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ポートフォリオ🧠 AI精査

ポートフォリオ理論・AI精査

保存済みポートフォリオを、条件付きストレス分析として安全にAIへ渡すための理論と出力契約。

THEORY BOUNDARY

現在の主役は「最適化」ではなく、条件付きの弱点発見

現在の10因子は、金利や為替などを一つずつ動かしたときの局所的な理論価値の変化です。 期待収益率、β、共分散を観測していないため、Markowitz最適化やCAPMの期待収益率、 Black–Littermanの事後リターン、リスクパリティの最適配分とは呼びません。

10局所ストレス因子
4一次理論論文
v1AI出力契約

このサイトでの方式の使い分け

理論方式と現在の採用範囲
方式このサイトで使う箇所不足時の扱い
conditional_factor_stress保存済み10因子、金利高・インフレ・危機などの条件付き比較常時使用。ただし初期値・AI推定ならindicative_only
erc履歴から共分散を検証できた場合のリスク寄与比較観測数・PSD・通貨が不足すればnot_computable
mean_variance期待収益率と共分散を明示入力した場合だけ10因子から期待収益率を推定しない
capm_context市場βとリスクフリー金利を揃えた要求収益率の参考10因子感応度をβに置換しない
black_litterman市場時価総額の事前分布と明示的ビューを揃えた場合だけビュー・信頼度・tau不足なら計算しない

一次理論論文

論文は方式の根拠であり、個別銘柄の将来株価や売買判断を保証するものではありません。

Portfolio Selection

1952

Harry Markowitz

DOI: 10.1111/j.1540-6261.1952.tb01525.x

一次論文を開く ↗

Portfolio Selectionをサイトでどう扱うか
このサイトで使う箇所このサイトで使わない箇所
共分散と制約を用いる平均分散比較の理論的背景。履歴データが揃った場合だけ任意方式として使う。現在の10因子局所感応度から期待収益率や共分散を作る根拠にはしない。

Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk

1964

William F. Sharpe

DOI: 10.1111/j.1540-6261.1964.tb02865.x

一次論文を開く ↗

Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Riskをサイトでどう扱うか
このサイトで使う箇所このサイトで使わない箇所
市場β・リスクフリー金利・ERPを揃えた場合の要求収益率の文脈として参照する。保存済みの10因子感応度をβとみなさず、現行の条件付き比較をCAPMと呼ばない。

Global Portfolio Optimization

1992

Fischer Black and Robert Litterman

DOI: 10.2469/faj.v48.n5.28

一次論文を開く ↗

Global Portfolio Optimizationをサイトでどう扱うか
このサイトで使う箇所このサイトで使わない箇所
市場時価総額の事前分布・共分散・明示的な見解と信頼度を入力できる場合の拡張候補。AIやアプリがユーザーの見解・信頼度・均衡リターンを勝手に補う方式としては使わない。

On the Properties of Equally-Weighted Risk Contributions Portfolios

2010

Sébastien Maillard, Thierry Roncalli, Jérôme Teïletche

DOI: 10.3905/jpm.2010.36.4.060

一次論文を開く ↗

On the Properties of Equally-Weighted Risk Contributions Portfoliosをサイトでどう扱うか
このサイトで使う箇所このサイトで使わない箇所
共分散が検証できる場合のリスク寄与均等化（ERC）比較の根拠。現在の因子ストレス結果をリスクパリティや最適配分と表示しない。

10因子の単位と符号

保存済みポートフォリオを読み込むと表示します。

保存済みポートフォリオからAIへ渡す

端末内の保存済みポートフォリオを読み込み中…

保存データがありません。

ポートフォリオ画面で保有資産を保存してから、このページへ戻ってください。

AI出力契約：portfolio_theory_review_v1

AIは現在の賭け、10因子の集中、条件付き候補セクター3〜5個（各候補証券1〜3個）、 再配分実験、一次論文、限界を返します。実在証券は一次資料で同定・再計算し、 同定できない候補はカテゴリのまま未評価にします。

Schema ID: /ai/portfolio-theory-review.schema.json · 出力には必ずnot_investment_advice: trueを含めます。

AI用の理論JSON出力Schema JSON
出力Schemaの確認
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