OMXS30 Index
The OMXS30 (“OMX Stockholm 30”) is the blue-chip index of Nasdaq Stockholm, tracking the 30 largest and most actively traded Swedish companies. Nasdaq deliberately caps the constituent count at 30 so that all underlying shares have “excellent liquidity,” making the index well suited as a derivatives underlying; it trades as a cash index, as Nasdaq Nordic index futures and options, and via ETFs such as XACT OMX. The index is a modified free-float market-cap-weighted index rebalanced semi-annually, and from 1 July 2025 Nasdaq changed its primary inclusion criterion from liquidity to free-float market capitalisation. Crucially, while the OMXS30 is liquid within Sweden, it is small in absolute terms — total constituent market capitalisation is roughly USD 1.5 trillion, a fraction of the S&P 500 — so depth at the index and futures level is materially thinner and effective trading costs are higher.
In this vault the OMXS30 is the test market for Aronsson Folkesson 2023, a KTH bachelor-level degree project that discretised the index’s 2020-2023 daily returns into six magnitude-bucket states and evaluated a Markov Chain Trading Model for next-day prediction. The result was deflationary: a single discrete Markov chain predicted direction no better than random chance, and even a ten-chain voting ensemble cleared the 16.7% six-state random benchmark by only about 0.4 percentage points (17.1% vs 16.7%); the aggregated up/down version performed at or below 50/50. The study ran no trading backtest at all, so no return, Sharpe or drawdown figures exist.
The cost angle makes the OMXS30 a sharper case than it first appears. Aronsson & Folkesson excluded transaction costs entirely — but a ~0.4-percentage-point accuracy edge would be erased by any realistic cost, and on a smaller, less liquid market like the OMXS30, where spreads are wider than on the S&P 500, the cost hurdle for a frequently-rebalancing strategy is higher still. The OMXS30 thus illustrates the other side of the equity-index liquidity spectrum: developed-market indices outside the largest few combine the long histories that make them attractive testbeds with cost structures harsh enough to bury a marginal edge. It is the vault’s clearest worked example of how weakly a direct discrete-Markov price-transition chain performs out of sample — before costs are even considered.
Aronsson Folkesson 2023 [tests_strategy] Markov Chain Trading Model OMXS30 Index [relates] Transaction Costs and Slippage Transaction Costs and Slippage [contradicts] Markov Chain Trading Model
Connections
- Markov Chain Trading Model — trades_market, source: https://kth.diva-portal.org/smash/get/diva2:1823899/FULLTEXT01.pdf
- Aronsson Folkesson 2023 — tests_strategy (six-state chain, barely beats random), source: https://kth.diva-portal.org/smash/get/diva2:1823899/FULLTEXT01.pdf
- Transaction Costs and Slippage — relates (higher relative costs in a smaller market), source: https://indexes.nasdaqomx.com/Index/Overview/OMXS30
- Regime Classification — detects_regime (descriptive Markov characterisation of OMXS30), source: https://kth.diva-portal.org/smash/get/diva2:1823899/FULLTEXT01.pdf