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Strategy Library

Momentum Effect in Stocks in Small Portfolios

Introduction

The main reason for the momentum anomaly is the behavioral biases of the investor like underreaction and confirmation bias. Momentum strategy usually uses portfolios filled by thousands of stocks to compute the momentum factor return. This is not possible for small retail investors with small portfolios. They are constrained compared to big hedge funds and cannot diversify so well. In this tutorial, we'll construct a small portfolio consisting of up to 50 stocks to check the effect of momentum.

Method

The investment universe consists of all US listed companies. Stocks which have no fundamental data are ruled out from the universe. 

def CoarseSelectionFunction(self, coarse):
    if self.yearly_rebalance:
        # drop stocks which have no fundamental data
        self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)]
        return self.filtered_coarse
    else:
        return []

In FineSelectionFunction, stocks with the lowest market capitalization (25% of the universe) are excluded due to low liquidity. The momentum is defined as the stock market return over the previous 12 months. Momentum profits are calculated by ranking companies on the basis of yearly return. The ranking period is one year. 

  def FineSelectionFunction(self, fine):
      if self.yearly_rebalance:
          # Calculate the yearly return and market cap
          for i in fine:
              i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
          top_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)[:int(len(fine)*0.75)]
          has_return = []
          for i in top_market_cap:
              history = self.History([i.Symbol], timedelta(days=365), Resolution.Daily)
              if not history.empty:
                  close = history.loc[str(i.Symbol)]['close']
                  i.returns = (close[0]-close[-1])/close[-1]
                  has_return.append(i)
          sorted_by_return = sorted(has_return, key = lambda x: x.returns)
          self.long = [i.Symbol for i in sorted_by_return[-10:]]
          self.short = [i.Symbol for i in sorted_by_return[:10]]

          return self.long+self.short
      else:
          return []

The investor goes long in the ten stocks with the highest performance and goes short in the ten stocks with the lowest performance. The portfolio is equally weighted and rebalanced yearly. 

  def OnData(self, data):
      if not self.yearly_rebalance: return
      if self.long and self.short:
          stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
          # liquidate stocks not in the trading list
          for i in stocks_invested:
              if i not in self.long+self.short:
                  self.Liquidate(i)
          for i in self.short:
              self.SetHoldings(i, -0.5/len(self.short))
          for i in self.long:
              self.SetHoldings(i, 0.5/len(self.long))
          self.long = None
          self.short = None
          self.yearly_rebalance = False

Algorithm

Source

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