Quant Mashup

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.(...)

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(...)

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(...)

Parameter-free optimization [Trading the Breaking]

Let¡¯s talk plainly. Quant finance has spent years chasing complexity¡ªlayering indicators, stacking models, scaling clusters¡ªanything that might tease out an edge. We¡¯ve built whole infrastructures around that chase: faster data, bigger grids, deeper nets. Yet under all that polish sits an(...)

Cross-Sectional and Dollar Components of Currency Risk Premia [Quantpedia]

Currency strategies often appear simple on the surface ¨C 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,(...)

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(...)

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(...)

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

Every quant has been there. You¡¯re 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 ¨C(...)

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(...)

Intelligent Concentration: A Synopsis of Warren Buffett and Diversification [Alpha Architect]

Warren Buffett¡¯s 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 Hathaway¡¯s marketable equities portfolio. A historical(...)

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. You¡¯ll need to hit them directly on their site to get their full report. However, I read their very detailed white paper, and(...)

Leveraged ETFs in Low-Volatility Environments [Quantpedia]

Leveraged ETFs (such as SPXL ¨C (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(...)

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(...)

What Drives the Excess Bond Premium? [Quantpedia]

The Excess Bond Premium (EBP ¨C 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(...)

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 you¡ªunless you force it to¡ªis whether that winner is real. Financial data are dependent, heteroskedastic,(...)

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 long¨Cshort strategy, buying commodities with the most positive sentiment and shorting those with the most negative, delivers an 8.3% annualized return(...)

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(...)

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 Alpha¡¯s comprehensive research on trading SPX breakouts with zero-day-to-expiration (0DTE) credit spreads ¨C selling one option while buying a further OTM option for protection, collecting premium with defined risk. If you¡¯re not famliar with Option(...)

Weekly Research Recap [Quant Seeker]

The trade imbalance network and currency returns (Hou, Sarno, and Ye) While past work links a country¡¯s 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(...)

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(...)

A Better Stock Rotation System [Financial Hacker]

A stock rotation system is normally a safe haven, compared to other algorithmic systems. There¡¯s 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(...)

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(...)

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 distributions¡ªskewness¡ªthese strategies seek to capitalize on the(...)

Conditional Value at Risk [OS Quant]

Value at Risk (VaR) is the industry¡¯s go-to portfolio risk metric. But, it¡¯s a cutoff completely ignoring tail risk. It tells you how often you¡¯ll 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(...)

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(...)

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(...)

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(...)

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(...)

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(...)

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(...)

New open-source library: Conditional Gaussian Mixture Models (CGMM) [Sitmo]

I¡¯ve 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. It¡¯s built on top of scikit-learn (fits naturally into sklearn-style workflows and tooling). Example usage: In the(...)

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(...)

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(...)

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(...)

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(...)

The (hidden) trading value of central bank liquidity information [Macrosynergy]

Central banks regularly adjust the economy¡¯s 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(...)

Finding Edges [Robot Wealth]

How do we find edges? First, we must be clear about what constitutes a good idea. It isn¡¯t 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 won¡¯t let you sell naked options. You can¡¯t(...)

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(...)

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(...)

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(...)

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(...)

Walk-Forward optimization [Trading the Breaking]

I want to start by saying that the key is in the data, not in the model or its parameters. Therefore, if your data is garbage, no matter how much you parameterize it, the results will still be garbage. If you parameterize a model, it's to fine-tune something that already works. Period. Knowing(...)

Laurens Bensdorp - Building Strategies with Purpose [Algorithmic Advantage]

There¡¯s a special place in trading graveyards reserved for the back-test that looked gorgeous on paper and then detonated in production. I¡¯ve been there. If you trade long enough, you will too. We all know the over-fittings issues, and I¡¯ll get into that, but there¡¯s another reason why(...)

The Best Strategies for FX Hedging [Quantpedia]

Foreign exchange (FX) markets are a cornerstone of global finance, offering investors and corporations opportunities to manage currency risk, enhance returns, and optimize portfolio performance. Among the most critical challenges in FX is the design of robust hedging strategies to mitigate exposure(...)

Unlocking REIT Returns: Real Estate Investment Factors [Alpha Architect]

As of 2024, real estate investment trusts (REITs) have cemented their role as a $1.5 trillion segment within global capital markets, offering investors a liquid and regulated gateway to commercial real estate. With robust dividend mandates, leverage restrictions, and transparent operations, REITs(...)

Cesar Alvarez - A Novel Way to Combine Trend, Reversion, ETFs, Volatility & More [Algorithmic Advantage]

When I sat down recently with Cesar Alvarez of Alvarez Quant Trading, I knew I'd be tapping into a deep reservoir of quantitative trading wisdom. Cesar¡¯s journey into systematic trading began similarly to many of us¡ªstarting with discretionary trades, dabbling in mutual funds, and(...)

Quantifying Global Real Estate Returns Over Centuries [Quantpedia]

In the realm of quantitative finance, understanding the dynamics of real estate returns over extended periods is often overlooked, which is not good, as real estate constitutes a significant portion of investors¡¯ portfolios. The article titled Global Housing Returns, Discount Rates, and the(...)

Correlation Matrix Generation using Object Oriented Python [Quant Start]

In the last article Generating Synthetic Equity Data with Realistic Correlation Structure we discussed how to generate synthetic structured correlation matrices for the purposes of generating synthetic correlated equities data. This has a number of uses within systematic trading backtesting(...)

Weekly Research Recap [Quant Seeker]

Is Gold an Inflation Hedge? (Baur) Gold is not a consistent hedge against average inflation. Between 1971 and 2025, realized inflation explains less than 3% of gold¡¯s price variation, and the hedge effect evident in the 1970s¨C80s largely disappears thereafter. Gold does, however, respond(...)

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