Quantocracy

Quant Blog Mashup

Recent Quant Links from Quantocracy as of 10/06/2025

This is a summary of links recently featured on Quantocracy as of Monday, 10/06/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Book and Workshop Introduction: Generative AI for Trading and Asset Management [EP Chan]

    The world of finance is no stranger to artificial intelligence. Most quantitative asset managers are already familiar with discriminative models, for example, given yesterdays return, what is the probability that todays return will be positive? Many are also familiar with reinforcement learning, used for tasks like optimizing order execution or figuring out how to set the best capital
  • Gold s Rally and the Gold Mining Stocks Trap [Quantpedia]

    Gold has been in the headlines lately as it climbs to new highs, prompting many investors to look for ways to benefit from the rally. However, many institutional investors such as mutual funds and pension funds face restrictions on buying physical gold or gold-backed ETFs. Instead, they often turn to gold mining stocks to gain indirect exposure to golds price. That approach seems
  • The Role of Data in Financial Modeling and Risk Management [Relative Value Arbitrage]

    Much emphasis has been placed on developing accurate and robust financial models, whether for pricing, trading, or risk management. However, a crucial yet often overlooked component of any quantitative system is the reliability of the underlying data. In this post, we explore some issues with financial data and how to address them. How to Deal with Missing Financial Data? In the financial
  • Employing volatility of volatility in long-term volatility forecasts [Outcast Beta]

    We demonstrate how simple long-term volatility forecasts can be improved by incorporating the volatility of short-term volatility into forecasting models. The theoretical framework for modelling volatility of short-term volatility, along with its role in long-term forecasts, will be outlined. Empirical tests will then illustrate the value of including volatility of volatility measures in practice.
  • Weekly Research Recap [Quant Seeker]

    State-Dependent Market (In)Efficiency in Cryptocurrency Markets (Barak, Razmi, and Mousavi) Cryptocurrency return predictability is regime-dependent. This paper tests strategies based on Directional Change events, where a new trend is confirmed only once price moves a fixed percentage from the last extreme. A machine-learning model adaptively selects the optimal threshold each day. From

Recent Quant Links from Quantocracy as of 09/29/2025

This is a summary of links recently featured on Quantocracy as of Monday, 09/29/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Hedging Tail Risk with Robust VIXY Models [Quantpedia]

    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
  • Parameter-free optimization [Trading the Breaking]

    Lets talk plainly. Quant finance has spent years chasing complexitylayering indicators, stacking models, scaling clustersanything that might tease out an edge. Weve built whole infrastructures around that chase: faster data, bigger grids, deeper nets. Yet under all that polish sits an awkward truth we rarely lead with: most strategies lean on hand-picked knobs. Call them
  • Cross-Sectional and Dollar Components of Currency Risk Premia [Quantpedia]

    Currency strategies often appear simple on the surface go long high-yielding currencies, short low-yielding ones, or take a position on the U.S. dollar. But these trades actually mix two distinct components: a Dollar component, which bets on broad movements of the U.S. dollar against all others, and a Cross-Sectional (CS) component, which exploits relative differences across countries. The
  • A scorecard for global equity allocation [Macrosynergy]

    Macro-quantamental scorecards are systematic enhancements of discretionary portfolio management. They offer (a) information efficiency by structuring and condensing key macroeconomic data series, and (b) empirical validation of predictive power and trading value using historic point-in-time information. Scorecards can be readily built in Python, with pandas and existing classes and methods.
  • Volatility Risk Premium Across Different Asset Classes [Relative Value Arbitrage]

    The volatility risk premium has been studied extensively in the equity space, but less so in other asset classes. In this post, we are going to examine the VRP across different asset classes. Volatility Risk Premium Across Different Asset Classes The volatility risk premium (VRP) is the compensation investors receive for bearing the risk associated with fluctuations in market volatility, typically

Recent Quant Links from Quantocracy as of 09/25/2025

This is a summary of links recently featured on Quantocracy as of Thursday, 09/25/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Refining The 0DTE SPX Breakout Strategy with Evidence-Based Exclusions [Quantish]

    Every quant has been there. Youre backtesting a strategy, the overall results look good, but something feels off when you dig into the day-by-day breakdown. You keep digging while your gut says this feels like data mining, but the statistics keep pointing to the same uncomfortable truth some trading days are just bad for business. Thats where I found myself with the SPX opening
  • Weekly Research Recap [Quant Seeker]

    The Term Structure of European Carbon Futures and the Predictive Power of Speculators and Hedgers (Lautner, Dudda, and Klein) Shifts in the carbon futures term structure are driven by hedgers, not speculators. Using ESMA Commitment of Traders data, the authors show that a 1% increase in commercial long hedging demand predicts a 0.21% rise in the curve level one month ahead. Speculator activities,
  • Intelligent Concentration: A Synopsis of Warren Buffett and Diversification [Alpha Architect]

    Warren Buffetts diversification practices have been back in the spotlight over the past few years. Specifically, the level of concentration in his portfolio has come under scrutiny due to the size of the largest stock holding in Berkshire Hathaways marketable equities portfolio. A historical review of Buffetts implementation of diversification and concentration in practice, as well as his
  • A Golden Opportunity to Upgrade a 60/40? [Alpha Architect]

    Our friends Corey Hoffstein and Rodrigo Gordillo over at Return Stacked have done some interesting research on the potential for gold to improve your run-of-the-mill 60/40. Youll need to hit them directly on their site to get their full report. However, I read their very detailed white paper, and I thought the concept was intriguing and worth a look for Alpha Architect readers seeking tactical

Recent Quant Links from Quantocracy as of 09/22/2025

This is a summary of links recently featured on Quantocracy as of Monday, 09/22/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Leveraged ETFs in Low-Volatility Environments [Quantpedia]

    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
  • When Trading Systems Break Down: Causes of Decay and Stop Criteria [Relative Value Arbitrage]

    Decay and Stop Criteria Subscribe to newsletter A key challenge in system development is that trading performance often deteriorates after going live. In this post, we look at why this happens by examining the post-publication decay of stock anomalies, and we address a practical question faced by every trader: when a system is losing money, is it simply in a drawdown or has it stopped working
  • What Drives the Excess Bond Premium? [Quantpedia]

    The Excess Bond Premium (EBP the portion of corporate bond spreads not explained by default risk), a key metric in quantitative finance for gauging credit spreads, has long been a subject of intense scrutiny. Recent research sheds new light on its dynamics, moving beyond traditional macroeconomic factors to explore the role of information flow. By analyzing news attention across 180 topics, a

Recent Quant Links from Quantocracy as of 09/17/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 09/17/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Robust optimization protocol [Trading the Breaking]

    Parameter optimization is where good ideas go to either earn their keep or quietly fail. Given a fixed modeling recipe, the optimizer will always return a winner; what it cannot tell youunless you force it tois whether that winner is real. Financial data are dependent, heteroskedastic, regime-prone, and thin on signal. In that environment, any single backtest split can crown a parameter
  • Weekly Research Recap [Quant Seeker]

    News Sentiment and Commodity Futures Investing (Yeguang, El-Jahel, and Vu) Media news sentiment is a priced factor in commodity futures. A weekly longshort strategy, buying commodities with the most positive sentiment and shorting those with the most negative, delivers an 8.3% annualized return with a Sharpe ratio of 0.45, after costs. The premium remains significant after controlling for
  • Macro trading factors: dimension reduction and statistical learning [Macrosynergy]

    Macro trading factors are information states of economic developments that help predict asset returns. A single factor is typically represented by multiple indicators, just as a trading signal often combines several factors. Like signal generation, factor construction can be supported by regression-based statistical learning. Dimension reduction is particularly useful for factor discovery. It is
  • Volatility Targeting Across Asset Pricing Factors and Industry Portfolios [Relative Value Arbitrage]

  • Profitably Trading the SPX Opening Range. Code Included. [Quantish]

    This promising strategy comes from Option Alphas comprehensive research on trading SPX breakouts with zero-day-to-expiration (0DTE) credit spreads selling one option while buying a further OTM option for protection, collecting premium with defined risk. If youre not famliar with Option Alpha, and are serious about trading options, I highly recommend you check them out! (Disclosure: Im
  • Weekly Research Recap [Quant Seeker]

    The trade imbalance network and currency returns (Hou, Sarno, and Ye) While past work links a countrys trade balance to predictability of FX returns, this study shows that its position in the global network of deficits and surpluses matters too. The authors create a centrality-based measure (CBC), finding that going long highly central currencies and shorting peripheral ones delivers a Sharpe

Recent Quant Links from Quantocracy as of 09/08/2025

This is a summary of links recently featured on Quantocracy as of Monday, 09/08/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Surprisingly Profitable Pre-Holiday Drift Signal for Bitcoin [Quantpedia]

    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
  • A Better Stock Rotation System [Financial Hacker]

    A stock rotation system is normally a safe haven, compared to other algorithmic systems. Theres no risk of losing all capital, and you can expect small but steady gains. The catch: Most of those systems, and also the ETFs derived from them, do not fare better than the stock index. Many fare even worse. But how can you make sure that your rotation strategy beats the index? There is a way. In the
  • PCA analysis of Futures returns for fun and profit, part deux [Investment Idiocy]

    In my previous post I discussed what would happen if you did the crazy thing of doing a PCA on the whole universe of futures across assets, rather than just within US equities or bonds like The Man would want you to. In this post I explore how we could do something useful with them. There is some messy code here, to run all of it you'll need psystemtrade, but you can exploit big chunks with
  • Skewness Premium in Managed Futures: A Practitioner’s Guide [Invest ReSolve]

    Skewness-based managed futures strategies offer a unique opportunity to enhance portfolio performance by exploiting the asymmetry of return distributions across diverse asset classes. By focusing on the third moment of return distributionsskewnessthese strategies seek to capitalize on the tendency of assets with negative skewness to offer higher expected returns as compensation for tail
  • Conditional Value at Risk [OS Quant]

    Value at Risk (VaR) is the industrys go-to portfolio risk metric. But, its a cutoff completely ignoring tail risk. It tells you how often youll breach a threshold, not how bad losses are when you do. Conditional Value at Risk (CVaR) looks at that damage. It measures the average of your worst days. In this article we recap VaR, build intuition for CVaR, estimate it from historical returns,
  • Equity duration and predictability [Alpha Architect]

    Since 1945, a silent revolution has taken place in the way equity markets move. The classic view of stock prices responding mainly to changes in expected dividends no longer holds. Instead, expected returns now dominate. This paper digs into the reason: equity duration has increased dramatically. As firms reinvest more and delay payouts to the future, asset prices become more sensitive to changes
  • Tail Risk Hedging Using Option Signals and Bond ETFs [Relative Value Arbitrage]

    Tail risk hedging plays a critical role in portfolio management. I discussed this topic in a previous article. In this post, I continue the discussion by presenting different techniques for managing tail risks. Hedging with Puts: Do Volatility and Skew Signals Work? Portfolio hedging remains a complex and challenging task. A straightforward method to hedge an equity portfolio is to buy put

Recent Quant Links from Quantocracy as of 09/03/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 09/03/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bitcoin ETFs in Conventional Multi-Asset Portfolios [Quantpedia]

    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
  • Weekly Research Recap [Quant Seeker]

    Global News Networks and Return Predictability (Freire, Moin, Quaini, and Soebhag) News sentiment, extracted from a massive global article dataset, predicts daily equity index returns across 14 developed markets. Local sentiment strategies nearly double buy-and-hold Sharpe ratios (e.g., U.S. 1.34 vs. 0.62), with net alphas of about 16% after trading costs and one-third smaller drawdowns. Adding
  • Stochastic Volatility Models for Capturing ETF Dynamics and Option Term Structures [Relative Value Arbitrage]

    The standard Black-Scholes-Merton model is valuable in both theory and practice. However, in certain situations, more advanced models are preferable. In this post, I explore stochastic volatility models. Stock and Volatility Simulation: A Comparative Study of Stochastic Models Stochastic volatility models, unlike constant volatility models, which assume a fixed level of volatility, allow

Recent Quant Links from Quantocracy as of 09/01/2025

This is a summary of links recently featured on Quantocracy as of Monday, 09/01/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Combinatorial Purged Cross Validation for Optimization [Trading the Breaking]

    Traditional grid or Bayesian searches conducted on a single path reward parameters that overfit to this specific historical path. This inflates performance metrics through selection bias and temporal leakage. Combinatorial Purged Cross-Validation (CPCV) addresses this flaw by generating a multitude of chronology-respecting train-test partitions. Crucially, it purges any overlapping information,
  • New open-source library: Conditional Gaussian Mixture Models (CGMM) [Sitmo]

    Ive released a small, lightweight Python library that learns conditional distributions and turns them e.g. into scenarios, fan charts, and risk bands with just a few lines of code. Its built on top of scikit-learn (fits naturally into sklearn-style workflows and tooling). Example usage: In the figure below, a non-parametric model is fit on VIX conditioned on the VIX level, so it naturally
  • The Reversal Tendency of Labor Day Week [Quantifiable Edges]

    In the subscriber letter over the last several years I have demonstrated that the performance during the week of Labor Day has been impacted by the performance in the month leading up to it. Interestingly, is has been somewhat of a momentum reversal week. When SPX has rallied up to Labor Day, then it has struggled that week. And declines into Labor Day have seen positive performance. Below is an

Recent Quant Links from Quantocracy as of 08/31/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 08/31/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volume Shocks and Overnight Returns [Quantitativo]

    Albert Einstein had a way of capturing deep truths in simple words. His quote is a reminder, especially relevant to us when building models. Stripping away unnecessary complexity is vital, but going too far risks oversimplification: a model that looks neat but fails to capture reality. This week, we will implement the idea from the paper Volume Shocks and Overnight Returns, by lvaro Cartea,
  • The 5 Point Trade Quality Scoring System [Paper to Profit]

    Often we have a trading system with a countless number of trades (in my case 70,000,000) with little to no way to understand actually what is going on. Sure, we get massive printouts and tear sheets with a ton of figures that quantify our strategy. But, what about on a trade-by-trade basis? What we really need is to understand the quality of our trading systems on a trade-by-trade basis. Its
  • DataFrame Rec Tests with Recx [OS Quant]

    Code changes. Data changes. Outputs change. Somewhere between the first analysis and an odd position in production, little mismatches creep in: a misstated value, off-by-one date ranges, rounding shifts, subtle drift in calculations, missing IDs. The most reliable way to catch them is to compare a new DataFrame to a previously validated onea reconciliation, or rec, test. recx is a lightweight
  • The (hidden) trading value of central bank liquidity information [Macrosynergy]

    Central banks regularly adjust the economys monetary base through foreign exchange interventions and open market operations. Point-in-time information on such intervention-based liquidity expansion has predictive power for asset returns. That is because such operations often come in longer-term trends, and there are lagged effects, for example, through private sector portfolio rebalancing.
  • Finding Edges [Robot Wealth]

    How do we find edges? First, we must be clear about what constitutes a good idea. It isnt as simple as it having to make money. The risk profile must also be tolerable. This is a personal preference. Next, we need to be able to trade it. Robinhood wont let you sell naked options. You cant trade the Indian markets or crypto derivatives from the US. Retail cant trade OTC. These

Recent Quant Links from Quantocracy as of 08/29/2025

This is a summary of links recently featured on Quantocracy as of Friday, 08/29/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Neural Nets and Factor Models [Falkenblog]

    Gu, Kelly, and Xiu (2020) – "Empirical Asset Pricing via Machine Learning" and Chen, Pelger, and Zhu (2019) – "Deep Learning in Asset Pricing" examine various machine learning and neural net algorithms. Both find significant improvements to standard factor models. Several hidden parameter choices are not directly learned during training but significantly impact model
  • How Can We Explain the Low-Risk Anomaly? [Quantpedia]

    The low-risk anomaly in financial markets has puzzled researchers and investors, challenging the traditional risk-return paradigm (higher risk->higher return). This phenomenon, where low-risk assets outperform their high-risk counterparts on a risk-adjusted basis, has been observed across various asset classes, including stocks and mutual funds. What may be the possible explanation?
  • Cross-Sectional Momentum: Results from Commodities and Equities [Relative Value Arbitrage]

    Momentum strategies can be divided into two categories: time series and cross-sectional. In a previous newsletter, I discussed time series momentum. In this post, I focus on cross-sectional momentum strategies. Cross-Sectional Momentum in the Commodity Market Momentum trading is often divided into 2 categories: time-series momentum and cross-sectional momentum. Time-series based trading strategies
  • Weekly Research Recap [Quant Seeker]

    Asymmetry and Crude Oil Returns (Liu, Zhang, and Bouri) This paper introduces a new distribution-based asymmetry factor (OIS) for crude oil that strongly predicts WTI futures returns. A one-standard-deviation rise in OIS, signaling right-tail clustering, forecasts a 3.15% drop in next-month returns (t3.1, R=4.1%). Out-of-sample, OIS achieves an R of 4.2%, far exceeding standard