Use the tags as mental filters. The content is educational and process-focused — nothing here is a recommendation to buy or sell any specific security.
Hot Stocks • Earnings Momentum
Earnings Surprise Radar: Finding Stocks Wall Street Just Mispriced
Signals: surprise vs. expectations, revisions, post-earnings drift • Horizon: 1–3 months
This case study tracks companies that just beat or missed earnings expectations and
looks at how price reacts over the next few weeks. The model estimates a
“post-earnings drift score” based on surprise magnitude,
analyst revisions, and volume spikes, flagging stocks where history suggests the move
may not be finished yet.
Hot Stocks • Quality + Momentum
Quality Meets Momentum: Screening for Durable Runners
Signals: ROE, margin trends, debt levels, 6–12M price momentum • Horizon: 6–18 months
Here, the model blends fundamental strength with technical momentum. Stocks get a
quality score (profitability, balance sheet, earnings stability)
and a momentum score (trend strength vs. the market). The composite
“hot stock” score ranks names that are both fundamentally sound and already
attracting buyers.
Hot Stocks • Short Interest
Short Squeeze Radar: When High Short Interest Meets Positive Catalysts
Signals: short interest %, days to cover, news sentiment • Horizon: days–weeks
This study looks at heavily shorted stocks that suddenly see positive news,
improving sentiment, or technical breakouts. The model estimates a
“squeeze risk score”, highlighting situations where
bears may be forced to buy back shares, potentially driving sharp upside moves.
Hot Index Funds • Factor Tilt
Factor Rotation: When Value, Growth, or Small Caps Take the Lead
Signals: relative performance of style indices, macro regime markers • Horizon: 6–12 months
Instead of stock-picking, this model looks at style and size indices — value,
growth, small cap, large cap, quality, etc. It tracks rolling relative returns and
macro indicators to estimate a “factor tailwind score”
for each index fund, suggesting which tilts historically tend to run hot in similar environments.
Hot Index Funds • Trend Following
Simple Trend Models on Broad Market Index Funds
Signals: moving averages, drawdown, volatility regimes • Horizon: months–years
A trend-following case study that uses basic technical rules on big, broad index funds.
Think “above or below a long-term moving average”, combined with volatility
filters. The model produces a trend health score, reflecting
how favorable the backdrop has been historically for staying invested in that index.
Hot Index Funds • Risk Budgeting
Risk-Budgeted Index Blends: Who Deserves More Weight?
Signals: realized vol, correlations, return per unit risk • Horizon: strategic
This study treats each index fund as a building block in a portfolio and
asks a simple question: given recent return and risk, which funds deserve
a bigger slice of the risk budget? The output is a tilt score
that nudges weights toward indices that have delivered more return per unit of volatility.
Hot ETFs • Themes & Sectors
Thematic ETF Momentum: Surfing the Strongest Stories
Signals: 3–12M returns, dispersion vs. benchmark, news flow • Horizon: 3–12 months
This case compares theme and sector ETFs against a broad benchmark, tracking
which stories (cloud, energy, healthcare, dividends, etc.) have been leading.
The model outputs a theme momentum score that ranks ETFs based
on relative strength and persistence of their outperformance.
Hot ETFs • Flows & Liquidity
Follow the Money: ETF Flows and Liquidity Heat
Signals: daily net flows, turnover, bid-ask spreads • Horizon: days–months
Here the focus is on where new money is actually going. The model monitors net
inflows, trading volume, and liquidity measures to compute a
“flow heat score” for ETFs. Historically, persistent
inflows combined with tight spreads often line up with stronger, more stable trends.
Hot ETFs • Multi-Asset
Multi-Asset ETF Dashboards: Reading the Cross-Asset Weather
Signals: equity, bond, commodity, FX ETFs; cross-asset correlations • Horizon: tactical
This case study treats ETFs as proxies for entire asset classes and regions.
The model builds a market weather map from equity, bond, and
commodity ETFs to highlight risk-on vs. risk-off conditions, and which sleeve of the market
has historically run hottest in similar cross-asset environments.