Quantpedia Premium Update – February 23rd

New Strategies:

#1098 – Seasonality Patterns in the Crisis Hedge Portfolios

Period of rebalancing: Monthly
Markets traded: bonds, commodities, currencies
Instruments used for trading: ETFs
Complexity: Simple strategy
Backtest period: 2007-2024
Indicative performance: 5.87%
Estimated volatility: 8.91%

Source paper:

Vojtko, Radovan and Dujava, Cyril: Seasonality Patterns in the Crisis Hedge Portfolios
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5120553
Abstract:
This study investigates the integration of seasonal strategies into crisis hedge portfolios, focusing on their potential to enhance performance during market downturns. Traditional asset allocation methods often struggle in volatile conditions, prompting the exploration of seasonal timing to improve return-to-risk ratios. The research builds on historical findings of market seasonality, highlighting the dual impact of front-running behavior from market participants aiming to exploit these patterns. Utilizing a Black Swan Hedging Model, the study analyzes a cross-hedge portfolio comprising selected ETFs, examining both time-series and cross-sectional seasonality types. Results indicate that while traditional seasonal strategies may under-perform, front-running approaches yield superior risk-adjusted returns. Specifically, findings reveal that front-running seasonal signals outperform static strategies across various metrics, underscoring the effectiveness of incorporating seasonality into crisis hedge portfolios. The research contributes to the literature by bridging the gap between traditional asset allocation and seasonal strategies, offering actionable insights for portfolio managers seeking to enhance resilience in turbulent markets. The implications suggest understanding and leveraging seasonal patterns can significantly bolster downside protection and overall portfolio performance during crises.

#1099 – Margin Debt Levels Predict S&P 500 Returns

Period of rebalancing: Monthly
Markets traded: bonds, equities
Instruments used for trading: bonds, ETFs, futures
Complexity: Moderately Complex strategy
Backtest period: 1994-2014
Indicative performance: 16.74%
Estimated volatility: 16.78%

Source paper:

Deuskar, Prachi and Kumar, Nitin and Poland, Jeramia Allan: Margin Credit and Stock Return Predictability
https://www.stern.nyu.edu/sites/default/files/assets/documents/DKP_Margin_Credit_20160901.pdf
Abstract:
Margin credit, defined as the excess debt capacity of investors buying securities on the margin, is a very strong predictor of aggregate stock returns. It outperforms other forecasting variables proposed in the literature, in-sample as well as out-of-sample. Its out-of-sample R2, 7.45% at the monthly horizon and 35.68% at the annual horizon, is more than twice as large as that of the next best predictor. It produces a Sharpe Ratio of 1.42 over recessions and 0.96 over expansions and overall annualized Certainty Equivalent Return gain of 9.5%, all considerably larger than those for the other predictors. Further, margin credit predicts market crashes and avoids substantial parts of the stock market downturns around 2001 and 2008. Margin credit predicts future returns because it contains information about future discount rates as well as future cash flows.

#1100 – Time-Series Reversal in S&P 500 Using EOM Signal

Period of rebalancing: Monthly
Markets traded: bonds, equities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Simple strategy
Backtest period: 1975-2019
Indicative performance: 4.69%
Estimated volatility: 10.35%

Source paper:

Graziani, Giuliano: Time Series Reversal: An End-of-the-Month Perspective
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4349253
Abstract:
This paper introduces a novel aggregate reversal strategy that exploits monthly calendar effects. Specifically, I show that the end-of-the-month return of the S&P500 negatively correlates with one-month ahead returns. Contrary to the cross-sectional findings, strategies based on the novel aggregate pattern are extremely cost-effective, easy to implement, cyclical, and do not require short-selling. This novel pattern is consistent with pension funds’ liquidity trading to meet pension payment obligations.

#1101 – December Effect in Option Returns

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: options
Complexity: Complex strategy
Backtest period: 1996-2022
Indicative performance: 10.9%
Estimated volatility: 14.78%

Source paper:

Choy, Siu Kai and Wei, Jason and Zhang, Huiping: December Effect in Option Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5121679
Abstract:
This paper uncovers a December effect in option returns: The delta-hedged returns of options on both stocks and the S&P 500 index are substantially lower in December than in other months. Options are overvalued at the beginning of December due to option investors’ failure in anticipating the abnormally low volatility in the second half of December resulting from light trading of stocks during the festive Christmas holiday season. A trading strategy taking a short position in straddles at the beginning of December and closing the position at the month-end can generate a hedged return of 13.09% with a t-value of 6.70, compared with the unconditional sample mean of 0.88%. This paper is the first in the literature to uncover this December effect in option returns.

#1102 – Front-Running Rebalancing Signals

Period of rebalancing: Daily
Markets traded: bonds, equities
Instruments used for trading: CFDs, ETFs, futures
Complexity: Complex strategy
Backtest period: 1997-2023
Indicative performance: 9.92%
Estimated volatility: 9.2%

Source paper:

Harvey, Campbell R. and Mazzoleni, Michele G. and Melone, Alessandro: The Unintended Consequences of Rebalancing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5122748
Abstract:
Institutional investors engage in trillions of dollars of regular portfolio rebalancing, often based on calendar schedules or deviations from allocation targets. We document that such rebalancing has a market impact and generates predictable price patterns. When stocks are overweight, funds sell stocks and buy bonds, leading to a decrease in equity returns of 17 basis points over the next day. Our results are robust to controls for momentum, reversals, and macroeconomic information. Importantly, we estimate that current rebalancing practices cost investors about $ 16 billion annually—or $ 200 per U.S. household. Moreover, the predictability of these trades enables certain market participants to profit by front-running the orders of large institutional funds. While rebalancing remains a fundamental tool for investors, our findings highlight the costs associated with prevailing strategies and emphasize the need for innovative approaches to mitigate these costs.

New research papers related to existing strategies:

#1016 – Intraday Momentum Strategy for S&P500 ETF

Maróy, Ákos: Improvements to Intraday Momentum Strategies Using Parameter Optimization and Different Exit Strategies
https://ssrn.com/abstract=5095349
Abstract:
Building on the results of Zarattini, C., Aziz, A., & Barbon, A. (2024). Beat the market: An effective intraday momentum strategy for S&P500 ETF (SPY), we explore improvements to noise boundary based intraday momentum strategies by investigating different exit strategies and applying parameter optimization to all parameters of the strategies. We show that the returns of the momentum strategy can be significantly improved by such an approach. The best results are achieved with exits based on VWAP, VWAP & Ladder and Ladder exit strategies, with Sharpe ratios over 3.0 and annualized returns of over 50%, which are significant improvements against the baseline strategy.

#31 – Market Seasonality Effect in World Equity Indexes
#41 – Turn of the Month in Equity Indexes
#75 – Federal Open Market Committee Meeting Effect in Stocks
#83 – Pre-Holiday Effect
#102 – Option-Expiration Week Effect
#114 – January Effect in Stocks

Mohamed, Hussein: Time-Based Trading Patterns
https://ssrn.com/abstract=5101935
Abstract:
The research paper investigates the influence of various calendar anomalies on stock market returns across multiple indices, including the S&P 500, S&P 400, S&P 600, Russell 1,000 Growth, and Russell 1,000 Value. The study provides a comprehensive analysis of both individual and interaction effects of several well-known calendar anomalies, challenging the conventional understanding that these anomalies occur in isolation. The calendar effects studied include the Federal Open Market Committee (FOMC) meetings, Options Expiration Dates, the Holiday Effect, Sports Events Effect, Halloween Effect, Turn-of-the-Month Effect, January and September Effects, as well as Friday and Monday Effects. The research applies advanced regression techniques, incorporating an ARMA (1,1) model and an EGARCH (1,1) model with a t-distribution to account for both autocorrelation and volatility clustering in stock returns. The ARMA model, with exogenous variables representing the calendar anomalies and their interactions, captures the linear dependencies in return series. The EGARCH model further analyses the persistence and asymmetry in volatility, revealing how negative market shocks tend to increase volatility more than positive shocks. These models effectively highlight the impact of calendar anomalies on both the returns and volatility of the studied indices. The findings reveal that the Halloween Effect was the most significant anomaly observed, particularly impacting indices such as the S&P 500, S&P 400, S&P 600, and Russell 1,000 Value. These results suggest that this anomaly can be strategically leveraged by investors to optimize returns. While the research highlights the potential for using calendar anomalies as part of a time-based trading strategy, it also notes that the effects may vary across different indices, reflecting the unique characteristics of each market segment. The study contributes to the broader discourse on market efficiency by offering practical insights for investors and suggesting that these calendar-based anomalies may present exploitable opportunities within financial markets.

#620 – Long Term Time-series Momentum in India
#1083 – Adjusted Momentum Strategies in Indian Stocks

Pelluri, Rishikesh: Quantitative Analysis of Price Momentum in Indian Equity Markets
https://ssrn.com/abstract=5116091
Abstract:
This paper explores the effectiveness of a price momentum-based portfolio strategy in the Indian equity market using historical stock data. Momentum investing leverages the tendency of high-performing stocks (winners) to continue outperforming and low-performing stocks (losers) to persist in underperformance. The study constructs winner and loser portfolios, selecting the top quintile of stocks based on cumulative past returns, and evaluates both long-only and short-only strategies. Portfolio performance is assessed using two weighting approaches: equal weighting, assigning uniform weights to all stocks, and exponential weighting, prioritizing stocks with stronger recent performance. The analysis spans various formation periods (look-back windows) and holding periods (investment horizons). Findings highlight that portfolio outcomes are significantly impacted by the choice of weighting scheme, formation period and investment horizons, providing valuable insights for investors aiming to optimize returns in the Indian market.

#54 – Momentum and State of Market (Sentiment) Filters

Zhou, Hanchen: Are Upside and Downside Momentum Symmetrical?
https://ssrn.com/abstract=5033710
Abstract:
Building upon the extensive factor investing corpus, we further analyse the momentum factor to determine whether it exhibits return asymmetry in different market regimes, for further application in dynamic factor and low-net long/short portfolios. We use a market state identification indicator, following the study of Daniel and Moskowitz (2016), which uses the past 24 months’ cumulative return (CR) of the market price. Where the CR is positive, this indicates an “up” market, and when the CR is negative, indicates an “down” market. We utilize the indicator to assess whether the momentum factor performs symmetrically in “up” and “down” market periods to determine whether the factor is subject to upside or downside bias. We use the decile portfolio approach, mirroring the derivation of the momentum factor construction (French, 2024). We gained insights into momentum’s performance during different periods based on the results, leveraging our results to construct an optimal low-net dynamic momentum factor portfolio, with respect to our study findings and with respective to (external) macroeconomic shocks. To test if the portfolio is suitable for traditional investment products, we then apply Markowitz (1952) efficient frontier analysis to assess the diversification benefit of such a low-net dynamic momentum factor portfolio in diversifying a traditional asset allocation. Our results demonstrate that our portfolio is materially additive in achieving superior absolute and risk-adjusted return combinatory portfolios over a traditional allocation.

#528 – Equity Factors and Corporate Bonds

Gong, Zihan: Exploring Global Spillover Effects Between Stocks and Corporate Bonds: A Firm-Level Analysis of Market Efficiency and Information Flow
https://ssrn.com/abstract=5118257
Abstract:
This paper examines the global spillover effects between stocks and corporate bonds issued by the same company. Using twelve momentum strategies, we identify a significant equity-to-bond spillover effect globally, which is particularly notable in the U.S. market and less obvious in the non-U.S. markets, and we find no evidence of a global bond-to-equity spillover effect. With a 1-month formation period and a 1-month holding period, the equity-to-bond strategy yields a long-short corporate bond return of 36 basis points (bps) globally and 35 basis points (bps) in the U.S. market. The long-short returns decrease as the holding period extends. Our research underscores the importance of regional differences and highlights the predictive power of past stock returns on corporate bond returns.

#50 – FED Model

Xu, Xiang: Using CAPE to Estimate Future Stock Returns
https://ssrn.com/abstract=5100886
Abstract:
Recent Wall Street research suggests the prospect of relatively low stock returns over the next decade (e.g., 1%/y real return). This is a significant reduction compared to the prior decade’s realized real return of 10.5%/y. The prospect of low future returns stems from high stock market valuation as measured by CAPE (Shiller’s S&P 500 cyclically adjusted P/E). Historically, high levels of CAPE are associated with relatively low long-term future stock returns.

However, using CAPE to generate future stock return estimates can produce significantly different forecasts depending on how CAPE is specified in the forecast model and whether there are other explanatory variables. While CAPE is viewed as an important predictor of future stock returns, there is little consensus on how to use CAPE for future return estimation.

We compare various CAPE models and identify that (1/CAPE – R) (i.e., stock earnings yield – 10y US Treasury real yield) – also known as “stock excess yield” or the “Fed Model” – has delivered the best estimation performance (of those models considered), regardless of the level of CAPE.

#31 – Market Seasonality Effect in World Equity Indexes

Schroeder, Jan L. and Posch, Peter N.: A Puzzle Piece for The “Sell in May and Go Away” Anomaly: Regulatory Disclosures
https://ssrn.com/abstract=5104512
Abstract:
We propose a new puzzle piece for the Halloween effect, “Sell in May, and Go Away”, by identifying a seasonal pattern in SEC regulatory disclosures aligning with the summer and winter periods of the effect. From 2004-2023, winter sees a 473% surge in annual reports and audited financial statements, along with a 22% rise in insider trading, an 11% increase in activist investor activity, and a 13% uptick in private securities offerings compared to the summer. February consistently shows the highest disclosure volume, while September has the lowest. Similar patterns in European markets suggest a cross-market consistency in disclosure timing.

And several interesting free blog posts that have been published during the last 2 weeks:

Dangers of Relying on OHLC Prices – the Case of Overnight Drift in GDX ETF

Can we truly rely on the opening price in OHLC data for backtesting? While the overnight drift effect is well-documented in equities, we investigated its presence in gold using the GLD ETF and then extended our analysis to the GDX – Gold Miners ETF, where we observed an unusually strong overnight return exceeding 30% annualized. However, when we tested execution at 9:31 AM using 1-minute data, the anomaly diminished significantly, suggesting that the extreme return was partially a data artifact. This finding highlights the risks of blindly trusting OHLC open prices and underscores the need for higher-frequency data to validate execution assumptions.

Does the Image-Based Industry Classification Outperform?

For decades, investors and analysts have relied on traditional industry classifications like GICS, NAICS, or SIC to group companies into sectors and peer groups. However, these rigid categorizations often fail to capture the evolving nature of businesses, especially in an era of technological convergence and rapid industry shifts. Machine learning (ML) offers a more dynamic and data-driven alternative by analyzing company visuals—such as logos, product images, and branding elements—to identify similarities that go beyond predefined classifications. A recent study applies this approach to construct new industry groupings and tests them in industry momentum and reversal strategies. The results show that ML-generated groups lead to superior performance, once again highlighting the potential of image-based classification in financial analysis.

Using Inflation Data for Systematic Gold and Treasury Investment Strategies

Inflation significantly impacts the prices of gold and treasury bonds through various mechanisms. Gold is often viewed as a hedge against inflation, while treasury bonds exhibit a more complex relationship influenced by interest rates and investor behavior. This relationship between inflation, gold, and treasuries is well understood, but the real question is whether we can systematically capitalize on it. In this article, we explore how inflation data can be used to build trading strategies—and as our findings suggest, the answer is a definite yes.