“The strategy identifies Milan Stock Exchange stocks with abnormal trading volume and 1% daily gains, buying at close, holding one day, and rebalancing daily for short-term momentum gains.”
ASSET CLASS: stocks | REGION: Europe | FREQUENCY:
Daily | MARKET: equities | KEYWORD: Abnormal, Volume, Effect, Stock, Market
I. STRATEGY IN A NUTSHELL
The strategy analyzes stocks on the Milan Stock Exchange, potentially applicable to other markets. Daily, the investor identifies stocks with “abnormal” trading volume, defined as volumes exceeding 2.33 standard deviations above the 66-day average. Eligible stocks must show no abnormal volume in the prior 30 days and close with at least a 1% gain on the event day. These stocks are purchased at the market close and held for one day. Positions are equally weighted, and the portfolio is rebalanced daily. This approach leverages short-term trading volume spikes and price momentum for potential gains.
II. ECONOMIC RATIONALE
Academic research attributes this anomaly to insider trading, suggesting that uneven information distribution among market participants allows trading volumes to provide valuable insights. Large volume changes, particularly in the absence of news, may reflect non-public information, signaling potential future excess returns.
III. SOURCE PAPER
THE INFORMATION CONTENT OF ABNORMAL TRADING VOLUME [Click to Open PDF]
- Bajo, University of Bologna – Department of Management
<Abstract>
This paper empirically investigates how abnormal trading volume reveals new information to market participants. Trading volume is generally regarded as a good proxy for information flow and theory argues that it enhances the information set of investors. However, as yet, no research has related the presence of abnormal trading volume to firm characteristics, such as ownership and governance structure, which also has a theoretical link to information quality. I find strong excess returns around extreme trading levels, which is only moderately attributable to information disclosure. Moreover, these returns are not caused by liquidity fluctuations since prices do not reverse over the following period. In contrast, and in violation of the semi-strong form of market efficiency, there is evidence of price momentum, suggesting that traders can implement successful portfolio strategies based on the observation of current volumes. Consistent with the hypotheses presented in this study, the information content of abnormal trading volume is related to ownership characteristics, such as the level of control and the family-firm status.
IV. BACKTEST PERFORMANCE
| Annualised Return | 33.91% |
| Volatility | N/A |
| Beta | 0.74 |
| Sharpe Ratio | N/A |
| Sortino Ratio | 0.394 |
| Maximum Drawdown | N/A |
| Win Rate | 49% |
V. FULL PYTHON CODE
import numpy as np
from AlgorithmImports import *
from typing import List, Dict
from pandas.core.frame import DataFrame
class AbnormalVolumeEffectStockMarket(QCAlgorithm):
def Initialize(self) -> None:
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.data:Dict[Symbol, SymbolData] = {}
self.period:int = 66
self.leverage:int = 5
self.std_treshold:float = 2.33
self.performance_treshold:float = 0.01
self.long:List[Symbol] = []
self.selection_flag:bool = False
self.last_selection:List[Fundamental] = []
self.fundamental_sorting_key = lambda x: x.MarketCap
self.fundamental_count:int = 1000
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.settings.daily_precise_end_time = False
self.Schedule.On(self.DateRules.MonthStart(market), self.TimeRules.AfterMarketOpen(market), self.Selection)
def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(self.leverage)
def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
# Update the rolling window every day.
for stock in fundamental:
symbol:Symbol = stock.Symbol
if symbol in self.data:
# Store daily price and volume.
self.data[symbol].update(stock.AdjustedPrice, stock.Volume)
# return already selected universe during the month
if self.selection_flag:
self.selection_flag = False
selected:List[Fundamental] = [
x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.MarketCap != 0
]
if len(selected) > self.fundamental_count:
selected = sorted(selected, key = self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]
self.last_selection = selected
# Warmup price rolling windows.
for stock in self.last_selection:
symbol:Symbol = stock.Symbol
if symbol in self.data:
continue
self.data[symbol] = SymbolData(symbol, self.period, -1)
history:DataFrame = self.History(symbol, self.period, Resolution.Daily)
if history.empty:
self.Log(f"Not enough data for {symbol} yet")
continue
if 'close' in history and 'volume' in history:
closes:Series = history.loc[symbol]['close']
volumes:Series = history.loc[symbol]['volume']
for (time1, close), (time2, volume) in zip(closes.items(), volumes.items()):
self.data[symbol].update(close, volume)
# fundamental returned ready data
for stock in self.last_selection:
symbol:Symbol = stock.Symbol
if symbol not in self.data:
continue
if not self.data[symbol].is_ready():
continue
volumes:List[float] = [x for x in self.data[symbol]._volume]
volume_mean:float = np.mean(volumes)
volume_std:float = np.std(volumes)
volume:float = volumes[0] # Takes todays volume
closes:List[float] = [x for x in self.data[symbol]._price][:2] # First two are newest
todays_return:float = (closes[0] - closes[1]) / closes[1]
if volume > volume_mean + (self.std_treshold * volume_std):
# selects only firms with no abnormal volume over the preceding 30 trading days
if (self.data[symbol]._abnormal_date == -1) or (self.data[symbol]._abnormal_date < (self.Time - timedelta(days=30))):
# if the stocks finished the day with at least a 1% gain
if todays_return >= self.performance_treshold:
self.long.append(symbol)
self.data[symbol]._abnormal_date = self.Time
return self.long
def OnData(self, data: Slice) -> None:
# Trade execution
targets:List[PortfolioTarget] = [PortfolioTarget(symbol, 1. / len(self.long)) for symbol in self.long if symbol in data and data[symbol]]
self.SetHoldings(targets, True)
self.long.clear()
def Selection(self) -> None:
self.selection_flag = True
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))
class SymbolData():
def __init__(self, symbol:Symbol, period:int, abnormal_date:datetime):
self._symbol:Symbol = symbol
self._price:RollingWindow = RollingWindow[float](period)
self._volume:RollingWindow = RollingWindow[float](period)
self._abnormal_date:datetime = abnormal_date
def update(self, price:float, volume:float):
self._price.Add(price)
self._volume.Add(volume)
def is_ready(self) -> bool:
return self._price.IsReady and self._volume.IsReady
