New Strategies:
#1220 – Low-volatility strategies with stop-losses for highly liquid cryptocurrencies
Period of rebalancing: monthly
Markets traded: crypto
Instruments used for trading: crypto
Complexity: Moderate
Backtest period: 2017-2021
Indicative performance: 9%
Estimated volatility: 10%
Source paper:
Kaya, Orçun and Mostowfi, Mohdi: Low-volatility strategies for highly liquid cryptocurrencies
https://doi.org/10.1016/j.frl.2021.102422
Abstract: Managing extreme price fluctuations in cryptocurrency markets are of central importance for investors in this market segment. Using a sample of highly liquid cryptocurrencies from January 2017 to June 2021, this paper proposes a dynamic investment strategy that selects cryptocurrencies based on their historical volatility and is complemented by a simple stop-loss rule. Our results reveal that investing in highly concentrated low volatility cryptocurrency portfolios with six to twelve months volatility look-back and holding period generate statistically significant excess returns. By including a simple stop-loss rule, the downside risk of cryptocurrency portfolios is reduced markedly, and the Sharpe ratios are improved significantly.
#1221 – Large Moves in the Foreign Exchange Market
Period of rebalancing: Daily
Markets traded: currencies
Instruments used for trading: options
Complexity: Very Complex
Backtest period: 2006-2024
Indicative performance: 53.42%
Estimated volatility: 20.01%
Source paper:
Kostakis, Alexandros and McBride, Brandon A. and Sarno, Lucio and Wang, Junxuan: Large Moves in the Foreign Exchange Market
https://ssrn.com/abstract=5781462
Abstract: The timing of large currency moves – say three-standard deviations events – is often considered random. In contrast, we show that a non-trivial proportion of large moves in exchange rates is predictable in real time via the slope of the term structure of option-implied volatility (IV). Specifically, the difference between short-term and longer-term IVs is a significant and robust predictor of large currency returns in absolute value. This predictive relationship is exploitable in a trading strategy that buys strangles when the term structure of IV is sufficiently inverted, to profit from the subsequent realization of such large moves.
#1222 – Box Index Strategy
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Simple
Backtest period: 1998-2025
Indicative performance: 11.51%
Estimated volatility: 14.23%
Source paper:
Belobrad, David and Vojtko, Radovan: Alternative Market Signals: Investing with the Box Manufacturing Index
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5878883
Abstract: 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. For example, recent focus has been on construction inventory statistics, where large stockpiles have been interpreted as a signal of weakening demand for construction activity. This, in turn, may reflect broader consumer and corporate hesitancy to spend, implicitly suggesting a potential decline in asset prices. In this article, we examine a different type of alternative indicator: 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.
#1223 – Tech Momentum Alpha
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple
Backtest period: 1926-2025
Indicative performance: 5.05%
Estimated volatility: 15.78%
Source paper:
Beluská, Vojtko, Cmorej: Can Technology Sector Leadership Be Systematically Exploited?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5640430
Abstract: 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.
#1224 – Holiday Market Boost Strategy
Period of rebalancing: Daily
Markets traded: equities, commodities
Instruments used for trading: stocks, ETFs
Complexity: Simple
Backtest period: 2004 – 2024
Indicative performance: 7.66%
Estimated volatility: –
Source paper:
Pauchlyová, Margaréta and Vojtko, Radovan and Cmorej, Peter: Thanksgiving and Christmas Trading Strategies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5700602
Abstract: 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.
#1225 – Characteristics-Managed Momentum
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex
Backtest period: 2003 – 2025
Indicative performance: 11.9%
Estimated volatility: 12.8%
Source paper
Beckmeyer, Heiner and Wiedemann, Timo: All Days Are Not Created Equal: Understanding Momentum by Learning to Weight Past Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5702162
Abstract: By flexibly weighting the information contained in past realized returns, we construct a momentum strategy that outperforms and subsumes the performance of traditional stock momentum. The strategy performs well in crises and continues to work in the most recent decades. We show that the way past returns are weighted is in line with the strategy exploiting an underreaction to the information contained in realized returns, but also investigate alternative behavioral and risk-based explanations. We find that the response to earnings announcements, market-wide jumps and large individual returns realized in the formation period are most informative about future stock returns.
#1226 – Social Media and Crude Oil Market
Period of rebalancing: Daily
Markets traded: commodities
Instruments used for trading: futures
Complexity: Very complex
Backtest period: 2016 – 2022
Indicative performance: 48.25%
Estimated volatility: 51.75%
Maa, Gaoping and Tourani-Rad, Alireza and Xu, Yahua and Zhou, Z. Ivy: Is Social Media Information Noise or Fundamentals? Evidence from the Crude Oil Market
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5387113
Abstract: This paper examines the role of social media sentiment in predicting returns in the crude oil market. We find that social media sentiment significantly predicts crude oil market returns, with its predictive power stemming from both fundamental information and noise. Positive sentiment is primarily associated with fundamental information, leading to lasting price changes, whereas negative sentiment is largely noise-driven, resulting in temporary price changes that eventually revert. In addition, only positive sentiment predicts crude oil inventory changes, further emphasizing its link to fundamental information. Furthermore, we isolate social media insights from news media and find that social media provides unique, incremental information beyond traditional news outlets.
#1227 – Bond Factor Momentum
Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Very complex
Backtest period: 2002 – 2022
Indicative performance: 9%
Estimated volatility: 10.46%
Keshavarz, Javad and Sirmans, Stace: Bond Factor Momentum and Its Predictability for Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5242278
Abstract: This paper investigates momentum effects in corporate bond factors and spillover into equity markets. We find robust evidence of momentum within bond factors, with past winners outperforming losers by economically significant margins (up to 11.2% per year). This factor-level momentum subsumes security-level bond momentum, suggesting that bond return predictability is primarily a systematic rather than idiosyncratic phenomenon. We also discover significant cross-market momentum spillovers from bond to stock factors, with stronger effects for factors exposed to credit risk. These bond-to-stock spillovers intensify during market stress and improve with bond market liquidity. Our findings suggest bond markets play a unique role in processing systematic information, particularly related to credit conditions, challenging the notion of equity market informational dominance.
#1228 – Multi-Timeframe Bitcoin Trend Strategy
Period of rebalancing: Intraday
Markets traded: crypto
Instruments used for trading: crypto
Complexity: Moderate
Backtest period: 2018 – 2025
Indicative performance: 14.82%
Estimated volatility: 13.8%
Source paper:
Vojtko, Radovan and Mesíček, David: How to Design a Simple Multi-Timeframe Trend Strategy on Bitcoin
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5748642
Abstract: This study investigates the construction and evaluation of a simple multi-timeframe trend-following strategy applied to Bitcoin. We begin with a naïve baseline model and progressively enhance it using daily–hourly trend confirmation and a trailing exit mechanism. Using BTC/USD data from Gemini Exchange between 2018 and 2025, we demonstrate how multi-timeframe structure improves stability, reduces drawdowns, and generates superior risk-adjusted performance compared to both buy-and-hold and a pure MACD strategy. Results show meaningful improvements in Sharpe and Calmar ratios as complexity increases logically without parameter optimization.
#1229 – Gold Silver Pair Trading Using Machine Learning
Period of rebalancing: Daily
Markets traded: commodities
Instruments used for trading: futures, ETFs
Complexity: Very Complex
Backtest period: 2023 – 2025
Indicative performance: 11.2%
Estimated volatility: 15.8%
Source paper:
Mittal, Vineet Kumar and Mittal, Richa: Gold Silver Pair Trading – Mean Reversion Strategy Using Machine Learning
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5710242
Abstract: The gold–silver relationship has long served as a benchmark for relative-value and mean-reversion trading in commodity markets. This study develops a quantitative framework that integrates classical cointegration analysis with modern machine learning (ML) techniques to enhance trading performance in the gold–silver spread. Using futures and ETF data (COMEX GC–SI, GLD–SLV) from 2015–2025, the paper first confirms long-term cointegration and then applies dynamic hedge estimation via Kalman filtering. Mean reversion signals are standardized through z-score normalization and augmented with ML-based regime filters (Gradient Boosting, Support Vector Machine) trained on volatility, macro, and sentiment features to distinguish stable versus unstable spread conditions. Backtests reveal that ML-filtered mean reversion trades outperform static statistical arbitrage by improving entry precision and reducing drawdowns, particularly during high-volatility regimes such as the 2020 COVID crisis, the 2022 inflation spike, and the 2024 commodity rally. The proposed hybrid framework demonstrates that integrating adaptive learning models into classical econometric approaches significantly enhances profitability and risk control for commodity pair trading.
A new research paper related to existing strategies:
#1071 – Interday Cross-Sectional Momentum
Haendler, Charlotte and Heston, Steven L. and Korajczyk, Robert A. and Sadka, Ronnie: The Intra-Day Stock Return Periodicity Puzzle
https://ssrn.com/abstract=5749704
Abstract: Heston et al. (2010) find puzzling intra-day patterns in stock returns, which are particularly strong at the beginning and end of the trading day. We demonstrate the patterns persist out-of-sample and test several explanatory variables, including trading frictions and proxies for trader type. They jointly explain all the open and midday periodicity, and up to 30% of the closing interval periodicity. Our proxies for institutional trading-changes in lendable shares, VWAP-like trading, and index inclusion-are the most significant explanatory variables. Overall, the results suggest that open periodicity is driven by VWAP trading while close periodicity by market-on-close trading.
#172 – Cold IPOs Effect
Libson, Adi and Chaim, Danielle A. and Mugerman, Yevgeny: The Price of Power: The Big Three and IPO Underpricing
https://ssrn.com/abstract=5711482
Abstract: The meteoric rise of asset management giants over the last two decades has ignited intense scrutiny among legal scholars and economists. The control wielded by these behemoths over vast capital pools and their outsized influence over corporate America is argued to pose myriad economic, social, and corporate governance challenges. In this Article, we uncover a critical yet overlooked arena in which the concentration in the asset management industry and the rising dominance of giant institutional investors manifest additional deleterious consequences: capital markets, specifically in the context of Initial Public Offerings (IPOs). We present empirical evidence that concentrated market power in the hands of a core group of giant asset managers has exacerbated IPO underpricing-defined as the difference between the offer price and the stock’s closing price on the first day of trading. Our analysis indicates that from 2002 to 2022, the simultaneous participation of the three largest asset managers-BlackRock, Vanguard, and Fidelity-in IPOs increased underpricing levels by an average of 16.7 percentage points. Even after controlling for IPO size, bookrunner, industry, and year fixed effects, this impact remains substantial at 9.7 percentage points. The participation of such market-moving institutional investors can drive up underpricing through various mechanisms. Our analysis pinpoints several channels through which these investors signal their bidding intentions, share information, and even coordinate their positions during the IPO process. Some of these mechanisms warrant closer scrutiny, as they may constitute collusive behavior by institutional investors in their role as competing bidders in IPOs-potentially violating antitrust laws. Our novel analysis of underpricing through the lens of institutional-investor market power adds a crucial piece to the IPO underpricing puzzle and illuminates the marked correlation between rising underpricing levels and the ascendancy of asset manager capitalism. Notably, over the past decade, underpricing has soared to extraordinary levels, resulting in an unprecedented $90 billion left ‘on the table’ by issuers. To counteract this effect of asset manager capitalism, we propose a three-pronged approach: first, introducing market-structure changes to limit the size and curb the market power of asset managers; second, enhancing transparency within the book-building process; and third, imposing communication restrictions among prospective bidders during the pricing process.
#974 – Trend-Following Strategy in Cryptocurrencies
#1131 – Catching Crypto Trends
Karassavidis, Ioannis and Kateris, Lampros and Ioannidis, Maximos: Quantitative Evaluation of Volatility-Adaptive Trend-Following Models in Cryptocurrency Markets
https://ssrn.com/abstract=5821842
Abstract: Cryptocurrency markets exhibit extreme volatility, heavy-tailed return distributions, and structurally persistent inefficiencies arising from fragmented liquidity and heterogeneous market participants. In addition to posing a challenge to conventional portfolio theory, these features offer a special testing ground for momentum-based trading systems. This paper presents a trend-following framework that adjusts to volatility in high-frequency cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). The proposed structure uses multi-horizon moving averages to detect trends. It uses a multi-timeframe Relative Strength Index (RSI) filter to confirm momentum and employs Average True Range (ATR) scaling to manage risk based on volatility. Trade exits are set by combining ATRbased trailing stops with Exponential Moving Average (EMA) slope reversal detection. This combination creates an exit system that balances staying in the trend while managing risk effectively. Furthermore, cross-correlation analysis reveals that BTC and ETH share a time-varying but persistently high correlation structure (ρ ≈ 0.8-0.9), suggesting common volatility regimes. This validates the use of joint parameter optimization across correlated assets as a robustness-enhancing constraint. Collectively, the findings indicate that volatility-adjusted, momentum-confirmed trendfollowing systems can generate statistically and economically significant excess returns in cryptocurrency markets, provided that their optimization framework is adaptive, crossvalidated, and sensitive to dynamic volatility states.
#1038 – Presidential Cycle and Performance of the Stock Market
Han, Yufeng and Lu, Yueliang and Xu, Weike and Zhou, Guofu: From Red to Blue: The Long-Run Inversion of Political Cycles and Stock Market Returns Over 153 Years
https://ssrn.com/abstract=5749464
Abstract: We show that U.S. political cycles have shaped stock market returns in a time-varying manner driven by long-run shifts in party ideology and policy. Using 153 years of data, we document a striking inversion: before 1929, excess returns were about 7% higher under Republican presidents, reflecting the Republican Party’s pro-business policies of sound money, low taxes, and limited federal intervention, consistent with a consumption-risk channel. After the New Deal realignment, Democratic administrations earn a premium exceeding 9%, aligning with their emphasis on economic stabilization and social insurance and a stronger risk-aversion channel. Political ideology and policy thus critically shape asset prices.
#527 – Employee Satisfaction, ESG and Stock Returns
#692 – Employee Satisfaction Factor
#806 – Employee Sentiment and Stock Returns
Cai, Wei and Campbell, Dennis and Chen, Yaxuan and Chen, Yufei and Prat, Andrea: Measuring Quality
https://ssrn.com/abstract=5840182
Abstract: Quality is fundamental to firm value creation, yet existing measures are limited in coverage, lack cross-firm comparability, and primarily rely on lagging indicators that capture product or service failures only after they materialize. We develop a novel measure of firm-year-level product and service quality using over 4.3 million employee written reviews on Glassdoor. Leveraging machine learning models trained on a subset of firms with third-party customer satisfaction data, we construct quality indices for S&P 1500 firms spanning 2008 to 2023. The resulting quality measures exhibit meaningful variation across firms and within firms over time. In out-of-sample tests, our text-based quality indices demonstrate strong predictive power for future quality provision, emerging as the single most important predictor relative to firm fundamentals and Glassdoor ratings. We validate our measure by examining its association with alternative quality metrics. We show that our quality measures are useful in predicting important quality-related firm outcomes such as product recalls, brand value, and profitability. We also construct an alternative set of quality measures using a zero-shot prompt-based approach as well as a supervised fine-tuning approach with GPT models to assess the potential of large language models and generative AI in capturing firm-level quality provision. Our paper shows the value of employee voices as a powerful, forward-looking, and scalable signal of firm quality provision. The paper offers implications for stakeholders seeking to identify quality-related risks and opportunities before they become externally visible.
#530 – Jump Risk in Stocks
Bahcivan, Hulusi and Dam, Lammertjan and Gonenc, Halit, Dark Side of the Day: Overnight Price Jumps and Short-Term Return Predictability
https://ssrn.com/abstract=5648748
Abstract: Using 9,283 stocks listed on the NYSE, AMEX, and NASDAQ, we identify investor overreaction to overnight information shocks and show that cumulative overnight jump returns negatively predict short-term returns in both positive and negative episodes. This relationship remains robust across sub-periods, including times of market distress, and across quintile portfolios formed on firm-specific factors. A zero-cost contrarian strategy based on extreme deciles of lagged overnight jump returns yields a 0.6% risk-adjusted loss over a one-month horizon, implying that the overreaction is transient. Overall, the findings suggest that the price discovery is contingent on an active trading environment as well, and overreaction to overnight information shocks and return reversals are short-lived market phenomena.
And several interesting free blog posts that have been published during the last month:
Can We Blame Index Funds for More Volatile Financial Markets?
Over the past seven decades, U.S. equity-market volatility has roughly doubled—from about 10% to 20%—and this increase is concentrated at the market level and at high frequencies (daily volatility up by ~130%, weekly by ~75%, monthly by ~40%). A new paper by Lars Lochstoer and Tyler Muir argues that this structural change is not driven by macroeconomic fundamentals or firm-level shocks but by the dramatic growth of index-level trading (futures, ETFs, index mutual funds, and extended trading hours). Using statistical investigations—the 1997 introduction of E‑mini S&P 500 futures and historical NYSE trading‑hour changes—the authors provide causal evidence that easier and larger trading of the market portfolio has raised aggregate volatility through higher trading volume and a shift toward systematic demand shocks.
Can We Use U.S. Government Shutdowns as a Signal for Investment Decisions?
In recent times, we have observed heightened volatility across financial markets. Concerns surrounding government shutdowns, as well as the uncertainty they create, do little to calm these fluctuations. Rather than being purely disruptive, however, such events raise an intriguing question: could these episodes of political and economic uncertainty actually be leveraged to our advantage in investment strategies? In this article, we will examine several asset classes and attempt to assess whether this phenomenon provides a sufficiently relevant signal for investment decisions.
Understanding Gold – Hedge, Diversifier, or Overpriced Insurance?
In Understanding Gold, Claude B. Erb and Campbell R. Harvey examine gold’s enduring reputation as a safe-haven asset and contrast popular narratives with empirical evidence. While gold has preserved purchasing power over millennia—what the authors call the “golden constant”—this does not translate into reliable short- or medium-term inflation hedging. Gold’s volatility is comparable to equities, while inflation itself is far more stable, making gold an unreliable hedge over typical investor horizons. The key insight is that gold’s real long-run return is approximately zero, which is precisely what one should expect from a hedging asset rather than a growth asset.
Cross-Asset Price-Based Regimes for Gold
This article develops a price-based macro–financial model of gold that formally links its medium-horizon return dynamics to cross-asset risk-premium configurations. Although gold has traditionally been conceptualized as a non-yielding inflation hedge or safe-haven asset, contemporary empirical evidence reveals a substantially more intricate structure: gold’s forward returns are systematically conditioned by the joint momentum of (i) gold itself and (ii) long-duration U.S. Treasury total-return indices. The alignment of these two signals appears to encode macroeconomic information—specifically the direction of real interest rates, the stance and expected trajectory of Federal Reserve policy, and the prevailing global risk-appetite regime.
Top Ten Blog Posts on Quantpedia in 2025
One year is again behind us (in this case, it was 2025), and we are all a little older (and hopefully richer and/or wiser). Turn-of-the-year period is usually an excellent time for a short recap. Over the past 12 months, we have kept our pace and published nearly 70 short analyses of academic papers and our own research articles. So let’s summarize 10 of them, which were the most popular (based on the Google Analytics ranking). The top 10 is diverse, as usual; once again, we hope that you may find something you have not read yet …
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
1208 – Leveraged ETFs in Asset Allocation: Opportunity or Trap?
1212 – Post-FOMC Drift in the Equity Options Market
1213 – Factor Momentum in REITs
1218 – Negative Overnight Return in China
1219 – Lazy Man’s Country Momentum Strategy
1223 – Tech Momentum Alpha
1224 – Holiday Market Boost Strategy
