| r"""Robustness table for the real-data run. |
| |
| Rows are alternative specifications; each is computed from saved predictions: |
| |
| baseline the main run (kappa = 120, 20 bp, out of sample 2000-) |
| oos 2005 the main run's forecasts for target months from 2005 on. Models are |
| refitted every January on an expanding window, so these forecasts are |
| identical to a rerun with --oos-start 2005. |
| --run label=dir separate full reruns: kappa 60 / 240, equal-weighted and uncapped |
| value-weighted factors, time-varying market classification |
| cost 10 / 40 bp the main run's forecasts with a different cost per unit of turnover |
| |
| python scripts/robustness.py --base reports/jkp_em \ |
| --run '$\kappa = 60$=reports/jkp_em_kappa60' --run 'Equal-weighted factors=reports/jkp_em_ew' |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) |
|
|
| from emft.evaluate import portfolio_returns, portfolio_table, r2_table |
|
|
| CELLS = [("enet", "own"), ("enet", "dm_only"), ("gbrt", "own"), ("gbrt", "dm_only")] |
| PORTS = ["static_ew", "shrink_mean:rule", "enet:own", "enet:dm_only", "gbrt:own", "gbrt:dm_only"] |
|
|
|
|
| def _check(run: Path) -> None: |
| m = json.loads((run / "run_manifest.json").read_text()) |
| if m.get("provenance") != "JKP": |
| raise SystemExit(f"{run} is not a real-data run") |
|
|
|
|
| def summarise(preds: pd.DataFrame, cost_bps: float, label: str) -> dict: |
| r2 = r2_table(preds).set_index(["model", "scope"]) |
| port = portfolio_table(portfolio_returns(preds, cost_bps=cost_bps)).set_index("strategy") |
| row = {"spec": label, "shrink_vs_hist": r2.loc[("shrink_mean", "rule"), "r2_vs_hist_pct"], |
| "months": int(preds["target_month"].nunique())} |
| for m, s in CELLS: |
| row[f"r2_{m}_{s}"] = r2.loc[(m, s), "r2_vs_shrink_pct"] |
| row[f"t_{m}_{s}"] = r2.loc[(m, s), "cw_t_shrink"] |
| for p in PORTS: |
| row[f"srnet_{p}"] = port.loc[p, "sharpe_net"] |
| return row |
|
|
|
|
| def _f(x: float, d: int = 2) -> str: |
| return "--" if pd.isna(x) else f"{x:.{d}f}".replace("-", "$-$") |
|
|
|
|
| def to_tex(df: pd.DataFrame) -> str: |
| lines = [r"\begin{table}[!ht]", r"\centering\footnotesize", r"\caption{Robustness}", r"\label{tab:robust}", |
| r"\begin{tabular}{lrrrrrrrrr}", r"\toprule", |
| r"\multicolumn{10}{l}{\textit{Panel A: $R^2_{OS}$ (\%) and Clark--West $t$ against the shrinkage mean}} \\", |
| r"\addlinespace", |
| r" & Shrink.\ vs.\ & \multicolumn{2}{c}{Elastic net, EM} & \multicolumn{2}{c}{Elastic net, DM}" |
| r" & \multicolumn{2}{c}{GBRT, EM} & \multicolumn{2}{c}{GBRT, DM} \\", |
| r"\cmidrule(lr){3-4}\cmidrule(lr){5-6}\cmidrule(lr){7-8}\cmidrule(lr){9-10}", |
| r"Specification & own mean & $R^2$ & $t$ & $R^2$ & $t$ & $R^2$ & $t$ & $R^2$ & $t$ \\", r"\midrule"] |
| stat = df[~df["spec"].str.startswith("Cost")] |
| for _, r in stat.iterrows(): |
| cells = [r["spec"], _f(r["shrink_vs_hist"], 3)] |
| for m, s in CELLS: |
| cells += [_f(r[f"r2_{m}_{s}"], 3), _f(r[f"t_{m}_{s}"])] |
| lines.append(" & ".join(cells) + r" \\") |
| lines += [r"\bottomrule", r"\end{tabular}", "", r"\vspace{10pt}", |
| r"\begin{tabular}{lrrrrrr}", r"\toprule", |
| r"\multicolumn{7}{l}{\textit{Panel B: net Sharpe ratios of timing portfolios}} \\", r"\addlinespace", |
| r" & & Shrinkage & \multicolumn{2}{c}{Elastic net} & \multicolumn{2}{c}{GBRT} \\", |
| r"\cmidrule(lr){4-5}\cmidrule(lr){6-7}", |
| r"Specification & Static & mean & EM & DM & EM & DM \\", r"\midrule"] |
| for _, r in df.iterrows(): |
| lines.append(" & ".join([r["spec"]] + [_f(r[f"srnet_{p}"]) for p in PORTS]) + r" \\") |
| lines += [r"\bottomrule", r"\end{tabular}", "", r"\vspace{4pt}", |
| r"\begin{minipage}{0.97\linewidth}\footnotesize", |
| r"\textit{Notes:} Emerging-market test set. Baseline: $\kappa=120$, 20 bp per unit of turnover, " |
| r"out of sample 2000--2025. ``OOS from 2005'' keeps the baseline forecasts for target months from " |
| r"2005, which equal those of a rerun starting in 2005 because models are refitted annually on an " |
| r"expanding window. $\kappa$ rows rerun the full pipeline, changing both the benchmark and the " |
| r"learned models' anchor. Factor-weighting rows rerun it on equal-weighted or uncapped " |
| r"value-weighted JKP factors. ``Time-varying classification'' assigns each country-month the MSCI " |
| r"class then in force (Appendix~\ref{app:classification}). Cost rows change only the charge per unit of timing turnover. " |
| r"``Shrink.\ vs.\ own mean'' is the $R^2_{OS}$ of the shrinkage mean against the own historical mean. " |
| r"EM / DM = trained on emerging / developed markets only.", |
| r"\end{minipage}", r"\end{table}"] |
| return "\n".join(lines) + "\n" |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--base", default="reports/jkp_em") |
| ap.add_argument("--run", action="append", default=[], help="label=run_dir, repeatable") |
| ap.add_argument("--out", default="reports/robustness") |
| ap.add_argument("--tex", default="paper/tables/robustness.tex") |
| a = ap.parse_args() |
| base = Path(a.base) |
| _check(base) |
| preds = pd.read_parquet(base / "predictions.parquet") |
| rows = [summarise(preds, 20, r"Baseline"), |
| summarise(preds[preds["target_month"].dt.year >= 2005], 20, "OOS from 2005")] |
| for spec in a.run: |
| label, d = spec.rsplit("=", 1) |
| _check(Path(d)) |
| rows.append(summarise(pd.read_parquet(Path(d) / "predictions.parquet"), 20, label)) |
| for c in (10, 40): |
| rows.append(summarise(preds, c, f"Cost {c} bp")) |
| df = pd.DataFrame(rows) |
| out = Path(a.out) |
| out.mkdir(parents=True, exist_ok=True) |
| df.to_csv(out / "robustness.csv", index=False) |
| Path(a.tex).write_text(to_tex(df)) |
| with pd.option_context("display.width", 250, "display.max_columns", 30): |
| print(df.round(3).to_string(index=False)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
