New strategies:
#333 – Earnings Announcement Seasonality Effect in Equities
Period of rebalancing: daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1972-2013
Indicative performance: 40.73%
Estimated volatility: not stated
Source paper:
Chang, Hartzmark, Solomon, Soltes: Being Surprised by the Unsurprising: Earnings Seasonality and Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2460166
Abstract:
We present evidence consistent with markets failing to properly price information in seasonal earnings patterns. Firms with historically larger earnings in one quarter of the year (“positive seasonality quarters”) have higher returns when those earnings are usually announced. Analysts have more positive forecast errors in positive seasonality quarters, consistent with the returns being driven by mistaken earnings estimates. We show that investors appear to overweight recent lower earnings following positive seasonality quarters, leading to pessimistic forecasts in the subsequent positive seasonality quarter. The returns are not explained by risk-based explanations, firm-specific information, increased volume, or idiosyncratic volatility.
#334 – Volatility-Adjusted Momentum in Corporate Bonds
Period of rebalancing: monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Complex strategy
Bactest period: 1994-2015
Indicative performance: 3.61%
Estimated volatility: 3.12%
Source paper:
Zundert: Momentum in the Cross-Section of Corporate Bond Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2880097
Abstract:
This paper revisits the empirical finding that there is no momentum in the cross-section of investment-grade corporate bonds. Using a comprehensive dataset of investment grade and high yield USD corporate bonds from 1994 to 2015, I find that the large dispersion in volatility between corporate bonds causes standard decile portfolio sorts to be far from an optimal momentum portfolio as suggested by theory. Scaling bonds, i.e. momentum signals and positions, with an ex-ante estimate of volatility remedies this problem and reveals significant momentum effects. The momentum effect cannot be attributed to systematic risk, is robust to the choice of the ex-ante volatility measure, is present across formation and holding period lengths and is not driven by liquidity.
New research papers related to existing strategies:
#68 – Combining Earnings, Revenue and Price Momentum
Huang, Zhang, Zhou: Dual Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2894068
Abstract:
Firms’ fundamentals collectively have strong predictive power on future returns. The top 20% high fundamental firms outperform the bottom 20% by 88 bps per month, yielding a significant fundamental momentum portfolio, which has similar performance to, but little correlation with, the widely analyzed price momentum portfolio. Combining price momentum and fundamental momentum generates a dual momentum, with an average return more than the simple sum of price momentum and fundamental momentum. This dual momentum cannot be spanned by extant risk factor models, nor can it be explained by short-sale impediments and investor sentiment.
#233 – Using Straddles to Trade on Earnings Announcements
Chung, Louis: Earnings Announcements and Option Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2886040
Abstract:
While prior studies find that returns on option straddles are generally negative, we show that returns on straddles purchased prior to earnings announcements are actually positive. The earnings announcement impact is compounded when the pre-portfolio formation volatility is low (high) and the pre-expiration realized volatility is high (low). Apparently, the average option trader underestimates future volatility before forthcoming earnings announcements, particularly after a period of relatively low volatility, and overestimates future volatility after recent earnings announcements, particularly after a period of relatively high volatility. The overestimation of future volatility after recent earnings announcements also increases with the magnitude of the earnings surprise.
Two additional related research papers have been included into existing free strategy reviews during last 2 week:
A short but interesting academic paper about differences in a well-known CBOE PutWrite and BuyWrite Indexes (#63 – Trendfollowing Combined with Volatility Premium):
Israelov: PutWrite versus BuyWrite: Yes, Put-Call Parity Holds Here Too
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2894610
Abstract:
The CBOE PutWrite Index has outperformed the BuyWrite Index by approximately 1.1 percent per year between 1986 and 2015. That is pretty impressive. But troubling. Yes – troubling – because the theory of put-call parity tells us that such outperformance should be almost impossible via a compelling no-arbitrage restriction. This paper explains the mystery of this outperformance, which has implications for portfolio construction.
And an amazing academic paper about multiple equity factor models and about the way how to pick the best one:
Cooper, Maio, Philip: Multifactor Models and the APT: Evidence from a Broad Cross-Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2883765
Abstract:
We seek to describe the broad cross-section of average stock returns. We follow the APT literature and estimate the common factor structure among a large cross-section containing 278 decile portfolios (associated with 28 market anomalies). Our statistical model contains seven common factors (with an economic meaning) and prices well both the original portfolio returns and an efficient combination of these portfolios. This model clearly outperforms the empirical workhorses in the literature when it comes to pricing this broad cross-section. Augmenting the empirical models with new factor-mimicking portfolios, based on APT principles, significantly improves their performance.
