# 市场有效性与信息处理：从公开事件到可检验的证据
按size加权合约量重建公告前流量代理，核对信息窗口、回报基准与分组标准化，避免把关联当收益。
Entry: zh-m23 | Node: M23 | Language: zh | Editorial revision: 2026-09-21
## Teaching instructions
你正在教M23《市场有效性与信息处理：从公开事件到可检验的证据》，内容版本2026-09-21-MEFG-review-v3。读者已有高年级本科至研究生的数学基础。
先确认选用本篇共同正文和哪些分支，再实际读取agent_packet列出的当前必读完整原文。PDF需读脚注、表图与符号；只拿到摘要/目录/搜索片段不算完成。记录实际版本、范围与内容对应，不沿用编辑端“已读”充当本次读取；同会话已完整取得相同版本单元可以复用。若所需单元失败，尝试机构正式等价全文；仍缺失则指出具体单元，不凭记忆补成已读教学。runtime_reading_log从空开始。
独立学习任务：用四类size加权合约量算TOI，标出事件前可用信息，并解释标准化回归而不是宣称策略利润。
专属诊断与正向讲解：先问30/10/25/35的单位，若答笔数要求补每笔size；核TOI.30和PCR1.5。然后按τ−300至−46、τ−40至−10、τ−1、τ、[0,1]排序。
反馈尺度：.0283是原文宏观分组标准化下的系数，不能说2.83%收益；发起分类不是动机；不把事后消息分类倒入交易信号。
读者要求直接讲解时，按本篇连贯推导讲清，不反复问已会先修。静态例、实验、练习必须使用本包同一输入和单位；实例价、合成价、模型价、规则时点不能混换。练习要给完整解析，不仅打分。仅当选读分支被采用时，将其optional reading转入当前必读。
Before substantive teaching, actually retrieve every required reading unit for the selected scope. Read its complete designated section, including necessary assumptions, tables and footnotes. A working URL or an editorial access date is not a runtime reading receipt. Record the actual version, location, scope and what it supports. If unavailable, use a previously verified equivalent source; if the required unit remains unavailable, identify that gap rather than teach it from memory. Start runtime_reading_log empty. Once reading is complete, use a substantive diagnostic or follow the reader's request for direct explanation. Advance one complete reasoning task at a time; skip mastered basics. Distinguish original facts, supplied teaching assumptions and inference.
## Required readings and runtime protocol
```json
{
"export_mode": "public",
"required_readings": [
{
"source_id": "MEFG-MIT-EFFICIENCY",
"version": "Spring 2003",
"access": {
"kind": "pdf_full_text",
"uri": "https://ocw.mit.edu/courses/15-433-investments-spring-2003/4491842813dd9719421bce96c282b39c_1543322marketefficiency.pdf",
"verified_access_at": "2026-09-21"
},
"required_unit": {
"locator": "完整pp1–11，重点信息集、事件研究、成本与限制",
"scope": "完整指定单元、必要脚注与表图；不以搜索摘要代替",
"purpose": "效率的相对信息集和基准"
},
"supports": "信息集、检验基准与实施成本的基本框架；历史基金绩效不是当前市场事实。",
"id": "M23-READ-01",
"title": "15.433 Investments, Class 22: Market Efficiency",
"authors": [
"MIT OpenCourseWare"
],
"retrieved_at": "2026-09-21"
},
{
"source_id": "MEFG-KIT-2025",
"version": "Manuscript 2025-03-07; no journal status claimed",
"access": {
"kind": "pdf_full_text",
"uri": "https://derivate.fbv.kit.edu/download/Information_Processing_in_the_Option_Market%202025.pdf",
"verified_access_at": "2026-09-21"
},
"required_unit": {
"locator": "§2.2、完整§3（含§3.4.1式2–3）、§4.1.1及Figure1、完整§4.2与Tables2–3，止于§4.3前",
"scope": "完整指定单元、必要脚注与表图；不以搜索摘要代替",
"purpose": "size加权合约量、事件/估计窗口、CAR、macro/nonmacro分别标准化与关联"
},
"supports": "size加权的发起方向合约成交量代理、事件时间、市场模型基准、分组标准化关联；无交易动机识别或可执行净收益证明。",
"id": "M23-READ-02",
"title": "Information Processing in the Option Market Around Earnings and Macroeconomic Announcements",
"authors": [
"Jelena Eberbach",
"Marliese Uhrig-Homburg",
"Xiaoxiao Yu"
],
"retrieved_at": "2026-09-21"
}
],
"optional_readings": [],
"runtime_reading_log": [],
"supplied_inputs": {
"content_version": "2026-09-21-MEFG-draft-v1",
"data": {
"schema": "markets-mefg-adopted-inputs-v1",
"content_version": "2026-09-21-MEFG-draft-v1",
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"M16",
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"source_file": "data/qt-f-shared-state-experiment.json",
"source_version": "2026-09-21-v1",
"source_sha256": "8489fd3b74f469c3424d32a2b0738280e4e83709f081dfb24282d9afcfa8804b",
"source_pointer": "two_state / three_state_incomplete / three_state_augmented_complete",
"identity": "Exact received QT-F/M21 frozen contract; file preserved byte-for-byte; generated browser data is a derived copy",
"physical_up_probability_variants": [
0.2,
0.6,
0.8,
0.9
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"period_unit": "one abstract period; R is gross growth, not annual net rate"
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"initial_wealth_usd": 5000,
"spot_usd": 50,
"strike_usd": 50,
"premium_per_share_usd": 5,
"shares_per_contract": 100,
"stock_position_shares": 100,
"mixed_contracts": 1,
"all_option_contracts": 10,
"mixed_cash_usd": 4500,
"simple_annual_cash_rate": 0.0325,
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62,
58,
54,
50,
46,
42,
38
],
"cost_convention": "No fees, tax or stock dividends in base; terminal economic payoff is not a funding guarantee for physical exercise"
},
"cash_examples": {
"identity": "Synthetic amounts under documented product rules; not historical quotes/series/accounts",
"equity_call": {
"multiplier": 100,
"contracts": 1,
"strike": 100,
"purchase_premium": 4,
"later_underlying_bid": 105,
"later_option_bid": 6,
"later_option_ask": 6.2,
"exercise_gross_cash_assumption": 10000,
"exercise_model": "sequential gross payment first; no assumed netting/credit; broker policy not inferred"
},
"spxw_call": {
"multiplier_usd_per_point": 100,
"strike_points": 5000,
"purchase_premium_points": 50,
"settlement_value_points": 5025,
"shares_delivered": 0
},
"uncovered_equity_call_margin": {
"contract_type": "OCC ordinary equity uncovered call minimum; not ETF/index or broker house rules",
"multiplier": 100,
"strike": 105,
"initial_spot": 100,
"initial_option_premium": 4,
"later_spot": 120,
"later_option_mark": 17,
"base_fraction": 0.2,
"minimum_call_fraction": 0.1,
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}
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"identity": "Separate teaching market, not EXP-STATE-01",
"spot": 50,
"strike": 100,
"up_factor": 1.2,
"down_factor": 0.9,
"gross_cash_growth_per_period": 1.02,
"periods": [
1,
2
],
"dividend": 0,
"time_unit": "one abstract period; not a year; American exercise at every displayed node"
},
"parity_quotes": {
"identity": "Separate synthetic quote/funding scenario; no friction toggle on EXP-STATE-01",
"strike": 105,
"gross_cash_growth": 1.02,
"stock": {
"bid": 99.95,
"ask": 100.05
},
"call": {
"bid": 5.8,
"ask": 6.2
},
"put": {
"bid": 8.7,
"ask": 9.1
},
"extra_fee_per_underlying_unit": 0,
"cost_convention": "same borrowing/lending, zero dividend European same K/T; all legs synchronous and fully fillable only by explicit teaching assumption",
"execution_assumptions": {
"borrowing": true,
"stock_short": true,
"synchronous": true,
"depth": true,
"exercise_terms_match": true
},
"default_capital_available": 1
},
"surface": {
"experiment_id": "EXP-MEFG-IV-01",
"identity": "Entirely synthetic SPX-sized European call/put quotes; actual multiplier/tick only; no historical chain",
"quote_date": null,
"spot_index_points": 5000,
"annual_continuous_rate": 0.04,
"annual_continuous_dividend_yield": 0,
"dividend_note": "zero is teaching assumption, not S&P500 dividend forecast",
"day_count": "ACT/365 teaching whole-day expiries",
"strikes_points": [
4500,
5000,
5500
],
"maturities": [
{
"days": 30,
"sigma_by_strike": [
0.28,
0.24,
0.22
]
},
{
"days": 90,
"sigma_by_strike": [
0.27,
0.235,
0.22
]
},
{
"days": 180,
"sigma_by_strike": [
0.25,
0.23,
0.22
]
}
],
"quote_half_width_points": 0.2,
"contract_multiplier_usd_per_point": 100,
"min_quote_tick_below3": 0.05,
"min_quote_tick_at_or_above3": 0.1,
"greek_example": {
"strike": 5000,
"days": 90,
"sigma": 0.235,
"spot_changes_points": [
50,
500
],
"vol_changes_absolute": [
0.01,
0.1
]
},
"validation_scope": "displayed grid only; not a global no-arbitrage surface or observed chain"
},
"vix": {
"official_sample": {
"identity": "Methodology v6.0 Appendix3 explicitly hypothetical (2022-09-27 10:45:15 ET label is not observed VIX)",
"minutes_near": 34484,
"minutes_next": 44954,
"target_minutes": 43200,
"year_minutes": 525600,
"near_variance": 0.019233906,
"next_variance": 0.019423884,
"displayed_T_near": 0.0656088,
"displayed_T_next": 0.0855289,
"displayed_vix": 13.93,
"one_strike": {
"strike": 1370,
"delta_K": 5,
"mid_quote": 0.2,
"R_rounded": 0.000317,
"T_rounded": 0.0656088,
"displayed_contribution": 5.328e-07
}
},
"two_term_teaching_variant": {
"identity": "separate variance interpolation, not a VIX-strip reproduction",
"days_near": 24,
"days_next": 36,
"target_days": 30,
"near_vol": 0.2,
"next_vol": 0.24
}
},
"information_example": {
"identity": "Synthetic contract volumes classified by initiating side; size-weighted KIT Eq(2), not unweighted transaction counts; not motives",
"unit": "option contracts",
"buyer_call_contracts": 30,
"seller_call_contracts": 10,
"buyer_put_contracts": 25,
"seller_put_contracts": 35,
"non_event_mean_TOI": 0.05,
"baseline_window": "non-announcement days tau-40 through tau-10 per paper; 0.05 here a teaching value",
"table_2_column_3": {
"identity": "published regression coefficients with macro/non-macro separately standardized variables; not raw-return slopes/profits",
"OI_coefficient": 0.0033,
"OI_macro_interaction": 0.025,
"column_1_OI_coefficient": 0.0078,
"sample_observations": 51522
}
},
"runtime_reading_log": []
},
"additional_files": [],
"experiment_id": "EXP-MEFG-M23-INFORMATION"
},
"entry_id": "zh-m23",
"node_id": "M23",
"content_version": "2026-09-21-MEFG-review-v3",
"experiment_ids": [
"EXP-MEFG-M23-INFORMATION"
],
"source_id_aliases": {
"MEFG-ODD": "MA-OCC",
"MEFG-OCC-EQUITY": "PFH-EQUITY",
"MEFG-OCC-ETF": "PFH-ETF",
"MEFG-OIC-T1": "PFH-T1",
"MEFG-SPX": "MA-SPX",
"MEFG-MIT-OPTIONS": "PFH-MIT-OPTIONS",
"MEFG-MIT-KOGAN": "PFH-MIT-BSM",
"MEFG-OIC-PARITY": "PFH-PARITY",
"MEFG-OIC-EXERCISE": "PFH-EXERCISE",
"MEFG-CHICAGOFED-2025": "PFH-CHIFED2025"
},
"source_paths": {
"shared_inputs.json": "https://ou-liu-red-sugar.github.io/notebook/labs/m-efg/inputs.json",
"data/qt-f-shared-state-experiment.json": "https://ou-liu-red-sugar.github.io/notebook/labs/qt-f/data/qt-f-shared-state-experiment.json",
"static/M23.html": "https://ou-liu-red-sugar.github.io/notebook/labs/m-efg/static/M23.html"
}
}
```
## Supplied entry
## 一、“信息已反映在价格里”，究竟是什么意思
一句“市场有效”必须说明相对于什么信息。过去价格、全部公开信息、甚至私人信息，形成不同的信息集。弱式、半强式与强式的区分，就在于可用信息的范围，而不是把市场分成“聪明”或“愚蠢”。价格迅速反应也不意味着每一刻都等于研究者心中的唯一真实价值。[^MEFG-MIT-EFFICIENCY]
检验还需要正常回报的基准。发现公告后股票上涨，可能是意外好消息，也可能是共同市场变化；发现某信号与未来回报有关，也可能是风险补偿而不是可免费取得的超额收益。因而检验信息效率常与检验回报模型交织在一起。没有声明信息集和比较基准，只拿一段涨跌图，不能完成这项工作。
信息处理本身要花时间，交易要支付价差与费用，某些持仓还需要借券和资本。所以本篇不从“存在统计关系”直接跳到“投资者可以靠它稳定赚钱”。我们读一项真实研究，逐项看作者观察了什么、怎样定义变量，以及结论停在哪里。
## 二、研究对象不是交易者脑中的想法
采用Eberbach、Uhrig-Homburg和Yu的 *Information Processing in the Option Market Around Earnings and Macroeconomic Announcements*，2025-03-07工作稿。其美国股票期权样本覆盖2004年1月至2017年10月，结合LiveVol成交／报价、OptionMetrics、I/B/E/S及宏观公告资料。它研究公告附近的期权流量和随后股票价格关系，不是2026年实时信号。[^MEFG-KIT-2025]
第一步已经有测量问题：一笔成交由买方还是卖方主动发起，是根据报价附近的成交分类等规则推断的，并非直接观察交易者真实动机。买入put可能为对冲，也可能为方向判断；卖出put可能有不同组合背景。作者构造的是可观察信息处理的**代理量**，不是给每位交易者贴“知情”标签。
图形阅读也要看时间。原文§4.1.1、Figure 1（PDF第19页）按事后消息分类比较公告周围的异常流量。这能帮助描述消息与交易之间的关系，但事后才知道的好／坏消息标签，不能被放进公告前的策略输入。
## 三、先按合约份数加权，再读方向
KIT §3.4.1式(2)用每笔成交的方向符号与**成交合约份数size**计算
$$
TOI=\frac{\sum_i \operatorname{sign}_i\,\operatorname{size}_i}
{\sum_i\operatorname{size}_i}.
$$
买方发起的call和卖方发起的put取正；卖方发起的call和买方发起的put取负。它是方向性合约成交量的标准化差，不是未加权的“有几笔买、有几笔卖”。[^MEFG-KIT-2025]
现在用一个明确的合成例。四行是发起方向分类后的**合约份数**，不是实际公司数据，也不是四组交易者意图：
| 分类 | 成交合约份数 | 符号 | 带符号合约量 |
|---|---:|---:|---:|
| 买方发起call | 30 | +1 | +30 |
| 卖方发起call | 10 | −1 | −10 |
| 买方发起put | 25 | −1 | −25 |
| 卖方发起put | 35 | +1 | +35 |
| 合计 | 100 | — | 30 |
所以 $TOI=(30+35-10-25)/100=0.30$。若对应的非事件基线均值为0.05，异常OI为 $0.30-0.05=0.25$。原文的基线使用公告前指定非公告日窗口，这里只把其均值0.05作为教学输入，不伪造那段真实历史。
同一组数据的put／call总量比是
$$
\frac{25+35}{30+10}=\frac{60}{40}=1.5.
$$
“put量更多”和“方向代理量为正”没有矛盾。前者不区分发起方向，后者区分并按合约量加权。更不能把1.5直接改写成“60%的交易者看空”。
再想一个更小的核对：若上表恰好来自四笔不同大小的成交，每笔各算一票会有两个正、两个负，未加权结果为0；按size加权才是0.30。只给笔数而不给每笔规模，就没有足够输入复算论文的这个变量。原研究还设成交量等筛选；本例数值达到100份，不能因此声称整个研究数据清理已经复现。
## 四、信息必须在使用时已经可得
以公告日为 $\tau$，本篇采用原文的几组不同窗口：
| 环节 | 相对时间 | 用途 |
|---|---|---|
| 市场beta估计 | $\tau-300$ 至 $\tau-46$ | 形成异常回报的市场模型基准 |
| 非事件流量基线 | $\tau-40$ 至 $\tau-10$ 中相应非公告日 | 计算平常的TOI均值 |
| 公告前流量 | 例如 $\tau-1$ | 作为随后价格反应的解释变量 |
| 公告／消息 | $\tau$ | 确定事件，具体盘中盘后对齐沿原研究 |
| 公告回报窗口 | 例如 $[0,1]$ | 度量公告附近价格反应 |
这些窗口不能仅因为都在同一张图上，就当成同一时点已经知道的信息。预测者在 $\tau-1$ 不知道事后实现的公告回报，也不能用完整未来样本重新估计当时的beta。[^MEFG-KIT-2025]
原文的市场调整累计回报也不是随便把股票涨幅叫alpha。用 $G_i=\prod(1+r_{i,t})$、$G_m=\prod(1+r_{m,t})$、$G_f=\prod(1+r_{f,t})$ 表示指定窗口的累计总增长量，其比较结构为
$$
CAR=(G_i-G_f)-\widehat\beta_i(G_m-G_f).
$$
$\widehat\beta_i$ 由上述更早的估计窗口取得。这里解释的是原文比较口径；没有原始股票和市场序列，我们不为这条式子填一个似乎真实的CAR。
## 五、0.0283为什么不是“赚2.83%”
Table 2（印刷p22／PDF第23页）将公告窗口回报与公告前异常OI、宏观日指标及其交互联系。用简写表示其中一部分：
$$
CAR=\alpha+\beta_1OI+\beta_2MD
+\beta_3(OI\times MD)+\text{控制项}+\varepsilon.
$$
$MD$ 是相应宏观公告日指示变量。作者在宏观／非宏观两组分别标准化相关变量；因此表中系数不能直接作为未经标准化的OI每增加1时，真实回报百分点会增加多少。[^MEFG-KIT-2025]
| 原文比较 | 采用值 | 正确阅读 |
|---|---:|---|
| 第3列OI系数 | 0.0033 | 该列指定基组与标准化下的系数 |
| 第3列OI×宏观日交互 | 0.0250 | 同一回归中宏观日斜率增量 |
| 两者之和 | 0.0283 | 作者解释的宏观日对应系数 |
| 第1列合并样本OI系数 | 0.0078 | 另一列合并回归的参照 |
作者正文采用 $0.0033+0.0250=0.0283$ 并与0.0078比较，数值比约3.63。这个加法在原回归定义和分组标准化说明下有意义，却不等于“发现信号后能赚2.83%”。还差交易价格、持有规则、可用信息、费用、资金和风险等整条策略链；统计显著性本身不会补齐它。
这也说明一种检验的边界：期权流量与随后股票反应有关，支持作者对信息处理的讨论，但不直接识别每笔交易者的动机，不单独证明内幕交易，也不构成已经实施的样本外策略。信息可能来自对公开资料的处理，可能与风险暴露和交易需求相关；要在这些解释之间作更强判断，需要额外识别。
## 六、图形和计算各承担什么
第一个视角把四类合约量画成正负贡献，再计算TOI、异常OI和put／call总量比；第二个视角显示估计、基线、流量、公告和回报窗口。只有前者使用这里的合成数字。原研究Figure 1与Table 2保留公开原文定位，不提供虚构的公司选择或历史回测按钮。
总量为0时TOI没有定义；call总量为0时put／call比也不能强制显示为0。少量合成数据可以算教学定义，但不因此满足原研究全部样本筛选。每一个输出都应先有足够的分母与合格的时间身份。
## 七、从结果回到证据
**题一：上表只有四行，为什么不能算成“二正二负，TOI为0”？**
**解析。** 论文按size加权，四行分别汇总30、10、25、35份合约。带符号量为30，总量100，得0.30。即使恰好来自四笔成交，也不能丢掉不同size。
**题二：put／call比1.5与TOI=+0.30各告诉我们什么？**
**解析。** 前者只有call与put总量，不含发起方向；后者对四类方向加权。两者都不是交易者心理的直接测量，也不能凭这两个数建立必赚信号。
**题三：把Figure 1事后好消息组的标签加入 $\tau-1$ 策略，会犯什么错？**
**解析。** 该标签依赖后来公告，属于未来信息。用它训练或执行公告前规则，会把事后解释误当成当时可用的预测信息。
**题四：0.0283和0.0078的比约3.63，能否说宏观日策略收益高3.63倍？**
**解析。** 不能。它比较原文指定回归和标准化口径下的系数；不是同资本、同成本的两套交易收益。要作策略比较，须另定义价格、仓位、信息、持有期和可执行费用。
**题五：一条公开消息之后价格仍缓慢变化，足以推翻半强式有效吗？**
**解析。** 单一观察不足。先确认何时消息真正公开、市场基准、风险补偿、样本选择及交易成本；再检验可重复的异常结果。否则既可能漏掉真正信息处理，也可能把正常风险或测量偏差错当成低效率。
[^MEFG-MIT-EFFICIENCY]: MIT OpenCourseWare，*15.433 Investments, Class 22: Market Efficiency*，Spring 2003。[原文](https://ocw.mit.edu/courses/15-433-investments-spring-2003/4491842813dd9719421bce96c282b39c_1543322marketefficiency.pdf)。本篇定位：Full physical pp1–11; p2 information sets, pp4–6 event/cost/limits, p9 framing。
[^MEFG-KIT-2025]: Jelena Eberbach, Marliese Uhrig-Homburg, Xiaoxiao Yu，*Information Processing in the Option Market Around Earnings and Macroeconomic Announcements*，Manuscript 2025-03-07; no journal status claimed。[原文](https://derivate.fbv.kit.edu/download/Information_Processing_in_the_Option_Market%202025.pdf)。本篇定位：§2.2 proxy; §3 full data/variables: physical pp11–18；§4.1.1, Figure1 physical p19；§4.2 including Tables2–3, physical pp21–26 before §4.3; Table2 p23；§3.4.1 Eq(2)–(3): physical pp14–15。
## Additional teaching material
### 本篇默认结果与静态等价
| 发起方向 | 合约份数 | 符号 |
|---|
| 买call | 30 | + |
| 卖call | 10 | − |
| 买put | 25 | − |
| 卖put | 35 | + |
| 量 | 计算 |
|---|
| TOI | (30+35−10−25)/100=0.30 |
| 异常OI | 0.30−0.05=0.25 |
| put/call量 | 60/40=1.5 |
| 宏观日标准化系数 | .0033+.0250=.0283 |
| 与合并列比较 | .0283/.0078≈3.6282 |
单位是按发起方向分类的成交合约份数，size加权。论文按宏观/非宏观组标准化；系数不是交易收益。估计窗口τ−300…−46，基线τ−40…−10，公告前流量τ−1，CAR[0,1]。事后消息标签不能倒放。
完整冻结输入：https://ou-liu-red-sugar.github.io/notebook/labs/m-efg/inputs.json。来源内的包路径按 source_paths 取得。
## Experiment inputs and static equivalents
```json
[
{
"id": "EXP-MEFG-M23-INFORMATION",
"title": "合约成交量与事件时间",
"anchor": "m23-explore",
"description": "同一组put量可以更多，而带方向的TOI仍为正。先核单位，再解释系数。",
"data_identity": "四组数均为按发起方向分类的合约份数，不是未加权笔数；不是交易者动机或真实流量。",
"shared_dataset_ids": [],
"inputs": {
"canonical_common": "shared_inputs.json",
"state_contract": null,
"controls": [
{
"name": "bc",
"label": "买方发起call（份）",
"type": "number",
"default": 30,
"domain_or_choices": {
"min": 0,
"step": 1
}
},
{
"name": "sc",
"label": "卖方发起call（份）",
"type": "number",
"default": 10,
"domain_or_choices": {
"min": 0,
"step": 1
}
},
{
"name": "bp",
"label": "买方发起put（份）",
"type": "number",
"default": 25,
"domain_or_choices": {
"min": 0,
"step": 1
}
},
{
"name": "sp",
"label": "卖方发起put（份）",
"type": "number",
"default": 35,
"domain_or_choices": {
"min": 0,
"step": 1
}
},
{
"name": "baseline",
"label": "非事件TOI均值（教学）",
"type": "number",
"default": 0.05,
"domain_or_choices": {
"min": -1,
"max": 1,
"step": 0.01
}
}
]
},
"algorithm": "TOI=(BC+SP-SC-BP)/(BC+SC+BP+SP), all counts are size-weighted contract volumes. Abnormal=TOI-baseline; PCR=(BP+SP)/(BC+SC). Total0=>undefined; call_total0=>PCR undefined. Published standardized macro slope=.0033+.025=.0283; ratio to pooled=.0283/.0078; not return percent.",
"outputs": {
"default": {
"bc": 30,
"sc": 10,
"bp": 25,
"sp": 35,
"total": 100,
"calls": 40,
"puts": 60,
"signed": 30,
"TOI": 0.3,
"abnormal": 0.25,
"putCallRatio": 1.5,
"baseline": 0.05,
"status": "toy_computed_not_research_replication",
"standardizedMacroSlope": 0.028300000000000002,
"pooledCoefficient": 0.0078,
"ratioToPooled": 3.6282051282051286,
"unit": "contracts classified by initiating side, size weighted; not transaction counts"
},
"static_equivalent": "https://ou-liu-red-sugar.github.io/notebook/labs/m-efg/static/M23.html",
"unit_note": "四组数均为按发起方向分类的合约份数，不是未加权笔数；不是交易者动机或真实流量。"
},
"boundaries": [
"合约份数非交易笔数，非动机",
"size总量0不除0；call量0PCR null",
"低于10份仅toy，不能称通过原研究筛选",
"事后消息不能用于公告前信号",
"分组标准化coefficients不改写为交易收益"
],
"views": {
"first": "数值/单位/现金表",
"second": "定价、现金、状态或事件图；见同一实验实现"
},
"implementation": {
"html": "/notebook/labs/m-efg/interactions.html?experiment=EXP-MEFG-M23-INFORMATION",
"engine": "/notebook/labs/m-efg/engine.js",
"static_available_without_js": true
},
"source_id_aliases": {
"MEFG-ODD": "MA-OCC",
"MEFG-OCC-EQUITY": "PFH-EQUITY",
"MEFG-OCC-ETF": "PFH-ETF",
"MEFG-OIC-T1": "PFH-T1",
"MEFG-SPX": "MA-SPX",
"MEFG-MIT-OPTIONS": "PFH-MIT-OPTIONS",
"MEFG-MIT-KOGAN": "PFH-MIT-BSM",
"MEFG-OIC-PARITY": "PFH-PARITY",
"MEFG-OIC-EXERCISE": "PFH-EXERCISE",
"MEFG-CHICAGOFED-2025": "PFH-CHIFED2025"
},
"input_uri": "https://ou-liu-red-sugar.github.io/notebook/labs/m-efg/inputs.json",
"static_equivalent": "https://ou-liu-red-sugar.github.io/notebook/labs/m-efg/static/M23.html"
}
]
```
## Sources
- [Information Processing in the Option Market Around Earnings and Macroeconomic Announcements](https://derivate.fbv.kit.edu/download/Information_Processing_in_the_Option_Market%202025.pdf): size加权的发起方向合约成交量代理、事件时间、市场模型基准、分组标准化关联；无交易动机识别或可执行净收益证明。
- [15.433 Investments, Class 22: Market Efficiency](https://ocw.mit.edu/courses/15-433-investments-spring-2003/4491842813dd9719421bce96c282b39c_1543322marketefficiency.pdf): 信息集、检验基准与实施成本的基本框架；历史基金绩效不是当前市场事实。
## Content relations
```json
[
{
"from": "zh-m23",
"relation": "part_of",
"to": "markets-pricing",
"reason": "主要 topic 归属"
},
{
"from": "zh-m23",
"relation": "requires",
"to": "zh-m02",
"reason": "仅需要这项先备能力，不由推荐次序自动生成",
"required_competence": "区分发行、二级交易与参与者的信息路径"
},
{
"from": "zh-m23",
"relation": "uses_method",
"to": "zh-m03",
"reason": "本篇相应段落就地调用报价、履约、时间价值或价格敏感度，不要求重新学习整篇"
},
{
"from": "m23-information",
"relation": "supported_by",
"to": "MEFG-MIT-EFFICIENCY",
"reason": "信息集、检验基准与实施成本的基本框架；历史基金绩效不是当前市场事实。",
"locator": "Full physical pp1–11; p2 information sets, pp4–6 event/cost/limits, p9 framing",
"scope": "本段具名采用内容；教学生成数字仍按自身身份",
"at_section": "m23-information"
},
{
"from": "m23-study",
"relation": "supported_by",
"to": "MEFG-KIT-2025",
"reason": "size加权的发起方向合约成交量代理、事件时间、市场模型基准、分组标准化关联；无交易动机识别或可执行净收益证明。",
"locator": "§2.2 proxy; §3 full data/variables: physical pp11–18；§4.1.1, Figure1 physical p19；§4.2 including Tables2–3, physical pp21–26 before §4.3; Table2 p23；§3.4.1 Eq(2)–(3): physical pp14–15",
"scope": "本段具名采用内容；教学生成数字仍按自身身份",
"at_section": "m23-study"
},
{
"from": "m23-volume",
"relation": "supported_by",
"to": "MEFG-KIT-2025",
"reason": "size加权的发起方向合约成交量代理、事件时间、市场模型基准、分组标准化关联；无交易动机识别或可执行净收益证明。",
"locator": "§2.2 proxy; §3 full data/variables: physical pp11–18；§4.1.1, Figure1 physical p19；§4.2 including Tables2–3, physical pp21–26 before §4.3; Table2 p23；§3.4.1 Eq(2)–(3): physical pp14–15",
"scope": "本段具名采用内容；教学生成数字仍按自身身份",
"at_section": "m23-volume"
},
{
"from": "m23-timing",
"relation": "supported_by",
"to": "MEFG-KIT-2025",
"reason": "size加权的发起方向合约成交量代理、事件时间、市场模型基准、分组标准化关联；无交易动机识别或可执行净收益证明。",
"locator": "§2.2 proxy; §3 full data/variables: physical pp11–18；§4.1.1, Figure1 physical p19；§4.2 including Tables2–3, physical pp21–26 before §4.3; Table2 p23；§3.4.1 Eq(2)–(3): physical pp14–15",
"scope": "本段具名采用内容；教学生成数字仍按自身身份",
"at_section": "m23-timing"
},
{
"from": "m23-regression",
"relation": "supported_by",
"to": "MEFG-KIT-2025",
"reason": "size加权的发起方向合约成交量代理、事件时间、市场模型基准、分组标准化关联；无交易动机识别或可执行净收益证明。",
"locator": "§2.2 proxy; §3 full data/variables: physical pp11–18；§4.1.1, Figure1 physical p19；§4.2 including Tables2–3, physical pp21–26 before §4.3; Table2 p23；§3.4.1 Eq(2)–(3): physical pp14–15",
"scope": "本段具名采用内容；教学生成数字仍按自身身份",
"at_section": "m23-regression"
},
{
"from": "m23-explore",
"relation": "illustrated_by",
"to": "EXP-MEFG-M23-INFORMATION",
"reason": "同一组put量可以更多，而带方向的TOI仍为正。先核单位，再解释系数。",
"at_section": "m23-explore"
}
]
```
## Related entries