New Strategies:
#1212 – Post-FOMC Drift in the Equity Options Market
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: options, futures
Complexity: Simple
Backtest period: 1996-2023
Indicative performance: 1.45%
Estimated volatility: –
Source paper:
Zhang, Yu and Kappou, Konstantina and Urquhart, Andrew: Post-FOMC Drift in the Equity Options Market
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5464630
Abstract: In this paper, we document significant excess returns on equity options on the day following scheduled Federal Open Market Committee (FOMC) meetings after controlling for volatility-related variables. Our findings reveal that this drift stems from market participants’ disagreement in interpreting interest rate decisions. Further, price jumps triggered by monetary surprises also contribute to the observed pattern. Our paper highlights the dynamic response of derivative markets to FOMC decisions and offers insights into the role of effective communication between central banks and financial markets.
#1213 – Factor Momentum in REITs
Period of rebalancing: Monthly
Markets traded: REITs
Instruments used for trading: stocks
Complexity: Simple
Backtest period: 1998-2021
Indicative performance: 5.68%
Estimated volatility: 4.2%
Tomtosov, Aleksandr and Rechmedina, Svetlana and Dobrynskaya, Victoria: Momentum Factor or Factor Momentum in REITs Market?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5631072
Abstract: We study the application of factor investing in the market for real estate (REITs). The analysis of traditional factor strategies, such as momentum, value, size and profitability, reveals their unstable and atypical behavior in comparison to the equity market, however, there is a strong momentum effect in the factor strategies. We propose a new dynamic strategy, which is based on the factor momentum effect. The strategy buys the factor portfolios with the best past performance and sells short the factor portfolios with the worst past performance, with monthly rebalancing. The factor momentum strategy yields a significant positive average return and alpha of about 6% pa, it has high Sharpe and Sortino ratios. The strategy outperforms all traditional single-factor strategies in different market states and sub-periods. The superior returns in adverse market conditions makes it particularly attractive.
#1214 – Clustering-Based Lead-Lag Portfolio Strategy
Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 2000-2024
Indicative performance: 23.35%
Estimated volatility: 15%
Source paper:
Lu, Yutong and Zhang, Ning and Reinert, Gesine and Cucuringu, Mihai: A tug of war across the market: overnight-vs-daytime lead-lag networks and clustering-based portfolio strategies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5371952
Abstract: In this research, we show that the tug of war is not only at individual stock level but also a networked trading behaviour across the entire market. By decomposing daily returns into overnight and daytime components, we construct directed networks to capture the lead-lag relations between overnight speculations and daytime price corrections, and vice versa, across stocks. We originate a novel clustering-based framework to construct portfolios to capture the cross-stock tug of war. In order to identify disjoint leader and lagger groups in directed lead-lag networks, we develop a specialized spectral clustering algorithm. By generating trading signals exclusively from the leader stocks to predict and trade lagger stocks, we isolate pure cross-stock interactions from autocorrelation within individual stocks. Our empirical results support the conclusion that both noise traders and arbitrageurs trade at the portfolio level and disseminate the tug of war across stocks. With backtests spanning from 2000-01-03 to 2024-12-31, the cross-stock lead-lag portfolios generate remarkable returns and significant alphas on top of portfolios representing firm-level tug-of-war reversals and other the pricing factors. Moreover, the performance of cross-stock lead-lag portfolios grow in recent years, while the stock-specific reversals decay.
#1215 – Short Volatility Strategy Execution with Large Language Models
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: options
Complexity: Very Complex
Backtest period: 2023-2025
Indicative performance: 8.2%
Estimated volatility: 5.13%
Source paper
Garmash, Dmitry: Short Volatility Strategy Execution with Large Language Models
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5320847
Abstract: This paper is devoted to the Short Volatility Strategy by using Large Language Models (LLM), comparing efficiency and profitability of LLaMA2 and GPT3.5. There are several methods for implementing a Short Volatility Strategy. Short Volatility Strategy is employed when an investor expects low volatility and minimal price fluctuation. This often involves selling options or engaging in strategies like writing covered calls. The premise here is to generate returns through the premium received from selling these options, with the expectation that the options will expire worthless or decrease in value due to the low volatility. Short volatility, offering more consistent returns in stable conditions, poses a significant risk during market upheavals. Investments in a Short Volatility Strategy are typically made through Short Volatility Exchange-Traded Funds (ETFs) and Short Volatility Options: 1) Short Volatility ETFs: these ETFs are designed to perform inversely to volatility indices like the VIX. They tend to increase in value when market volatility decreases, offering a straightforward method for investors to benefit from stable market conditions; 2) Short Volatility Options: this involves selling options, such as writing put or call options. The idea here is to collect the premiums from the options sold. If the market remains stable or volatility declines, the options will likely expire worthless or decrease in value, allowing the investor to keep the premium as profit. Implementing a Short volatility strategy is most effective under specific circumstances: Market Conditions: it is best employed in tranquil market environments where volatility is low or expected to diminish. This strategy is prevalent during extended periods of economic growth or market stability. Income Generation: for investors seeking consistent income, short volatility can be an appealing strategy. It provides regular returns through the collection of premiums from sold options. The advantages of a Short Volatility Strategy include steady income generation and performance in stable market conditions. However, the primary disadvantage lies in its risk profile. While the strategy offers consistent small returns, it exposes the investor to potentially unlimited losses in the event of a sudden spike in market volatility. This was notably evident during events like the 2018 volatility spike, where short volatility positions experienced significant losses. Variance swaps, volatility swaps, volatility futures and options are all vehicles that can be used to implement and trade in a short volatility view. In particular, a strategy that takes a systematic approach to selling index options may generate stable returns through a variety of market conditions. Typically, there are six variables that affect option pricing: security price, strike price, time to maturity, interest rate, dividend yield, and implied volatility. If all other variables are held constant, as implied volatility increases, so too does the value of an option. Rising expected volatility reflects a higher likelihood of a larger market move. Therefore, there is greater desire for insurance, resulting in higher option prices. Using direct options transactions to sell volatility involves being short both puts and calls on the same underlying security or instrument. A short straddle, which combines a short put and a short call of equal strike price, maturity, and size is one of the most basic approaches an investor can use. Another example is a short strangle, or simultaneously selling both out of the money puts and calls with the same maturity and size. These strategies are employed by sophisticated investors and fund managers. As published in January 2025 research report from Cambridge Associates, since 1990 implied volatility has been higher than realized volatility in 86,9% of monthly observations, with a mean difference of 4,5%. Our own research suggests similar results. Using monthly observations, and time periods of 1, 3, 6 and 12 months, I found that implied volatility remains higher than realized volatility approximately 85% of the time with a mean spread of 2,7% and a median spread of 3,0%. Furthermore, the spread between implied and realized volatility exists, regardless of the duration of maturity or time of measurement. A process of systematically selling both puts and calls on the S&P500 would have resulted in better returns than owning the total return of the index outright over the same period of time. In addition, this approach would have exhibited lower volatility while offering comparable transparency and liquidity. I suspect that such findings are not specific to the U.S. equity markets, as long as the markets in which these options are sold have sufficient liquidity and depth. Since these strategies result in being a net-seller of
#1216 – Attention Factors for Statistical Arbitrage
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 1998-2021
Indicative performance: 7.93%
Estimated volatility: 3.62%
Source paper:
Epstein, Elliot and Wang, Rose and Choi, Jaewon and Pelger, Markus: Attention Factors for Statistical Arbitrage
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5588830
Abstract: Statistical arbitrage exploits temporal price differences between similar assets. We develop a framework to jointly identify similar assets through factors, identify mispricing and form a trading policy that maximizes risk-adjusted performance after trading costs. Our Attention Factors are conditional latent factors that are the most useful for arbitrage trading. They are learned from firm characteristic embeddings that allow for complex interactions. We identify time-series signals from the residual portfolios of our factors with a general sequence model. Estimating factors and the arbitrage trading strategy jointly is crucial to maximize profitability after trading costs. In a comprehensive empirical study we show that our Attention Factor model achieves an out-of-sample Sharpe ratio above 4 on the largest U.S. equities over a 24-year period. Our one-step solution yields an unprecedented Sharpe ratio of 2.3 net of transaction costs. We show that weak factors are important for arbitrage trading.
#1217 – Disagreement on Tail
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 1967-2021
Indicative performance: 12.12%
Estimated volatility: 27.54%
Source paper:
Chen, Haiwei and Chen, Yong and Li, Jiangyuan and Luo, Dan: Disagreement on Tail
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5628930
Abstract: We propose a novel measure, DOT, to capture belief divergence on extreme tail events. Defined as the standard deviation of expected probability forecasts generated by distinct information processing functions and neural network models, DOT exhibits significant predictive power for future stock returns. A long-short portfolio based on DOT yields an average return of-1.07% per month. We validate the economic prediction of DOT with the risk-sharing framework proposed by Chen, Joslin, and Tran (2012). Finally, we find that disagreement on tail events accounts for a disproportionately large share of overall belief dispersion and exhibits high persistence.
#1218 – Negative Overnight Return in China
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very Complex
Backtest period: 2017-2022
Indicative performance: 12.4%
Estimated volatility: –
Source paper:
Jiang, Haobo and Li, Xinping: Adverse Selection and Overnight Returns: Information-Based Pricing Distortions Under China’s “T+1” Trading
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5674862
Abstract: Contrary to the US, Chinese stock markets exhibit negative overnight returns that appear to be highly affected by the extent of information asymmetry. China’s “T+1” trading rule, which prohibits same-day selling, exacerbates adverse selection for uninformed buyers by limiting them to react to post-trade information. An information asymmetry-driven price discount thus emerges at market open, generating negative overnight returns, which further decrease with information asymmetry. Consistent with adverse selection, empirical evidence reveals lower overnight returns during market declines and high-volatility periods, with robust negative relationship between overnight returns and information asymmetry proxied by firm size, analyst coverage, and earnings announcement proximity. A model is introduced to rationalize our findings. This framework also sheds light on China’s “opening return puzzle”, the phenomenon that prices rise rapidly in the initial 30 minutes of trading, by showing how reduced adverse selection enables rapid price recovery during opening session.
#1219 – Lazy Man’s Country Momentum Strategy
Period of rebalancing: 6 months
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Simple
Backtest period: 1970-2024
Indicative performance: 13.09%
Estimated volatility: 16.88%
Source paper:
Estrada, Javier: The Lazy Man’s Momentum Strategy
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5644131
Abstract: Momentum strategies have a long track record of profitability across countries and asset classes. Institutional investors have embraced them long ago, but individual investors find them difficult to implement, at least in part due to the frequent rebalancing these strategies typically require. This article proposes a lazy man’s momentum strategy, based on semi‐annual rebalancing and small portfolios. The results reported here show that this lazy strategy beats a passive benchmark, delivering substantially higher return and compounding power, and higher risk‐adjusted return.
A new research paper related to existing strategies:
#2 – Momentum Asset Allocation Strategy
#3 – Sector Momentum – Rotational System
#14 – Momentum Factor Effect in Stocks
#15 – Momentum Factor Effect in Country Equity Indexes
#136 – Residual Momentum Factor
#162 – Momentum Effect in Stocks in Small Portfolios
Baltussen, Guido and Dom, M. Sipke and Van Vliet, Bart and Vidojevic, Milan: Momentum Factor Investing: Evidence and Evolution
https://ssrn.com/abstract=5561720
Abstract: Momentum is a foundational factor in equity markets. We review its evolution in the literature and analyze the momentum factor empirically across a wider variety of tests. Our empirical analyses demonstrate robust empirical support for the momentum factor over domestic and global stock markets spanning up to 150 years of data and a wide variety of design choices, establishing momentum’s resilience against data mining and arbitrage concerns. Momentum has transitioned from pure price-based trends to advanced fundamental, firm-specific, and network-based trends that improve the effectiveness of the momentum factor. Finally, momentum is exposed to crash risk, but we find that risk-managed momentum strategies mitigate the crash risk and improve the risk efficiency of the momentum factor. Overall, the momentum factor premium is sizable, robust, persistent, and fundamentally multi-dimensional.
And several interesting free blog posts that have been published during the last 2 weeks:
Systematic Edges in Prediction Markets
Prediction markets are financial platforms where participants trade contracts linked to future events, with prices reflecting collective probabilities. While these markets efficiently aggregate information, systematic inefficiencies create trading opportunities. Notable strategies include inter- and intra-market arbitrage, exploiting price differences across platforms or mispricing within a single market. Behavioral biases, such as the longshot bias, lead traders to overvalue underdogs and undervalue favorites, while bookmakers may manipulate odds to mislead naive participants. Experienced traders can exploit these patterns to secure profits. This article reviews common systematic edges in prediction markets, illustrates their practical application, and highlights the potential for profitable trading.
Alternative Market Signals: Investing with the Box Manufacturing Index
Investors are increasingly exploring alternative indicators to gain an edge in financial markets. Traditional signals, such as earnings reports or macroeconomic data, often come with delays or may already be priced in. As a result, unconventional metrics have attracted attention. In this article, we examine the Producer Price Index (PPI) for the Corrugated and Solid Fiber Box Manufacturing industry, including corrugated boxes and pallets. Our motivation is to evaluate this index’s effectiveness as a predictive signal for the S&P 500 ETF, sector-specific ETFs, and individual stocks such as Amazon (AMZN), one of the largest consumers of materials tracked by this index. We present several investment strategies that incorporate this indicator and assess whether it can enhance risk-adjusted returns.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
1196 – Robust Skewness Multi-Asset Strategy
1205 – Investor Memory and Stock Returns
1207 – Volatility Decay and Arbitrage in Leveraged ETFs
1209 – Santa Claus Rally
