Quantpedia Premium Update – July 9th

New Strategies:

#1019 – Volatility-Managed Volatility Trading

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: futures, options
Complexity: Very complex strategy
Backtest period: 1990-2023
Indicative performance: 12.3%
Estimated volatility: 15.6%

Source paper:

Yang, Aoxiang: Volatility-Managed Volatility Trading
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4761614
We develop volatility risk premium (VRP) timing strategies that involve trading two assets: a volatility asset and a risk-free asset. We first analyze a benchmark portfolio with a constant negative weight on volatility assets each month. Then, we show that a volatility-managed portfolio, which reduces selling of volatility assets during periods of heightened volatility, considerably enhances long-run performance. Our findings are robust across three types of volatility assets – variance swaps, VIX futures, and S&P 500 straddles – and in the presence of transaction costs. An ex-post study indicates that timing portfolios yield positive alpha and reduce exposure relative to constant-weight portfolios, mostly during volatility-spike periods rather than stable periods. Our findings help differentiate asset pricing theories on risk-return relations in the volatility asset market.

# 1020 – Momentum and Investors’ Lottery-Like Preference

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1962-2023
Indicative performance: 34.49%
Estimated volatility: 32.01%

Source paper:

Haghighi Zadeh, Reihaneh, Momentum and Investors’ Lottery-like Preference
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4796780
Abstract:
Previous empirical studies have consistently shown that lottery-like stocks exhibit unconditionally low average returns. However, we find that when the momentum strategy is jointly conditioned on MAX stocks, high-MAX stocks offer significantly higher future returns compared to low-MAX stocks. Our analysis indicates that for value-weighted portfolios, implementing a momentum strategy on high-MAX stocks results in an average monthly return of 2.5%, which significantly outperforms the same strategy when applied to low-MAX stocks, where the average monthly return is only 0.25%. We observe that our MAX-Momentum strategy outperforms the momentum strategy of Jegadeesh and Titman (1993), and this outperformance can be attributed to the performance of high-MAX losers. For value-weighted portfolios, under the MAX-Momentum strategy, high-MAX winners’ (losers) stocks earn an average monthly return of 1.17% (-1.33%), whereas a momentum strategy alone yields an average monthly return of 1.27% (0.01%) for past winners (losers), respectively. Similar trends are observed for equal-weighted portfolios. Our findings indicate that high-MAX losers experience more substantial losses compared to their counterparts in the momentum strategy alone.

#1021 – Timing the Factor Zoo in the US

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1926-2020
Indicative performance: 17.99%
Estimated volatility: 14.68%

Neuhierl, Andreas and Randl, Otto and Reschenhofer, Christoph and Zechner, Josef: Timing the Factor Zoo
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4376898
Abstract:
We provide a comprehensive analysis of the timing success for equity risk factors. Our analysis covers over 300 risk factors (factor zoo) and a high dimensional set of predictors. The performance of almost all groups of factors can be improved through timing, with improvements being highest for profitability and value factors. Past factor returns and volatility stand out as the most successful individual predictors of factor returns. However, both are dominated by aggregating many predictors using partial least squares. The median improvement of a timed vs. untimed factor is about 2% p.a. A timed multifactor portfolio leads to a 8.6% increase in annualized return relative to its naively timed counterpart.

#1022 – Pairs Trading Strategy With Connected Stocks

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2005-2021
Indicative performance: 13.35%
Estimated volatility: 19.02%

Source paper:

Lin, Fengjiao and Qiu, Zhigang: Pairs Trading Strategy and Connected Stocks: Evidence from China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4790701
Abstract:
We develop a pairs trading strategy in China’s stock market based on non-fundamental comovement driven by fund common ownership. The stocks connected through their common mutual fund owners is used to identify pairs, and the divergence from connected stocks is used for the trading strategy. Specifically, a long-short hedging strategy based on return divergence can achieve 1.73% monthly return. Because the pairs trading strategy is based on non-fundamental comovement driven by fund flows, the trading profits is more pronounced following months with fund outflows, proxied by negative market returns and high market volatility. Finally, we show that the profits of the pairs trading are related to investor attention, lottery features, valuation uncertainty, market beta or downside beta, arbitrage risk and past one-month winners or losers.

#1023 – Front-Running the Goldman Roll

Period of rebalancing: Daily
Markets traded: commodities
Instruments used for trading: CFDs, futures
Complexity: Moderately complex strategy
Backtest period: 2000-2010
Indicative performance: 3.78%
Estimated volatility: 4.78%

Source paper:

Mou, Yiqun: Limits to Arbitrage and Commodity Index Investment: Front-Running the Goldman Roll
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1716841
Abstract:
This paper studies the unique rolling activity of commodity index in futures markets and shows that the resulting price impact is statistically and economically significant. Two trading strategies, devised to exploit this anomaly, yielded excess returns with positive skewness and Sharpe ratios as high as 4.39 from 2000 to March 2010. The profitability of the strategies is positively correlated with the net result of two opposite forces: the size of index investment and the amount of arbitrage capital employed. Due to the price impact, investors forwent 3.6\% annual return, 48\% higher Sharpe ratio, and billions of dollars over the period.

#1024 – Investigation of Lead-Lag Effect in Easily-Mistyped Tickers

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple
Backtest period: 2019-2024
Indicative performance: 3.78%
Estimated volatility: 4.78%

Source paper:

Vojtko, Radovan and Dujava, Cyril: Oh My! I Bought A Wrong Stock! – Investigation of Lead-Lag Effect in Easily-Mistyped Tickers
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4873469
Abstract:
The academic paper titled “Oh My! I Bought A Wrong Stock! – Investigation of Lead-Lag Effect in Easily-Mistyped Tickers” aims to explore the lead-lag effect between well-known stocks and lesser-known stocks with similar ticker symbols (e.g., TSLA / TLSA). This phenomenon has received limited attention in financial literature. The study investigates whether investors, espe- cially retail investors, inadvertently trade on less-known stocks due to ticker symbol confusion, impacting their price movements in a manner correlated with leading stocks. Understanding this effect could provide valuable insights and potentially lead to strategies that exploit such inefficiencies, offering a promising prospect for more informed and profitable investments.

New research papers related to existing strategies:

#20 – Volatility Risk Premium Effect

Lu, Yunpeng: Trading Theta: A Strategy Exploiting Time Decay
https://ssrn.com/abstract=4792284
Abstract:
This paper presents a trading strategy that takes advantage of the characteristic of Theta. Theta, known as the time value of the option, is always negative, at least in theory. The simple non-mathematical rationale is that any financial instruments lose value in the sense of time passing by. In terminology, time value of money is what we are referring to. Hence, shorting Theta and Theta alone is guaranteed to be profitable. The strategy involves three instruments – SPY, SPX and E-mini. By shorting SPY or SPX, we get a position of shorting Theta, as well as other Greeks. To gain a position of Delta, Gamma, and Vega neutral, a hedge is taken. The hedge can either be in SPY and E-mini, or SPX and E-mini. Four modules/algorithms are required to implement this strategy – contract selection, hedge contracts weight calculation, position adjustment cost calculation, daily Greek exposure and PnL calculation. The result shows the trading setting with main contracts of SPX Put and hedge contracts of SPY Put has the highest performance.

#348 – The Tax Day Trade

Jalbert, Terrance: Is there an April Effect in Stock Returns?
https://ssrn.com/abstract=4843739
Abstract:
This paper examines the extent that retirement account inflows around the April 15th U.S. income tax filing deadline affect U.S. equity prices. Beginning in 1975, the U.S. Federal tax system allowed individuals to realize tax advantages by placing funds in specialized retirement accounts. Individuals can put money into these accounts until April 15th following the end of the tax year. Further, evidence suggests a disproportionate number of taxpayers file their returns near the April 15th deadline. This paper examines if money flowing into retirement accounts around the April 15th deadline produce a calendar-based stock-return pattern. This paper posits these market inflows result in higher average daily stock returns around April 15th. Results show large and significant April effects with event-window daily returns as much as eight times larger than daily returns for the rest of the year. Results hold for both U.S. and international stock indexes.

#766 – Sentiment Factor in the Cross-Section of Commodity Futures
#988 – Media Emotion Intensity and Commodity Futures Pricing

Vu, Thanh and Chi, Yeguang and El-Jahel, Lina: News Sentiment and Commodity Futures Investing
https://ssrn.com/abstract=4870724
Abstract:
We investigate the role of media news sentiment in commodity futures investing. The weekly rebalanced long-short portfolio sorted by news sentiment generates a significant average annualized return of around 10%. The time-series spanning test reveals that the abnormal return of the long-short portfolio sorted by news sentiment still remains above 7% and is statistically significant after controlling for various benchmark factors. The premium of the news sentiment factor is also significantly priced at above 8% in the cross-section of commodity futures returns. Furthermore, we show that incorporating the news-sentiment factor into commodity futures investment portfolio leads to meaningful performance enhancement.

#811 – Intraday Closing Momentum in Futures

Jin, Huixing: Market Intraday Momentum in Japan: Evidence from the Nikkei Stock Index
https://ssrn.com/abstract=4816793
Abstract:
This study examines whether the intraday momentum pattern observed in the US market―where the first half-hour return on the market measured from the previous day’s close predicts the last half-hour return―also applies to the Japanese market. Findings indicate a significant presence of intraday momentum, particularly on days with high trading volume, volatility, and positive opening returns. However, unlike in the US, intraday momentum in Japan is absent before the 2008 Global Financial Crisis (GFC) and during periods of low volume and volatility. Additionally, the study examines the impact of the Bank of Japan’s monetary policy, revealing that ETF purchases during market downturns weaken intraday momentum. In terms of economic significance, our analysis suggests that intraday momentum investment strategies bear economic significance, particularly during periods of high market uncertainty. Finally, the study extends its analysis beyond the Nikkei 225 Index to include other representative market indexes, and we find that intraday momentum exists across different types of market return indexes in Japan.

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

A Few Thoughts on Pragmatic Asset Allocation

One of the main reasons why the Pragmatic Asset Allocation Model was designed is to give investors a tax-efficient possibility to invest in a global equity portfolio with a lower risk than the passive buy&hold approach. Therefore, the PAA model is not the “absolute return” model but rather the tactical model that prefers to invest in the equity risk premium and move to the hedging portfolio (gold, treasuries, or cash), only for short periods and only when it’s absolutely necessary. We use price trend+momentum indicators and yield curve inversion as signals for such situations when (based on the past data) there is a higher probability of recessions and equity bear markets. What is unusual in the current situation is the length of time that the YC is inverted (19 months at the moment), which makes it the 2nd longest YC inversion in the last 100 years, and we are analyzing the implications for the PAA model.

How to Construct a Long-Only Multifactor Credit Portfolio?

There exist two most common techniques for constructing multifactor portfolios. The mixing approach creates single-factor portfolios and then invests proportionally in each to build a multifactor portfolio. The integrated approach combines single-factor signals into a multifactor signal and then constructs a multifactor portfolio based on that multifactor signal. Which methodology is better? It is hard to tell, and numerous papers show each method’s pros and cons. The recent paper from Joris Blonk and Philip Messow explores this question from the standpoint of the credit fixed-income portfolio manager and offers their analysis, which shows that an integrated approach is probably better in this particular asset class.