New Strategies:
#1194 – Tactical Allocation for Vanguard Investors
Period of rebalancing: Monthly
Markets traded: equities, bonds, commodities
Instruments used for trading: funds
Complexity: Simple strategy
Backtest period: 2000-2025
Indicative performance: 6.16%
Estimated volatility: 4.09%
Source paper:
Carlson, Thomas D.: Tactical Allocation for Vanguard Investors: A Defensive Strategy for Retirement Portfolios
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5369659
Abstract: This paper introduces a streamlined, transparent, and empirically grounded tactical allocation strategy tailored for retirement-focused investors. The model rotates among four distinguished mutual funds available through Vanguard’s platform: Vanguard Wellington (VWELX), Vanguard Wellesley Income (VWINX), Permanent Portfolio Fund (PRPFX), and Vanguard Market Neutral (VMNFX). These funds span diverse economic exposures—growth, income, inflation protection, and market neutrality—and the allocation is evaluated bimonthly using a dual momentum framework. From January 2000 through June 2025, the model delivered a compound annual growth rate (CAGR) of 6.16% with volatility of just 4.09%, achieving a Sharpe ratio of 1.02 and a worst calendar year return of -1.87%. Its low drawdowns significantly outperformed both equal-weight blends and the Vanguard Balanced (60/40) portfolio. Fully rules-based and composed of no-load, low-cost funds, the strategy is easily executed using end-of-month data. The long tenures of the component funds—with inception dates ranging from 1929 to 1998—lend institutional-grade credibility and out-of-sample robustness. In plain terms, this strategy offers a simple way to help retirement investors avoid deep losses and stay invested through market turbulence. By rotating among a small number of proven mutual funds based on performance trends, it delivers steadier returns with less risk—no Wall Street complexity required.
#1195 – Elastic Momentum Factor in Cryptocurrencies
Period of rebalancing: Weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Complex strategy
Backtest period: 2014-2024
Indicative performance: 46.9%
Estimated volatility: 9.34%
Source paper:
Li, Jennifer and Liao, Li and Yang, Siyuan and Zhang, Hong: Predictive Crypto Crashes and Asset Pricing Implications: An Inelastic Market Perspective
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5328940
Abstract: Frequent and large-scale crashes are hallmarks of cryptocurrencies. We propose a new mechanism to explain this phenomenon: blockchain-based capital inelasticity. Empirically, inelastic cryptocurrencies underperform elastic ones, reflecting inelasticity-induced crash risk. This effect persists even among past winners, suggesting that crash risk dominates momentum among inelastic cryptocurrencies. Consequently, only elastic cryptocurrencies deliver significant momentum returns, allowing a strategy that combines inelastic crashes and elastic momentum to generate higher returns with longer durations than momentum. Analysis of ICO-induced Ethereum blockchain congestion supports a causal interpretation of our mechanism. Our results highlight the importance of inelastic capital in shaping cryptocurrency prices.
#1196 – Robust Skewness Multi-Asset Strategy
Period of rebalancing: Monthly
Markets traded: equities, commodities, bonds, currencies
Instruments used for trading: futures, forwards
Complexity: Complex strategy
Backtest period: 1999-2024
Indicative performance: 5.78%
Estimated volatility: 10%
Suvak, Colin and Masturzo, Jim: A Tail of Five Skews
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5554621
Abstract: We show that a highly statistically significant total skewness risk premium is embedded in the cross-section of returns within a large universe of multi-asset futures and forwards, within a broad set of 215 long/short style factors formed on that universe, and within the cross-sectional equity factor zoo. The skewness risk premium is most robust when it is measured in a relatively new, intuitive way that minimizes the impact of outliers while still capturing information in the tails, which we demonstrate by evaluating five candidate methods across a battery of empirical tests. We show there is compensation for bearing skewness risk in both the long run and ex ante on a point-in-time basis available to investors.
#1197 – An Information Factor
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex strategy
Backtest period: 1996-2019
Indicative performance: 14.3%
Estimated volatility: 11.2%
Source paper:
Ma, Matthew and Martin, Xiumin and Zhou, Guofu: An Information Factor: What Are Skilled Investors Buying and Selling?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3205660
Abstract: We construct a novel information factor (INFO) using the informed trades of corporate insiders, short sellers, and option traders. INFO strongly predicts future stock returns – a long-short portfolio formed on INFO earns monthly alphas of 1.07%, substantially outperforming existing strategies including momentum. INFO explains hedge fund returns in the time-series and cross-section. Moreover, funds with higher covariation between their returns and INFO outperform by 0.29% per month. The results support theoretical predictions that trading volume contains unique information that is not contained in prices.
#1198 – Bitcoin Trading with Quantum Belief Networks
Period of rebalancing: Daily
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Very Complex strategy
Backtest period: 2019-2019
Indicative performance: 130.5%
Estimated volatility: 22.6%
Source paper:
Biswas, Aniruddha and Zaman, Saad: Bitcoin Trading with Quantum Belief Networks
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5281675
Abstract: The field of quantitative finance is constantly seeking new tools to exploit the complexities of the financial markets. With classical computers having limitations, the burgeoning field of quantum computing offers immense computational capabilities. Though Belief Networks have been useful in quantitative finance, their towering computational demands on classical systems limit their efficacy. On the other hand, Bitcoin’s popularity has increased in the last few years due to its unique features, such as decentralization and blockchain. Being relatively new Bitcoin’s market possesses huge potential. Price of the Bitcoin depends upon various economic and market factors, also possesses quite high volatility making traders worried while dealing with it. This project tries explore the potential of quantum computing technologies and Belief networks for developing new long-short Bitcoin trading strategy by leveraging the strengths of both paradigms.
#1199 – Volatility-Adjusted Leverage Strategy
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: ETFs, CFDs, futures
Complexity: Simple strategy
Backtest period: 2013-2025
Indicative performance: 29.81%
Estimated volatility: 37.48%
Source paper:
Beluská, Soňa and Vojtko, Radovan: Leveraged ETFs in Low-Volatility Environments
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5543518
Abstract: Leveraged ETFs (such as SPXL – (Direxion Daily S&P 500 Bull 3X Shares) offer amplified exposure to the S&P 500, promising high returns but exposing investors to volatility drag caused by daily rebalancing. This effect can significantly erode performance over longer horizons, particularly during periods of elevated market volatility. Inspired by recent research, The Volatility Edge, A Dual Approach For VIX ETNs Trading, focused on volatility-linked ETNs, we propose a volatility filter that adjusts ETF exposure based on the relationship between short-term realized volatility and implied volatility. By reducing exposure in high-volatility periods and maintaining it in calmer markets, this approach aims to harness leverage effectively while mitigating the most damaging drawdowns.
New research papers related to existing strategies:
#1199 – Volatility-Adjusted Leverage Strategy
Bezdjian, Rob: Deception by Design: Leveraged ETFs, Structural Fraud, and Proof of Outperformance
https://ssrn.com/abstract=5347238
Abstract: This white paper delivers a forensic examination of Leveraged and Inverse Exchange-Traded Products (LETPs), including both ETFs and ETNs, revealing that these instruments are structurally engineered to shift wealth from public investors to issuers. Through asymmetric decay, inverse underperformance, and embedded design flaws, LETPs create persistent, predictable imbalances that reward issuers by design, not by chance. Leveraging rules-based models and decades of historical data, the analysis shows these flaws are not merely observable, they are systematically exploitable. The core framework, Handsome Rob, achieves consistent outperformance of major benchmarks with lower volatility, beta, and drawdowns, posting a 10-year CAGR near 20% across the full Direxion LETF universe, with all backtest results included herein. This work forms the foundation for the follow-up paper Am I the Patsy? LETF Issuance Is Signal, Not Noise (Bezdjian, SSRN ID: 5360727), which transforms issuer share-issuance behavior into a tradable signal. Together, these studies prove that LETFs leave a repeatable profit footprint, one that can be tracked, modeled, and reversed to the issuer’s detriment. These findings underpin a broader whistleblower submission to the U.S. Securities and Exchange Commission (SEC) and are supplemented by the author’s book, Structured Fraud: Strategies to Profit from Wall Street’s Insatiable Desire to Defraud Investors. Every calculation, dataset, and rule is fully disclosed for independent validation, replication, and legal review. The conclusions herein reflect the author’s analysis of publicly available information and should be read as opinion, not as a definitive finding of liability.
#777 – Combined Momentum and Nearness to 52-week High
Scott, David J. and Scott, David J. and Geels, Harry J.: Maximizing Momentum with Double-Sorting
https://ssrn.com/abstract=5418174
Abstract: Several papers highlight the demise of momentum in U.S. equity markets in recent decades. Our analysis finds significant abnormal returns from 6-month momentum, but lack thereof from 12month and price-to-high momentum. We construct double-sorted portfolios to isolate 12-month and price-to-high effects. Within subgroups of stocks closer to their 52-week high, 12-month momentum factors deliver larger and more significant returns (t-statistics > 3). However, among long-only portfolios, 12-month momentum returns are higher within stocks farther from their 52-week high and lower within stocks closer to the high. Discrepancy between factor returns and long-only returns has implications for efficient portfolio construction.
And several interesting free blog posts that have been published during the last 2 weeks:
Can Technology Sector Leadership Be Systematically Exploited?
The U.S. equity market has periodically been dominated by a few technology-driven stocks, most recently the so-called “Magnificent Seven.” Historically, similar dominance occurred during the Nifty Fifty era in the 1960s–1970s and the dot-com boom in the 1990s. These periods of concentrated leadership often led to temporary outperformance, but systematically capturing such gains has proven challenging. Our study investigates the potential to exploit technology sector dominance using momentum-based strategies across Fama–French 12 industry portfolios, analyzing whether long-only, long-short, and rolling-basis approaches can generate persistent alpha, and assessing the limitations of simple timing methods.
The End-Of-Month Effect in Value–Growth and Real‑Estate–Equity Spreads
The clustering of excess returns on the final trading days of the month constitutes a robust empirical regularity with significant implications for portfolio construction. We document a month-end premium that is both statistically and economically significant, distinct from the canonical turn-of-the-month (ToM) effect. Our strategy highlights systematic style rotations—particularly shifts in value versus growth exposures, as proxied by the IVE–IVW spread—and documents parallel contemporaneous dislocations between real-estate and broad-equity benchmarks, as measured by the IYR–SPY spread.
Cryptocurrency as an Investable Asset Class – 10 Lessons
Cryptocurrencies have matured from experimental curiosities into a viable investable asset class whose return-generation and risk characteristics merit treatment within empirical asset pricing. A recent paper by Nicola Borri, Yukun Liu, Aleh Tsyvinski, Xi Wu summarizes ten facts from the literature that show cryptocurrencies share important similarities with traditional markets—comparable risk-adjusted performance and a small set of cross-sectional factors—while retaining distinctive features such as frequent large jumps and price signals embedded in blockchain data. Key themes include portfolio diversification, factor structure, market microstructure, and the evolving role of regulation and derivatives in shaping market discovery and stability.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
1173 – Crypto ETF Spread Mean-Reversion
1174 – Buying the Worst-Performing S&P 500 Equities
1186 – April Inflow Strategy
1199 – Volatility-Adjusted Leverage Strategy
