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On November 4, 2026, Barry Ritholtz, founder of Ritholtz Wealth Management, published a short essay on the United States midterm elections. He recorded how three tools he previously used to track political trends all failed simultaneously that night: stock index futures had already sold off deeply by 6 p.m.; prediction markets such as Polymarket severely underestimated the win probability of Democratic candidates; and while opinion polls got the general direction roughly right, they exhibited significant magnitude errors, completely failing to predict the election of Democratic senators in Kansas and South Carolina, or the sharp reversal in Texas. By noon on November 4, the Dow Jones Industrial Average had rebounded approximately 3% from its previous day's low, while the S&P 500 and Nasdaq Composite each rose about 2%.

Ritholtz attributes these failures to three factors he considers inherent human cognitive defects: tribalism, a bad information diet, and the difficulty of accurately assessing one’s own abilities. He describes these three elements as fundamental obstacles when analyzing social events and politics, stating bluntly that when this combination is applied to capital allocation decisions, the consequences are fatal. He cites screenwriter William Goldman’s famous quote, "Nobody knows anything," to encapsulate this unknowability, but immediately admits that using this quote feels like cheating—it exploits cognitive limitations formed in an evolutionary environment to explain prediction failures in modern financial markets, limitations that most people either do not know about or do not care about.

However, Ritholtz also explained why this particular election was especially difficult to predict. He noted that the President won in 2024 by broadening traditional Republican demographics, adding young males, less-educated groups, and Hispanic voters to the coalition, which made the Republican alliance vulnerable in a normal midterm cycle. He listed three specific policy factors—the aggressive immigration enforcement by Immigration and Customs Enforcement (ICE), a muscular foreign policy supporting multiple wars, and chaotic tariff policies—as catalysts for the coalition's disintegration. Exit polls showed that 43% of voters cast their ballots to oppose Trump, compared to only 16% who voted to support him; turnout models completely underestimated the mobilization intensity among voters leaning toward the Democratic Party.

For investors, the value of this short essay lies not in providing a new predictive model, but in exposing an operational issue through a concrete case: when futures, prediction markets, and opinion polls all point in the same direction, does this consistency truly represent triple independent verification? Ritholtz does not directly answer this question, nor does he offer an alternative solution. However, his approach of juxtaposing the failures of the three tools and then attributing them to the same set of human cognitive defects forces readers to re-examine the evidentiary logic behind relying on multi-signal cross-confirmation for political risk.

What is the real question when a systematic investor faces N trading strategies or N leverage levels? Rob Carver provides a clear reframing in his blog: whether bootstrapping historical returns to obtain a distribution of final Compound Annual Growth Rate (CAGR), or bootstrapping different leverage levels to generate a series of discrete possible outcomes, both are fundamentally about choosing which of N outcome distributions you prefer. You are not selecting the average performance of a specific strategy, but rather which complete outcome distribution you favor.

Carver distinguishes three selection methods. The first is the strict expectation method: selecting the distribution with the best median value, i.e., the best 50th percentile. As he has previously argued, this is mathematically equivalent to Kelly optimal, which maximizes long-term compound growth. The second is the gambler's method: selecting the distribution with the best right tail, such as the option with the most favorable 1% right-tail point. This corresponds to high-leverage, cryptocurrency, or short-gamma strategies—paths that do not blow up in the best possible alternative histories generated by the bootstrap. Carver's assessment is that nobody should be a gambler, except occasionally. The third is the conservative method he favors: selecting the distribution with the best left tail, i.e., the option with the most favorable 1st, 10th, or 25th percentile point, meaning the option that yields the best outcome if bad things happen.

The extreme version of the conservative method is MinMax from game theory: selecting the option with the best worst-case outcome. Carver points out that this is exactly what all chess-playing software does—treating the market as an adversary and striving for the least bad result in the worst possible state. However, he immediately acknowledges that if the number of bootstrap iterations is sufficiently large, the lower bound of all distributions will converge toward zero (since compounded returns and absolute returns greater than -100% cannot mathematically fall below zero), making them hard to distinguish. Therefore, the most realistic extreme version in practice is selecting the option with the most optimal 1st-percentile left-tail point. Given that this is likely to be negative, it would be the option with the least worst 1st-percentile tail.

Carver does not suggest directly optimizing for the 1st-percentile point. He cites an old saying in the trading community: there are no old, bold traders. Good traders know how to quantify risk and when to take risks, but they also understand the importance of keeping their capital mostly intact for the future. His conclusion is that optimizing around the 10th or 25th percentile point is a reasonable compromise in practice. The 1st-percentile point is hard to distinguish in bootstrapping, and more extreme results likely exceed the range predictable from historical samples; while the 50th-percentile point (Kelly optimal) maximizes long-term growth, the corresponding path volatility may exceed the psychological tolerance of most people. The 10% to 25% interval implies that in every four to ten possible outcomes, you are optimizing for the worse-performing instance rather than the worst-case scenario.

Systematic trading practitioner Rob Carver has proposed a new method for handling trading costs in the 11th article of his 2026 portfolio optimization series. His starting point is a widely accepted observation in quantitative investment: trading costs are easier to predict than expected returns. Costs are relatively deterministic figures, whereas return estimates are filled with noise. So, when optimizing predicted weights, how can this asymmetry be naturally reflected in the results?

Carver lists six existing solutions, ranging from completely ignoring costs and using net returns, to multiplying costs by a factor greater than 1 to artificially amplify their weight. He argues that while the multiplier method seems intuitively correct—since costs are more certain, they should carry greater weight—it introduces an additional parameter, 'factor,' which requires further optimization and increases complexity. His proposed new solution is to first perform a shrinkage estimate on the gross-return Sharpe ratio, and then directly deduct each transaction's cost from the shrunk value.

The logic behind this method is that shrinkage itself already handles the uncertainty of return estimates. When you shrink a noisy Sharpe ratio estimate, you are essentially saying, 'I am not very confident about how good this return prediction is.' Costs do not require shrinkage because they are known. Therefore, shrinking first and then deducting naturally achieves the effect of giving more weight to certain costs and less weight to uncertain returns, without needing to manually set a multiplier. The specific parameters Carver provides are: a Sharpe ratio shrinkage of 0.5, a correlation coefficient shrinkage of 0.75, and higher shrinkage when data spans less than five years.

However, Carver admits that this method suffers from a cliff effect: he sets a threshold of 0.01 Sharpe ratio points as the cost cap; tools exceeding this threshold are dynamically excluded by the optimizer. An instrument with a cost of 0.012 and one with a cost of 0.008 receive drastically different treatment in the optimization. His mitigation strategy is to cap costs at 0.01 at the optimization level, making high-cost instruments behave as if their cost is exactly 0.01 during weight calculations, but still excluding them at the actual execution level. He acknowledges this remains a double standard and somewhat messy, but it is better than the previous approach of completely ignoring costs.

The article concludes by presenting Carver's current 'gold standard' architecture for forecast weight optimization: from outside to inside, it consists of rolling out-of-sample optimization, cross-instrument average pooling (averaging optimizations based on three datasets for gross returns: instrument-specific, asset-class-level, and full-sample), reverse weight processing (allowing for the discovery of effective strategies the author did not anticipate), clustering (targeting six clusters), and finally, innermost shrinkage plus cost deduction. Instrument weights use the same structure but omit the pooling step. Carver explicitly states that the next article will test this architecture on a smaller, tractable portfolio.

The results of the United States midterm elections have created a unique political landscape in Washington, D.C.: a Republican president alongside a Democratic-controlled Congress. James Picerno, author of The Capital Spectator, argues that this divided government is not a negative factor for the market; rather, it provides new support logic for asset pricing through two distinct channels.

The first channel is policy predictability. A divided government means that tax code revisions, large-scale deregulation, or expansive fiscal stimulus face significant hurdles in Congress. Picerno points out that reducing policy surprises itself provides businesses and investors with a more stable operating environment, a level of certainty that markets have historically preferred.

The second channel is fiscal discipline. Picerno contends that the combination of a Democratic-controlled Congress and a Republican White House makes it harder to pass legislation that significantly expands the budget deficit. While this gridlock is unlikely to reverse the long-term upward trend of federal debt, it lowers the probability of new, large-scale fiscal stimulus. A verifiable anchor for this judgment is the yield on the 10-year U.S. Treasury note: if yields stabilize or decline post-election, it may indicate that bond market investors recognize divided government as an effective constraint on deficit expansion; if yields continue to rise, it suggests investors remain concerned that the trajectory of federal debt will not change regardless of which party controls Congress. Picerno notes that the 10-year yield has recently fallen back below 5%, implying that the market is temporarily less worried about inflation and fiscal risk.

However, Picerno simultaneously identifies a sectoral exception: AI data center infrastructure. Citing a poll released by Politico last week, he notes that approximately half of Americans oppose building data centers within a few miles of their homes, a figure that has risen sharply from 28% in January. As public sentiment shifts toward supporting restrictions, Democrats may seek stricter regulation of data center development. The market appears to be pricing in this possibility: two ETFs serving as proxies for data center and digital infrastructure stocks have fallen sharply in recent weeks, while the S&P 500 ETF (SPY) has remained largely flat. This implies that the transmission mechanism of divided government to the AI infrastructure sector is opposite to its effect on the broader stock market—policy predictability benefits the broad market, but expectations of tighter regulation weigh on specific sectors.

Picerno’s analysis also leaves open two risk channels still under the President’s unilateral control. First is tariffs: even with a divided Congress, the President can impose or raise tariffs through executive action and existing trade laws, limiting Congress’s ability to restrain such moves. Second is geopolitics: some analysts argue that a lame-duck president may feel fewer constraints in pursuing a more aggressive foreign policy agenda, including a tougher stance toward Iran. Any escalation in tensions could disrupt oil supplies, push up energy prices, and further complicate the inflation outlook. Currently, the U.S. benchmark crude oil, West Texas Intermediate (WTI), is trading below $100 per barrel. Picerno poses the question: does this reflect a durable easing of geopolitical concerns, or merely a temporary pause before markets reassess the risks of renewed tensions in the Middle East?

Why do two borrowers who appear nearly identical in credit score and income level receive significantly different annual percentage rates (APRs) when applying for a credit card at the same bank? In a study published on November 4, 2026, on Liberty Street Economics, the blog of the Federal Reserve Bank of New York, Kyle Herkenhoff, a consultant at the Federal Reserve Bank of Minneapolis, and Juan M. Morelli, a research economist in the Research and Statistics Group of the Federal Reserve Bank of New York, provide a mechanistic explanation: credit card issuers pass through changes in funding costs to a lesser extent in segmented markets where they hold a larger market share.

Understanding this conclusion requires an understanding of the structure of the credit card market. Issuers do not compete in a single, undifferentiated national market; rather, they design products and pricing strategies targeting specific types of borrowers. Industry reports, regulatory guidance, and academic research all document market segmentation based on credit risk, income levels, card characteristics, and acquisition channels. A prime borrower applying online for a standard non-co-branded card belongs to a completely different market than a subprime borrower accepting a pre-approved offer for a co-branded card. The same issuer can dominate one such segment while holding a very small share in another.

Guided by this framework, Herkenhoff and Morelli divided the national market into 960 distinct segments, combining four credit-score groups, card and product types, acquisition channels, and income groups based on the borrower’s ZIP code. In their dataset, an issuer operates in approximately 200 of these markets on average per week. Within each market, the issuer’s share is defined as its revenue relative to total revenue in that market.

They first utilized confidential account-level data collected by the Office of the Comptroller of the Currency (OCC), a U.S. banking regulator, to observe the relationship between interest rates and market share. After finely grouping accounts by characteristics such as income, credit score, and card type, they found a positive correlation of 54% between the average APR within each group and the average market share of the issuing lender within that group. This means that among borrowers with similar characteristics, cards issued by lenders with higher market shares carry higher interest rates.

However, correlation does not imply causation. High-share markets may differ in unobserved dimensions that also affect rates. To obtain more direct evidence, the authors turned to a natural experiment: when an issuer’s funding costs undergo an exogenous change, do its pricing adjustments differ between high-share and low-share markets?

The analysis focused on January 2008, which saw one of the largest unexpected drops in the federal funds rate (25 basis points). For new accounts, issuers set the initial APR and its spread over the benchmark rate at origination. The authors compared cards issued shortly before and after this unexpected rate change by the same issuer, stripping out market-wide common rate movements and controlling for observable differences in borrower credit scores, income, and card types. What remained was the difference in pass-through between high-share and low-share markets for the same issuer under the same exogenous shock.

US-based asset manager Franklin Templeton officially launched two new Exchange-Traded Funds (ETFs) on October 29, 2026: the Franklin U.S. Equity Bitcoin DRIP Index ETF (NASDAQ: FBEQ) and the Franklin U.S. Innovation Bitcoin DRIP Index ETF (NASDAQ: FIBT). The core innovation of these funds lies in their "Dividend Reinvestment Plan" (DRIP) strategy—they do not use stock dividends to purchase more shares but instead systematically convert them into Bitcoin exposure.

FBEQ tracks approximately 500 large-cap U.S. stocks, while FIBT focuses on 100 Nasdaq innovation companies. In their initial allocation, both maintain a 95% equity position and a 5% Bitcoin position. To control risk, the funds rebalance quarterly: if Bitcoin exposure exceeds 5%, it is reduced to 4.5%; if it is at or below 5%, the allocation remains unchanged. This structure aims to allow investors to retain traditional stock market returns while gradually accumulating digital assets through compounding effects.

However, this strategy comes with significant trade-offs. Compared to traditional dividend reinvestment, Bitcoin's high volatility means this portion of capital may face larger drawdown risks. As of early November 2026, Bitcoin prices were down approximately 38.5% from their October 2025 all-time highs, amid what is being called a "crypto winter." While the fund's allocation caps limit single-asset concentration, they cannot eliminate losses during broader market downturns. For investors with higher risk tolerance and longer horizons, this serves as an automated method to accumulate Bitcoin; for conservative investors, it adds unnecessary portfolio volatility.