5 Goals for Hedge Hiker in 2021
Building a dynamic UI that will allow users to create custom subsets of hedge funds and track top consensus and conviction ideas each quarter.
In Q4 2021, Hoan and I are going to launch Hedge Hiker, a platform that will allow users to create groupings of hedge funds to hunt for ideas.
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Here are our 5 big milestones for the rest of 2021.
Finalize Hedge Fund Sample (done): Finalize sample. 13F disclosures often have typos and data errors; we’ll clean that as best we can. Append sample with fund-level data that will help inform custom groupings later on (fund strategy, simulated turnover, simulated active share, fund size, etc.). Append key data on an individual security level (sector, industry, market cap, “style”, etc.).
Create Time Series (in progress): Scrape current holdings & historical 13F data for the sample. Test what signals (consensus, etc.) work best for replication, among hedge funds overall as well as within individual strategies of funds. Previous research suggests that L/S, L Only, Event Driven, and Market Neutral strategies with low turnover and high active share are best for replication.
Append Time Series Data to Sample: Analyze time series and append meaningful fund-level data into the sample, such as simulated past performance (1-year, 3-year, etc.) and simulated “success rate” by sector, industry, “style”, geography, or market cap. Many hedge funds have reputations for expertise in particular areas. Would be neat to measure that.
Launch Website with Static UI: Launch website in beta with static user interface. Assign funds into subsets that will represent standard groupings in the initial website. Groupings will be based on fund strategies and other meaningful characteristics that aren’t mutually exclusive (Tiger Cubs, Buffett Acolytes, Micro-Sized Funds, Small Cap Focused, China Focused, Technology Focused, Value Oriented, GARP, etc.). User will select between pre-set groupings and the interface will display subset-level visualizations (subset consensus picks, subset performance over time, subset sector exposure over time, etc.).
Move to Dynamic UI (by December 2021): Transition website to a dynamic user interface that lets users create/save customizable subsets of hedge funds and displays subset-level visualizations.
In conjunction with the website, we’ll also share original research from a time series analysis. This study will seek to contribute to research on the following questions.
What’s the most effective way to use 13F’s for idea generation?
What’s the signal and what’s the noise in 13F data?
Do consensus / conviction picks outperform?
Are these signals any stronger among a subset of funds that are more fundamentals-based and long-term in their approach?
Using quantitative and/or qualitative considerations, how can hedge funds be grouped to create a stronger signal for specific areas of the market?
The usefulness of our platform will depend on the quality of the dataset so we’re planning ample time for data collection, cleaning the data, and building out the dataset. Our goal is to have a website up with a static UI (milestone 1-4) by Q3 13F day in mid-November.
We think this platform will add value for folks so we’re pretty excited to get it built. See you in November.
My junior analyst Everett says hi. Pictured below sitting like a human.