Tech stocks

This paper tests whether that rotation in attention leaves a systematic footprint. Using Donchian breakout signals across QQQ and Bitcoin, with cash as a fallback during periods of consolidation, we examine whether investors can harvest momentum without remaining permanently exposed to either asset’s full drawdown profile. The results suggest that the answer is yes: retail attention does not move randomly. It rotates, it concentrates, and—when measured through price breakouts—it can be systematically exploited.

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.

Corporate conferences have long been recognized as pivotal events in financial markets, serving as catalysts that signal upcoming innovations and strategic shifts. Scheduled corporate events induce market reactions that can be systematically analyzed to reveal predictable return patterns. In this work, we focus on examining the return drift exhibited by technology stocks in the days surrounding their respective conferences, employing simple quantitative methods with daily price data.

The hypothesized return drift is premised on the notion that investor sentiment and market dynamics are significantly altered by the information disseminated at these conferences. Investors, reacting to both anticipatory signals and post-announcement adjustments, tend to drive prices in a measurable manner in the windows immediately preceding, during, and after the events. By systematically analyzing stocks of companies such as Apple, Google, and Microsoft, this study aims to validate the existence of these drift patterns and shed light on the underlying mechanisms, thereby enhancing mutual understanding of event-driven asset pricing dynamics.