Original paper

SSRN-id3097420.pdf1147.4KB

Abstract

We document that the fundamental strength (FSCORE) of a firm exerts a significant influence on the performance of short-term reversal strategies. Past losers with strong fundamentals significantly outperform past winners with weak fundamentals. Our FSCORE approach is complementary to Da et al. (2014) cash flow news metrics based on analysts’ forecast revisions in that many firms do not have analyst following. Our approach also seems capable of capturing the lagged effects from past fundamental news shocks. After controlling for fundamental strength, we find that investor sentiment plays a more dominant role than do liquidity shocks in explaining return reversal.

Keywords: Short-term return reversal; Fundamental strength; Analyst forecast revision; Slow incorporation of information

Trading rules

Targeted assets: Common shares traded on NYSE, AMEX, and NASDAQ (with a share code of 10 or 11).
Exclude shares priced under $5 at formation period end
FSCORE range: 0-9 points (1 point for each positive signal)

Positive return-on-assets (ROA)
Positive cash flow from operations
Positive change in ROA
Positive difference between net income and accruals
Decrease in long-term debt-to-total-assets ratio
Positive change in current ratio
No new common equity issuance
Positive change in gross margin ratio
Positive change in asset turnover ratio

Construct quarterly FSCORE with latest financial statement information
Match monthly reversal data with most recent quarterly FSCORE
Firm classification:

Fundamentally strong (FSCORE 7-9)
Fundamentally middle (FSCORE 4-6)
Fundamentally weak (FSCORE 0-3)

Identify large stocks: Those in the top 40% by market value at the termination of the formation month.
Sort stocks considering their 1-month historical performances and their most recent quarterly FSCORE.
Take long position in past losers with strong fundamentals (7-9) and a short position in past outperformers with weak fundamentals (0-3).
Employ an equal-weighted approach, with monthly adjustments.

Python code

Backtrader

Here is the Backtrader Python code for the given trading rules:

import backtrader as bt

class FScoreStrategy(bt.Strategy):
    def __init__(self):
        self.price = bt.indicators.Close(self.data)
        self.market_cap = self.data.market_cap
        self.roa = self.data.return_on_assets
        self.cfo = self.data.cash_flow_operations
        self.delta_roa = self.roa - self.roa(-1)
        self.ni_accruals = self.data.net_income - self.data.accruals
        self.delta_debt_ratio = self.data.long_term_debt(-1) / self.data.total_assets(-1) - self.data.long_term_debt / self.data.total_assets
        self.delta_current_ratio = self.data.current_ratio - self.data.current_ratio(-1)
        self.new_common_equity = self.data.new_common_equity
        self.delta_gross_margin = self.data.gross_margin - self.data.gross_margin(-1)
        self.delta_asset_turnover = self.data.asset_turnover - self.data.asset_turnover(-1)

    def prenext(self):
        self.next()

    def next(self):
        if self.inds[self.data]['fscore'] is None:
            return

        long_list = []
        short_list = []

        for d in self.getdatanames():
            if self.getposition(d).size:
                self.sell(data=d)

            if self.price[d][0] < 5:
                continue

            fscore = 0
            fscore += self.roa[d][0] > 0
            fscore += self.cfo[d][0] > 0
            fscore += self.delta_roa[d][0] > 0
            fscore += self.ni_accruals[d][0] > 0
            fscore += self.delta_debt_ratio[d][0] < 0
            fscore += self.delta_current_ratio[d][0] > 0
            fscore += self.new_common_equity[d][0] == 0
            fscore += self.delta_gross_margin[d][0] > 0
            fscore += self.delta_asset_turnover[d][0] > 0

            self.inds[d]['fscore'][0] = fscore

            if fscore >= 7 and self.data1_month[d][0] < 0:
                long_list.append(d)
            if fscore <= 3 and self.data1_month[d][0] > 0:
                short_list.append(d)

        long_weight = 1.0 / len(long_list) if long_list else 0
        short_weight = 1.0 / len(short_list) if short_list else 0

        for d in long_list:
            self.buy(data=d, size=long_weight * self.broker.get_cash())

        for d in short_list:
            self.sell(data=d, size=short_weight * self.broker.get_cash())

Note that this code assumes that the required data, such as return on assets, cash flow from operations, and other financial ratios, have been added to the Backtrader data feed. You may need to preprocess the data or modify the code to accommodate your specific data sources.

Fundamental Strength and Short-Term Return Reversal

Original paper

Abstract

Trading rules

Python code

Backtrader