equities · Global · ETFs · Monthly Free

Overreaction effect: evidence from an emerging market (Shanghai stock market)

Academic paper

AuthorsKrishna Reddy; Muhammad Tahir ul Qamar; Nawazish Mirza; Fangwei Shi

Institute

COMSATS University Islamabad
Excelia Business School
University of Waikato

Paper linkhttps://doi.org/10.1108/ijmf-01-2019-0033

Teaser

Rank the book by trailing return and hold the top-N names equal-weight. Universe: EWZ, FXI, EWT, EWY, EIDO, THD, EPHE, ECH, EPOL, EZA, ARGT, TUR. Parameters: lookback=21; top_n=3; rebalance=monthly; invert=True. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage.

Strategy in a nutshell

Purpose The purpose of the study is to examine overreaction effect in the Chinese stock market after the global financial crisis (GFC) of 2007 for all the stocks listed in Shanghai Stock Exchange (SSE) Composite 50 index. Design/methodology/approach To capture overreaction effect in the stock listed at SSE 50 Index, a time series analysis of average cumulative abnormal return within a unified framework is applied for the period of January 2009 to December 2015. From these loser and winner portfolios, contrarian strategy is applied to build arbitrage portfolio, which is the difference of mean reversions between loser and winner portfolios. The portfolio construction is based on a 12-month formation period and 6-month testing period for intermediate-term analysis and. for short-term analysis

Economic rationale

Assets with stronger recent relative performance tend to continue outperforming over intermediate horizons; rotating into leaders harvests that premium. Related evidence from “Overreaction effect: evidence from an emerging market (Shanghai stock market)”: Purpose The purpose of the study is to examine overreaction effect in the Chinese stock market after the global financial crisis (GFC) of 2007 for all the stocks listed in Shanghai Stock Exchange (SSE) Composite 50 index. Design/methodology/approach To capture overreaction effect in the stock listed at SSE 50 Index, a time series analysis of average cumulative abnormal return within a unified framework is applied for the period of January 2009 to December 2015. From these loser and winner portfo

Entry & exit

Entry

On each month, rank EWZ, FXI, EWT, EWY, EIDO, THD, EPHE, ECH, EPOL, EZA (+2 more) by 21-day return and invert the sort (reversal). Equal-weight the weakest 3 name(s), expecting a bounce.

Exit

At the next rebalance, sell names that are no longer in the bottom 3 and rotate into the new laggards.

Market data required

Needs at least 21 adjusted daily closes on EWZ, FXI, EWT, EWY, EIDO, THD (+6 more) before the first signal is ready. ETF columns print on exchange sessions only. The lab loads Yahoo Finance via backtest.data.load_daily_prices.

Symbols

EWZ, FXI, EWT, EWY, EIDO, THD, EPHE, ECH, EPOL, EZA, ARGT, TUR

Frequency

Monthly · Daily adjusted close; signals rebalance on the month change (last available session)

History needed

≥ 21 trading days per symbol (about 1 months), plus a few extra bars so the first signal can fire.

Fields

Adjusted close (Yahoo auto_adjust)
Session date index

Credentials

None for paper and the onsite lab
Broker or exchange API keys only for live orders

APIs & providers

Quant Buffet Data API

The lab calls load_daily_prices for the ASSETS panel of adjusted closes, then caches CSV so backtests replay the same series.

Provider: backtest.data.load_daily_prices
Auth: None for paper / sandbox backtests
Endpoints: load_daily_prices(ASSETS, start="2000-01-01")

API / docs → /en/docs/data-api

Yahoo Finance via yfinance

US equities and ETFs, adjusted OHLCV, free for research (rate limits apply).

Provider: yfinance → Yahoo Finance
Auth: None for public history
Endpoints: Ticker.history / yf.download(auto_adjust=True)

API / docs → github.com/ranaroussi/yfinance

Onsite backtest IDE

Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for Overreaction effect: evidence from an emerging market (Shanghai stock market) in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →

IDE · 42 lines

"""
Quant Buffet interactive lab — overreaction-effect-evidence-from-an-emerging-market-shanghai-stock-market
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS = ['EWZ', 'FXI', 'EWT', 'EWY', 'EIDO', 'THD', 'EPHE', 'ECH', 'EPOL', 'EZA', 'ARGT', 'TUR']
LOOKBACK = 21
TOP_N = 3
INVERT = True  # True = short-term reversal (rank ascending)

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    rets = prices[cols].pct_change(LOOKBACK)
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        row = rets.loc[dt]
        if row.isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        ranked = row.dropna().sort_values(ascending=INVERT)
        picks = list(ranked.head(TOP_N).index)
        if not INVERT:
            picks = [s for s in picks if ranked[s] > 0] or picks[:1]
        w = {} if not picks else {s: 1.0 / len(picks) for s in picks}
        engine.set_target_weights(dt, w)

    ready = rets.dropna(how="all").index.min() if rets.notna().any().any() else None
    return on_day, ready

"""
Quant Buffet interactive lab — overreaction-effect-evidence-from-an-emerging-market-shanghai-stock-market
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS = ['EWZ', 'FXI', 'EWT', 'EWY', 'EIDO', 'THD', 'EPHE', 'ECH', 'EPOL', 'EZA', 'ARGT', 'TUR']
LOOKBACK = 21
TOP_N = 3
INVERT = True  # True = short-term reversal (rank ascending)

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    rets = prices[cols].pct_change(LOOKBACK)
    state = {"last": None}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        row = rets.loc[dt]
        if row.isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        ranked = row.dropna().sort_values(ascending=INVERT)
        picks = list(ranked.head(TOP_N).index)
        if not INVERT:
            picks = [s for s in picks if ranked[s] > 0] or picks[:1]
        w = {} if not picks else {s: 1.0 / len(picks) for s in picks}
        engine.set_target_weights(dt, w)

ready = rets.dropna(how="all").index.min() if rets.notna().any().any() else None
    return on_day, ready

Quant Buffet syntax cheat sheet (copy / insert)

Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.

Required imports

Only these libraries are allowed in the sandbox.

from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS list (whitelisted ETFs)

Module-level list. Tickers must be in the Quant Buffet whitelist.

ASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]

make_on_day contract

Must return (on_day, ready). on_day calls engine.set_target_weights.

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    sma = prices[cols].rolling(200, min_periods=200).mean()
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        if sma.loc[dt].isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        long = [
            s for s in cols
            if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
            and prices.at[dt, s] > sma.at[dt, s]
        ]
        weights = {} if not long else {s: 1.0 / len(long) for s in long}
        engine.set_target_weights(dt, weights)

    ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
    return on_day, ready

Set target weights

Weights should sum to about 1.0. Empty dict = 100% cash.

engine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})

Live backtest performance

CAGR

9.15%

Sharpe

0.45

Max DD

-64.99%

Vol

28.33%

Sortino

0.71

Beta

1.07

Stored preview only. Run backtest to simulate every daily session from Yahoo Finance adjusted closes.

Run the backtest to load orders, then mark them on the equity chart.

Equity curve (indexed = 100)

Accent = strategy · dashed grey = buy-and-hold benchmark. Hover or tap any point for its date and values.

Drawdown

Metrics bar chart

Monthly returns

Order records

Run the backtest to load every order fill alongside performance.

Export to your platform

Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.

Target platform

Run in: QuantConnect Cloud or LEAN CLI · QCAlgorithm with Equity securities and monthly rebalance.

Detected pattern: Momentum rotationAssets: EWZ, FXI, EWT, EWY, EIDO, THD, EPHE, ECH, EPOL, EZA, ARGT, TUR

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Overreaction effect: evidence from an emerging market (Shanghai stock market)
# Detected pattern: Momentum rotation
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.

from AlgorithmImports import *


class QuantBuffetExport(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2010, 1, 1)
        self.SetCash(100000)
        tickers = ["EWZ", "FXI", "EWT", "EWY", "EIDO", "THD", "EPHE", "ECH", "EPOL", "EZA", "ARGT", "TUR"]
        self.symbols = []
        for t in tickers:
            if "-" in t:  # crypto proxy e.g. BTC-USD
                self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
            else:
                self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
        self.Schedule.On(
            self.DateRules.MonthStart(self.symbols[0]),
            self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
            self.Rebalance,
        )
        # Logic: Hold top 3 by 21-day return; monthly.

    def Rebalance(self):
        scores = {}
        for symbol in self.symbols:
            hist = self.History(symbol, 21 + 5, Resolution.Daily)
            if hist.empty: continue
            close = hist["close"]
            if hasattr(close, "unstack"):
                close = close.unstack(level=0).iloc[:, 0]
            if len(close) < 21 + 1: continue
            scores[symbol] = float(close.iloc[-1] / close.iloc[-21 - 1] - 1)
        ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:3]
        for symbol in self.symbols:
            self.SetHoldings(symbol, 0)
        if ranked:
            w = 1.0 / len(ranked)
            for symbol, _ in ranked:
                self.SetHoldings(symbol, w)

Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.