Haris is a quantitative equity researcher and portfolio manager with experience across top-tier institutions, including BlackRock. Born in Greece, he studied applied computer science and applied mathematics, worked at the European Central Bank, then moved to the US for a Master's in Financial Engineering, and later built systematic equity models in the industry.

In this episode, we go into how quant research is done when time matters. Haris breaks down the prototype mindset, how to build a fast research pipeline, and what gets rejected immediately before you waste months. We talk about signal evaluation, residual momentum, sector and risk neutralization, and why correlation with your existing book can kill a "great" signal.

We also unpack execution realities, trading costs, backtest vs. realized PnL, Monte Carlo and holdouts, and the optimizer's approach to sizing, constraints, turnover, and robustness. Finally, we cover where machine learning and deep learning fit, why short horizons give data-hungry models an edge, and how LLM agents are accelerating the pace of research.

*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.