Literature Review
1 Classical Foundations of Risk-Based Asset Pricing
Modern finance rests on the principle that asset returns compensate for exposure to systematic risk. Foundational models such as the Capital Asset Pricing Model (CAPM), the Fama-French factor models, and the Betting Against Beta (BAB) framework formalize this idea under assumptions of frictionless trading, rational expectations, and arbitrage-free equilibria.
In CAPM, expected returns scale linearly with market beta. The Fama-French Three- and Five-Factor models extend this by incorporating size, value, investment, and profitability factors (Fama and French 1993, 2015). Alternative models such as Black–Litterman (Black and Litterman 1992) or Bayesian tilting (Brandt, Santa-Clara, and Valkanov 2009) adjust this equilibrium using investor views. Despite their theoretical elegance, these frameworks rely heavily on convex market structure, homogeneous expectations, and efficient capital mobility — conditions increasingly absent in post-crisis financial markets.
2 Empirical Challenges and Structural Frictions
2.1 Factor Models and Fragility Under Structural Shifts
The empirical performance of multi-factor models is often weak out-of-sample. Applying Fama-MacBeth regressions (Fama and MacBeth 1973) to industry or quantile portfolios reveals unstable betas, multicollinearity among explanatory variables, and residual alpha persistence (Fama and French 1997). Recent replication studies (e.g., Hou, Xue, and Zhang (2020)) show that most anomalies lack robustness, particularly when controlling for micro-cap bias and rebalancing frictions.
Even advanced selection methods, such as Lasso or Ridge-regularized regressions (Kozak, Nagel, and Santosh 2020), identify few stable predictors. These results suggest that the dominant source of return variation may lie outside the domain of classical factor structures.
2.2 Nonlinearity and the Failure of Beta as a Pricing Kernel
The BAB model posits that low-beta portfolios offer higher Sharpe ratios after adjusting for leverage constraints (Frazzini and Pedersen 2014). However, empirical tests using SIC-based portfolios reveal a non-monotonic relationship: optimal risk-adjusted returns often cluster around beta ≈ 1, contradicting the implication of continuous beta-based mispricing. This points to risk inertia rather than reward for bearing volatility.
2.3 Practical Limitations of Optimization-Based Portfolios
Mean-Variance optimization (Markowitz 1952) and its extensions frequently fail in practice. Portfolio weights are unstable and overfit, leading to high turnover and inferior real-world performance (DeMiguel, Garlappi, and Uppal 2009). Constrained variants reduce instability but underperform simple passive benchmarks like SPY or QQQ.
2.4 Post-2010 Failures of Predictive Allocation
Dynamic asset allocation models using Bayesian tilting or factor timing (Brandt, Santa-Clara, and Valkanov 2009) show limited edge post-2010. In regimes dominated by non-Gaussian returns, policy shocks, and arbitrage limits, these models fail to anticipate structural capital movements or benefit from persistent factor exposures.
3 Structural Interpretations: Rent Extraction and Capital Lock-in
A growing literature interprets return generation as increasingly governed by non-risk factors such as capital scale, policy insulation, and rank persistence:
- Large banks exhibit lower risk-adjusted returns, suggestive of implicit guarantees (Gandhi and Lustig 2015).
- Small-cap segments contain a rising share of “zombie” firms — structurally unproductive entities sustained by policy support (Acharya et al. 2024).
- ETF flows bias price discovery toward large-cap incumbents (Jiang, Vayanos, and Zheng 2020; Glosten, Li, and Zhang 2021).
- Concentration of capital and returns parallels macro trends in labor share decline and superstar firm dominance (Autor et al. 2020).
These findings frame market returns not as compensation for risk, but as rents accruing to structural position within the capital hierarchy.
4 Positioning TBTF within the Literature
This study contributes to this emerging paradigm by introducing:
- Rank-based capital lock-in frameworks via percentile transition matrices, inspired by social mobility models.
- A low-turnover, high-performance TBTF strategy, rooted in capital persistence rather than information efficiency.
- A critique of post-QE capital dynamics, where ETF-driven flows reinforce rank-based inequality in financial markets.
In contrast to conventional pricing models assuming rapid mean-reversion and marginal arbitrage, the TBTF strategy illustrates how capital stratification can sustain excess returns through structural mechanisms rather than informational edges.
Its continued success does not reject modern finance—it reflects its breakdown under structural stress, necessitating models that account for capital rigidity, transition asymmetry, and institutional drift.