"""Write the paper's results tables from one run directory. Every number in paper/tables/*.tex written by this script comes from the CSVs and predictions of a single `python -m emft run`, so the paper can be regenerated after any rerun instead of being edited by hand. python scripts/paper_tables.py --run reports/jkp_em --out paper/tables """ 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 newey_west_t # noqa: E402 MODEL_LABEL = { "ols": "OLS", "enet": "Elastic net", "gbrt": "Gradient-boosted trees", "nn": "Neural network", "hist_mean": "Own historical mean", "shrink_mean": "Shrinkage mean", "fmom": "Factor momentum", } SCOPE_LABEL = {"own": "EM", "dm_only": "DM only", "pooled": "Pooled", "rule": "--"} COUNTRY = { "are": "United Arab Emirates", "bra": "Brazil", "chl": "Chile", "chn": "China", "col": "Colombia", "cze": "Czech Republic", "egy": "Egypt", "grc": "Greece", "hun": "Hungary", "idn": "Indonesia", "ind": "India", "kor": "Korea", "kwt": "Kuwait", "mex": "Mexico", "mys": "Malaysia", "per": "Peru", "phl": "Philippines", "pol": "Poland", "qat": "Qatar", "sau": "Saudi Arabia", "tha": "Thailand", "tur": "Turkey", "twn": "Taiwan", "zaf": "South Africa", "aus": "Australia", "aut": "Austria", "bel": "Belgium", "can": "Canada", "che": "Switzerland", "deu": "Germany", "dnk": "Denmark", "esp": "Spain", "fin": "Finland", "fra": "France", "gbr": "United Kingdom", "hkg": "Hong Kong", "irl": "Ireland", "isr": "Israel", "ita": "Italy", "jpn": "Japan", "nld": "Netherlands", "nor": "Norway", "nzl": "New Zealand", "prt": "Portugal", "sgp": "Singapore", "swe": "Sweden", "usa": "United States", } LEARNED_ORDER = ["ols", "enet", "gbrt", "nn"] SCOPE_ORDER = ["own", "dm_only", "pooled"] def _f(x: float, d: int = 2) -> str: return "--" if pd.isna(x) else f"{x:.{d}f}".replace("-", "$-$") def _notes(text: str) -> list[str]: return ["", r"\vspace{4pt}", r"\begin{minipage}{0.95\linewidth}\footnotesize", r"\textit{Notes:} " + text, r"\end{minipage}"] def coverage_table(cov: pd.DataFrame, manifest: dict) -> str: cov = cov[cov["region"].isin(["DM", "EM"])].copy() cov["name"] = cov["country"].map(COUNTRY) lines = [r"\begin{table}[!ht]", r"\centering\footnotesize\setlength{\tabcolsep}{4pt}", r"\caption{Coverage of the JKP factor returns by market}", r"\label{tab:coverage}", r"\begin{tabular}{lrllr@{\hspace{6pt}}lrllr}", r"\toprule"] half = r"Market & Factors & First & Last & Obs." lines += [half + " & " + half + r" \\", r"\midrule"] for reg, title in (("DM", "Developed markets"), ("EM", "Emerging markets")): g = cov[cov["region"] == reg].sort_values("name").reset_index(drop=True) lines.append(rf"\multicolumn{{10}}{{l}}{{\textit{{{title} ({len(g)})}}}} \\") k = (len(g) + 1) // 2 for i in range(k): cells = [] for j in (i, i + k): if j < len(g): r = g.loc[j] cells += [r["name"], str(int(r["factors"])), r["first"], r["last"], f"{int(r['obs']):,}"] else: cells += [""] * 5 lines.append(" & ".join(cells) + r" \\") lines.append(r"\addlinespace") lines += [r"\bottomrule", r"\end{tabular}"] lines += _notes( "Monthly capped value-weighted factor returns of \\citet{jensen2023replication}, downloaded from " "jkpfactors.com. Country-factor-months in which a leg holds fewer than " f"{manifest['min_stocks']} stocks are dropped. ``Obs.'' counts country-factor-months after that " "filter. Markets are classified by the current MSCI classification.") lines.append(r"\end{table}") return "\n".join(lines) + "\n" def forecast_slopes(preds: pd.DataFrame) -> pd.DataFrame: """Pooled slope of the realised deviation from the shrinkage mean on the forecast deviation. Descriptive only: estimated on the whole out-of-sample period, so rescaling forecasts by it would use future data. A slope between 0 and 1 means forecasts point the right way but are too large, which is how a positive Clark-West statistic can accompany a negative R2. """ rows = [] for (m, s), g in preds[preds["model"].isin(LEARNED_ORDER)].groupby(["model", "scope"]): x = (g["pred"] - g["shrink_mean"]).to_numpy() y = (g["target"] - g["shrink_mean"]).to_numpy() rows.append({"model": m, "scope": s, "slope": float(x @ y / (x @ x)), "sd_forecast_dev_pct": 100 * float(x.std())}) return pd.DataFrame(rows) def r2_table(r2: pd.DataFrame, manifest: dict, slopes: pd.DataFrame) -> str: rows = [] for m in LEARNED_ORDER: for s in SCOPE_ORDER: h = r2[(r2["model"] == m) & (r2["scope"] == s)] if not h.empty: rows.append(h.iloc[0]) rules = [r2[(r2["model"] == m) & (r2["scope"] == "rule")] for m in ("fmom", "shrink_mean")] first, last = manifest["oos_start"], manifest.get("last_year", "") lines = [r"\begin{table}[!ht]", r"\centering\small\setlength{\tabcolsep}{4pt}", rf"\caption{{Out-of-sample $R^2$ for emerging-market factor returns, {first}--{last}}}", r"\label{tab:r2}", r"\begin{tabular}{llrrrrrr}", r"\toprule", r" & & \multicolumn{2}{c}{vs.\ own historical mean} & \multicolumn{2}{c}{vs.\ shrinkage mean} & & \\", r"\cmidrule(lr){3-4}\cmidrule(lr){5-6}", r"Model & Training & $R^2_{OS}$ (\%) & CW $t$ & $R^2_{OS}$ (\%) & CW $t$ & Slope & Obs. \\", r"\midrule"] sl = slopes.set_index(["model", "scope"])["slope"] prev = None for r in rows: name = MODEL_LABEL[r["model"]] if r["model"] != prev else "" prev = r["model"] lines.append(f"{name} & {SCOPE_LABEL[r['scope']]} & {_f(r['r2_vs_hist_pct'], 3)} & {_f(r['cw_t_hist'])} & " f"{_f(r['r2_vs_shrink_pct'], 3)} & {_f(r['cw_t_shrink'])} & " f"{_f(sl.get((r['model'], r['scope']), np.nan))} & {int(r['obs']):,} \\\\") lines.append(r"\addlinespace") for h in rules: if h.empty: continue r = h.iloc[0] lines.append(f"{MODEL_LABEL[r['model']]} & -- & {_f(r['r2_vs_hist_pct'], 3)} & {_f(r['cw_t_hist'])} & " f"{_f(r['r2_vs_shrink_pct'], 3)} & {_f(r['cw_t_shrink'])} & -- & {int(r['obs']):,} \\\\") lines += [r"\bottomrule", r"\end{tabular}"] lines += _notes( "Pooled over all emerging-market country-factor-months. $R^2_{OS} = 1 - \\sum (r-\\hat r)^2 / " "\\sum (r-b)^2$ for benchmark $b$. CW $t$ is the Clark--West statistic: the adjusted MSPE " "difference is averaged across the cross-section each month and its mean tested with Newey--West " "standard errors (3 lags); one-sided 5\\% critical value 1.645. Training scope: EM = emerging markets " "only; DM only = developed markets only (the transfer test); Pooled = both. Learned models forecast " "the deviation from the shrinkage mean and are refitted every January. The shrinkage mean is the " "benchmark itself, so its own row against that benchmark is zero by construction. Slope: pooled " "regression slope, without intercept, of $r-\\tilde r$ on $\\hat r-\\tilde r$ over the whole " "out-of-sample period; a descriptive diagnostic that uses future data and is not a tradable rescaling.") lines.append(r"\end{table}") return "\n".join(lines) + "\n" def country_table(preds: pd.DataFrame, model: str) -> tuple[str, pd.DataFrame]: """R^2 vs the shrinkage mean and CW t by country, EM-trained vs DM-trained, for one model.""" out = [] for c, g in preds[preds["model"] == model].groupby("country"): row = {"country": c} for s in ("own", "dm_only"): h = g[g["scope"] == s] y, p, b = h["target"].to_numpy(), h["pred"].to_numpy(), h["shrink_mean"].to_numpy() row[f"r2_{s}"] = 100 * (1 - np.sum((y - p) ** 2) / np.sum((y - b) ** 2)) cw = pd.Series(2 * (y - b) * (p - b)).groupby(h["target_month"].to_numpy()).mean().to_numpy() row[f"t_{s}"] = newey_west_t(cw, 3)[0] row["obs"] = len(h) out.append(row) df = pd.DataFrame(out) df["name"] = df["country"].map(COUNTRY) df = df.sort_values("name").reset_index(drop=True) lines = [r"\begin{table}[!ht]", r"\centering\small", rf"\caption{{Transfer by country: {MODEL_LABEL[model].lower()} trained on emerging vs.\ developed markets}}", r"\label{tab:country}", r"\begin{tabular}{lrrrrr}", r"\toprule", r" & \multicolumn{2}{c}{EM-trained} & \multicolumn{2}{c}{DM-trained} & \\", r"\cmidrule(lr){2-3}\cmidrule(lr){4-5}", r"Market & $R^2_{OS}$ (\%) & CW $t$ & $R^2_{OS}$ (\%) & CW $t$ & Obs. \\", r"\midrule"] for _, r in df.iterrows(): lines.append(f"{r['name']} & {_f(r['r2_own'], 3)} & {_f(r['t_own'])} & {_f(r['r2_dm_only'], 3)} & " f"{_f(r['t_dm_only'])} & {int(r['obs']):,} \\\\") n_pos = {s: int((df[f"r2_{s}"] > 0).sum()) for s in ("own", "dm_only")} n_sig = {s: int((df[f"t_{s}"] > 1.645).sum()) for s in ("own", "dm_only")} lines += [r"\midrule", rf"Markets with $R^2_{{OS}}>0$ & \multicolumn{{2}}{{c}}{{{n_pos['own']} of {len(df)}}} & " rf"\multicolumn{{2}}{{c}}{{{n_pos['dm_only']} of {len(df)}}} & \\", rf"Markets with CW $t>1.645$ & \multicolumn{{2}}{{c}}{{{n_sig['own']} of {len(df)}}} & " rf"\multicolumn{{2}}{{c}}{{{n_sig['dm_only']} of {len(df)}}} & \\", r"\bottomrule", r"\end{tabular}"] lines += _notes( "$R^2_{OS}$ and Clark--West $t$ against the shrinkage mean, computed within each market on the same " "observations for both training scopes. Country-level tests are not adjusted for multiple comparisons.") lines.append(r"\end{table}") return "\n".join(lines) + "\n", df def portfolio_table(port: pd.DataFrame, manifest: dict) -> str: def label(s: str) -> str: if s == "static_ew": return "Static equal-weight", "--" m, sc = s.split(":") return MODEL_LABEL[m], SCOPE_LABEL[sc] order = ["static_ew"] + [f"{m}:{s}" for m in LEARNED_ORDER for s in SCOPE_ORDER] + \ ["shrink_mean:rule", "hist_mean:rule", "fmom:rule"] port = port.set_index("strategy") lines = [r"\begin{table}[!ht]", r"\centering\small\setlength{\tabcolsep}{5pt}", r"\caption{Emerging-market factor-timing portfolios}", r"\label{tab:portfolios}", r"\begin{tabular}{llrrrrr}", r"\toprule", r"Strategy & Training & Mean (\%) & Vol.\ (\%) & SR gross & SR net & Turnover \\", r"\midrule"] prev = None for s in order: if s not in port.index: continue r = port.loc[s] name, sc = label(s) if s == "fmom:rule": name = "Factor momentum (sign)" if s == "shrink_mean:rule": lines.append(r"\addlinespace") if sc == "EM" and prev is not None: lines.append(r"\addlinespace[2pt]") shown = "" if (name == prev and sc != "--") else name prev = name name = shown lines.append(f"{name} & {sc} & {_f(r['mean_ann_pct'])} & {_f(r['vol_ann_pct'])} & " f"{_f(r['sharpe_gross'])} & {_f(r['sharpe_net'])} & {_f(r['turnover_monthly'])} \\\\") if s == "static_ew": lines.append(r"\addlinespace") prev = None lines += [r"\bottomrule", r"\end{tabular}"] lines += _notes( "Within each emerging market and month, the timing portfolio holds each factor in proportion to its " "forecast, scaled to unit gross exposure; the static portfolio holds every available factor equally. " "Country portfolios are averaged with equal weights. Mean and volatility are annualized, gross of " f"timing costs. Net Sharpe ratios deduct {manifest['cost_bps']:.0f} basis points per unit of monthly " "turnover; factor returns are already gross of their own rebalancing costs. The factor-momentum rule " "holds each factor long or short by the sign of its trailing 12-month mean.") lines.append(r"\end{table}") return "\n".join(lines) + "\n" OTHER_NAMES = {"arg": "Argentina", "jor": "Jordan", "mar": "Morocco", "pak": "Pakistan", "rus": "Russia", "ven": "Venezuela"} def classification_table(path: Path) -> str: """Appendix table of the reclassification events in config/market_history.yaml.""" import yaml ev = pd.DataFrame(yaml.safe_load(path.read_text())["events"]).sort_values(["from_month", "country"]) names = {**COUNTRY, **OTHER_NAMES} lab = {"DM": "Developed", "EM": "Emerging", "OTHER": "Frontier/standalone"} lines = [r"\begin{table}[!ht]", r"\centering\footnotesize\setlength{\tabcolsep}{4pt}", r"\caption{MSCI reclassifications used for the time-varying classification}", r"\label{tab:classification}", r"\begin{tabular}{llll}", r"\toprule", r"Market & In force from & New class & MSCI event \\", r"\midrule"] for _, e in ev.iterrows(): note = e["note"].replace("->", r"$\to$") lines.append(f"{names[e['country']]} & {e['from_month']} & {lab[e['region']]} & {note} \\\\") lines += [r"\bottomrule", r"\end{tabular}"] lines += _notes( "Each market takes the new class from the first full month after MSCI implemented the change; before " "its first event a market has its prior class, and markets without events keep their current class. " "First inclusion in the MSCI indexes and partial-inclusion steps are not modelled. Sources: MSCI's " "list of market reclassifications and MSCI announcements, cited in \\texttt{config/market\\_history.yaml}.") lines.append(r"\end{table}") return "\n".join(lines) + "\n" def strategy_label(s: str) -> str: if s == "static_ew": return "Static equal-weight" m, sc = s.split(":") return MODEL_LABEL[m] if sc == "rule" else f"{MODEL_LABEL[m]} ({SCOPE_LABEL[sc]}-trained)" def cumulative_figure(monthly: pd.DataFrame, port: pd.DataFrame, path: Path, cost_bps: float) -> None: """Same selection rule as the run report (static + the three best net Sharpe ratios), readable labels.""" import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt best = port[port["strategy"] != "static_ew"].sort_values("sharpe_net", ascending=False)["strategy"].head(3) fig, ax = plt.subplots(figsize=(7.5, 4)) for st in ["static_ew", *best]: g = monthly[monthly["strategy"] == st].sort_values("target_month") x = pd.PeriodIndex(g["target_month"].astype(str), freq="M").to_timestamp() ax.plot(x, 100 * np.cumsum(g["net"].to_numpy()), label=strategy_label(st), linewidth=1.4) ax.axhline(0, color="0.6", linewidth=0.8) ax.set_ylabel(f"Cumulative net return, % (net of {cost_bps:.0f} bp per unit turnover)", fontsize=8) ax.legend(fontsize=8, frameon=False) ax.spines[["top", "right"]].set_visible(False) fig.tight_layout() fig.savefig(path, dpi=200) plt.close(fig) def main() -> None: ap = argparse.ArgumentParser() ap.add_argument("--run", default="reports/jkp_em") ap.add_argument("--out", default="paper/tables") ap.add_argument("--country-model", default=None, help="default: the learned model with the highest R2 vs the shrinkage mean (same rule as the CLI)") a = ap.parse_args() run, out = Path(a.run), Path(a.out) out.mkdir(parents=True, exist_ok=True) manifest = json.loads((run / "run_manifest.json").read_text()) if manifest.get("provenance") != "JKP": raise SystemExit(f"{run} is not a real-data run (provenance={manifest.get('provenance')})") preds = pd.read_parquet(run / "predictions.parquet") manifest["last_year"] = int(preds["target_month"].max().year) if hasattr(preds["target_month"].iloc[0], "year") \ else int(str(preds["target_month"].max())[:4]) (out / "coverage.tex").write_text(coverage_table(pd.read_csv(run / "coverage.csv"), manifest)) slopes = forecast_slopes(preds) slopes.to_csv(run / "forecast_slopes.csv", index=False) (out / "r2_oos.tex").write_text(r2_table(pd.read_csv(run / "r2_oos.csv"), manifest, slopes)) r2 = pd.read_csv(run / "r2_oos.csv") model = a.country_model or r2[r2["model"].isin(LEARNED_ORDER)].iloc[0]["model"] tex, by_country = country_table(preds, model) (out / "transfer_by_country.tex").write_text(tex) by_country.to_csv(run / f"transfer_by_country_{model}.csv", index=False) (out / "portfolios.tex").write_text(portfolio_table(pd.read_csv(run / "portfolios.csv"), manifest)) (out / "market_history.tex").write_text( classification_table(Path(__file__).resolve().parents[1] / "config" / "market_history.yaml")) fig_dir = out.parent / "figures" fig_dir.mkdir(exist_ok=True) cumulative_figure(pd.read_csv(run / "portfolio_monthly.csv"), pd.read_csv(run / "portfolios.csv"), fig_dir / "cumulative_net.png", manifest["cost_bps"]) print(f"wrote tables to {out} and figure to {fig_dir}") if __name__ == "__main__": main()