New Strategies:
#1201 – One-Month Factor Momentum Strategy
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 1963-2024
Indicative performance: 2.11%
Estimated volatility: 4.49%
Source paper:
Rönkkö, Mikael and Holmi, Joonas: Revisiting factor momentum: A one-month lag perspective
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=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.
#1202 – Bitcoin Pre-Holiday Momentum Strategy
Period of rebalancing: Daily
Markets traded: crypto
Instruments used for trading: futures, ETFs, CFDs, crypto
Complexity: Simple
Backtest period: 2018-2025
Indicative performance: 8.62%
Estimated volatility: 10.96%
Source paper:
Vojtko, Radovan and Dujava, Cyril and Cmorej, Peter: Surprisingly Profitable Pre-Holiday Drift Signal for Bitcoin
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5499178
Abstract: Cryptocurrency markets have matured into a distinct asset class characterized by extreme volatility, deep liquidity pools, and worldwide retail participation. Traditional equity and commodity markets exhibit a well-documented pre-holiday effect, where returns on trading days immediately preceding public holidays tend to outperform other days. Given that Bitcoin is often described as the archetypal absolute risk asset, it is natural to hypothesize that any calendar-driven anomalies observed in equities should manifest—or even amplify—in crypto markets. However, unlike equity markets, where institutional investors and marketing calendars drive collective behavior, crypto markets are more dispersed, retail-dominated, and influenced by nontraditional information flows. This article investigates whether the classic pre-holiday effect applies to Bitcoin and assesses the extent to which it can be amplified by an attention-grabbing momentum filter based on local price highs.
#1203 – Bitcoin ETFs in Conventional Multi-Asset Portfolios
Period of rebalancing: Daily
Markets traded: equities, commodities, bonds, crypto
Instruments used for trading: ETFs, CFDs, futures
Complexity: Moderate
Backtest period: 2018-2025
Indicative performance: 18.72%
Estimated volatility: 14.1%
Source paper:
Belobrad, David and Vojtko, Radovan: Bitcoin ETFs in Conventional Multi‐Asset Portfolios
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5499268
Abstract: Understanding how Bitcoin-related instruments can fit into traditional portfolios is increasingly relevant for investors. Some risk-averse investors do not like to hold cryptocurrencies in their portfolios strategically; however, they may be open to investing in crypto-linked assets on a tactical level. In this context, our goal is to explore how we can provide short-term Bitcoin exposure while contributing to overall portfolio balance and potential downside protection.
#1204 – VIXY Tail Hedge Strategy
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: ETFs, CFDs, futures
Complexity: Complex
Backtest period: 2004-2025
Indicative performance: 18.39%
Estimated volatility: 15.52%
Source paper:
Belobrad, David and Vojtko, Radovan and Cmorej, Peter: Hedging Tail Risk with Robust VIXY Models
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5557058
Abstract: Extreme market events, once perceived as statistical outliers, have become a central concern for investors. The persistence of sharp drawdowns and volatility spikes demonstrates that the cost of ignoring tail risks is not tolerable for long‐term portfolio resilience. While diversification can mitigate ordinary fluctuations, it often fails when markets move in unison under stress. This makes explicit protection against severe downside events not just desirable but necessary. Tail hedging addresses this need by providing a structured defense against the most damaging scenarios, ensuring that portfolios remain robust when traditional risk management tools fall short. Using VIXY ETF, we will present and test a range of hedging strategies designed to protect portfolios under stress. By applying robust testing frameworks, we aim to evaluate how different implementations of VIXY ETF‐based tail hedges perform across a variety of market environments, highlighting both their strengths and inherent trade‐offs.
#1205 – Investor Memory and Stock Returns
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex
Backtest period: 1963-2024
Indicative performance: 10.16%
Estimated volatility: 12.24%
Source paper:
Liu, Jinpeng and Wang, Chenguang: Investor Memory and Stock Returns: Empirical Evidence
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5566638
Abstract: We measure investor memory bias (MB) as the proportion of negative daily returns that investors tend to forget or suppress. We find that stocks with high MB earn significantly lower subsequent returns than stocks with low MB. The high-minus-low MB portfolio generates a return spread of -0.90% per month for equal-weighted portfolios and -0.81% per month for value-weighted portfolios. The negative relation between MB and future returns remains after controlling for fifteen established stock characteristics and stays statistically significant under multiple asset-pricing factor models. Further analysis reveals that the MB effect is stronger when investor sentiment is high, when investors use closing prices rather than opening prices, and when arbitrage constraints are tighter. High-MB stocks also exhibit significantly greater abnormal trading volume, consistent with memory-induced reinvestment behavior. Our findings provide the first direct empirical evidence that positive memory bias systematically affects aggregate stock prices.
#1206 – Revaluation Alpha
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex
Backtest period: 1973-2022
Indicative performance: 4.53%
Estimated volatility: 7.96%
Source paper:
Arnott, Rob and Ehsani, Sina and Harvey, Cambell R. and Shakernia, Omid: Revaluation Alpha
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5451754
Abstract: We define revaluation return as the portion of a factor’s historical return that comes from changes in its valuation ratio. A factor may have impressive historical performance, but if the high return was driven by rising valuations, it is unwise to expect similar future returns because valuation ratios cannot rise indefinitely, and often mean-revert. We define structural return as the historical factor return net of the revaluation return. For decades, the factor literature has implicitly assumed that revaluation alpha is equally predictive to structural alpha. We challenge that assumption. Structural returns predict future returns better than raw historical returns and add incremental value beyond well-known timing signals such as momentum.
New research papers related to existing strategies:
#332 – Contrast Effect During the Earnings Announcements
#932 – Timing Betting-Against-Beta (BAB) Anomaly v.2
#1038 – Presidential Cycle and Performance of the Stock Market
Velinova, Ines: From the White House to Wall Street: How U.S. Political Regimes and Policy Uncertainty Shape the Low-Risk Anomaly
https://ssrn.com/abstract=5503138
Abstract: This study examines the relationship between U.S. political regimes, economic policy uncertainty innovations, and the abnormal returns of the Betting Against Beta (BAB) strategy. Using rolling six-factor alphas and an updated BAB portfolio construction that addresses recent methodological critiques, the analysis investigates how risk-adjusted returns vary across different political and policy environments. Sector-level abnormal returns are further evaluated across 11 Fama-French industries to identify regime and policy dependent patterns.
#1171 – Sunspot Wheat Timing Strategy
Belkin, Vladimir: Global Metal Price Index and Solar Activity (1992 – 2024)
https://ssrn.com/abstract=5409588
Abstract: The report uses a methodological approach based on W. S. Jevons and A. L. Chizhevsky. During the study period, the years of solar cycles were numbered according to the order established in solar astrophysics, grouped and compared with the arithmetic mean values of global metal price indices. The resulting models have approximation reliability coefficients equal to 1.0 and 0.95. The study suggests a significant decrease in the world metal price index in 2025 and 2026.
#31 – Market Seasonality Effect in World Equity Indexes
Schroeder, Jan L.: A New Puzzle Piece for the “Sell in May, and Go Away” Anomaly: Regulatory Disclosures
https://ssrn.com/abstract=5399725
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 that aligns with the effect’s summer and winter periods. From 2004 to 2023, SEC filing volumes are 17% higher in winter (November-April) than in summer (May-October). Winter also sees a 22% rise in insider trading, 13% more private securities offerings, 12% more activist investor activity, a 96% increase in shareholder meetings, and 473% more annual reports. February consistently marks the peak in disclosures, and September the low. Similar patterns across European markets suggest global consistency. As regulatory filings contain material pricerelevant information, this seasonal disclosure pattern offers a new contributing factor to the Halloween effect puzzle.
#42 – Alpha Cloning – Following 13F Fillings
Schroeder, Jan L.: Outperforming the Market: Portfolio Strategy Cloning from SEC 13F Filings
https://ssrn.com/abstract=5399672
Abstract: Can mirroring of hedge fund strategies lead to market-outperforming returns? Our findings demonstrate that cloned portfolios in the top quartile, derived from SEC Form 13F filings, replicate the funds’ performances and exceed the S&P 500 index by 24.3% on an annualized risk-adjusted basis. Analyzing over 150,000 portfolios between 2013 and 2023, we compare original versus replicated strategies across twelve performance metrics. Except for annualized volatility, maximum drawdown and tracking error, we demonstrate that cloned portfolios rebalanced on the disclosure date of filings, successfully mirror the performance of the original funds, including both market-underperforming and-outperforming funds.
#118 – Time Series Momentum Effect
Etienne, Alban and Ohana, Jean-Jacques and Benhamou, Eric and Guez, Béatrice and Setrouk, Ethan and Jacquot, Thomas: Revisiting the Structure of Trend Premia: When Diversification Hides Redundancy
https://arxiv.org/abs/2510.23150
Abstract: Recent work has emphasized the diversification benefits of combining trend signals across multiple horizons, with the medium-term window-typically six months to one year-long viewed as the “sweet spot” of trend-following. This paper revisits this conventional view by reallocating exposure dynamically across horizons using a Bayesian optimization framework designed to learn the optimal weights assigned to each trend horizon at the asset level. The common practice of equal weighting implicitly assumes that all assets benefit equally from all horizons; we show that this assumption is both theoretically and empirically suboptimal. We first optimize the horizon-level weights at the asset level to maximize the informativeness of trend signals before applying Bayesian graphical models-with sparsity and turnover control-to allocate dynamically across assets. The key finding is that the medium-term band contributes little incremental performance or diversification once short- and long-term components are included. Removing the 125-day layer improves Sharpe ratios and drawdown efficiency while maintaining benchmark correlation. We then rationalize this outcome through a minimum-variance formulation, showing that the medium-term horizon largely overlaps with its neighboring horizons. The resulting “barbell” structure-combining short- and long-term trends-captures most of the performance while reducing model complexity. This result challenges the common belief that more horizons always improve diversification and suggests that some forms of time-scale diversification may conceal unnecessary redundancy in trend premia.
And several interesting free blog posts that have been published during the last 2 weeks:
Thanksgiving and Christmas Trading Strategies
This article examines the impact of major consumer holidays, Thanksgiving and Christmas, on financial markets. Using historical price data from 2004 to 2024, we analyze daily performance trends in the 10 trading days before and after each holiday to determine whether seasonal spending influences asset prices. Our findings suggest that seasonal consumer spending influences financial markets, with Amazon benefiting around Thanksgiving and gold during Christmas.
How to Value Overvalued MicroStrategy?
MicroStrategy has become one of the most polarizing companies in public markets. Once a conventional business intelligence firm, it has transformed into the world’s largest publicly traded Bitcoin proxy, holding over a million BTC on its balance sheet and continuously raising capital to buy more. Supporters praise it as a visionary “Bitcoin ETF with leverage,” while critics argue it is an irrationally overvalued vehicle whose market capitalization regularly trades far above the fair value of its underlying assets. The persistent premium — the gap between MicroStrategy’s equity value and the market value of its Bitcoin holdings — has puzzled analysts, defied traditional valuation logic, and raised the question: why does this spread exist, and why does it not close through arbitrage? A recent academic paper, Valuing MicroStrategy, offers a structural model that explains this phenomenon and sheds light on how the firm’s unique financing mechanics allow its stock price to exceed the value of its assets.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
1192 – Defense First – A Multi-Asset Tactical Model for Adaptive Downside Protection
1193 – Grid Trading Strategy for BTC
1202 – Bitcoin Pre-Holiday Momentum Strategy
1203 – Bitcoin ETFs in Conventional Multi-Asset Portfolios
1204 – VIXY Tail Hedge Strategy
