| # Correction of the earlier version of this work |
|
|
| This repository holds the reworked version of the MambaATM study. The earlier version, submitted to |
| *Evolving Systems* as Ms. No. EVOS-D-26-02015, reported results that the code and the data do not |
| support. The review identified the discrepancy; this note records it in full so that anyone who |
| encounters the earlier release knows what changed and why. |
|
|
| ## The architecture that was described was not the architecture that ran |
|
|
| The manuscript described attention *guiding the state update* of the state-space branch. The code |
| computed a sigmoid gate over the two branch outputs and mixed them. Worse, in the submitted |
| configuration the sequence-model slot was filled by `test_correctness(q, k, v, n)` from the |
| authors' `ssd_minimal.py` — a numerical correctness check whose discretisation step and state |
| transition are drawn from a fixed seed on every forward pass — so the proposed model held **zero** |
| learnable parameters, while its own baselines instantiated a real Mamba2 block. The proposed model |
| was structurally weaker than the models it was compared against. |
|
|
| The rework implements both mechanisms: the gate that the code actually used, and the guided |
| modulation that the text claimed, with zero-initialised projections so that the guided variant |
| starts as the plain branch. Section 4 of the reworked manuscript and Table 1 list the differences. |
|
|
| ## The causality test did not pass |
|
|
| The manuscript stated that every model passed a causality test with a change of exactly zero before |
| the cutoff. Measured on the submitted class, the change was 0.004361 at the submitted configuration |
| and 0.006600 in the configuration `main.py` used. The cause is `nn.Conv1d(kernel_size=3, padding=1)` |
| in both attention classes: symmetric padding lets position *t* read *t+1*. The reworked models pass |
| the same test with a change of exactly zero. |
|
|
| ## The dataset that was released does not match the dataset that was described |
|
|
| `Stock 1.csv` to `Stock 4.csv` on the linked data repository hold 500 rows, not the 2,435 described; |
| 258 columns, of which 248 (`extra_feature_*`) are identically zero in every row and were dropped by |
| the submitted loader only for `日期` and `涨跌幅`; the period is 2023-01-10 to 2025-02-07, not |
| 2015 to 2025; the prices are per-column z-scores over the whole sample, not values in $[0, 1]$, so |
| test-period statistics entered the training inputs through the standardisation; and the return column |
| does not agree with the price columns of the same file — |
| corr(quoted return, price-implied return) is $-0.041$, $+0.032$, $-0.148$ and $-0.115$ for the four |
| stocks, with mean absolute differences of 0.42, 0.49, 0.17 and 1.39 against returns whose own scale |
| is about 0.02. The four assets are not named, so the experiment cannot be repeated on them. |
|
|
| Those four files are byte-identical to `000001`, `000006`, `000007` and `000008` of the processed |
| folder of the project, which is how the defect was traced. The reworked study uses the panel those |
| files were derived from: 232 named A-share stocks, forward-adjusted prices, 2015-01-06 to |
| 2025-12-31, screened, with the return column agreeing with the price columns to machine precision |
| (mean absolute difference $5\times10^{-17}$). |
|
|
| ## What the reworked experiments find |
|
|
| On the rebuilt panel the nine learned models, including DLinear, PatchTST, iTransformer and |
| TimesNet, lie within about two percentage points of accuracy around a majority-class rate of 0.53. |
| MambaATM is the most accurate of them, the difference is smaller than the cross-sectional dispersion |
| across the 232 stocks, it does not survive a Holm-corrected Wilcoxon test against the stronger |
| baselines, and its equal-weight portfolio return is dominated by turnover and cost. The |
| attention-guided variant is not better than the plain gate. |
|
|
| The earlier reported accuracies of 0.60 to 0.85 are not reproducible on data that passes the |
| integrity checks, and the reworked manuscript states the negative result instead. |
|
|
| ## Files |
|
|
| * `code/` — the models, the walk-forward pipeline, the trading simulator, the four audit scripts and |
| the scripts that generate every table, figure and number in the manuscript. |
| * `code/panel/` — the panel builder and the screening rule, so the data can be rebuilt from source. |
| * `data/` — the screened panel and the same panel before screening. |
| * `EVIDENCE.md` — the measurements behind every claim on this page, with the script that produces it. |
| * `results/` — the per-stock and per-fold metrics, the saved predictions and the run summaries. |
|
|
| The earlier code is kept in `code/legacy_amt.py`, verbatim, so that the behaviour described above |
| stays reproducible. |
|
|
