Quantpedia Premium Update – December 8th

New strategies:

#1076 – Impact of EIA Inventory Announcements on Crude Oil Prices

Period of rebalancing: Intraday
Markets traded: commodities
Instruments used for trading: CFDs, ETFs, futures
Complexity: Simple strategy
Backtest period: 2006-2019
Indicative performance: 4.14%
Estimated volatility: 1.51%

Source paper:

Indriawan, Ivan and Lien, Donald and Wen, Zhuzhu and Xu, Yahua: Intraday Return Predictability in the Crude Oil Market: The Role of EIA Inventory Announcements
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3822093
Abstract:
We study the impact of the announcements released by the US Energy Information Administration (EIA) crude oil storage every Wednesday at 10:30 ET (the beginning of the third half-hour interval) on intraday return predictability, that is, intraday momentum. Our results indicate that returns on the third half-hour on EIA announcement days can significantly and positively predict the returns in the last half-hour, whereas, on non-EIA announcement days, only returns in the first half-hour have significant predictability. The dominant source of prediction in the first half-hour return mainly comes from the overnight component. EIA announcements contribute to intraday momentum because they attract more informed traders and because the period surrounding their release is often associated with a reduction in liquidity. Substantial economic gains can be made by using efficient intraday predictors as trading signals.

#1077 – Predicting the Holdings of Top Mutual Funds

Period of rebalancing: Quarterly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2004-2023
Indicative performance: 6.45%
Estimated volatility: 6.1%

Source paper:

van Brakel, Jean-Paul: Machine Learning from the Best: Predicting the Holdings of Top Mutual Funds
https://ssrn.com/abstract=4924423
I show that machine learning models, by exploiting the nonlinearities and interactions in stock characteristics, can better predict the stocks owned by top-performing mutual fund managers than suggested by their most recent holdings or a linear model. Previous ownership by mutual funds and the market cap and volume of the stock are identified as the most important predictors. The predictions also prove useful in separating stocks based on their future return potential. Shorting stocks predicted to be disliked by top managers provides higher returns than can be explained by common factors, either systematically or over time.

#1078 – Peer Option Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: options
Complexity: Very complex strategy
Backtest period: 1996-2020
Indicative performance: 83.94%
Estimated volatility: 23.87%

Source paper:

Li, Yang: Peer Option Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4964637
Abstract:
We document option momentum spillovers across peer firms with shared analyst coverage. Firms whose peers have higher past-12-month delta-hedged straddle returns tend to have higher future option returns. Peer option momentum has a magnitude similar to the option momentum documented by Heston et al. (2023), but it is distinct from option momentum. Past option returns of a firm’s peers can predict the firm’s realized variance even after we control the firm’s option-implied variance and its own past option returns. In addition, peer momentum is stronger for indirect linkages. The findings are consistent with limited investor attention on volatility information from peer firms. Compared with peer stock momentum, peer option momentum persists longer and exhibits spillovers via more economic linkages that cannot be subsumed by analyst-based linkages.

#1079 – Commodities Seasonal Front-Run Strategy

Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: ETFs, futures
Complexity: Moderately complex strategy
Backtest period: 2007-2024
Indicative performance: 7.62%
Estimated volatility: 9.43%

Source paper:

Vojtko; Dujava: Trader’s Guide to Front-Running Commodity Seasonality
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5048183
Abstract:
Seasonality is a well-known phenomenon in the commodity markets, with certain sectors exhibiting predictable patterns of performance during specific times of the year. These patterns often attract investors who aim to capitalize on anticipated price movements, creating a self-reinforcing cycle. But what if you could stay one step ahead of the crowd? By front-running these seasonal trends—buying sectors with expected positive performance (or shorting those with negative seasonality) before their favorable months begin—you can potentially gain a significant edge over traditional seasonality-based strategies. In this blog post, we explore how to construct and backtest a systematic strategy using commodity sector ETFs to exploit this seasonal front-running effect.

New research papers related to existing strategies:

#470 – Macroeconomic Announcement Beta Strategy

Petrasek, Lukas and Kukacka, Jiri: US Equity Announcement Risk Premia
https://ssrn.com/abstract=4978270
Abstract:
We analyze the announcement risk premia on the US market between September 1987 and March 2023 and find that the market index exhibits average excess returns of 8.3 bps for macroeconomic announcement days. This strongly contrasts with 1.4 bps returns for non-announcement days. We further measure the individual stocks’ sensitivities to macroeconomic data announcements over various lookback periods and show that stocks in the high-sensitivity portfolios offer investors significantly higher returns than stocks in the low-sensitivity portfolios. The average returns on the difference portfolios amount to 18 bps per month for the 60-month sensitivities. The Fama-MacBeth regression coefficients for the announcement sensitivity are positive and statistically significant across all lookback periods.

#161 – Lunar Cycle in Precious Metals

Goti, Matius: Bearish New Moon and Bullish Full Moon: Analyzing Lunar Cycle Patterns in Gold Price Movements
https://ssrn.com/abstract=4977360
Abstract:
This article investigates the relationship between lunar phases—specifically the new moon and full moon—and gold price movements, a topic that has captivated traders and researchers alike. We explore historical associations of the lunar cycle with human behavior and decision-making, positing that these celestial events may influence market psychology and trading strategies. Through rigorous back testing and data analysis, we examine whether these lunar phases correlate with bullish or bearish trends in the gold market, a commodity known for its role as a financial safe haven. Despite limited empirical studies on this subject, our findings suggest that lunar cycles may offer valuable insights into market dynamics, potentially challenging conventional trading wisdom. This research contributes to a deeper understanding of how psychological factors, influenced by astronomical phenomena, can shape trading decisions and outcomes in the financial markets.

#41 – Turn of the Month in Equity Indexes

Chen, Haiwei and Shin, Sang Heon and Sun, Xu: Return-enhancing strategies with international ETFs: Exploiting the turn-of-the-month effect
https://www.semanticscholar.org/paper/Return-enhancing-strategies-with-international-ETFs-Chen-Shin/adb5b5b07cf735bd45633f935128039895bfc70b
Abstract:
We show that the average return over the four-day period surrounding the turn of the month is significantly positive in eight out of the nine international exchange-traded funds (ETFs). The strategy of buying-and-holding an ETF during turn-of-the-month (TOM) period and switching to holding T-bills during non-TOM period produces significantly positive monthly average returns. This ETF- T-bills switching strategy also has the lowest risk and highest Sharpe ratio and Sortino ratio than the traditional strategy of buying-and-holding either an index fund or an ETF. Investors pursuing this switching strategy generate a terminal value twice larger than the next best strategy of buying-and- holding an ETF.

#582 – Carbon Risk in the Cross Section of Corporate Bond Returns
#614 – Climate sentiment, carbon prices and Emission minus Clean Portfolio
#679 – Carbon Emmision Intensity in Stocks
#707 – Benchmarks Portfolios with Decreasing Carbon Footprints

Tham, Eric and Kang, Young Dae: How Do Carbon Prices Impact the Carbon Premium?
https://ssrn.com/abstract=4260719
Abstract:
Governments institute carbon pricing to curb emissions. We show increasing carbon futures prices on the European Trading Scheme (ETS) decrease the carbon \textit{news} equity premium but only in brown firms. This premium is obtained from a panel regression on firm-level sentiment scores. The premium varies across industries and countries, and separate into short and long term components identified by news topics on emissions and environmental innovation respectively. The changes in carbon premium are in line with increased investors’ concerns on environmental issues post COP 2016. Regression results on the carbon premium with the carbon futures returns as explanatory economic variables show $R^2$ up to $41\%$ in Germany and $21\%$ in United States. A firm model of investment is developed to explain the economic impact of carbon prices on the carbon equity premium and validate the empirical findings.

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

Trader’s Guide to Front-Running Commodity Seasonality

Seasonality is a well-known phenomenon in the commodity markets, with certain sectors exhibiting predictable patterns of performance during specific times of the year. These patterns often attract investors who aim to capitalize on anticipated price movements, creating a self-reinforcing cycle. But what if you could stay one step ahead of the crowd? By front-running these seasonal trends—buying sectors with expected positive performance (or shorting those with negative seasonality) before their favorable months begin—you can potentially gain a significant edge over traditional seasonality-based strategies. In this blog post, we explore how to construct and backtest a systematic strategy using commodity sector ETFs to exploit this seasonal front-running effect.