To explore the rationality and competitiveness of the mutual fund industry, we analyze the alpha of active and index mutual funds from a global sample of more than 60,000 equity and fixed income funds and test the null hypothesis that alphas to investors are zero. We distinguish between institutional and retail investors since there are significant differences in management fees, economies of scale, and information asymmetries between these two groups. Using a new robust statistical test, we cannot reject our null hypothesis for the majority of investment categories. We find that the average active fund has less exposure to traditional risk factors, but higher sensitivity to alternative risk premia. Fund persistence and the impact of size and fees add further support to our conclusion that the mutual fund industry is highly competitive, except for US domestic funds. This set of funds is excessively overfunded compared with other fund categories.

Despite the enormous growth of the asset management industry during the past decades, little is known about the asset pricing implications of investment intermediaries. Standard models of investment theory neither address the distinction between individual and institutional investors nor the potential implications of direct investing and delegated investing. In a model with endogenous delegation, the authors find that delegation leads to a more informative price system and lower equity premia. In the presence of relative return objectives, stocks exhibiting high correlations with the benchmark have significantly lower returns than stocks with low correlations. The authors' empirical results support the model's predictions.

We investigate the implications of technological innovation and non-diversifiable risk on entrepreneurial entry and optimal portfolio choice. In a real options model where two risk-averse individuals strategically decide on technology adoption, we show that the impact of non-diversifiable risk on the option timing decision is ambiguous and depends on the frequency of technological change. Compared to the complete market case, non-diversifiable risk may accelerate or delay the optimal investment decision. Moreover, strategic considerations regarding technology adoption play a central role for the entrepreneur’s optimal portfolio choice in the presence of non-diversifiable risk.

We analyze the impact of funding costs and margin requirements on index options traded on the CBOE. Assuming differential borrowing and lending rates, we derive no-arbitrage bounds for European options. We show that funding costs and the CBOE’s margin requirements lead to a price increase, which translates into skew and smile patterns for implied volatility curves even under constant volatilities. Empirical tests confirm that our model-implied slopes have significant statistical power in explaining the slopes observed in the market. Hence, at least in part, funding costs and collateral requirements offer an institutional explanation of the volatility smile phenomenon.

Financial sentiment analysis models, which extract meaning from vast amounts of unstructured data, play a crucial role in sentiment-driven financial decisions. However, the complex and domain-specific language used in finance poses unique challenges for adversarial attacks. To address these challenges, we propose a novel, white-box attack methodology leveraging a pre-trained general-purpose language model (GPT-4o). We employ carefully designed instructions and incorporate a new loss function based on embedding similarity to ensure semantic coherence while producing syntactically diverse samples. Our experimental results demonstrate that both FinBERT and Fin-GPT, leading models in financial sentiment analysis, exhibit significant susceptibility to our proposed adversarial attacks. Specifically, the sentiment predictions of these models were successfully altered for a substantial proportion of the samples across three public datasets, including Financial Phrase Bank (FPB), Twitter Financial News Sentiment (TFNS), and Sentimence and Entity Annotated Financial News (SEntFiN). Our findings emphasize the need for enhanced robustness in financial classification models against adversarially targeted attacks. By understanding and addressing these vulnerabilities, it is possible to improve the reliability and security of automated financial systems.

To simultaneously account for the properties of interest-rate term structure and foreign exchange rates within one arbitrage-free framework, we propose a class of multi-currency quadratic models (MCQM) with an (m + n) factor structure in the pricing kernel of each economy. The m factors model the term structure of interest rates. The n factors capture the portion of the exchange rate movement that is independent of the term structure. Our modeling framework represents the first in the literature that not only explicitly allows independent currency movement, but also guarantees internal consistency across all economies without imposing any artificial constraints on the exchange rate dynamics. We estimate a series of multi-currency quadratic models using U.S. and Japanese LIBOR and swap rates and the exchange rate between the two economies. Estimation shows that independent currency factors are essential in releasing the tension between the currency movement and the term structure of interest rates.

This paper performs specification analysis on the term structure of variance swap rates on the S&P 500 index and studies the optimal investment decision on the variance swaps and the stock index. The analysis identifies 2 stochastic variance risk factors, which govern the short and long end of the variance swap term structure variation, respectively. The highly negative estimate for the market price of variance risk makes it optimal for an investor to take short positions in a short-term variance swap contract, long positions in a long-term variance swap contract, and short positions in the stock index.

We estimate a flexible affine model using an unbalanced panel containing S&P 500 and VIX index returns and option prices and analyze the contribution of VIX options to the model’s in- and out-of-sample performance. We find that they contain valuable information on the risk-neutral conditional distributions of volatility at different time horizons, which is not spanned by the S&P 500 market. This information allows enhanced estimation of the variance risk premium. We gain new insights on the term structure of the variance risk premium, present a trading strategy exploiting these insights, and show how to improve S&P 500 return forecasts.

We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of return prediction factors using various machine learning algorithms. Contrasting previous studies for the US market, liquidity emerges as the most important predictor, leading us to closely examine the impact of transaction costs. The retail investors’ dominating presence positively affects short-term predictability, particularly for small stocks. Another feature that distinguishes the Chinese market from the US market is the high predictability of large stocks and state-owned enterprises over longer horizons. The out-of-sample performance remains economically significant after transaction costs.

This paper examines the effects of an educational program on Sustainable Finance Literacy (SFL) and its influence on sustainable investment decisions. Through a randomized controlled trial and an incentivized choice experiment, we found that our SFL program significantly improves literacy. The program also increased the probability of investing in a highly sustainable fund by 6 percentage points on the extensive margin and decreased allocations between 3.2% and 2.7% for the less sustainable funds on the intensive margin. Among participants who already held pro-sustainability attitudes, the treatment additionally led to more investments in the highly sustainable fund on the intensive margin. Higher SFL further led to more critical sustainability assessments of mid-tier funds and reduced tendencies to chase past high returns.