How AI and Passive Investing Affect Stock Market Behavior

Artificial Intelligence (AI) is increasingly in the spotlight, attracting widespread attention across industries. One question on investors’ minds is how AI will change investment dynamics. In this context, Reference [1] examines whether AI and passive investing weaken the relationship between stock returns and economic fundamentals while increasing return autocorrelation.

The study analyzes 12 Fama-French U.S. industry portfolios using quarterly data from 2002 Q1 to 2025 Q2, comprising 1,128 industry-quarter observations. To measure AI sentiment, the author uses NVIDIA’s excess returns over the S&P 500, detrended and standardized.

The paper pointed out,

Five findings stand out. First, AI intensity exerts a robust negative effect on quarterly industry returns, acting as a sectoral redistributive force that diverts capital from non-AI segments during sentiment spikes – consistent with Danielsson and Uthemann’s (2025) prediction of intensified procyclical, synchronised capital behaviour – without directly severing the informational relationship between prices and economic activity. Second, fundamental anchoring persists over the full 2002–2025 period but is weakening. Industrial production growth retains its role as a positive and significant predictor of industry returns, although rolling-window and split-sample analyses reveal a sustained deterioration from 2015 Q2 that intensifies in the post-COVID era. Third, no evidence of feedback dynamics emerges: the lagged-return coefficient fails to attain statistical significance throughout the sample and across both sub-periods, providing no support for the market-fragility hypothesis and aligning instead with informationally efficient pricing. Fourth, passive investing flows exert no direct effect on returns, suggesting their influence operates through indirect structural channels–factor exposure, amplified comovement, and liquidity transformation– in line with the microstructure mechanisms identified by Ben-David et al. (2018) and Da and Shive (2018). Fifth, aggregate market returns remain the dominant driver of industry variation, a pattern that neither AI sentiment nor passive growth has yet materially altered.

In short, the paper finds that when AI-related stocks perform particularly well, other industries tend to underperform. This suggests that investors may be shifting capital toward AI companies and away from traditional sectors. The study also finds that stock returns have become less closely linked to economic fundamentals since 2015, although the evidence of this weakening is relatively modest.

This paper contributes to an emerging body of research on the impact of AI on investing, although its real-world effects remain to be seen.

An interesting aspect of the study is its use of NVIDIA’s relative performance as a proxy for AI sentiment. Trading system developers could adopt a similar approach by incorporating similar AI-related variables into investment models.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Piscopiello, M. (2026). Artificial Intelligence, Price Anchoring, and Feedback Dynamics in U.S. Equity Markets, Kent Economics Undergraduate Research Journal, Issue 4.

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The Relationship Between Leverage, Investor Behavior, and Returns

Margin lending and borrowing are integral parts of a well-functioning financial system. However, excessive use of margin, or leverage, can create significant risks. In this context, Reference [1] examines the returns and trading behavior of retail investors using margin financing in the Chinese stock market.

The study uses transaction data from nearly five million brokerage accounts between May and July 2015, covering China's equity market bubble and crash. The authors report an average assets-to-equity ratio of 1.4 for brokerage margin accounts and 6.4 for shadow accounts. They then construct a distance-to-margin-call measure (Z), which estimates how many standard deviations of portfolio losses would bring an account to its liquidation threshold.

The paper  pointed out,

We find that margin traders tend to hold stocks with more scope for “informed trading” and lottery-like features, though they do not outperform their unlevered counterparts. Our results thus suggest that lottery preferences, partly driven by overconfidence, can help explain why investors are willing to pay an annual interest rate of up to 25% in order to lever up.

Both shadow and brokerage margin investors heavily sell their holdings when their account-level leverage edges toward their Pingcang Lines (the maximum leverage limit, or maintenance margin), controlling for stock-date and account fixed effects. In other words, we find strong empirical support for both forced and preemptive margin-induced trading. The fact that investors react asymmetrically to positive and negative return shocks suggests a leverage constraint channel in addition to a rebalancing motive. We exploit the discontinuity around the eligibility of brokerage margin trading and the variation in Pingcang Lines across certain shadow accounts to provide unique evidence that leverage constraints induce selling. Moreover, we find that margin investors are relatively more likely to sell non-lottery stocks as the leverage constraint binds in a down market.

Aggregating trading behavior across margin investors, we find a significant return spillover in the near future: a stock’s return can be strongly forecasted by a portfolio of stocks with which it shares common margin-investor ownership. This pattern is subsequently reversed, and is present only in market downturns.

In short, the paper finds that,

• Retail margin borrowing is associated with lottery preferences and possible overconfidence.
• Margin-induced liquidation creates a feedback loop between declining asset prices and forced selling, transmitting shocks across otherwise unrelated stocks.

Although the study focuses on leveraged trading in the Chinese stock market, its conclusions may also be applicable to developed markets. We would be interested to hear from readers who are aware of similar studies in developed markets.

Another interesting contribution is the adaptation of the Merton credit risk model to leveraged trading, providing a framework for analyzing margin-related risks.

Let us know what you think in the comments below or in the discussion forum.

References

[1]  Jiangze Bian, Zhi Da, Zhiguo He, Dong Lou, Kelly Shue, and Hao Zhou, The Drivers and Implications of Retail Margin Trading, Journal of Finance, 2026.

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Revisiting Gambler’s Ruin: Trading Edge, Drawdowns, and Survival

The gambler's ruin problem is a classical probability problem that examines the likelihood of a gambler reaching a specified wealth target or losing all available capital. Reference [1] revisits this problem. Specifically, it seeks to answer the following questions:

Suppose a gambler enters a casino with finite capital,

• What is the probability of reaching a target wealth level or going bankrupt, and
• What is the expected duration of play?

The author first derives analytical formulas to address these questions, then uses Monte Carlo simulations to validate the theoretical results. Finally, the findings are applied to a real-world investment problem using daily S&P 500 data from January 1990 through September 2026.

The author pointed out,

Applied to daily S&P 500 returns, the model estimates a win rate of p = 0.5369 and finds that the sign sequence is statistically distinguishable from an independent one while being, in magnitude, very nearly independent. The more serious failure is elsewhere. When the fixed unit stake is replaced by a bootstrap of the actual return distribution, holding the win rate and the mean absolute move constant, the projected probability of ruin rises from 5.0% to 42.7%. The model’s weakness is not that markets are predictable but that they do not move in equal steps. That is a useful thing to know about a model, and it is the kind of thing that only becomes visible when the mathematics is exact enough that the discrepancy cannot be blamed on the algebra.

A central finding of the paper is that risk models based solely on the frequency of winning/losing days can substantially underestimate the risk of ruin when actual return magnitudes are unequal and heavy-tailed. In other words, the size of losses relative to gains, as well as the presence of fat tails, is critical in determining ruin probabilities.

Another interesting finding is that a relatively small increase in the probability of winning can dramatically reduce the probability of ruin. For example, at a 51% win rate, the probability of ruin is 44.93%, whereas at a 56% win rate, it falls to just 0.8%.

This paper makes an important contribution by formally confirming two principles already familiar to practitioners, namely

• Keeping losses and drawdowns small, and
• Increasing the trading edge whenever possible.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Jasman Mander, Random Walks and the Gambler’s Ruin Problem: Exact Results, Independent Derivations, and an Empirical Application to Equity Index Returns, Working Paper, 2026

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Market Timing Strategies Using the Implied Volatility Surface

Market timing is typically conducted using technical, macroeconomic, and volatility indicators. However, there is a growing trend in the research community toward using options-implied volatility to extract information for constructing market-timing strategies. For example, we have discussed how option-implied distributions can be used to time the market. Along the same line, Reference [1] develops an approach that uses the implied-volatility surface (IVS) to identify market entry points.

Using the IVS, the authors develop two groups of indicators,

1. Directional-expectation indicators: Directional Sentiment Bias (DSB) and Relative Upside Conviction (RUC).
2. Risk-stability indicators: Tail Risk Concentration (TRC) and Intertemporal Uncertainty Spread (IUS).

These indicators are then combined into three bullish structural regimes:

1. Extreme Hedging Reversal: panic/hedging conditions remain elevated, but directional sentiment has already turned bullish.
2. Trend-Stabilizing Correction: short-term stress has subsided relative to longer-term uncertainty while bullish sentiment remains.
3. Persistent Trend Potential: low short-term stress and longer-term uncertainty combined with bullish conviction, interpreted as conditions favorable to continued trends.

The authors test their approach on AAPL, TSLA, NVDA, and SPY. They generate an entry signal when IVS structural indicators cross dynamically calibrated thresholds into a defined regime, using only the first day of each structural transition as the entry point.

The paper pointed out,

The timely identification of effective market entry points is critical for capturing favorable risk-return opportunities while mitigating downside exposure. To address this challenge, the proposed framework leverages the structural information contained in the IVS through an economically motivated segmentation into the Central Sentiment and Peripheral Risk domains. Structural factors extracted from these domains disentangle directional sentiment from extreme risk pricing, while regime-specific thresholds identify distinct structural market regimes. By focusing on IVS state transitions rather than continuous price movements, this approach identifies discrete, effective entry points. Experimental results show that the framework outperforms conventional price-based and volatility-level baselines in directional accuracy and risk-adjusted returns. These findings highlight the practical value of structurally informed IVS analysis for precise, early-phase entry point detection and tactical decision-making in financial markets.

In short, the authors find that the IVS strategy generally delivers higher risk-adjusted performance, although the magnitude varies materially across assets. Their interpretation is that combining directional information with structural risk conditions improves entry timing.

A noteworthy conclusion is that this approach produced more precise and robust early-phase entry points than the technical and sentiment benchmarks tested, particularly because it uses economically interpretable information from different regions of the IV surface rather than continuously forecasting prices. Consequently, the approach can be used as a standalone regime-detection framework.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Yi-Chieh Sung, Shiou-Chi Li, and Jen-Wei Huang, Market Timing via Structural Transition Detection in the Options Implied Volatility Surface, in Advances in Computational Collective Intelligence, Springer 2026

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Statistical Arbitrage in Prediction Markets

Prediction markets are markets in which participants trade contracts based on the outcomes of future events, and they have become increasingly popular in recent years. Given their rising popularity, an important question is how to develop quantitative betting strategies.

Reference [1] proposes an interesting approach based on an adaptation of the classic pairs-trading strategy. Specifically, the paper adapts financial-market pairs trading and mean reversion to U.S. presidential betting markets. But instead of modeling the price spread between two stocks, it models the sum of the implied probabilities of the presidential nominees.

The combined probability is modelled as a latent Ornstein-Uhlenbeck (OU) process with a time-varying mean plus additive observation noise. The OU component represents short-term mean reversion, while the moving equilibrium represents gradual changes in the election market.

The authors utilize political betting odds obtained through BetData. The 2020 election (Trump/Biden) is used for model construction and training, while the 2024 election (Trump/Harris) is used for out-of-sample testing. They pointed out,

This study demonstrates the feasibility of adapting a pairs-trading framework to political prediction markets. We model the combined implied probability of the two major-party nominees using a latent time-varying-mean Ornstein-Uhlenbeck process with additive observation noise, use bootstrap-based prediction bounds to identify trading signals, and apply a Bradley-Terry pairwise comparison model to select the candidate-specific odds. In the 2024 out-of-sample evaluation, the strategy generated 130 completed trades, with a mean realized odds-price return of 1.86% and an unannualized per-trade Sharpe-type ratio of 1.12. These results illustrate the potential of the integrated signal-generation and candidate-selection framework in electoral markets dominated by two leading candidates.

In short, the authors present the framework as a proof of concept showing that a pairs-trading-style mean-reversion model can be adapted to two-candidate political betting markets. They point to the 2024 130-trade out-of-sample result, +1.86% mean synthetic odds-price return and 1.12 per-trade Sharpe-type ratio as evidence for the integrated signal-generation/candidate-selection framework.

This is an interesting paper, as it provides a foundation for developing quantitative strategies in prediction markets.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Haoyu Liu, Len Thomas, Benjamin Baer, and Carl Donovan, Adapting Pairs Trading to Gambling Markets: A Case Study of the U.S. Presidential Election, arXiv 2609.22639v1, 2026

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