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13 OctTue
Tue
CFA Institute Research and Policy Center: PublicationsAI Score4949
When evaluating AI heat risk models used in company valuations, investors should focus on whether they fully cover the entire transmission chain from heat hazards to asset exposure, operational sensitivity, financial impact, and ultimately valuation. Existing heat risk estimates from different institutions show significant discrepancies and often omit key impact dimensions such as employee productivity. Investors can reference the case of Électricité de France (EDF) and require companies to disclose quantifiable operational thresholds, financial sensitivities, and adaptation costs to determine whether heat-related losses are already reflected in company value.
In a trading lab at the Robinhood Summit, the author used Nike’s (NKE) after-hours earnings release on October 1, 2025, as a backdrop to demonstrate how to express a specific directional view using vertical spreads. At the time, NKE traded around $36, and the $36 straddle implied an expected move of about $3 (8%). The author chose to buy the 38.5/39 call vertical for a premium of $0.12, which would pay $0.50 if the stock closed above $39 at expiration, for a risk-reward ratio of roughly 3:1.
The author participated in an options buying education session and a roundtable for trading platform data providers at the Robinhood 2026 Summit in Houston, recapping the Kelly Criterion supporting course, the profit and loss comparison logic of buying options versus holding the underlying stock, and the analytical approach for unusual INTC options flow.
Moontower author Kris Abdelmessih released this newsletter, first sharing Adam Mastroianni's article on 'thickness', distinguishing between deep works that require attention to extract value and content-free AI-generated 'slop', then step-by-step deriving the optimal Kelly bet size formula from first principles.
This article introduces a method for automatically identifying structural breakpoints in the Sharpe ratio of trading rules, using independent t-tests to compare mean differences across subsamples, recursively splitting the sample while requiring each subsample to retain at least 5 years of data. The authors provide complete Python implementation code, using the momentum4 rule for CORN futures as an example, with the algorithm automatically identifying August 26, 1982 as the breakpoint.
This article is the sixth part of a series on statistical arbitrage for independent traders. It explains the three core modules of triangulated statistical arbitrage and their resource allocation priorities. High-quality pair screening is the core foundation: using 24 months of historical data, the top 10% of pairs are screened monthly and divided into 5 quality tiers, with the best tier achieving a Sharpe ratio of 1.6 and the worst only 0.42. The author discloses only the general methodology and does not reveal specific implementation details.
This article is the fifth in a statistical arbitrage series for independent traders, focusing on the triangulated statistical arbitrage method. Using the energy sector spread network as an example, the author explains how to cross-validate multiple overlapping spreads to identify genuinely mispriced individual stocks, rather than treating deviations in a single spread as a direct trading signal.
This article introduces a triangulated statistical arbitrage approach. Using 8 spread groups constructed from 6 energy stocks as an example, the z-score signal of each spread is decomposed into opposite votes for the two individual stocks, which are then aggregated into long-short signals at the individual stock level. This allows direct trading of undervalued or overvalued stocks, replacing the traditional pairs-trading model of hedging both legs simultaneously. The approach can improve capital efficiency, reduce transaction costs, and increase signal utilization, while achieving variance control similar to pairs trading through a long-short basket portfolio. The article will further explain how to evaluate the quality of aggregated signals.
The author argues that mean reversion, trend, and momentum are merely labels for price movements rather than trading edges. A true edge requires first identifying the underlying structural cause and then validating it with data. The article uses three types of mean-reversion scenarios as examples: forced liquidation by leveraged funds, scheduled rebalancing of crypto leveraged tokens, and month-end stock-bond rebalancing by wealth management institutions. All three scenarios involve identifiable trading flows that are insensitive to price.
