Market timing

For anyone trading or investing in Bitcoin, a familiar indicator can become a dangerous comfort blanket. Josh Molnar’s Bitcoin Runs on a Clock argues that classic cycle signals—such as MVRV, the Mayer Multiple and Pi Cycle Top—have weakened as Bitcoin’s price swings become less extreme. Thresholds calibrated to earlier cycles may consequently stop triggering, leaving investors waiting for a sell signal that never arrives. The practical takeaway is that a strong historical record does not make an indicator’s settings permanent. If the market changes, the dashboard needs to change with it.

This paper investigates whether longer historical training periods improve the out-of-sample performance of a Hidden Markov Model (HMM) used for identifying financial market regimes. Four four-state Gaussian HMMs are compared, differing only in the starting point of their training data (1970, 1980, 1990, and 2000), while being evaluated on the same test period. The model uses five market-based features capturing volatility, drawdowns, credit conditions, and sector rotation to identify latent risk regimes.

The study highlights that validation methodology and evaluation criteria are as important as model design and data length when assessing regime-based investment models.

We analyze a multi-asset calendar anomaly around U.S. federal elections that is consistent across assets and simple to trade. While our previous study documents a robust pre-election drift, the cross-asset pricing of its D+1 resolution remains unmapped beyond SPY. Using a diversified ETF basket spanning technology, emerging markets, real estate, high-yield credit, gold, oil, and foreign exchange, we show that election day itself is neutral to slightly positive — an equal-weight D0 long earns a modest 1.03% p.a. at a Sharpe of 0.19, which we document as the control arm of the experiment but do not trade — while the following session delivers a systematic sell-off across all seven legs as the political uncertainty premium collapses. An equal-weight short on D+1 yields 1.86% per annum with a Sharpe of 0.31 and a maximum drawdown of -4.16%, achieved over 13 trading days in 26 years. Intermediate Treasuries exhibit the inverse pattern, selling off in the four days before the vote and rallying from D0 to D+2, with a Sharpe of 0.48.

Intraday trading strategies have gained increasing attention as advances in computing power and market data availability have made intraday strategy analysis more accessible. While many trading strategies are traditionally developed and evaluated using daily price data, shorter timeframes can provide additional opportunities to identify and exploit market trends within a single trading session. In this article, we investigate the performance of trend-following strategies based on selected technical indicators computed from one-minute price data for SPY ETF. The historical dataset, provided by Algoseek, serves as the basis for designing, backtesting, and comparing several intraday trading approaches.

We document a persistent intramonth momentum cycle in U.S. sector ETFs that yields meaningful risk-adjusted returns when properly sequenced. Using the nine original Select Sector SPDR ETFs and SPY as the market benchmark from December 1998 through June 2026, we show that trailing 252-day sector momentum generates a positive spread on the first trading day of the month—and then sharply reverses on days two and three. A third, independent leg of the cycle emerges in the window from ten to five trading days before month-end, consistent with the intramonth momentum cycle recently documented at the single-stock level by Nathan, Suominen and Tasa (2026). Stitching the three legs together into a single composite strategy delivers 5.99% annualized return at a 0.55 Sharpe ratio for the long-short variant, and 3.77% at 0.54 for the market-neutral variant—all while being invested fewer than half the trading days each month. Our contribution is twofold: we extend the calendar-anomaly literature from individual equities to sector-level portfolios, and we provide practitioners with a transparent, low-turnover framework that translates these academic patterns into actionable trade schedules.

One of the advantages of the Quantpedia Pro platform and its Portfolio Analysis toolkit is the ability to analyze not only multi-asset and multi-strategy portfolios but also individual equity curves. Users can upload virtually any return series or analyze assets already present in the database. The same analytical tools used for portfolio construction can therefore also be applied to single assets.

Given the current macro-driven environment, commodity markets—particularly crude oil—offer a relevant case study. The United States Oil Fund (USO) ETF serves as a practical proxy for oil price dynamics. By analyzing its equity curve through Quantpedia Pro, we can explore whether persistent patterns, behavioral effects, or structural inefficiencies exist and whether they can be transformed into systematic trading strategies.