild a ranking tree of factors and composites, choose directions, scopes and NA policies, test it with the bucket test, and use the rank in a screen or backtest.
• How to simulate a strategy in StoxaRun a screen and a ranking as a real portfolio: buy rules, sell rules, positions and caps, stops, a hedge sleeve and costs, then read the equity curve, drawdown, statistics and trades.
• How to follow a house strategy in StoxaRead a house strategy page, its risk score and trading commitment, follow it with a paper portfolio (jump in or scale in), keep it in step with the drift badge and rebalance diff, or clone its rules.

The theory: valuation

1. 1 Value versus priceWhat intrinsic valuation tries to do and how to read the result.
2. 2 Free cash flow to the firmThe cash flow the DCF discounts, and how each forecast year is built.
3. 3 Growth, margin and tax pathsHow the drivers move from today’s numbers to steady state.
4. 4 ReinvestmentGrowth is not free: the capital a company must spend to grow.
5. 5 Discount rate: cost of equity and WACCCAPM, beta estimation, synthetic ratings and the weighted average.
6. 6 Terminal valueValuing everything after the forecast horizon, and why it dominates.
7. 7 From enterprise value to a share priceSubtract what lenders own, add what the business does not need, divide.
8. 8 Reverse DCF: what the price impliesSolve the model backwards to find the growth the market is paying for.
9. 9 Relative valuationPricing against peers and the company’s own history.
10. 10 Dividend discount modelEquity valued from the cash actually paid to shareholders.
11. 11 Residual income modelBook value plus the value of earning more than the cost of equity.
12. 12 Sensitivity, Monte Carlo and price pathsTurning a point estimate into a range you can act on.
13. 13 Reading the warningsEvery check the engine runs, what triggers it and what to change.

The theory: rules-based investing

14. 14 Point-in-time data and backtestingOnly using what was known on the date, so backtests do not cheat.
15. 15 Screening: universes, funnels and fair comparisonsWhat a screen is and is not, liquidity floors, relative-to-industry fields and survivorship.
16. 16 Ranking systems and the bucket testPercentile ranks, composite trees, NA policies, scope, and how to test that a ranking works.
17. 17 Reading a backtest: every statisticTotal return, CAGR, volatility, Sharpe, Sortino, drawdown, beta, alpha, turnover, cost drag and rolling tests.
18. 18 Transaction costs: why they decide whether a strategy is realCommissions, slippage tiers by liquidity, the square-root impact model, and turnover times cost.
19. 19 Portfolio simulation: rules, stops and hedgesBuy and sell rules, the daily loop, position sizing, stops, hedging, and what is not modelled.
20. 20 Reading a live strategyBacktest versus live record, risk score, trading commitment, capacity, drift, and patience.
21. 21 The eight house strategiesThe idea, the origin, the exact rules as implemented and what can go wrong, for each one.

Books: the reading list, from first read to deepest reference →