The Sophron Network

Amay Patel joins The Sophron® Network for our first in-person episode on what it takes to build and run a market-neutral crypto hedge fund.

An OG member of Amsterdam Investment Club, Amay went from independent quant trader, to an Amsterdam-based fund, to launching his own fund. We dig into the strategies, deal-making, and risk discipline behind that path.

Amay Patel is Partner and Head of Investments at Atomic Digital, launched in 2025. The fund runs market-neutral strategies across OTC structured products, DeFi, quantitative, fixed income, and credit markets. Its flagship Market Neutral Fund is offered in USD and Bitcoin-denominated share classes, alongside separately managed accounts for institutional clients, aiming for consistent, low-volatility returns across market regimes.

Before Atomic Digital, Amay was a Quantitative Developer at an Amsterdam-based fund and spent five years as an independent quantitative trader. He studied Finance at the University of Amsterdam and now splits his time between London and Dubai.

We cover the honest version of becoming a trader: starting at 16, years of losses he calls "paying tuition to the market," and the jump from retail size to deploying seven and eight figures at a fund. From there, we move into the mechanics that matter at institutional scale: capital efficiency, borrow rates, margining, and why two funds running the same signal can post very different returns.

We also discuss Atomic Digital, multi-strategy crypto investing, locked-token and OTC structured-product deals, risk discipline, managing inflows with flex capital, durable yield, airdrops, liquidity risk, and flying.

LinkedIn: https://linkedin.com/in/amaypatel

00:00 - Welcome and introductions
02:26 - Trading your own book vs trading for a fund
03:17 - From equities to crypto in the COVID crash
05:23 - DeFi yields, Pendle and Boros
10:03 - Why capital efficiency separates funds
18:51 - Atomic Digital: multi-strategy from day one
21:05 - Locked-token edge and OTC products
30:31 - Risk discipline and the Turkish bond trade
39:22 - Managing inflows with flex capital
44:35 - Will DeFi become as efficient as TradFi
49:22 - Rapid fire: airdrops, liquidity risk, flying

• Launching a market-neutral hedge fund
• Multi-strategy digital asset portfolios
• OTC structured products and locked-token deals
• DeFi yield trading, Pendle, Boros, and basis trades
• Capital efficiency, margining, and borrow rates
• Deal negotiation and counterparty relationships
• Liquidity risk and flex capital
• Scaling from seed capital to external LPs
• Independent trader to fund founder at 24

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Dr. Yves Hilpisch joins The Sophron® Network to discuss what happens to quants in the AI era. We trace two decades of Python in quantitative finance, from a language barely on the radar in 2005 to today's industry standard, and ask how generative AI and agentic coding are reshaping quant careers. The conversation moves from the practical gap between a backtest and a live strategy to the skills that stay valuable no matter how fast the tools change.

Dr. Yves Hilpisch is the Founder and CEO of The Python Quants and The AI Machine. He holds a diploma in business administration and a PhD in mathematical finance, with a long-standing focus on derivatives analytics and risk-neutral pricing. He is the author of several standard references published by O'Reilly and Wiley, including Python for Finance, Derivatives Analytics with Python, Listed Volatility and Variance Derivatives, Artificial Intelligence in Finance, Reinforcement Learning for Finance, and Financial Theory with Python. He directs the Certificate in Python for Finance and the Data Scientist, AI Engineer, and Crypto Engineer programs, and has run developer meetups and training for financial institutions across Europe for over fifteen years.

We examine how Python went from a hobby project to the backbone of modern quant work, with pandas and bank-wide migrations displacing MATLAB and a patchwork of proprietary tools. The conversation then shifts to AI: why the bar for junior quants has risen rather than fallen, why "you can outsource your thinking but not your understanding," and what agentic coding means for entry-level roles. We dig into the backtest-to-live "last-mile problem," economic profit versus a statistical edge, why FX is a sensible starting point for retail algo trading, and why anyone selling a strategy is a red flag. We conclude with the timeless skills, math, programming, and judgment, and why a GitHub portfolio is worth more than a polished CV.

Follow Dr. Yves Hilpisch on LinkedIn:

https://www.linkedin.com/in/dyjh/

Listen on Spotify: SPOTIFY_LINK_HERE

Core Timestamps

02:02 – Founding The Python Quants in 2005, and Python's early signal

06:45 – From hobby to business: pandas and the rise of Python

08:37 – Bank-wide migrations and the displacement of MATLAB

10:51 – The four programs: Python for Finance, Data Scientist, AI Engineer, Crypto Engineer

17:32 – Where AI demand is heading, and the uncertainty in the job market

19:35 – "Coding is solved" and the raised bar for juniors

23:52 – Outsourcing thinking versus understanding

25:33 – Backtest to live: the last-mile problem

28:07 – Why you should not backtest to death

36:31 – Why FX is the natural starting point for retail algo trading

40:12 – The red flag of selling strategies

43:28 – The superquant and the end of the model quant

51:13 – From evangelist to guide through the jungle

60:16 – Timeless skills and portfolios over CVs

Main Topics Covered

• Twenty years of Python in quantitative finance

• pandas, Wes McKinney, and the displacement of MATLAB

• Generative AI and the rising bar for junior quants

• Agentic coding and "outsourcing thinking versus understanding"

• The backtest-to-live last-mile problem

• Economic profit versus a statistical edge

• FX as a starting point for retail algorithmic trading

• The crypto engineering discipline beyond price speculation

• The superquant and timeless, durable skills

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Marc Ostwald joins The Sophron® Network to discuss how the drivers of markets have changed across four decades, why the financial economy has drifted from the real one, and where the next generation of edge will come from. The conversation moves from central bank evolution and conditioned buy-the-dip behaviour into refining bottlenecks, the helium and rare-earth chokepoints behind the AI boom, and the rise of resource nationalism. We also examine what cross-asset relationships still work in this regime — and where investors are still trading from a 2010s playbook that no longer applies.

Marc Ostwald is Chief Economist and Global Strategist at ADM Investor Services International, with more than forty years of experience across FX, fixed income, commodities, equities and global asset allocation. He is one of the most senior macro voices in the City of London, with a regular media presence on Bloomberg, CNBC, Reuters and the BBC. Marc is known for cross-asset, positioning-aware reading of markets rather than orthodox macro — placing physical flows, processing capacity and geopolitics on equal footing with monetary policy.

Follow Marc Ostwald on LinkedIn:

https://www.linkedin.com/in/marcostwald/

Core Timestamps

02:31 – Forty years of markets: what has actually changed

03:34 – From Volcker's opacity to the Greenspan put

06:22 – Active to passive: the ETF regime and Pavlovian flows

07:23 – Four crises in six years and the buy-the-dip reflex

09:21 – Why the post-2008 banking system only looks safer

11:32 – Andy Haldane: risk dissipates but doesn't disappear

13:50 – Buffett, valuations, and what "cheap" really means now

17:41 – $20 of credit, $1 to the real economy — then and now

19:23 – Cooperation breaking down between crises

20:14 – Processing and refining: the bottleneck most investors miss

22:31 – Nitrogen, sulfur and helium out of the Persian Gulf

24:31 – AI infrastructure, helium prices and semiconductor costs

25:28 – Rare earths, derivatives, and why China owns the chain

29:14 – AI capex, power, water and the new era of scarcity

36:21 – Where helium actually comes from

38:50 – Single points of failure and the move to just-in-case

42:32 – Europe's internal trade barriers and the ECB tariff study

45:01 – Resource nationalism, paper vs physical oil, spike volatility

51:44 – Cross-asset: FX, crypto, commodities, shipping

55:11 – Momentum trading and what's missing under the surface

55:48 – Advice for the next generation: psychology, engineering, power

Main Topics Covered

• Forty years of structural change across FX, rates, commodities and equities

• Central bank evolution from Volcker opacity to unconventional QE

• Buy-the-dip behaviour as a conditioned response to four recent crises

• The illusion of post-2008 banking stability and where risk really sits

• The widening gap between the financial system and the real economy

• Refining and processing bottlenecks in commodity and energy value chains

• Helium, sulfur and nitrogen as hidden inputs to the AI capex cycle

• Rare earths, supply chain control, and why no proper derivatives market exists

• Resource nationalism, single points of failure and just-in-case inventory

• Europe's internal trade barriers and the ECB's hidden-tariff study

• Cross-asset relationships that still work versus the 2010s playbook

• What the next generation of traders and investors should master first

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Nikita Granger joins The Sophron® Network to discuss how quantitative finance operates in physical commodity markets — from pricing exotic structured derivatives in power and gas to managing risk across Shell's global infrastructure portfolio. We explore why energy markets function fundamentally differently from equities, how spread options and compound options are used to model real assets, and what it takes to hedge in some of the most illiquid markets in finance.

Nikita Granger is a Quantitative Developer at Shell, working across algorithmic trading, energy markets, and structured derivatives. He started his career as a Data Analyst at Priogen Energy, supporting front-office power trading, before joining Shell as a Data Scientist. At Shell, he built reinforcement learning models for gas and power trading, developed price forecasts across US and European markets, and worked on derivatives linked to green certificates and flexible demand assets. He then moved into deal structuring, designing and pricing complex energy contracts, before transitioning into his current role building trading algorithms around exotic structured products in power markets. Outside of Shell, Nikita is developing a honey futures market to help beekeepers manage credit and price risk.

Follow Nikita Granger on LinkedIn:

  https://www.linkedin.com/in/nikita-granger-27831389/

Core Timestamps

00:00 – Introduction and Nikita's background

02:44 – How Nikita's path led to energy markets

05:04 – Why commodity markets are fundamentally different from equities

06:19 – Energy assets as options: gas plants, pipelines, and the financial-to-physical transition

08:49 – Grid constraints, alternating frequency, and power market design

15:22 – Types of deals at Shell: leasing pipelines, transmission cables, gas plants, batteries

18:00 – Kirk's approximation and spread options as the workhorse of commodity pricing

20:19 – Exotic structured contracts: compound options on gas plants

24:18 – Biggest pricing challenges: parameterization, correlation, and compute time

26:35 – Liquidity problems and dirty hedging with proxy instruments

29:32 – Hedge simulators and risk premiums in illiquid markets

33:09 – Shell as a risk warehouse: how large commodity traders create value

35:51 – Full deal lifecycle: origination, structuring, term trading, cash trading, and scheduling

43:37 – P&L attribution challenges and mark-to-market accounting

45:39 – Collateral, margin calls, and the 2022 European energy crisis

50:10 – Building a honey futures market: credit risk, colony collapse, and commodity innovation

56:22 – Why commodities trading has real-world impact on energy prices and infrastructure

Main Topics Covered

• Commodity markets as futures-driven systems shaped by physical storage constraints

• Energy assets modeled as spread options and compound options

• Kirk's approximation for pricing multi-commodity spread options

• Liquidity challenges and dirty/proxy hedging strategies

• Shell's risk warehouse model: leasing infrastructure and absorbing market risk

• Deal lifecycle from origination through real-time grid scheduling

• Mark-to-market accounting and collateral requirements

• Grid frequency control, resource adequacy, and power market design

• Honey futures: applying derivatives innovation to agricultural commodities

• Real-world impact of doing commodities trading well

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Johannes Muhle-Karbe joins The Sophron® Network to discuss market impact, model misspecification, and why simple trading strategies often perform surprisingly close to complex nonlinear optima. The conversation spans three of his recent research areas: the P&L consequences of using incorrect impact models, Bayesian methods for estimating true out-of-sample Sharpe ratios, and the efficiency of linear approximations in markets with power-law impact.

Johannes Muhle-Karbe is Head of Mathematical Finance at Imperial College London and Director of the CFM-Imperial Institute of Quantitative Finance. His research sits at the intersection of mathematical finance and real-world market practice, with work spanning market microstructure, liquidity, price impact, transaction costs, and optimal execution. Prior to joining Imperial, he held faculty positions at Carnegie Mellon University, the University of Michigan, and ETH Zürich. His research has made him a widely recognized figure in quantitative finance, particularly for his contributions to understanding how trading strategies perform in markets shaped by frictions, uncertainty, and complex dynamics.

We examine why market impact is the dominant trading friction for large systematic funds and how misspecifying impact models can destroy profitability — with the key insight that overestimating liquidity is far more dangerous than underestimating it.

Follow Johannes Muhle-Karbe on LinkedIn:

linkedin.com/in/johannes-muhle-karbe-77428a105

Core Timestamps

04:25 – Introduction to Johannes Muhle-Karbe and his background

06:17 – What is market impact and why it matters for systematic trading

07:14 – Why impact models are always wrong and how much that matters

09:32 – Why you can't just backtest without a model

10:25 – Do firms use academic research on misspecification directly?

13:05 – When does impact misspecification matter most?

15:03 – The asymmetry of P&L: overestimating liquidity vs. underestimating it

20:27 – Dimensionality challenges in fitting impact models

22:14 – Bayesian estimation of Sharpe ratios and the complexity haircut

23:25 – Why the 50% haircut rule is too simplistic

25:51 – What happens when you add more predictors and complexity

31:05 – How signal autocorrelation affects the framework

35:12 – Linear approximations for power-law impact

37:55 – Simple parametric strategies vs. machine learning optimization

39:07 – Why statistics matter more than optimization

42:02 – Is there an optimal execution algorithm?

44:27 – How much is still unknown in market impact research

47:15 – Current research: a unified theory connecting market regularities

50:47 – Will AI and LLMs fundamentally change quantitative finance?

Main Topics Covered

• Market impact as the dominant friction for systematic trading strategies

• The asymmetric P&L consequences of impact model misspecification

• Why overestimating liquidity is far more dangerous than being too conservative

• Bayesian Sharpe ratio estimation and the complexity haircut

• How model complexity widens the gap between in-sample and out-of-sample performance

• The surprising efficiency of simple linear strategies under nonlinear impact

• Why statistical estimation matters more than optimization sophistication

• Connecting square-root impact, rough volatility, and order flow regularities

• The need for better industry–academia data sharing in market microstructure

• The uncertain but transformative potential of AI in quantitative finance

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Alex Zhong joins The Sophron® Network to discuss how quantitative traders build systematic strategies from scratch — from signal discovery and model validation to portfolio construction across dozens of uncorrelated strategies. The conversation covers what separates alphas that survive out-of-sample from those that don't, why crypto markets offer unique advantages for smaller systematic traders, and how to manage a portfolio of 50+ strategies in a 24/7 market.

Alex Zhong is a quantitative trader focused on systematic strategies in crypto markets. At WorldQuant, one of the largest quantitative investment firms in the world, he ranked #27 globally and #2 in China on the Alpha Creation Engine (ACE), and was a Top-5 global out-of-sample performer. His experience includes research with Trexquant Investment, where he built a Sharpe 2 equity earnings prediction strategy that went live, and participation in DRW's Crypto Prediction Challenge, where he ranked in the global Top 50. Alex holds an MSc in Quantitative Finance from the University of Amsterdam and a BSc in Applied Physics from South China University of Technology. He currently runs his own systematic crypto trading desk, managing over 50 strategies in a risk parity framework.

We examine how to build a trading strategy from a simple baseline and iterate toward a mature system, covering the full pipeline from data collection and feature engineering to ML modeling and position management. Alex explains why economic intuition matters more than raw backtest performance, how indirect overfitting through multiple testing can fool even experienced researchers, and why studying your losers is one of the best ways to improve a system. The conversation then shifts to crypto markets — why they offer a more level playing field for small players, how sentiment and momentum dynamics differ from value-driven equity markets, and how to manage extreme volatility and tail risk. We conclude with practical advice for students and aspiring quants on breaking into the industry.

Follow Alex Zhong on LinkedIn: linkedin.com/in/alex-zhongs/

Core Timestamps

00:44 – Introduction to Alex Zhong and his background

03:20 – Differences between building strategies independently vs. at a professional firm

04:46 – How out-of-sample overfitting happens indirectly

06:10 – Building a Sharpe 2 strategy from scratch: feature design to position management

09:21 – Distinguishing real alpha from overfitting

11:44 – Why Alex transitioned from equities to crypto

14:30 – The DRW Crypto Prediction Challenge approach

18:25 – Red flags that signal a strategy will fail

20:17 – Managing thinning margins in high-frequency trading

22:57 – Building and managing a portfolio of 50+ strategies

28:35 – Managing correlation between strategies in tail-risk scenarios

31:17 – Position sizing framework: volatility targeting vs. Kelly criterion

36:21 – Biggest lesson from going live: when backtests don't translate

38:33 – Advice for students breaking into quantitative finance

42:08 – Predictions for crypto markets in the next 3–5 years

46:39 – Rapid fire: book recommendation, programming language, best advice

Main Topics Covered

• Building quantitative strategies from scratch using iterative improvement

• Out-of-sample validation and the dangers of indirect overfitting

• Economic intuition vs. data mining in strategy development

• Why crypto markets are more accessible for small systematic traders

• Managing a portfolio of 50+ strategies in a risk parity framework

• Position sizing and volatility targeting in 24/7 crypto markets

• Detecting bad strategies early through parameter sensitivity testing

• The transition from paper trading to live execution

• Career advice for aspiring quantitative traders

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Lionel Martellini joins The Sophron® Network to discuss the structural evolution of modern portfolio construction — from the rise of passive investing to hidden factor exposures, broken retirement products, and the real implications of quantum technology for finance.We examine why cap-weighted indices dominate global portfolios, why equally weighted portfolios often outperform on a risk-adjusted basis, and how investors unknowingly concentrate risk through implicit factor tilts. The conversation then shifts to fixed income and retirement design, where Professor Martellini argues that most retirement products are structurally misaligned with real-world liabilities.

We conclude with a deep dive into the second quantum revolution — separating genuine technological progress from hype, and discussing where quantum computing may (and may not) meaningfully impact finance.

Follow Lionel Martellini on LinkedIn: https://fr.linkedin.com/in/lionel-martellini-b549a0129Core

Timestamps

04:14 – Active vs Passive: Why passive took over

08:37 – The structural issues with cap-weighted indices

12:15 – Why equally weighted portfolios often outperform

18:31 – The “Effective Number of Bets” and hidden concentration

31:52 – Why fixed income is fundamentally broken

35:48 – Retirement bonds and liability-driven design

49:08 – The second quantum revolution

54:03 – Quantum in finance: real applications vs hype

Main Topics Covered

• Active vs passive investing and structural alpha

• Hidden growth and large-cap bias in benchmarks

• Factor exposure and true diversification

• Retirement bond design and liability matching

• The future of fixed income innovation

• Quantum computing and financial markets

• Avoiding technological “washing” in finance

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Jean-Philippe Bouchaud is Chairman of Capital Fund Management (CFM). He founded ‘Science and Finance’ in 1994, the research arm of CFM with Jean-Pierre Aguilar, which merged with CFM in 2000. He supervises the research team alongside Marc Potters. Jean-Philippe maintains strong links with the academic world and is a professor at École Normale Supérieure (ENS). Prior to CFM, Jean-Philippe was a researcher at the Centre National de la Recherche Scientifique until 1992. Following this he spent a year at the Cavendish Laboratory in Cambridge before joining the Service de Physique de l’État Condensé at the Commissariat à l’Energie Atomique in Saclay, France. He holds a PhD in theoretical physics from the ENS in Paris.Follow Jean-Philippe Bouchaud on LinkedIn Here are the questions we covered during this session and their respective timestamps:04:23 — Arnav“So professor, over the 30 years you’ve witnessed numerous regime changes. So the dot com bubble, the 08 crisis, Covid, and the 2022 inflation shock, for example, which regime change challenged CFM’s systematic approaches? And what did you learn about building strategies that can survive multiple regimes such as these?"12:19 — Nihar“So if you like, in next 20 years, what, which technology will be the regime change like AI and ML. Sorry, can you — so what would you say the next kind of technology or something that could change, like AI for example, would cause the next regime change? That’s also important for you as a fund.”15:27 — Vincent“So how then would you like prepare yourself in the sense that, okay, you don’t know what’s going to happen even tomorrow. But what are some things where if you’re if you have a trading firm or a fund or any financial institution, or you’re maybe running your own strategy, how would you say then to mitigate risk or try to prepare yourself for these sort of unknown things that are going to happen?”19:16 — Nihar“So my main question is, you mentioned in the Global Trading interview that the COVID vaccine announcement November 2020 hurt CFM badly because it created a strong divergence between stay home and go out industries that wasn’t expressing risk before the pandemic. How do you systematically detect emerging risk factors that aren’t in their historical data? Is this even possible or did we just have to accept getting hit occasionally?”26:38 — Arnav“So what do you think the trade off between publishing openly and maintaining proprietary advantage and what role should systematic hedge funds play in advancing academic finance?”45:26 — Vincent“Where would you say are things that align most with physics and mathematics and then the financial world?"51:03 — Vincent“Do you then also think that we’re entering sort of like almost like a dangerous space where fewer and fewer people actually when it comes down to it really understand like the complexity and like what it actually means or what the system is actually doing? Do you think that’s also dangerous in that sense for maybe the entire financial system?”58:30 — Vincent“So just very quickly maybe in like one, two points, what do you think the industry will look like in 10, 20 years?”Mentioned during this episode:- CFM Official Website - Bouchaud, J-P. (2024). "The Self-Organized Criticality Paradigm in Economics & Finance." arXiv:2407.10284. https://arxiv.org/abs/2407.10284- Bouchaud, J-P. & Potters, M. (200U). "Financial Applications of Random Matrix Theory: a short review." arXiv:0910.1205. https://arxiv.org/abs/0910.1205- Bouchaud, J-P. (2021). Radical Complexity. Entropy, 23(12), 1676. https://doi.org/10.3390/e23121676- Risk.net Podcast: "CFM's Bouchaud on agent-based models and ESG investing." September 2020. - J Doyne Farmer. Market force, ecology and evolution. Industrial and Corporate Change, 11(5):895–953, 2002.We also like to share with you some of our socials: Instagram LinkedIn X

Vincenzo Riflesso is Head of Risk at Maverix Securities, bringing over two decades of experience across trading and risk management.

Before his current role, he spent nearly a decade at Leonteq as Head of Market Risk, IPV and PnL Reporting. Earlier in his career, he worked as an Equity Derivatives Trader at BNP Paribas, Intesa Sanpaolo, and UniCredit, after beginning as an Equity Analyst at Merrill Lynch.

With a career spanning global trading floors and senior leadership roles, Vincenzo shares insights into risk management, market dynamics, and the lessons learned from navigating today's complex financial landscape.

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• Quantitative Portfolio Management
• Static vs Dynamic Portfolio Construction
• Risk-Scaling and Momentum-Based Allocation Models
• Rotational and Multi-Factor Equity Portfolios
• Multi-Asset Portfolios (Equities, Bonds, Alternatives)
• Golden Butterfly–Style Portfolio Enhancements
• Portfolio Performance Metrics (Returns, Drawdowns, Volatility)
• Backtesting Pitfalls and Overfitting Risks
• Portfolio Behavior During Market Crises
• Risk Management, Leverage, and Survivability
• How Portfolio Construction Improves Trading Discipline

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Image: Merrill Lynch & Co., Inc. by Hänsel und Gretel, licensed under Creative Commons Attribution-ShareAlike 3.0 Unported.

Sankarshan Basu is Professor of Finance and Accounting at the Indian Institute of Management Bangalore (IIMB). He has held senior academic leadership roles at IIMB and served as Dean at Amrut Mody School of Management, Ahmedabad University, with extensive international teaching experience across Europe and Asia. A former derivatives researcher at ICICI Treasury, he bridges academic rigor with market practice and is co-author with John C. Hull of the Indian version of a leading book on derivatives and financial markets.Here are the questions we covered during this session and their respective timestamps:6:46 — Arnav“For someone starting their quantitative finance journey in 2026, what are the three most fundamental mathematical concepts they absolutely must master before touching anything more advanced?”12:56 — Vincent“How do you see that now with these new models, with AI being used in the markets?”20:18 — Nihar“Based on your teaching experience, what’s the biggest misconception that students have about option pricing when they first encounter the Black–Scholes model?”23:25 — Vincent“Are these [Black–Scholes type models] still used in practice… with different assumptions?”27:13 — Arnav“In your experience training and working with professionals at banks, what basic concepts do even experienced traders frequently misunderstand, and what are the biggest differences between trading options in practice and in theory?”37:35 — Nihar“Why do retail derivative traders in India suffer catastrophically high loss rates — backtests look perfect, but live trading leads to losses?”43:07 — Nihar“How many of the 10% who make money are making money because of skill versus randomness/luck?”45:52 — Nihar“Where is still the edge for a retail trader to make money, given large firms trading with advanced algorithms and PhD-level talent?”48:29 — Nihar“From the perspective of SEBI recently introducing new F&O rules, how would this change the game for retail traders like us versus institutional players?”54:55 — Vincent“Is it better from a regulator’s perspective to completely take highly leveraged instruments out of the market, or is it better to inform people?”59:00 — Vincent“On very short time frames (day trading), is there still an edge to consistently make money?”1:01:19 — Arnav“Do you think SEBI’s new regulations would deter a lot of small retail participants?”1:03:52 — Nihar“On the Jane Street situation: SEBI accused them of artificially increasing expiry-date prices; Jane Street says it’s basic arbitrage — what’s your view?”1:08:16 — Nihar“With local stock exchanges shutting down and listings concentrated in BSE/NSE, how should we view this — especially for smaller local companies that want to list?”1:11:42 — Vincent“What are one or two key points a country needs — for both retail and institutional participants — to get better, more developed capital markets?”Mentioned during this episode:

• LinkedIn CEO at XTX Markets Post
• Application of Time Series Analysis to Finance by Sankarshan Basu
• Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets, a book by Nassim Nicholas Taleb
• SEBI Update
• SEBI New F&O Rules
• Reuters Jane Street

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