This is my third post in a series about optimisation and fitting. In my previous post I used random data to calibrate and evaluate many portfolio optimisation techniques. It's worth quoting in full from that post: Random data is not real data: Well duh. But why is this important? Because random data is drawn from a fixed and well behaved distribution. This means the optimiser only has to

A Tale of Two Prices (the core idea of stat arb) Moneyball (finding undervalued pairs using unconventional metrics) The Winter of our Pairs Trading Discontent (problems, limitations, frustrations) The Metamorphosis (from pairs to portfolio) When is a Mispricing Not a Mispricing? (something looks mispriced why?) At its simplest, stat arb is a bet on convergence: two related stocks drift

Combine a three-asset risk-premia portfolio with a trend-following program and the Sharpe ratio jumps from 1.1 to nearly 1.5. It looks like free diversification. But a trend program is long equities, bonds and metals and a risk-premia core is equities, bonds and metals. So before we accept the free lunch, we should ask what part of it we are paying for twice, and what part is genuinely new.

As I said in my last post I'm currently in the process of a mega-sized research project on fitting. In the first post I examined the correct way to cluster combinations of trading rules and instruments. This next post is rather meatier, and is about evaluating and calibrating some portfolio optimisation techniques. We might call this 'meta optimisation', since we want to find the

Two different sources each tell you something about where a stock is going. The first is insider trading filings: SEC Form 4, which executives, directors, and large shareholders are required to submit within two business days of buying or selling shares in their own company. The second is the news cycle: analyst reports, earnings coverage, breaking stories. Each source has its own academic

How much do we really know about corporate bond returns before the modern data era? Until recently, the answer was: not enough. Most empirical work in corporate bond pricing has relied on relatively short samples, especially the post-2002 TRACE period, leaving open the question of whether observed risk premia are robust over longer horizons. Ghaderi, Plante, Roussanov, and Seo (2026) Ghaderi,

This will be the first in a series of posts about portfolio optimisation. Main reason being I'm planning to write a book about backtesting, and that will include a big chunk of material on optimisation. Yes, I know, my latest book isn't out yet (it's out in December – in time for Christmas). But this backtesting book is going to be quite deep (and probably long!) so I need to start

At Concretum Group, a relevant part of our research effort goes into developing strategies for external clients, each arriving with different requirements about what market behavior to model and, just as importantly, about how much capital a given strategy is meant to run on. This brings us to a very delicate part of our work, which might not seem exciting at first, but becomes crucial before

If youve ever scrolled through Polymarket or Kalshi wondering whether the wisdom of crowds is actually wisdomor just organized noiseyoure not alone. A new paper, How Wise is the Crowd? Bias and Edge in Prediction Markets, tears into the microstructure of modern prediction markets to ask a practical question: Whos actually making money, and whos just paying for the

Measuring Bubbles via Put-Call Disparity: A Model-Free Approach Robert A. Jarrow (Cornell U.) and Simon Kwok (U. of Sydney) May 2026 This paper introduces simple, model-free lower and upper bounds for measuring the size of asset price bubbles. Assuming only that the market satisfies no-free-lunch-with-vanishing-risk and that all trading strategies are admissible, our framework avoids restrictive

Hello again! Todays post was originally published in the November 2025 issue of Wilmott Magazine. What if you could make it a lot easier for readers to replicate your paper? That was the idea I followed when Wilmott called for articles to be published in a special issue on the replication crisis. Subscribe to my Substack to receive my posts in your inbox, or follow me on Twitter or LinkedIn if

Regression is one of the oldest and widely used statistical techniques. It has found applications across the social sciences, engineering, natural sciences, and finance. Despite the rapid rise of machine learning and AI, regression remains a useful tool for modeling relationships, making forecasts, and extracting signals from data. In this post, we revisit regression-based trading systems and

Every strategy and Model Portfolio now includes a Return Contribution analysis, showing each assets contribution to overall annual return. We further aggregate results by asset category and risk on/off, as well as estimate the drag from trading friction (transaction costs + slippage). Lets walk through a sample return contribution analysis using the most popular strategy on our

Part 1 left a gap. Regressing the synthetic backtrack against the whole universe at once recovered the program in ten contracts at a Sharpe of 0.84, against the programs 1.03 a fifth of a Sharpe unaccounted for. I argued there that the gap lived in the regressions blindness to the structure inside the program: equities and bonds, energy and grains, all blended into one optimisation, so

Last time, I showed you a pattern in energy spreads and asked what it meant. The answer seemed obvious: XOM is the outlier. Every spread involving XOM is stretched. The spreads not involving XOM are near zero. But on this seemingly obvious map of mispricings, XOM may not mark the spot The name Triangulated Stat Arb comes from triangulation, the navigation technique. One bearing on a landmark

In-sample looks great. Live trading is where the truth lives. A few weeks ago we wrote about how we use AI alongside Trading Blox what it does well, what it doesnt, and the workflow we run. The single biggest risk we flagged was overfitting specifically, the way AI overfitting in trading systems quietly destroys live performance after pretty backtests. That deserves its own post,

Weve added Safe and Perpetual Withdrawal Rates to your custom Model Portfolios. New here? Learn more: What is a Model Portfolio? What are Withdrawal Rates? The Safe Withdrawal Rate (SWR) measures the max amount that could have been withdrawn each year in retirement (with an annual adjustment for inflation) without running out of money over the worst retirement period. Its the source of the

Welcome to the Systematic Trading with TradeQuantiX newsletter, your go-to resource for all things systematic trading. This publication will equip you with a complete toolkit to support your systematic trading journey, sent straight to your inbox. Remember, its more than just another newsletter; its everything you need to be a successful systematic trader. I recently launched a

Asset prices are often viewed through a simple lens. Investors form expectations, discount future cash flows, and determine prices accordingly. But in reality, expectations themselves are complex. They vary across institutions, across asset classes, and over time. This paper introduces a new perspective. Institutional expectations are not random or purely behavioral. They are structured,

More and more traders are using Claude Code, ChatGPT, Cursor, and other LLMs to build and automate their trading systems. It works. You can go from strategy idea to a working bot in a day. The code compiles, the backtest looks good, orders fire on paper trading, and you move to production. Then stuff breaks. Not the strategy logic – the infrastructure around it. Over the years, weve repeatedly

The published literature on trend-following replication treats the program being copied as a black box. When the program is your own, this is the wrong way around and fixing it changes the result more than I expected. The story of trend following as a systematic strategy reaches back to the 1970s, when a handful of futures traders observed that prices in commodity markets tended to persist in

Last week, we shared some findings of an intraday short-selling signal taken from our internal research archives. Today, picking up on the same theme, we present some evidence behind an effect we believe stems from the very presence of short sellers in stocks with the same characteristics highlighted in our previous piece. We recommend you first read our original analysis here. Identifying Stocks

Two previous articles, Trend-Following Filters Part 7 [1] and Trend-Following Filters Part 9 [2], examined, from a digital signal processing (DSP) time domain perspective, digital filters commonly used by technical analysts to aid in making trading decisions. The filters examined in Part 7 include moving average (MA), linear weighted moving average (LWMA), and exponential

The Sharpe stability ratio measures the consistency of risk-adjusted PnL value generation. It divides the mean Sharpe ratio over sequential overlapping lookback periods by its estimated standard error. Thereby, it quantifies significance and intertemporal stability. Both are critical for selecting factors and for assessing the commercial viability of a strategy. If two strategies produced the same

Welcome to the Quantpedia Awards 2026 winners announcement. For the third time, we are proud to celebrate excellence in quantitative research and recognize the researchers behind innovative studies in quantitative trading. We are also pleased to see that the Quantpedia Awards have become an established and recognized brand within the quant community. This is the moment we have all been waiting

That line, usually pinned to Einstein, fits this article rather well. In trading strategy research, we can spend a long time counting the wrong thing: like, as Martyn Tinsley says – whether the single best in-sample parameter set survives out-of-sample testing. Martyn Tinsleys novel new approach, Walk Forward Correlation, argues that this is often a comforting illusion. Conversely, the

The academic literature on legal insider trading is unusually mature. Sixty years of work, replicated across multiple samples and methodologies, has converged on a few consistent claims: insider purchases carry information, insider sales mostly do not, cluster buying by multiple insiders is stronger than individual transactions, and the alpha has been compressing for decades as the market got

Pairs trading remains a feasible approach for the indie trader. But, as we saw last time, there are inherent limitations. Trading both legs eats a lot of buying power and limits the number of pairs you can trade. Trading only the mispriced leg helps, but introduces a ton of variance. Essentially, the trade-off is accepting a wilder ride in exchange for higher expected returns and better capital

Our study explores a weekly-rebalanced dual-momentum-based Global Tactical Asset Allocation (GTAA) strategy applied to a diversified set of ETFs. The strategy selects assets based on relative momentum and applies an absolute momentum filter to avoid declining investments. Ultimately, a single combined strategy was created by merging two sub-strategies, incorporating both shorter- and longer-term

Without a shade of doubt, Market Wizards books have been a staple in the upbringing of whole generations of traders and investors, and rightfully so we ourselves have been inspired by the exceptional stories within them. The series, authored by Jack Schwager, began in 1989: what has made it so enduring is not the trading insights alone, but the human stories behind them, of rigor, discipline,

Choi, Huh and Su have a very good paper entitled Tighter uniform bounds for BlackScholes implied volatility and the applications to root-finding. Whats particularly great is that it gives both a decent lower bound and a proof a monotone convergence using Newtons method starting from this lower bound. The industry standard for solving the Black-Scholes implied volatility is Peter Jckel

For decades, academic researchers have catalogued hundreds of patterns in the stock market statistical regularities linking firm characteristics to future returns. These persistent return patterns, unexplained by standard risk models, are known as anomalies. They now form the intellectual backbone of a multi-trillion-dollar industry called factor investing, implemented through mutual funds,

In managing our book, we run trend strategies across multiple asset classes and at different speeds, with exposure ranging from slower multi-day systems to faster intraday signals. Regardless of model specifications, we keep observing the same pattern: small implementation details can produce surprisingly large differences in realized performance. As we like to say dispersion is in the details.

For U.S. equities, fixed income, and commodities, reconstructing long-term historical datasets is relatively straightforward, and we have already explored these challenges in several previous studies, including 100 Years of Multi-Asset Trend Following, Extending Historical Daily Bond Data to 100 Years, and Extending Historical Daily Commodities Data to 100 Years. Moreover, the broader methodology

Welcome to the Systematic Trading with TradeQuantiX newsletter, your go-to resource for all things systematic trading. This publication will equip you with a complete toolkit to support your systematic trading journey, sent straight to your inbox. Remember, its more than just another newsletter; its everything you need to be a successful systematic trader. Introduction: This is the

Last night the pipeline pulled 1,199 financial news articles tagged across nine GICS sectors. It started at 9 PM Mountain Time and finished around 1 AM. By morning we had sorted the catch. One hundred and six articles carried direction. The other one thousand ninety-three were ore. Thanks for reading! Subscribe for free to receive new posts and support my work. If you mine gold in the western

In the high-stakes arena of prediction markets, a counterintuitive pattern emerges: retail traders who correctly pick winners more than half the time still lose money, while automated traders with coin-flip accuracy pocket nine-figure profits. Using 222 million prediction market trades with directly observable terminal payoffs, the paper Who Profits from Prediction? Execution, Not

Hedging is a fundamental risk management tool. The most common hedging instruments are futures and options associated with a given underlying asset, when available. For equity exposure, index options are also widely used for hedging. However, hedging can be done not only through equity index options, but also through volatility derivatives, although the latter are considerably more complex and

There is by now a small mountain of vendor material claiming that AI agents will run hedge funds. The reality on the ground for those of us who actually do the work is more interesting and more useful. Agentic workflows, properly constructed, materially accelerate the parts of quant research that consume the most time. They also fail in specific, predictable ways that you can defend

Static currency hedging is a coin toss with predictable losers. The investor either pays the interest-rate differential as carry every month for years, or accepts the full drawdown when the dollar reverses. A trend-following forecast on EUR/USD, combined with carry treated honestly as a cost rather than a feature, sizes the hedge dynamically and finishes ahead of the US-domiciled investor over

Commodity markets are back in investors focus. After years in which equities and growth assets dominated portfolios, the recent rise in geopolitical tensions, inflation uncertainty, supply-chain fragmentation, and renewed resource nationalism has reminded allocators that commodities remain a critical macro asset class. That is why a newly released research paper, An Index of Commodity Futures

A few days ago on X, Charlie Bilello pointed out SPX had gained more than 16% over the previous six weeks and thats one of the biggest six-week rallies of all time. I looked back at the top 20 non-overlapping 30-trading-day rallies since 1950. They can all be found in the table below. Shaded rows are those instances where there was less than a 20% drawdown at the start of the rally. The current

In one of my recent articles, we looked at a paper that proposed a measure of how many strategies you effectively tested in-sample. I found the idea of such a measure really interesting and useful, so I went deeper into it, uncovered problems with existing measures, and ultimately came up with my own measure that has all the properties I desire from such a measure! What is the point of such a

Are the markets like they were in March 2000, right at the top of tech-bubble? I have been seeing lots of facts about how expensive or extended this market is. The Schiller P/E ratio near the 2000 top. The Buffett Indicator at over 2 standard deviations, similar to the 2000 top. US Total Stock Market Value/GDP at all-time highs, far exceeding the 2000 top. The weight of the top 10 stocks in the

This is the third installment in a series on selecting Tactical Asset Allocation (TAA) strategies based on recent performance. Read Part 1 and Part 2. We advocate combining multiple TAA strategies together into Model Portfolios to limit the risk of any single strategy underperforming. In our previous studies we selected strategies for our Model Portfolio based on recent return. In this

Even if you have skill, you can look wrong for a very long time. Cliff Asness A backtest (or even many of them) can tell you whether a strategy survived a historical test. It cannot tell you whether you were testing the right idea, in the right way, for the right purpose. That gap matters. There are plenty of methodologies for minimising over-fitting and increasing confidence that an

Why does sentiment predict returns at all? The textbook answer is that markets are slow. A positive headline drops at 4:01 PM. By 4:30, sell-side analysts at maybe a dozen banks are scrambling to update their models. By 6 PM, three of them have published preliminary notes. By 9 AM the next day, the buy-side has read those notes, decided, and placed orders. By close on day one, the price reflects