Original paper

SSRN-id3036761.pdf1129.2KB

Abstract

Recent evidence shows that investor sentiment is a contrarian predictor of stock returns with speculative stocks earning lower (higher) future returns than safe stocks following high (low) sentiment states. We extend this argument by conditioning expected stock returns on sentiment dynamics and show that the mispricing of speculative and safe stocks worsens with sentiment continuations but is corrected with sentiment transitions, consistent with the view that the mispricing of these stocks is sentiment-driven. We show that the unconditional contrarian return predictability of sentiment, at least in the short-run, is due to the returns of stocks in sentiment transitions. Results show that ex post, sentiment is a momentum predictor if subsequent sentiment continues; and a contrarian predictor if subsequent sentiment transitions. We show that the MAX effect can either be positive or negative contingent on sentiment dynamics. The absence of a negative MAX effect following Low sentiment states suggested by prior studies is due to the completely offsetting negative MAX effect when sentiment continues in a Low state and the positive MAX effect when sentiment transitions from a High to a Low state.

Keywords: sentiment dynamics, cross-section, arbitrage, asset pricing, maximum returns

Trading rules

Investment scope: Stocks from NYSE, AMEX, and NASDAQ priced over $5 and holding a capitalization higher than the NYSE's 50th percentile.
Indicator for skewness: Maximum daily return in the past month.
Monthly sorting routine:

Organize stocks into five groups depending on projected skewness (highest daily yield).
Further categorize stocks into five groups based on the accumulated returns over the past year.

Momentum specification: 12-month formation period, 1-month holding period, skip 1 month.
Monthly portfolio setup:

Long position: Most negatively skewed winners
Short position: Most positively skewed losers

Portfolio management: Equally weighted, rebalanced monthly

Python code

Backtrader

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

import backtrader as bt
import pandas as pd
import numpy as np

class SkewMomentumStrategy(bt.Strategy):
    params = (
        ('formation_period', 12),
        ('holding_period', 1),
        ('skip_period', 1),
        ('min_price', 5),
        ('capitalization_percentile', 0.5),
    )

    def __init__(self):
        self.inds = {}
        for d in self.datas:
            self.inds[d] = {}
            self.inds[d]['max_return'] = bt.indicators.Highest(d.close(-1) / d.close(-2), period=21)
            self.inds[d]['momentum'] = d.close / d.close(-self.p.formation_period) - 1

    def prenext(self):
        self.next()

    def next(self):
        if self._count % (self.p.holding_period + self.p.skip_period) != 0:
            return

        stocks = self.datas
        stocks = [d for d in stocks if d.close[0] > self.p.min_price]
        market_caps = np.array([d.market_cap[0] for d in stocks])
        threshold = np.percentile(market_caps, self.p.capitalization_percentile * 100)
        stocks = [d for d, mc in zip(stocks, market_caps) if mc >= threshold]

        skews = np.array([self.inds[d]['max_return'][0] for d in stocks])
        momentum = np.array([self.inds[d]['momentum'][0] for d in stocks])
        skew_quintiles = pd.qcut(skews, 5, labels=False)
        momentum_quintiles = pd.qcut(momentum, 5, labels=False)

        long_candidates = [d for d, sq, mq in zip(stocks, skew_quintiles, momentum_quintiles) if sq == 0 and mq == 4]
        short_candidates = [d for d, sq, mq in zip(stocks, skew_quintiles, momentum_quintiles) if sq == 4 and mq == 0]

        long_weight = 1.0 / len(long_candidates) if long_candidates else 0
        short_weight = -1.0 / len(short_candidates) if short_candidates else 0

        for d in long_candidates:
            self.order_target_percent(d, target=long_weight)

        for d in short_candidates:
            self.order_target_percent(d, target=short_weight)

        for d in set(self.datas) - set(long_candidates + short_candidates):
            self.order_target_percent(d, target=0)

This code defines a SkewMomentumStrategy class that implements the trading rules. The strategy calculates skewness proxies and past momentum for each stock, sorts them into quintiles, and constructs a long-short portfolio according to the specified criteria. The positions are equally weighted and rebalanced monthly.

Investor Sentiment Dynamics, the Cross-Section of Stock Returns and the MAX Effect

Original paper

Abstract

Trading rules

Python code

Backtrader