The Blushing Quants Podcast

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The Blushing Quants is a candid look at the intersection of quantitative finance and machine learning. We discuss the hard truths of building ML-based investment systems. What works, what fails, and why. We leave the LLMs to the chatbots and focus on the heavy hitters of quantitative finance: Neural Networks, Time Series Analysis, and Statistical Learning. *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

  1. 1D AGO

    Ben Charoenwong: Academia, Hedge Funds, AI, and Applied Finance | Blushing Quants #24

    Ben Charoenwong is a finance professor, researcher, and fund manager working at the intersection of academia, quantitative investing, and applied market practice. As an associate professor at INSEAD and co-founder of Chicago Global, he brings a rare perspective shaped by both rigorous academic training and the real constraints of building and managing investment strategies in live markets. In this episode, we talk about what it actually means to bridge academia and industry in finance, and why that gap is both narrower and harder than most people think. Ben shares how academic research can still produce ideas that matter in practice, but also why the market is a humbling force that quickly exposes weak theories, poor signals, and overconfident models. We get into financial education, opportunity cost, critical thinking, student training, and why AI is forcing universities to rethink not just how they teach, but what they are really supposed to teach. We also dive into fund design, market inefficiencies versus risk premia, diversification, investor fit, long-short construction, mid-frequency strategies, and the importance of building portfolios around the end client's actual risk tolerance. Ben breaks down how he approaches explainability, feature engineering, theory-driven quant research, model simplicity, alternative data, fraud signals, and geopolitical shocks, and why the best quantitative work still requires judgment, discipline, and a clear understanding of what kind of edge you are really trying to build.   *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

    1h 35m

  2. APR 27

    Garret Brennan: Deterministic AI for Institutional Quant Workflows | Blushing Quants #23

    Garret Brennan is the co-founder and CEO of Epoch, an AI-native quantitative research startup building tools for institutional investors who want to integrate AI into their workflows without sacrificing rigor, determinism, or trust. With a background on the fixed income desk at Bank of Montreal in New York, Garret brings both market experience and startup urgency to the problem of making quantitative research faster, more accessible, and more usable in real institutional settings. In this episode, we talk about what it actually takes to build AI infrastructure for quantitative finance in a market that is both highly technical and deeply conservative. Garret explains why Epoch is not trying to let language models perform the computation itself, but instead uses AI as a structured interface atop a traditional institutional-grade research stack. We get into determinism, hallucination risk, backtesting infrastructure, internal libraries, multi-agent systems, and why infrastructure has to come before AI hype if you want a product that can survive real production use. We also discuss customer workflows across the buy side and sell side, what different types of traders actually need, how to think about product-market fit in a fragmented market, and why speed to decision is one of the clearest sources of value. Garret also shares lessons from his path from sales and trading into entrepreneurship, how his market experience shaped the product, and why building for quantitative finance requires a rare mix of technical depth, workflow understanding, and obsessive attention to correctness.   *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

    45 min

  3. APR 14

    Roman Isachenko: Alpha Decay, Derivatives, and the Reality of Quant | Blushing Quants #22

    Roman Isachenko is a quantitative researcher with a background in applied mathematics and rocket science who moved from engineering into finance, derivatives, and systematic trading. His experience spans risk management, derivative pricing, asset management, and small hedge fund environments, giving him a grounded view of how quant research actually works when capital, time, and market reality put every idea under pressure. In this episode, we talk about the realities of building and rebuilding quant strategies in an environment where alpha decays quickly, and competition keeps getting tougher. Roman shares his path from engineering into quant finance, explains why derivatives first pulled him into the field, and reflects on the difference between elegant theory and what survives in live markets. We discuss pricing models, calibration, volatility, and why even strong ideas can stop working when market structure changes. We also get into research discipline, overfitting, noise testing, Monte Carlo comparisons, strategy decay, and the hard decisions small teams face when a model starts to fail. Roman breaks down the differences between research at large institutions and at small funds, why execution and exits often matter more than entry signals, and why leadership, judgment, and genuine passion still matter as much as technical skill in quantitative finance.   *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

    1 hr

  4. APR 9

    Zach Marx: Where Retail Sentiment Meets Systematic Equities | Blushing Quants #21

    Zach Marx is the Chief Investment Officer of Vineyard Quant Capital, where he works at the intersection of systematic equity investing, institutional flow, and data-driven portfolio construction. In this episode, we get into what it actually takes to build a quantitative investment process around how institutions and retail investors make decisions, and how that can be turned into a systematic equity strategy. We talk about Zach’s path from market data and S&P to running an investment strategy at Vineyard, why understanding how different allocators behave can be just as important as understanding the data itself, and how his team uses point-in-time information, forward expectations, and cross-sectional ranking to scale a fundamentally informed process across thousands of stocks. We also get into sector- and industry-level clustering, orthogonal factor construction, seasonality, in-sample versus out-of-sample testing, stock-level and portfolio-level risk management, and why running a successful quant strategy is as much about operations, relationships, and business building as it is about research. The conversation also touches on retail sentiment, alpha-capture frameworks, and how Zach approaches using AI to evaluate processes, without yet trusting it to make investment decisions on its own.   *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

    1h 5m

  5. APR 6

    Mark Aron Szulyovszky: Crypto, Alpha Factors, and Market Neutrality | Blushing Quants #20

    Mark Aron Szulyovszky is a crypto quant and an entrepreneur focused on cross-sectional alpha factors in digital assets. He works to surface crypto-native factors, build market-neutral portfolios, and turn research on derivatives, microstructure, and token-specific behavior into tradable products for both internal use and external clients. In this episode, we get into what it actually takes to build and trade crypto-native alpha factors in a market that is volatile, fragmented, and still structurally different from traditional finance. We talk about why Mark moved away from a more machine-learning-heavy approach toward simpler, more interpretable factor research, how he thinks about market microstructure and derivatives data in crypto, and why cross-sectional alpha remains more abundant there than in many traditional markets. He explains how his team classifies and cleans data, works with tradable universes, controls turnover, and looks for signals that can survive transaction costs rather than just look good in backtests. We also get into retail versus institutional flow, the relationship between spot and perpetual futures, portfolio construction under extreme non-stationarity, and why, in crypto, the biggest challenge is often not finding a signal but sizing and risk-managing it properly when the market regime shifts. The conversation also touches on his entrepreneurial path, including selling his first company and applying that builder mindset to quantitative finance.   *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

    1h 9m

  6. APR 2

    Manuel Ritsch: AI, Asset Management, and the Business of Funds | Blushing Quants #19

    Manuel Ritsch is the founder of Alpha Rho Technologies, where he is building AI-native investment infrastructure for asset management. After seeing how much of the industry still relied on outdated tools and manual processes, he set out to replicate the work of human analysts with AI and turn that into a real operating model for funds. In this episode, we step slightly outside pure quant research to explore what it actually takes to build an AI-driven investment business from the ground up. We talk about the gap between traditional asset managers and newer AI-native approaches, why low-frequency investing may be one of the clearest use cases for agentic systems, and how Manuel structured a fund run by AI analysts, CIOs, and investment committees. We also get into go-to-market, fundraising, bank partnerships, product positioning, client education, and the hard reality that institutional investors still want a track record, transparency, and a process they can trust. Manuel also explains why explainability matters so much in asset management, why models themselves are becoming commodities, and why the real edge increasingly comes from orchestration, usage, and product design rather than just model access.   *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

    29 min

  7. MAR 30

    Francisco Prack: Tape Reading, RL, and Sequential Decision-Making | Blushing Quants #18

    Francisco Prack is a quant, economist, and portfolio manager with 30+ years of experience across financial markets, and a background spanning traditional finance, quantitative research, algorithmic trading, and crypto. In this episode, we get into how a deeply model-driven way of thinking can shape an entire career in markets, from economics and traditional finance to algorithmic trading, reinforcement learning, and crypto. We talk about why Francisco sees markets as a sequential problem rather than a static one, how he studies the tape day by day to extract patterns, and why understanding market rules and order types matters before touching the data at all. He explains how he thinks about institutional footprints, why replaying and re-reading past market sequences can be more useful than forcing generic statistical frameworks onto trading, and how reinforcement learning fits into his process by helping adapt parameter choices and position sizing across different market conditions. We also get into the practical differences between TradFi and crypto, the importance of writing conservative code for extreme market events, and why he still prefers to write the core logic himself rather than outsource the brain of the system.   *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

    1h 10m

  8. MAR 27

    Denis Lukyanov: Quant Research, GenAI Agents, and Trading Systems | Blushing Quants #17

    Denis Lukyanov is a quantitative researcher and AI/ML practitioner working at the intersection of finance, machine learning, and agentic systems. In this episode, we get into what it really takes to integrate agentic systems and large language models into quant workflows, and why the hard part is not generating ideas quickly, but building something structured, testable, and useful in practice. We talk about the gap between quick AI prototypes and production-grade systems, why planning still matters more than coding speed, and how domain expertise remains the real bottleneck even as the tools improve. Denis breaks down how he thinks about combining traditional machine learning and deep learning with GenAI agents: LLMs can add real value as orchestrators, analysts, and research accelerators, but they still should not be trusted to make decisions. We also get into context windows, knowledge systems, guardrails, model-as-judge workflows, regime detection, quantitative research loops, and why serious trading systems still need explicit logic, strong data, and human control. If you care about how AI is actually being used inside quant research today, what separates real systems from AI theater, and how to think clearly about agents, models, and market structure without losing rigor, don’t miss this one.   *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

    50 min
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About

The Blushing Quants is a candid look at the intersection of quantitative finance and machine learning. We discuss the hard truths of building ML-based investment systems. What works, what fails, and why. We leave the LLMs to the chatbots and focus on the heavy hitters of quantitative finance: Neural Networks, Time Series Analysis, and Statistical Learning. *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Information

  • Creator
    theblushingquants
  • Years Active
    2025 - 2026
  • Episodes
    24
  • Rating
    Clean
  • Copyright
    © Copyright 2025 All rights reserved.
  • Show Website
    The Blushing Quants Podcast

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