RWH069: The Psychology of Investing w/ Emily Haisley

What It Covers

Emily Haisley, head of the behavioral finance team at BlackRock — the world's largest asset manager with $14 trillion in assets under management — explains how her team uses portfolio analytics, physiological data from Oura rings, AI-powered market simulations, and structured group decision-making processes to identify and reduce costly behavioral biases in professional fund managers.

Key Questions Answered

• •Quantifying Bias as Opportunity: BlackRock's behavioral analytics team measures specific biases — loss aversion, disposition bias, myopic loss aversion, overconfidence, and excess trading — directly from portfolio holdings and transaction history. When analytics reveal no detectable bias, that is not a positive outcome; random errors are harder to fix than systematic ones. Identifying a consistent, measurable mistake means the team can design a targeted process intervention to eliminate it and recover lost returns.
• •Default Position Sizing to Counter Myopic Loss Aversion: Portfolio managers routinely enter new positions at sizes too small relative to their conviction, driven by the unfamiliarity of new holdings and the concentrated career risk felt by individual analysts. The structural fix is establishing a team-agreed default entry size for all new positions, then requiring written documentation of any deviation from that default. This nudge shifts the cognitive burden onto justifying smaller sizes rather than justifying larger ones.
• •Disposition Bias Detection via Realized Gain/Loss Ratios: The team measures disposition bias by comparing the probability of realizing a winning position against the probability of cutting a losing one, benchmarked against historical norms. The bias is only flagged as costly when losing positions fail to mean-revert after being held and winning positions continue rising after being sold prematurely. Emotional resistance during the review conversation — anger, sadness, defensiveness — serves as a reliable signal that the bias is active and addressable.
• •Pre-Meeting Blind Voting to Eliminate Anchoring in Investment Committees: Before any preliminary investment committee meeting, each member independently rates a deal on a calibrated scale anchored against the historical distribution of previously approved deals — before group discussion begins. The chair receives all ratings privately and uses them to ensure dissenting views surface during the meeting. This single structural change eliminates anchoring to the first speaker, suppresses sunk-cost momentum, and forces minority perspectives into the room.
• •Pre-Mortem Analysis with an Outside Challenger: Assigning a designated challenger — drawn from outside the deal team — to conduct a formal pre-mortem before final investment committee approval reduces sunk-cost bias in private asset decisions. The challenger assumes the deal has already failed three years forward and works backward to identify what went wrong. This process either surfaces deal-killing risks, prompts renegotiation of terms, or creates an explicit early-warning framework for managing the asset if it begins deteriorating post-close.
• •Cortisol Levels and Portfolio Risk-Taking: Research shows that sustained elevated cortisol over approximately one week measurably shifts risk preferences toward excessive risk aversion in laboratory subjects. BlackRock's team links Oura ring physiological data — stress scores, sleep quality, recovery metrics — to individual portfolio risk decisions on a voluntary, confidential basis. When investors see their own drawdown periods correlated with elevated stress readings, the act of consciously acknowledging that stress frequently breaks the chronic stress cycle and restores baseline decision-making capacity.
• •AI War-Gaming to Rehearse Volatile Market Decisions: A simulation tool developed internally loads a team's actual portfolio positions and presents sequentially generated news headlines — some economically relevant, some noise — requiring real-time decisions to trade or hold. Teams discover under time pressure that their standard deliberative group process breaks down, forcing them to define explicit volatility-trading strategies in advance. Critically, participants experience the same drawdown scenarios as real markets but respond with engagement rather than distress, enabling behavioral learning without the psychological cost of actual losses.