Quantpedia Premium Update – July 24th

New Strategies:

#1152 – Tech Conference Return Drift Strategy

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 2011-2025
Indicative performance: 6.67%
Estimated volatility: 6.88%

Source paper:

Vojtko, Radovan and Dujava, Cyril: An Empirical Analysis of Conference-Driven Return Drift in Tech Stocks
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5342359
Abstract: Corporate conferences have long been recognized as pivotal events in financial markets, serving as catalysts that signal upcoming innovations and strategic shifts. Scheduled corporate events induce market reactions that can be systematically analyzed to reveal predictable return patterns. In this work, we focus on examining the return drift exhibited by technology stocks in the days surrounding their respective conferences, employing simple quantitative methods with daily price data. The hypothesized return drift is premised on the notion that investor sentiment and market dynamics are significantly altered by the information disseminated at these conferences. Investors, reacting to both anticipatory signals and post-announcement adjustments, tend to drive prices in a measurable manner in the windows immediately preceding, during, and after the events. By systematically analyzing stocks of companies such as Apple, Google, and Microsoft, this study aims to validate the existence of these drift patterns and shed light on the underlying mechanisms, thereby enhancing mutual understanding of event-driven asset pricing dynamics.

#1153 – Investment Base Pairs Futures Strategies

Period of rebalancing: Monthly
Markets traded: equities, commodities, bonds currencies
Instruments used for trading: futures
Complexity: Very Complex strategy
Backtest period: 1985-2023
Indicative performance: 10.4%
Estimated volatility: –

Source paper:

Goulding, Christian L. and Harvey, Campbell R.: Investment Base Pairs
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5193565
Abstract: Modern finance remains constrained by legacy portfolio construction techniques. While the conventional quantile approaches (e.g., long top 30%/short bottom 30%) and linear weighting schemes dominate cross-sectional strategies, we show that these methods discard crucial cross-asset information. We offer a new approach by decomposing common signals such as value, momentum, and carry, into base pair portfolios. Each signal-driven long-short position is shaped by five key drivers: own-asset predictability, cross-asset predictability, signal correlation, and signal mean and variance imbalances. Using 1,710 futures pair portfolios spanning equities, bonds, currencies, and commodities formed from common signal types, we show that targeting top pairs can triple average returns at fixed leverage over 20 years: the aggregate “All” portfolio rises from 3.4% to 10.4% annualized. Equity Value climbs from 3.6% to 14.3% and Currency Momentum reverses a -3.0% loss to a 10.3% gain. By harvesting cross-asset information and eliminating junk pairs, this approach offers a robust improvement over the status quo across diverse asset classes and time periods.

#1154 – Trading Theta: A Strategy Exploiting Time Decay

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: options, futures, ETFs
Complexity: Very Complex strategy
Backtest period: 2010-2022
Indicative performance: 20.04%
Estimated volatility: –

Source paper:

Lu, Yungpeng: 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.

#1155 – Insider Trading Signal Forecasts Anomaly Returns

Period of rebalancing: Quarterly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 1986-2019
Indicative performance: 31.22%
Estimated volatility: 26.13%

Source paper:

Tian, Jiaxing and Xiang, Hong and Xu, Minghai: Insider Trading and Anomalies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5237160
Abstract: We show that the insider trading pattern on anomaly long-short portfolio stocks can forecast anomaly returns. Specifically, we use the fraction of anomaly long-leg (short-leg) stocks being bought (sold) by insiders as a signal to extract insiders’ information on expected returns of the anomaly. Based on a composite anomaly measure that combines 11 prominent anomalies, we show that the insider trading signal significantly forecasts anomaly returns both in-sample and out-of-sample. These findings also help disentangle the risk-based and the mispricing-based explanation for anomaly returns.

New research papers related to existing strategies:

#1082 – Global Hairy Premium Strategy

Georgievska, Ljubica and Saunders, Anthony: The “Hairy Premium”: Incomplete Markets, Frictions, and Risk-Contaminated Swap Expectations
https://ssrn.com/abstract=4475821
Abstract: We document systematic departures from risk-neutral pricing in interest rate swap markets through a “Hairy premium” — the gap between swap rates and realized floating rates — averaging 2.7% annually for 10-year U.S. dollar swaps, with similar patterns across all G10 currencies. Market incompleteness (absent long-term risk-free FRNs and severe shorting constraints) prevents arbitrage, allowing risk premiums to contaminate the supposedly risk-neutral measures used to price the entire $684 trillion derivatives market. Our novel triangular methodology reveals markets expect these premiums ex-ante, not merely realize them ex-post. The evolving replication efficiency of swaps versus bond benchmarks explains the emergence of negative swap spreads as markets recognize improving relative market completeness.

#67 – Industry Momentum – Riding Industry Bubbles

Jarrow, Robert A. and Kwok, Simon: Riding a Bubble: A Study of Market-Timing Trading Strategies
https://ssrn.com/abstract=5200953
Abstract: This paper characterizes the probability distribution of local martingale price bubble processes in order to construct profitable trading strategies which exploit price bubble dynamics. Various such market-timing trading strategies are identified, all are based on the idea of “riding a bubble” by holding a stock during a bubbly period until the time when the bubble’s magnitude hits a predetermined barrier. We study these market-timing trading strategies via both simulations and back-tests using historical market prices. We show both in theory and practice that implementing such strategies in the presence of a bubble leads to increased performance relative to the traditional buy-and-hold trading strategy.

#007 – Low Volatility Factor Effect in Stocks

Soebhag, Amar and Baltussen, Guido and van Vliet, Pim: Factoring in the Low-Volatility Factor
https://ssrn.com/abstract=5295002
Abstract: Low-volatility stocks have historically delivered higher risk-adjusted returns than their high-volatility peers. Despite extensive evidence and widespread adoption in the investment industry, the so-called low-volatility factor is absent from standard asset pricing models. This paradox is attributable to asymmetry in factor legs and real-life investment frictions. A low-volatility factor substantially improves performance of factor models once accounting for these dimensions in various in-sample and out-of-sample exercises, across different low-risk measures and across methodological choices. We advocate integrating the low-volatility factor into asset pricing models, accounting for the asymmetry and frictions.

#685 – Boosted Trees and Cryptocurrency Return Prediction

Yalcin, Mustafa and Ertugrul, Omer Faruk: Ethereum Price Prediction Using Deep Learning
https://ssrn.com/abstract=5289331
Abstract: Artificial intelligence (AI) has significantly integrated into many aspects of our lives, greatly simplifying daily routines. This convenience is evident across various domains. In particular, in financial technologies, AI and machine learning (ML) methods have transformed the processes of prediction and analysis in cryptocurrency markets. In this context, price predictions of popular cryptocurrencies like Ethereum have become more accurate and reliable using advanced AI models. Investors can better understand market movements and make more informed decisions through these models. AI-based analyses not only improve investment strategies but also enhance risk management against market fluctuations. Thus, security and profitability are increased in the cryptocurrency world, supporting the sustainability of the digital financial ecosystem. This study investigates the use of AI models in predicting the price of the Ethereum cryptocurrency. The models used include Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN). Analyses were conducted on Ethereum price data from January 1, 2019, to January 18, 2024. The aim of the study is to evaluate the effectiveness of these models in price prediction and to demonstrate their forecasting capabilities in the cryptocurrency market. The findings provide significant insights into how AI techniques can be utilized in financial markets.

#510 – Factor Momentum
#534 – Time Series Factor Momentum

Rönkkö, Mikael and Holmi, Joonas: Revisiting Factor Momentum: A One-month Lag Perspective
https://ssrn.com/abstract=5333744
Abstract: Recent studies have questioned the relevance of factor momentum by showing that its profitability is driven by a static tilt toward factors with positive historical means and that only a minority of individual factors exhibit significant momentum. This paper shows that replacing the traditional one-year formation window with a one-month window yields significant alpha after controlling for tilt toward positive-mean factors and doubles the number of factors with significant momentum from roughly 20% to 40%. Furthermore, we show that the positive autocorrelation between the one-month formation window and the subsequent month’s return is twice as high as in the traditional one-year formation window. In the modern era of electronic trading, this autocorrelation is nearly 14 times higher. Our findings highlight that the robustness and profitability of factor momentum strategies depend critically on the formation window length.

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

How Fragile is Liquidity Across Asset Classes?

The paper “Through Stormy Seas: How Fragile is Liquidity Across Asset Classes?” is a very interesting examination of how liquidity properties have evolved over the past decade. Although the average bid–ask spread has declined, the kurtosis and skewness of the spread distribution have increased. What does this imply? On average, markets appear more liquid; however, liquidity evaporates more rapidly during stress events, amplifying tail risk and increasing execution slippage.

The Memorization Problem: Can We Trust LLMs’ Forecasts?

Everyone is excited about the potential of large language models (LLMs) to assist with forecasting, research, and countless day-to-day tasks. However, as their use expands into sensitive areas like financial prediction, serious concerns are emerging—particularly around memory leaks. In the recent paper “The Memorization Problem: Can We Trust LLMs’ Economic Forecasts?”, the authors highlight a key issue: when LLMs are tested on historical data within their training window, their high accuracy may not reflect real forecasting ability, but rather memorization of past outcomes. This undermines the reliability of backtests and creates a false sense of predictive power.

How to Identify Ponzi Funds?

Can we spot a Ponzi scheme before it collapses? That question haunts regulators, investors, and journalists alike. But what if some modern investment funds operate on dynamics that, while not technically illegal, closely resemble Ponzi-like behavior? A new paper by Philippe van der Beck, Jean-Philippe Bouchaud, and Dario Villamaina examines whether certain actively managed funds inflate their own performance — and in doing so, unwittingly mislead investors chasing past returns.