Agent skill

A-Share Corporate Event Analysis

by HKUDS in HKUDS/Vibe-Trading

Builds event-driven analysis for A-share companies: merger arbitrage spreads, shareholder buying and selling signals, equity incentives, placements and ST or delisting warnings; Chinese text.

MITAuto-check passedBusiness, Finance & HR

SKILL.md written in Chinese; this summary is our English description.

Install A-Share Corporate Event Analysis

skills CLI
$ npx skills add HKUDS/Vibe-Trading --skill corporate-events -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HKUDS/Vibe-Trading corporate-events --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent/src/skills/corporate-events .claude/skills/corporate-events && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
corporate-events
GitHub stars
35k
Token cost
~1.1k tokens
SKILL.md length
102 words
Files
1
Skills in repo
89
Repo updated
First seen
Licence
MIT

At a glance

Builds event-driven analysis for A-share companies: merger arbitrage spreads, shareholder buying and selling signals, equity incentives, placements and ST or delisting warnings; Chinese text.

  • Works in 3 steps: 定增/配股事件 → ST/退市预警 → 事件日历与交易时间窗
  • Calculating the spread on an A-share merger or takeover bid
  • SKILL.md covers 概述, 核心概念, 分析框架 and 输出格式, plus 2 more sections
  • Calls pip

What it does

Event-driven strategies rest on the idea that corporate announcements carry new information the market digests slowly, leaving systematic excess returns around the event. This skill covers merger and restructuring arbitrage in A-shares, including how to compute the spread and its annualized return, the conditions for spin-offs, signals from major shareholder and executive buying or selling, and how to read equity incentive terms. The instructions are written in Chinese.

It also covers private placements, rights issues and exchangeable bonds, the ST and delisting warning rules with an avoidance strategy, and an event calendar of trading windows before and after an announcement. A report layout is given, and a notes section warns about slow third-party data, insider trading risk around announcements, overlapping events, the effect of the registration system, tushare data lags and capping a single event strategy at 10% of the position. The code needs pandas and numpy.

When your agent uses it

  • Calculating the spread on an A-share merger or takeover bid
  • Reading the signal in a major shareholder's buying or selling plan
  • Evaluating a private placement or rights issue
  • Screening a portfolio for ST and delisting risk

Example prompts

  • “Work out the merger arbitrage spread and annualized return for a takeover bid that should close in six months.”
  • “Analyze 000001.SZ after its controlling shareholder announced a buying plan.”
  • “Which of my holdings risk ST or delisting under the 2024 rules?”
  • “Interpret the exercise price and vesting conditions of this equity incentive plan.”

Requirements

  • Python with pandas and numpy

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. 定增/配股事件
  2. ST/退市预警
  3. 事件日历与交易时间窗

What it can do on your machine

Read from SKILL.md and the folder at commit 8e43007. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

A-Share Corporate Event Analysis loads about 1.1k tokens when it runs. Until then it costs about 17 tokens; SKILL.md has 102 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~17
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HKUDS/Vibe-Trading at commit 8e43007, republished under its MIT licence (© HKUDS). 102 words, ~1,058 tokens.

Download SKILL.mdSave it as .claude/skills/corporate-events/SKILL.md (or your agent's skills folder).
name
corporate-events
description
公司事件驱动分析:并购套利价差计算、大股东增减持信号、股权激励解读、定增配股影响评估、A股ST/退市预警
category
flow

公司事件驱动分析

概述

通过公司层面的重大事件(并购、增减持、股权激励、再融资等)构建事件驱动交易策略。核心逻辑:事件公告包含增量信息,市场消化需要时间,事件前后存在系统性超额收益。

适用场景:

  • 并购重组套利(A股借壳/资产注入/吸收合并)
  • 大股东/高管增减持的信号提取
  • 股权激励的行权价与解锁条件分析
  • 定增/配股/可交债的折价套利
  • ST/*ST/退市预警的规避与投机

核心概念

并购套利(Merger Arbitrage)

A股并购特色:

类型频率超额收益风险
借壳上市低(注册制后减少)高(30-100%)极高(审批失败)
资产注入中中(10-30%)高(估值/审批)
吸收合并低低(5-15%)低(已达协议)
要约收购低低(3-8%)最低

价差计算:

并购套利价差 = (要约价 - 当前价) / 当前价
年化收益 = 价差 / 预计完成时间(年)

示例:
  A公司要约收购B公司, 要约价25元, B当前股价23.5元
  价差 = (25 - 23.5) / 23.5 = 6.38%
  预计3个月完成, 年化 = 6.38% × 4 = 25.5%

风险评估:
  审批确定性: 已获商务部/证监会预审 → 高确定性
  对价方式: 现金 > 股票+现金 > 纯股票 (确定性递减)
  交易条件: 是否有MAC条款(重大不利变化)

A股并购套利要点:

  • 停牌制度改革后,复牌首日涨跌幅受限(创业板/科创板20%)
  • 关注"筹划重大资产重组"公告 → 停牌 → 复牌的节奏
  • 审批链:董事会 → 股东大会 → 证监会(每一步都是风险点)
分拆/Spin-off
A股分拆上市条件:
  - 上市满3年
  - 子公司净利润 ≥ 母公司净利润10%
  - 子公司净资产 ≤ 母公司净资产30%

交易策略:
  预案公告 → 买入母公司(分拆预期提升估值)
  子公司上市 → 择机卖出母公司(利好兑现)

实例:
  2023年某科技公司分拆子公司至科创板
  预案公告后母公司20日涨幅18%
  子公司上市首日后母公司回调8%
大股东增减持信号

增持信号(看多):

信号强度条件超额收益(20日)
强控股股东增持 > 总股本1%+8~12%
中高管集体增持(≥3人)+5~8%
弱单一董事/监事增持+2~4%

减持信号(看空):

信号强度条件超额收益(20日)
强控股股东计划减持 > 总股本2%-5~10%
中解禁后首次减持-3~6%
弱高管离职减持-2~4%
关键窗口期:
  - 公告前6个月: 内幕交易高发区,异常放量可能是先知资金
  - 公告后3日: 信息冲击最大
  - 公告后20日: 超额收益基本消化

过滤规则:
  排除: 被动减持(质押平仓)、大宗交易折价减持(可能是换手不是看空)
  保留: 竞价减持(真正看空)、增持后短期再减持(反向信号)
股权激励解读
关键要素:
  1. 行权价/授予价: 相对当前股价的折价率
     折价 > 50% → 激励力度大但可能摊薄严重
     折价 < 20% → 管理层对股价有信心

  2. 解锁条件: 业绩目标是否有挑战性
     例: "未来3年净利润复合增长率≥20%"
     vs 行业平均增长率10% → 条件较高 → 正面信号

  3. 激励对象: 核心技术人员占比
     核心技术人员 > 50% → 绑定核心人才 → 正面
     纯管理层 → 可能是利益输送

  4. 股份来源:
     定向增发 → 摊薄每股收益
     回购 → 不摊薄,更正面

  5. 等待期/解锁期:
     等待期1年+解锁期3年 → 标准方案
     等待期短+解锁条件低 → 利益输送嫌疑

A股实证:
  激励方案公告后60日平均超额收益: +6.2%
  解锁条件超预期的: +10.5%
  行权价接近当前价的: +8.3%
  首次推出激励 > 再次推出: 超额收益更高

分析框架

1. 定增/配股事件

定增(定向增发):

事件时间线:
  预案公告 → 股东大会 → 证监会审批 → 发行 → 解禁

交易节点:
  1. 预案公告日: 关注折价率和募资用途
     折价率 = (当前价 - 发行底价) / 当前价
     折价率 > 20% → 利好(机构愿意折价买入 = 看好)

  2. 发行前: 定增对象有动力维护股价(锁定价格)
     公告后到发行前通常有正超额收益

  3. 解禁日: 定增股份解禁 → 卖压
     解禁前20日平均跌幅3-5%
     解禁后20日继续承压

募资用途评分:
  并购优质资产: +3分
  扩产/新项目: +2分
  补充流动资金: 0分 (中性偏负)
  偿还债务: -1分
  大股东认购比例 > 50%: +2分 (利益绑定)

可交债(EB):

可交换债券 = 大股东以持有股份为担保发行的债券
  换股价 = 发行时约定的换股价格
  当股价 > 换股价 × 130% → 投资者倾向换股 → 相当于大股东减持
  当股价 < 换股价 × 70%  → 投资者持有债券 → 大股东用低利率融资

交易信号:
  发行可交债 = 大股东可能计划减持,但比直接减持更温和
  临近换股期+股价接近换股价 → 关注大股东是否有意让股价突破换股价
2. ST/退市预警

ST标记规则(2024新规):

*ST (退市风险警示):
  - 最近一年净利润为负 + 营收 < 3亿(主板)/ 1亿(创业板)
  - 审计意见: 无法表示/否定
  - 财务造假

ST (其他风险警示):
  - 资金占用
  - 违规担保
  - 内控审计否定意见

退市条件:
  - 连续20个交易日收盘市值 < 3亿(主板)/ 5亿(创业板/科创板)
  - 连续20个交易日股价 < 1元
  - 财务类: *ST后下一年仍不达标

交易策略:

规避策略(推荐):
  - 持仓中排除所有ST/*ST
  - 排除 "最近一季度亏损 + 营收下滑 > 30%" 的潜在ST股
  - 排除审计机构出具保留意见的

投机策略(高风险,仅供研究):
  摘帽概念: *ST公司业绩扭亏 → 申请摘帽 → 涨停潮
  条件筛选:
    - 最近一季度盈利(扭亏拐点)
    - 有实质性资产重组/债务重组方案
    - 市值 > 10亿(远离面值退市线)
  风控: 仓位 < 5%, 止损 -15%
3. 事件日历与交易时间窗
T-30 至 T-1(事前窗口):
  增持/回购预案 → 逐步建仓
  并购预案 → 评估确定性后建仓

T(公告日):
  利好事件 → 集合竞价追入(注意涨停封单量)
  利空事件 → 开盘前挂单卖出

T+1 至 T+20(事后窗口):
  信息逐步消化,超额收益递减
  大事件(并购/重组): 消化期可达60天
  小事件(增持/回购): 20天基本消化

T+N(长期效应):
  股权激励解锁期前后: 管理层有动力维护股价
  定增解禁日: 确定性卖压

输出格式

事件驱动分析报告:

=== 事件概况 ===
标的: 000001.SZ 平安银行
事件: 控股股东增持计划公告
日期: 2026-03-25
增持规模: 10-20亿元 (占总股本0.8%-1.6%)

=== 信号评估 ===
信号强度: 强 (控股股东+金额大)
历史参考: 同类事件20日平均超额+8.2%
确定性: 高 (已公告增持计划, 6个月窗口期)

=== 策略建议 ===
操作: 公告次日开盘建仓
仓位: 5-8% (单事件上限)
持有期: 20-30个交易日
止损: -5% (低于公告日收盘价5%)
止盈: +12% 或 持有期满

=== 风险提示 ===
- 市场系统性下跌可能抵消事件效应
- 增持进度不及预期 → 关注月度增持公告
- 银行板块整体估值压制 → 超额收益可能偏低

注意事项

  1. 信息时效性:A股公告在交易所官网/巨潮资讯网首发,第三方平台有延迟,套利窗口可能已关闭
  2. 内幕交易风险:事件公告前的异常量价可能是内幕交易,跟随介入需谨慎(可能被监管调查)
  3. 事件聚集效应:同一标的多个事件叠加时信号增强(增持+回购+激励 = 强信号),但需排除"组合拳护盘"
  4. 注册制影响:借壳上市价值下降,传统壳资源套利空间大幅收缩
  5. 量化可获取性:tushare 提供增减持/股权激励/定增数据接口,但实时性不足(T+1或更慢)
  6. 仓位控制:单一事件驱动策略仓位不超过10%,事件失败(如并购被否)可能导致20%+跌幅

依赖

bash
pip install pandas numpy

© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in agent/src/skills/corporate-events of HKUDS/Vibe-Trading.

Open the folder on GitHubat commit 8e43007

Compare with similar skills

A-Share Corporate Event Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

A-Share Corporate Event Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
A-Share Corporate Event Analysis this skillHKUDS/Vibe-Trading35k—~1.1kAutomated safety check: PassMIT
Tushare Datazillionare/zillionare3222 repos~2.3kAutomated safety check: PassNone
Stock Deep Analysis Workflowwbh604/UZI-Skill7.1k—~9.1kAutomated safety check: NotesMIT
Supply Chain Bottleneck Hunterxbtlin/ai-berkshire17k—~2.6kAutomated safety check: PassMIT
A-Share Daily Reviewqusong0627/QuantMind1.7k—~1.9kAutomated safety check: PassAGPL-3.0
Futu OpenAPI Market and Trading Assistantqusong0627/QuantMind1.7k—~3.3kAutomated safety check: NotesAGPL-3.0

Similar skills

  • Tushare Data

    zillionare/zillionare

    面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求,转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。

    322 GitHub starsUsed in 2 repos~2.3k tokens
    Business, Finance & HRAuto-check passed
  • Runs a staged deep analysis of a single stock on China A-share, Hong Kong and US markets, ending in an HTML report with valuation models and investor-panel scores.

    7.1k GitHub stars~9.1k tokensUpdated 1 mo ago
    Business, Finance & HRAuto-check: notes
  • Scans a long-running industry trend for supply chain chokepoints, aiming to find second- and third-layer suppliers that the market has not yet priced in.

    17k GitHub stars~2.6k tokensUpdated 2 days ago
    Business, Finance & HRAuto-check passed
  • A-Share Daily Review

    qusong0627/QuantMind

    Produces a post-market review report for the China A-share market from local QuantDB data, news sentiment and model signals, ending in a next-day direction call.

    1.7k GitHub stars~1.9k tokensUpdated today
    Business, Finance & HRAuto-check passed
  • Queries Futu quotes, options, fundamentals and accounts and places orders through the Futu OpenAPI Python SDK, defaulting to simulated trading.

    1.7k GitHub stars~3.3k tokensUpdated today
    Business, Finance & HRAuto-check: notes
  • US Market Data Toolkit

    Geeksfino/finskills

    Free Python scripts that fetch US stock data, SEC filings, insider trades and macro indicators, and run financial score calculators and portfolio analytics.

    282 GitHub stars~1.2k tokensUpdated 7 mo ago
    Business, Finance & HRAuto-check passed

More from HKUDS/Vibe-Trading

All 89 skills in this repo
  • Eastmoney Market Data

    HKUDS/Vibe-Trading

    Index of Eastmoney's free, no-token market data interfaces for China A-shares and Hong Kong stocks: fund flows, dragon-tiger lists, margin trading, reports and news.

    35k GitHub stars~1k tokensUpdated today
    Auto-check passed
  • OKX Market Data

    HKUDS/Vibe-Trading

    Retrieves public OKX cryptocurrency market data such as spot prices, candlesticks, funding rates and open interest through the OKX V5 REST API, with no authentication.

    35k GitHub stars~1.3k tokensUpdated today
    Auto-check passed
  • SEC EDGAR Filings Fetcher

    HKUDS/Vibe-Trading

    Fetches U.S. SEC EDGAR data: resolves tickers to CIK numbers, lists recent 10-K, 10-Q and 8-K filings with document URLs, and pulls XBRL financial series.

    35k GitHub stars~1.4k tokensUpdated today
    Auto-check passed
  • A-Share ST Risk Screener

    HKUDS/Vibe-Trading

    Predicts whether a mainland China A-share company risks an ST or *ST warning after its next annual report, using financial thresholds and Sina penalty records.

    35k GitHub stars~4.9k tokensUpdated today
    Auto-check passed
  • Breaks a structural trend such as AI infrastructure into its physical supply chain and ranks lesser-known listed companies sitting on each bottleneck.

    35k GitHub stars~2.7k tokensUpdated today
    Auto-check passed
  • Plans and drafts an eight-part, roughly 120k-word investigative series on one company, built around a strict fact-check pass rather than fast drafting.

    35k GitHub stars~2.4k tokensUpdated today
    Auto-check passed

Questions about A-Share Corporate Event Analysis

What does A-Share Corporate Event Analysis do?

Builds event-driven analysis for A-share companies: merger arbitrage spreads, shareholder buying and selling signals, equity incentives, placements and ST or delisting warnings; Chinese text. Event-driven strategies rest on the idea that corporate announcements carry new information the market digests slowly, leaving systematic excess returns around the event. This skill covers merger and restructuring arbitrage in A-shares, including how to compute the spread and its annualized return, the conditions for spin-offs, signals from major shareholder and executive buying or selling, and how to read equity incentive terms.

When should I use A-Share Corporate Event Analysis?

A-Share Corporate Event Analysis fits situations like: calculating the spread on an A-share merger or takeover bid; reading the signal in a major shareholder's buying or selling plan; evaluating a private placement or rights issue; screening a portfolio for ST and delisting risk.

How do I install A-Share Corporate Event Analysis in Claude Code?

Run `npx skills add HKUDS/Vibe-Trading --skill corporate-events -a claude-code`. Or copy the skill folder (agent/src/skills/corporate-events in HKUDS/Vibe-Trading) into .claude/skills/corporate-events in your project. Claude Code loads it when a task matches its description.

How do I install A-Share Corporate Event Analysis in Codex?

Run `npx skills add HKUDS/Vibe-Trading --skill corporate-events -a codex`. Or copy the skill folder (agent/src/skills/corporate-events in HKUDS/Vibe-Trading) into .agents/skills/corporate-events in your project. Codex loads it when a task matches its description.

Can I use A-Share Corporate Event Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/Vibe-Trading --skill corporate-events -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/corporate-events, .gemini/skills/corporate-events, .github/skills/corporate-events and .opencode/skills/corporate-events in your project.

What does A-Share Corporate Event Analysis need to run?

Going by SKILL.md and its folder, A-Share Corporate Event Analysis needs the command-line tools its instructions call (pip). Our summary lists: Python with pandas and numpy.

Does A-Share Corporate Event Analysis access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is A-Share Corporate Event Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does A-Share Corporate Event Analysis use?

A-Share Corporate Event Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does A-Share Corporate Event Analysis use?

About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to A-Share Corporate Event Analysis?

Skills that share tags, products or a category with A-Share Corporate Event Analysis: Tushare Data (zillionare/zillionare, 322 stars), Stock Deep Analysis Workflow (wbh604/UZI-Skill, 7.1k stars), Supply Chain Bottleneck Hunter (xbtlin/ai-berkshire, 17k stars) and A-Share Daily Review (qusong0627/QuantMind, 1.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains A-Share Corporate Event Analysis?

HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 35,163 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 10, 2026.

Source: HKUDS/Vibe-Trading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.