--- title: "Founding Principles — Homodeus Research" description: "The principles behind Homodeus Research: evidence-led analysis, independent judgment, transparent assumptions and durable company research." canonical_url: "https://homodeus.one/founding-principles" markdown_url: "https://homodeus.one/founding-principles.md" access: "full" modified_date: "2026-08-21" source: "Homodeus Research" --- I. Origins ## The Tool That Changed Everything In late 2025, **Peter Steinberger** released **OpenClaw** — an open-source autonomous agent that connects to your messaging apps, integrates with leading AI models, and maintains persistent memory across sessions. Not a chatbot. An *execution shell* that acts on your behalf, continuously, in the background. Institutions called it a security nightmare. In the open, people are building financial intelligence layers on top of it anyway. Despite near-term security concerns, in my view, proliferation into the greater financial community is only a matter of time. II. A Personal Reckoning ## The Distribution Edge Equities research produced by banks and brokers has historically been the purview of institutional investors, gated behind closed garden portals only accessible to a select few. Open equities research is publicly available to all. With bots from both institutional and retail processing the research into their LLMs, open research will drive a disproportionately larger part of overall market sentiment. Distribution is the new edge. Figure 2 · Information Flow Model Comparison Then #### Prior Flow Model Now #### Future Flow Model Institutional — primary Institutional — indirect Bot direct channel Retail channel Market output Removed node ## Twenty Years, One Decision After two decades in institutional equities, I've decided to move from the sell-side to build an open research platform. The sell-side's edge was logistical more so than anything else. They were the loudest voice to market, feeding sentiment to institutional investors who drove market flows. With the change of market drivers augmented by the democratisation of agentic AI, this is all changing. Using tools like Claude Code and OpenClaw, I built a full autonomous agent in three weeks — persistent memory, live watchlist, accessible anywhere via Telegram. It never sleeps. Neither does the market. Figure 1 · Daily Turnover Composition · % of Value ASX Equity Turnover — Strategy & Investor Type 2013e Historical reconstruction · HFT directly anchored; passive and retail estimated 3% 27% 1% 59% 10% 2025e ~A$9.48B weighted avg daily turnover · Full-year actual inputs, estimated five-way split 10% 25% 3% 57% 5% 2030e Base case Forward scenario · Broader systematic adoption and continued passive growth 15% 30% 4% 47% 4% Scenario, not a statistical forecast. Sensitivity range: passive / index-linked 12–18%; Quant Institutional 27–33%. Institutional Non-HFT is the residual. **Passive / Index-linked**Index-tracking ETF-unit turnover plus non-ETF index-linked underlying flow **Quant Institutional**HFT baseline plus broader institutional systematic strategies **Quant Retail**Non-passive automated and rules-based retail turnover **Institutional Non-HFT**Traditional active and other non-HFT institutional flow; residual category **Fundamental Retail**Non-passive discretionary retail turnover **Model note:** No regulator or exchange publishes this exact five-way split. 2013e is a historical reconstruction; 2025e combines completed full-year turnover and ETF inputs with older HFT and retail proxies plus explicit assumptions; 2030e is a base-case scenario. Institutional Non-HFT is calculated as the residual. **Sources:** ASIC REP 331; ASIC REP 597; ASIC 2025 quarterly equity-market data; ASIC retail investor trading study (2020); ASIC trading systems and automated trading update (2025); ASX 2025 settlement calendar; Betashares 2025 ETF data; Vanguard Australia ETF index-tracking data. [Download the full estimation framework, source register and sensitivities (XLSX).](https://homodeus.one/whitepapers/ASX_Turnover_Estimation_Framework_2013_2025_2030.xlsx) Homodeus research is designed to be consumed by the entire active market - retail as well as institution. Our ground-up, agentic-friendly platform also allows our research to be read by bots as easily as humans, unlike legacy PDF-first research platforms. In partnership with [Sharewise](https://www.sharewise.com.au/), Homodeus Research has a retail distribution network of over 70k retail and high-net-worth clients. ![Sharewise × Homodeus Research](https://homodeus.one/logos/sharewise-homodeus-dark.png) ![Sharewise × Homodeus Research](https://homodeus.one/logos/sharewise-homodeus-light.png) III. The Structural Shifts ## i. The Passive Evolution In the US passive funds AUM crossed above active for the first time in 2024 — and the gap keeps widening. This trend has also been observed in Australia. Figure 3 · Active vs. Passive Share of Fund Assets — Comparative US & Australia: The Passive Migration, 2014–2025 United States 2014 30% 70% 2016 35% 65% 2018 42% 58% 2020 48% 52% 2022 50% 50% 2024 54% ← 46% 2025 56% 44% Passive assets ($19.1T) surpassed active ($16.2T) for the first time in 2024. Active funds have seen net outflows every year since 2014. Australia (ASX + CBOE) 2015 14% 86% 2017 18% 82% 2019 22% 78% 2021 28% 72% 2023 32% 68% 2024 35% 65% 2025 37% 63% → Active still dominates by AUM, but passive captured 74% of all 2025 ETF flows. Total ETF industry: AUD $330.6Bn (+34% YoY). Passive had net inflows in 23 of 24 consecutive quarters. Passive / Index Actively Managed Highlighted year US sources: Morningstar Fund Flow Data (Oct 2025); ICI Investment Company Fact Book 2025; Hartford Funds (Feb 2025).[1,2,3] · AU sources: Morningstar Australia Quarterly Fund Review (Dec 2025); Betashares Annual ETF Review 2025; S&P/ASX 25 Years of Indices Report (2025).[AU1,AU2,AU3] ## ii. The Volatility Conundrum As trading has progressively moved from active to passive, this has amplified market signals creating larger volatility in market moves. Figure 4 · Earnings Day Price Reactions Illustrative Average Absolute Single-Day Move on Earnings — S&P 500 Stocks, 2017–2025 Year Average absolute 1-day move at earnings (S&P 500) 2017~3.2% 2018~3.5% 2019~3.3% 2020~4.1% 2021~3.8% 2022~4.8% 2023~5.2% 2024~5.6% 2025e~5.9% Sources: SSRN "Earnings Announcement Volatility as a Market Advancement Indicator" (Kunehepon, 2024); S&P Dow Jones Indices US Equities Market Attributes 2025.[7,8] ## iii. The Sentiment Driver - Human in the Loop Algorithms now drive most equity volume — but the signal is still human. Management tone, analyst views, social media. With the shift from active to passive, the growth of systematic trading doesn't diminish human opinion. It *amplifies* it. Figure 5 · Systematic & Algorithmic Trading Growth Estimated Algorithmic Trading Market Size (USD Bn) & US Equity Volume Share Sources: Grand View Research Algorithmic Trading Market Report 2024; QuantifiedStrategies.com (Select USA data); Allied Market Research (2024).[4,5,6] IV. Synthesis ## Why Open Research Wins AI interpretation of numbers and sentiment will all but displace human analysis, but sentiment generation at its core is still human in the loop. With the proliferation of open source agentic AI (OpenClaw), algorithmic trading (both quant and sentiment) will become mainstream tools. Not just those used by institutional investors. Open research which is made publicly available and easily accessible to bots will have a disproportionately larger impact on market sentiment vs. traditional research produced by brokers, which have only been accessible to insititutional investors. In the age of agentic AI, widely distributed human opinion is the most powerful input to price formation. Open research feeds everyone's models. With macro indsutry trends driving ever larger market volatility having the appropriate messaging being widely disseminated is more critical than ever to ensure markets are properly capturing the value of your business.