Quantpedia Premium Update – January 23rd

New Strategies:

#1089 – Risk Premiums in the Cryptocurrency Market

Period of rebalancing: 6 Months
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Moderately complex strategy
Backtest period: 2017-2023
Indicative performance: 88.56%
Estimated volatility: –

Source paper:

Akbari, Guilda and Ekponon, Adelphe: Risk Premiums in the Cryptocurrency Market
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5056328
Abstract:
We examine the relationship between cryptocurrencies and the stock market to find out whether risk premiums exist in the cryptocurrency market. A double-sorted portfolio strategy that buys cryptocurrencies with the highest exposure to the stock market-high exposure to the market returns and low exposure to the market return volatility – and shorts cryptocurrencies with the lowest exposure to the stock market – low exposure to the market return and high exposure to the market return volatility – generates an excess return of 50% annually after surviving borrowing and trading costs. Exposure to the stock market using the principal component analysis of equity market returns and volatility produces monotonic returns and a long-short strategy return of more than 85% annually. We propose a reduced-form model that considers cryptocurrencies as levered assets on the equity market and provides evidence that investors have since 2018 employed coins in portfolio strategies which in turn have created a connection between the two markets.

#1090 – Refining ETF Asset Momentum Strategy

Period of rebalancing: Monthly
Markets traded: bonds, commodities, currencies, equities, REITs
Instruments used for trading: ETFs
Complexity: Moderately complex strategy
Backtest period: 2006-2023
Indicative performance: 9.36%
Estimated volatility: 14.25%

Source paper:

Pauchlyová, Margaréta and Vojtko, Radovan: Refining ETF Asset Momentum Strategy
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5095447
Abstract:
Today’s research introduces a refined ETF asset momentum strategy by combining a correlation filter with selective shorting. While traditional long-short momentum strategies usually yield suboptimal results, the long leg proves effective on its own, and the correlation filter demonstrates significant value for improving the timing and performance of the short leg. We propose a final strategy of going long on 4 top-performing ETFs while selectively shorting 1 ETF with a 30% weight. Our findings demonstrate that this combined long-short selective hedge strategy significantly outperforms standalone momentum strategies and the benchmark, delivering superior risk-adjusted returns and effective hedging during unfavorable market conditions.

#1091 – Aggregate Sales Growth Predicts Stock Index Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Complex strategy
Backtest period: 2005-2023
Indicative performance: 4.41%
Estimated volatility: 6.21%

Source paper:

Garfinkel, Jon A. and Hribar, Paul and Hsiao, Lawrence: Aggregate Sales Growth and Stock Market Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5066654
Abstract:
We examine the predictive content of aggregate sales growth (ASG) for stock market performance. ASG negatively predicts future stock market excess returns. In-sample tests show a one-standard-deviation increase in ASG leads to a decline of more than 6% in future annualized market excess returns. This negative relation is incremental to aggregate earnings growth and macroeconomic return predictors. In addition, the return-predicting power of ASG persists in out-of-sample tests, and mean-variance investors can construct a viable trading strategy via the forecasts based on ASG in real time. We explore potential channels. ASG negatively predicts various measures of aggregate earnings surprises, while being unrelated to subsequent discount rate proxies. Our findings suggest that the predictive ability of aggregate sales growth stems predominantly from a cash flow channel.

#1092 – Google Trends Unemployment Market Timing Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: CFDs, ETFs, futures
Complexity: Moderately complex strategy
Backtest period: 2007-2017
Indicative performance: 44.5%
Estimated volatility: –

Source paper:

Bock, Johannes: Quantifying macroeconomic expectations in stock markets using Google Trends
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3218912
Abstract:
Among other macroeconomic indicators, the monthly release of U.S. unemployment rate figures in the Employment Situation report by the U.S. Bureau of Labour Statistics gets a lot of media attention and strongly affects the stock markets. I investigate whether a profitable investment strategy can be constructed by predicting the likely changes in U.S. unemployment before the official news release using Google query volumes for related search terms. I find that massive new data sources of human interaction with the Internet not only improves U.S. unemployment rate predictability, but can also enhance market timing of trading strategies when considered jointly with macroeconomic data. My results illustrate the potential of combining extensive behavioral data sets with economic data to anticipate investor expectations and stock market moves.

New research papers related to existing strategies:

#433 – Computing Power Factor in Cryptocurrencies
#434 – Network Size Factor in Cryptocurrencies

Guidolin, Massimo and Ionta, Serena: Predictive Sorting of Cryptocurrencies Based On Fundamentals and Sentiment
https://ssrn.com/abstract=5079335
Abstract:
We study the relationship between cryptocurrency returns and fundamental blockchain characteristics, with a special emphasis on the predictive power of fundamentals and sentiment. In particular, we perform a systematic investigation of the predictive power of blockchain characteristics, such as the variation in the hashrate and the rate of growth of active users. We evaluate the effectiveness of these variables in forecasting returns for a large sample of cryptocurrencies, using metrics such as the out-of-sample R2 and certainty equivalents to validate the model’s reliability. Additionally, we use standard sorting methodologies in empirical asset pricing that identify portfolios that invest in cryptocurrencies with higher predictability while shorting those with lower predictability. This sorting strategy yields promising portfolio spreads. This emphasizes the predictive value potential of blockchain fundamentals and sentiment indicators in cryptocurrency markets. Our findings suggest that a combined approach based on both technical and behavioral factors provides robust insights into cryptocurrency price dynamics.

#144 – Trend-following Effect in Stocks

Zarattini, Carlo and Pagani, Alberto and Wilcox, Cole: Does Trend-Following Still Work on Stocks?
https://ssrn.com/abstract=5084316
Abstract:
This paper revisits and extends the results presented in 2005 by Wilcox and Crittenden in a white paper titled Does Trend Following Work on Stocks? Leveraging a survivorship-bias-free dataset of all liquid U.S. stocks from 1950 through November 2024, we examine more than 66,000 simulated long-only trend trades. Our results confirm a highly skewed profit distribution, with less than 7% of trades driving the cumulative profitability. These core statistics hold up out-of-sample (2005–2024), maintaining strong returns despite a modest decline in average trade profitability following the original publication. In the second part of this study, we backtest a long-only trend-following portfolio specifically aimed at capturing outlier returns in individual stocks. While the theoretical portfolio exhibits exceptional gross-of-fees performance from 1991 until 2024 (e.g., a CAGR of 15.19% and an annualized alpha of 6.18%), its extensive daily turnover poses a significant challenge once transaction costs are considered. Examining net-of-fee performance across various asset under management (AUM) levels, we find that the base trend-following approach is not viable for smaller portfolios (AUM less than $1M) due to the dampening effect of trading costs. However, by incorporating a Turnover Control algorithm, we substantially mitigate these transaction cost burdens, rendering the strategy attractive across all tested portfolio sizes even after fees.

#83 – Pre-Holiday Effect

Vidal-García, Javier and Vidal, Marta: The Holiday Effect
https://ssrn.com/abstract=5073776
Abstract:
This article studies the pre and post holiday effects, also known as holiday effect, for the most important stock markets around the world, through their main stock price indexes for a 34-year time frame from 1990 to 2024. Our results show that the pre-holiday effect exists for the Asian and North American markets. Particularly for Asia, returns for days prior to holidays are almost seven times higher than on regular days. We find post-holiday effects in Europe and North America. Returns for these days are accounted for as three times higher than a normal trading day. We do not find effects for the South African and South American markets. Additionally, we prove that the holiday effect is independent from other calendar anomalies, such as end-of-the-year or weekend effects.

#52 – Asset Growth Effect

Wang, Kai: Quarterly Asset Growth, the Cross Section of Stock Returns, and Subjective Beliefs
https://ssrn.com/abstract=5019875
Abstract:
I show that asset growth exhibits substantial quarterly variations within a firm’s fiscal year, indicating a potential informational advantage of quarterly asset growth compared to annual asset growth. However, unlike annual asset growth, quarterly asset growth shows no clear relation with subsequent stock returns. This unexpected result arises from the opposing return predictions of individual asset components at the quarterly level, which offset each other when aggregated. Constructing factor portfolios based on quarterly growth of individual asset components uncovers several anomalies not captured by existing models. A monthly-rebalanced factor on quarterly cash growth generates an annualized return of 4.7% (t = 8.2) and a Fama-French five-factor alpha of 4.7% (t = 7.6). For quarterly debt growth, these figures are -5.5% (t = -9.5) and -4.9% (t = -8.7). I argue that investors’ belief biases, driven by overreactions to investment rate information, largely explain the return predictability of investment-related asset growth, with empirical evidence supporting this claim.

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

Refining ETF Asset Momentum Strategy

Today’s research introduces a refined ETF asset momentum strategy by combining a correlation filter with selective shorting. While traditional long-short momentum strategies usually yield suboptimal results, the long leg proves effective on its own, and the correlation filter demonstrates significant value for improving the timing and performance of the short leg. We propose a final strategy of going long on 4 top-performing ETFs while selectively shorting 1 ETF with a 30% weight. Our findings demonstrate that this combined long-short selective hedge strategy significantly outperforms standalone momentum strategies and the benchmark, delivering superior risk-adjusted returns and effective hedging during unfavorable market conditions.

Detecting Wash Trading in Major Crypto Exchanges

Ever thought about investing in cryptocurrencies? Before diving in, it’s worth understanding the shadowy practice of wash trading, where fake trade volumes distort the market. Our latest blog explores how this manipulation impacts major assets like Bitcoin and Ethereum, the methods used to detect it, and why regulatory clarity is key to a more trustworthy crypto ecosystem.

Out-of-Sample Test of Formula Investing Strategies

Can we simplify the complexities of the stock market and distill them into a simple set of quantifiable metrics? A lot of academic papers suggest this, and they offer formulas that should make the life of a stock picker easier. Some of the most compelling methodologies within this realm are the F-Score, Magic Formula, Acquirer’s Multiple, and the Conservative Formula. These quantitative strategies are designed to identify undervalued stocks with robust fundamentals and potential for high returns. But do they really work out-of-sample? A new paper by Marcel Schwartz and Matthias X. Hanauer tries to answer this interesting question…