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Exploiting Mean-Reversion in Decentralized Prediction Markets: Evidence from Polymarket Binary Contracts

April 17, 2026

This study examines the profitability of mean-reversion trading strategies applied to binary outcome contracts on Polymarket, the world’s largest decentralized prediction market platform. We analyze three distinct contracts representing varying risk profiles: a quasi-risk-free instrument (No to “Will Jesus Christ return in 2025?”) and two high-yield speculative contracts (No to “Will China invade Taiwan in 2025?” and “Will the US confirm that aliens exist in 2025?”). Using high-frequency price data sampled at 10-minute intervals over approximately one year, we implement a parameterized mean-reversion framework across twelve strategy variants, testing robustness under varying liquidity constraints and transaction cost assumptions. Our findings reveal that while mean-reversion signals generate substantial alpha under passive limit-order execution (zero-spread scenario), strategy performance degrades significantly when more aggressive market orders are accounted for.

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Trading as a Small Business: What Beginner Investors and Traders Usually Learn Too Late

April 13, 2026

Many beginners enter the markets with the same silent assumption: if they study hard enough, find the right indicators, or discover the right strategy, they should eventually be able to generate high returns with manageable risk. The market appears full of examples that seem to confirm this belief. Screenshots of triple digit gains are everywhere. Backtests often look smooth. Social media makes it feel as if exceptional performance is common.

The reality is much harsher.

One of the most valuable lessons for a beginner is not how to optimize entries, build indicators, or use the latest machine learning model. It is learning how to frame trading correctly from the start. For a small retail trader, trading should not be treated as a shortcut to wealth. It should be treated as a business. And like any business, it requires realistic expectations, risk control, patience, and a clear understanding of where a small player can actually compete.

That perspective matters because most of the mistakes beginners make do not stem from a lack of ability or effort. They arise from starting with the wrong mental model and unrealistic expectations about how markets actually work.

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Systematic Tactical Allocation in Emerging Markets vs. U.S.: A Momentum-Based Approach

April 7, 2026

The global investment environment is going through a period of meaningful structural change. The dominance of the U.S. dollar is increasingly being questioned, geopolitical tensions are rising, and macroeconomic uncertainty remains elevated. Together, these forces challenge the post-Global Financial Crisis environment in which U.S. equities consistently outperformed most international markets. As a result, investors may be approaching a turning point where relative returns between U.S. equities and international markets—especially Emerging Markets (EM)—begin to shift.

This research focuses on a practical portfolio allocation question: when should investors increase or reduce exposure to Emerging Market equities relative to U.S. equities? Building on our earlier work analyzing the EAFE-USA spread, we extend the framework to Emerging Markets. Our hypothesis is that the relative performance between U.S. and EM equities is not random. Instead, it shows patterns driven by momentum and broader market trends. These patterns likely reflect persistent capital flows and the gradual way macroeconomic information spreads across global markets.

Rather than relying on static asset allocation approaches, we develop a dynamic allocation model that uses momentum and trend signals to generate practical timing signals between U.S. and EM equities. Emerging Markets are particularly interesting in this context because they tend to experience stronger regime shifts and larger performance cycles than developed international markets.

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One Year Later: Is ChatGPT Finally Worth Using for Quantitative Analysis?

April 1, 2026

One year ago, in our article “Can We Finally Use ChatGPT as a Quantitative Analyst?”, we explored the feasibility of leveraging ChatGPT for quantitative analysis. Since then, a lot has changed: newer models are now available (from OpenAI and also other vendors), and the ecosystem around AI-assisted analysis has evolved significantly. Back then, we encountered numerous challenges, ranging from model hallucinations and faulty code generation to excessive overfitting. In this article, we revisit these issues to assess what has improved and what remains unresolved, with the goal of finally answering whether we can use LLMs to assist with quantitative analysis tasks.

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Quantpedia’s Research Workflow: From Idea Discovery to Portfolio Construction

March 23, 2026

Quantitative strategy research is rarely about discovering a single “perfect” trading rule. In practice, robust portfolios emerge from a structured research process that filters ideas, evaluates evidence, and combines complementary strategies.

In this article, we demonstrate how such a workflow can be implemented using the tools available in Quantpedia Pro. Rather than focusing on maximizing the performance of a single strategy, we walk through the research process step by step—from thematic filtering to portfolio-level evaluation.

To make the process concrete, we use value-based equity strategies as our working example. However, the goal of the article is not to identify the ultimate value strategy, but to illustrate how a systematic research workflow can be used to build a diversified portfolio of strategies around any investment hypothesis.

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Timing Value vs. Growth: Evidence from 100 Years of Small Value–Large Growth Spread

March 18, 2026

The goal of our article is to examine the long-term relationship between small value and large growth stocks using more than 100 years of data and test whether the spread between small value and large growth portfolios shows trends that could help investors switch between the two styles. Using the Fama and French 2×3 and 5×5 size and book-to-market portfolios, we construct the small value minus large growth (SV–LG) spread and apply simple trend-following signals based on moving averages and momentum with horizons ranging from 3 to 12 months. Our results show that trend-following strategies are able to capture part of the value outperformance on the long side. Timing periods when growth stocks dominate is much more difficult.

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