Blog - QuantPedia

The Best Strategies for FX Hedging

August 22, 2025

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 to volatile currency movements. How does the financial industry deal with this task? We can draw inspiration from the paper written by Castro, Hamill, Harber, Harvey, and Van Hemert, which explores strategies such as dynamic hedging, trend-following, and momentum-based approaches, the concept of carry, and the interplay of these strategies with fundamental concepts like Purchasing Power Parity (PPP) and valuation metrics.

Quantpedia Premium Update – August 21st

August 21, 2025

Five new strategies have been added. Five new related research papers have been included into existing strategy reviews and one short free blog post has been published during last few weeks. Plus, four trading strategies have been backtested in QuantConnect in the previous two weeks.

Quantifying Global Real Estate Returns Over Centuries

August 18, 2025

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 Emergence of the Safe Asset, 1465-2024 fills the gap and provides a comprehensive historical overview of real estate yields, offering a chronological overview of real estate returns not just over a few decades but over several centuries.

Quantpedia in July 2025

August 11, 2025

Hello all,

What have we accomplished in the last month?

– New custom data import procedure
– 5th episode of our YouTube video series QuantBeats
– Interview with the winners of Quantpedia Awards 2025
– 10 new Quantpedia Premium strategies have been added to our database
– 9 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 8 new backtests written in QuantConnect code
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Quantpedia Premium Update – August 8th

August 11, 2025

Six new strategies have been added. Four new related research papers have been included into existing strategy reviews and two short free blog posts have been published during last few weeks. Plus, three trading strategies have been backtested in QuantConnect in the previous two weeks.

Cultural Calendars and the Gold Drift: Are Holidays Moving GLD ETF?

August 5, 2025

Financial markets exhibit persistent calendar anomalies, which often defy the efficient‐market hypothesis by generating predictable return patterns tied to institutional or cultural events. In this paper, we document a novel, globally pervasive drift in gold prices surrounding major wealth-oriented festivals across the four principal cultural and religious domains: Christianity, Islam, Hinduism, and East Asian syncretic traditions. While each community endows its principal holidays with gift‐giving rituals and conspicuous displays of wealth, the sole differentiator among regions is the precise timing of these festivities on the Gregorian calendar.

Our central thesis is that gold, owing to its dual role as a universal wealth reservoir and socio-cultural status symbol, experiences concentrated, holiday-induced buying pressure that yields persistent and economically material drift in the GLD ETF. By quantifying this effect across four distinct cultural calendars, we introduce a previously undocumented demand-side factor into commodity-pricing models.

Sunspots as a Natural Signal for Trading Wheat Futures?

July 29, 2025

When it comes to forecasting commodity prices, traders usually turn to weather patterns, supply-demand data, or economic indicators—but what if the sun itself could offer a clue? Our latest data analysis explores a surprising relationship: periods of high solar activity, measured by an increased number of sunspots, tend to precede lower long-term prices for agricultural staples like wheat and corn. The science behind it is simple—more sunspots often mean better growing conditions, which can boost crop yields and eventually put downward pressure on prices. It’s not a quick trade idea; the effects unfold over one to three years, as natural cycles gradually outweigh short-term noise from market speculation or temporary supply shocks. Unconventional? Yes. But in a market where every edge matters, even the sun might have something to say.

Quantpedia Premium Update – July 24th

July 24, 2025

Four new strategies have been added. Five new related research papers have been included into existing strategy reviews and three short free blog posts have been published during last few weeks. Plus, five trading strategies have been backtested in QuantConnect in the previous two weeks.

How to Identify Ponzi Funds?

July 23, 2025

Can we spot a Ponzi scheme before it collapses? That question haunts regulators, investors, and journalists alike. But what if some modern investment funds operate on dynamics that, while not technically illegal, closely resemble Ponzi-like behavior? A new paper by Philippe van der Beck, Jean-Philippe Bouchaud, and Dario Villamaina examines whether certain actively managed funds inflate their own performance — and in doing so, unwittingly mislead investors chasing past returns.

The Memorization Problem: Can We Trust LLMs’ Forecasts?

July 17, 2025

Everyone is excited about the potential of large language models (LLMs) to assist with forecasting, research, and countless day-to-day tasks. However, as their use expands into sensitive areas like financial prediction, serious concerns are emerging—particularly around memory leaks. In the recent paper “The Memorization Problem: Can We Trust LLMs’ Economic Forecasts?”, the authors highlight a key issue: when LLMs are tested on historical data within their training window, their high accuracy may not reflect real forecasting ability, but rather memorization of past outcomes. This undermines the reliability of backtests and creates a false sense of predictive power.