Asset Pricing

Transforming Japan Real Estate ArXiv ID: 2405.20715 “View on arXiv” Authors: Unknown Abstract The Japanese real estate market, valued over 35 trillion USD, offers significant investment opportunities. Accurate rent and price forecasting could provide a substantial competitive edge. This paper explores using alternative data variables to predict real estate performance in 1100 Japanese municipalities. A comprehensive house price index was created, covering all municipalities from 2005 to the present, using a dataset of over 5 million transactions. This core dataset was enriched with economic factors spanning decades, allowing for price trajectory predictions. The findings show that alternative data variables can indeed forecast real estate performance effectively. Investment signals based on these variables yielded notable returns with low volatility. For example, the net migration ratio delivered an annualized return of 4.6% with a Sharpe ratio of 1.5. Taxable income growth and new dwellings ratio also performed well, with annualized returns of 4.1% (Sharpe ratio of 1.3) and 3.3% (Sharpe ratio of 0.9), respectively. When combined with transformer models to predict risk-adjusted returns 4 years in advance, the model achieved an R-squared score of 0.28, explaining nearly 30% of the variation in future municipality prices. These results highlight the potential of alternative data variables in real estate investment. They underscore the need for further research to identify more predictive factors. Nonetheless, the evidence suggests that such data can provide valuable insights into real estate price drivers, enabling more informed investment decisions in the Japanese market. ...

Ponzi Funds ArXiv ID: 2405.12768 “View on arXiv” Authors: Unknown Abstract Many active funds hold concentrated portfolios. Flow-driven trading in these securities causes price pressure, which pushes up the funds’ existing positions resulting in realized returns. We decompose fund returns into a price pressure (self-inflated) and a fundamental component and show that when allocating capital across funds, investors are unable to identify whether realized returns are self-inflated or fundamental. Because investors chase self-inflated fund returns at a high frequency, even short-lived impact meaningfully affects fund flows at longer time scales. The combination of price impact and return chasing causes an endogenous feedback loop and a reallocation of wealth to early fund investors, which unravels once the price pressure reverts. We find that flows chasing self-inflated returns predict bubbles in ETFs and their subsequent crashes, and lead to a daily wealth reallocation of 500 Million from ETFs alone. We provide a simple regulatory reporting measure – fund illiquidity – which captures a fund’s potential for self-inflated returns. ...

Data-generating process and time-series asset pricing ArXiv ID: 2405.10920 “View on arXiv” Authors: Unknown Abstract We study the data-generating processes for factors expressed in return differences, which the literature on time-series asset pricing seems to have overlooked. For the factors’ data-generating processes or long-short zero-cost portfolios, a meaningful definition of returns is impossible; further, the compounded market factor (MF) significantly underestimates the return difference between the market and the risk-free rate compounded separately. Surprisingly, if MF were treated coercively as periodic-rebalancing long-short (i.e., the same as size and value), Fama-French three-factor (FF3) would be economically unattractive for lacking compounding and irrelevant for suffering from the small “size of an effect.” Otherwise, FF3 might be misspecified if MF were buy-and-hold long-short. Finally, we show that OLS with net returns for single-index models leads to inflated alphas, exaggerated t-values, and overestimated Sharpe ratios (SR); worse, net returns may lead to pathological alphas and SRs. We propose defining factors (and SRs) with non-difference compound returns. ...

Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective ArXiv ID: 2405.00697 “View on arXiv” Authors: Unknown Abstract This paper explores the implications of using machine learning models in the pricing of catastrophe (CAT) bonds. By integrating advanced machine learning techniques, our approach uncovers nonlinear relationships and complex interactions between key risk factors and CAT bond spreads – dynamics that are often overlooked by traditional linear regression models. Using primary market CAT bond transaction records between January 1999 and March 2021, our findings demonstrate that machine learning models not only enhance the accuracy of CAT bond pricing but also provide a deeper understanding of how various risk factors interact and influence bond prices in a nonlinear way. These findings suggest that investors and issuers can benefit from incorporating machine learning to better capture the intricate interplay between risk factors when pricing CAT bonds. The results also highlight the potential for machine learning models to refine our understanding of asset pricing in markets characterized by complex risk structures. ...

From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing ArXiv ID: 2403.06779 “View on arXiv” Authors: Unknown Abstract This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how 1) ML models, including supervised, unsupervised, semi-supervised, and reinforcement learning, provide versatile frameworks to address these complexities, and 2) the incorporation of advanced ML algorithms into traditional financial models enhances return prediction and portfolio optimization. These methods can adapt to changing market dynamics by modeling structural changes and incorporating heterogeneous data sources, such as text and images. In addition, this paper explores challenges in applying ML in asset pricing, addressing the growing demand for explainability in decision-making and mitigating overfitting in complex models. This paper aims to provide insights into novel methodologies showcasing the potential of ML to reshape the future of quantitative finance. ...

RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction ArXiv ID: 2403.02500 “View on arXiv” Authors: Unknown Abstract In recent years, the dynamic factor model has emerged as a dominant tool in economics and finance, particularly for investment strategies. This model offers improved handling of complex, nonlinear, and noisy market conditions compared to traditional static factor models. The advancement of machine learning, especially in dealing with nonlinear data, has further enhanced asset pricing methodologies. This paper introduces a groundbreaking dynamic factor model named RVRAE. This model is a probabilistic approach that addresses the temporal dependencies and noise in market data. RVRAE ingeniously combines the principles of dynamic factor modeling with the variational recurrent autoencoder (VRAE) from deep learning. A key feature of RVRAE is its use of a prior-posterior learning method. This method fine-tunes the model’s learning process by seeking an optimal posterior factor model informed by future data. Notably, RVRAE is adept at risk modeling in volatile stock markets, estimating variances from latent space distributions while also predicting returns. Our empirical tests with real stock market data underscore RVRAE’s superior performance compared to various established baseline methods. ...

Cyber risk and the cross-section of stock returns ArXiv ID: 2402.04775 “View on arXiv” Authors: Unknown Abstract We extract firms’ cyber risk with a machine learning algorithm measuring the proximity between their disclosures and a dedicated cyber corpus. Our approach outperforms dictionary methods, uses full disclosure and not devoted-only sections, and generates a cyber risk measure uncorrelated with other firms’ characteristics. We find that a portfolio of US-listed stocks in the high cyber risk quantile generates an excess return of 18.72% p.a. Moreover, a long-short cyber risk portfolio has a significant and positive risk premium of 6.93% p.a., robust to all factors’ benchmarks. Finally, using a Bayesian asset pricing method, we show that our cyber risk factor is the essential feature that allows any multi-factor model to price the cross-section of stock returns. ...

Economic Forces in Stock Returns ArXiv ID: 2401.04132 “View on arXiv” Authors: Unknown Abstract When analyzing the components influencing the stock prices, it is commonly believed that economic activities play an important role. More specifically, asset prices are more sensitive to the systematic economic news that impose a pervasive effect on the whole market. Moreover, the investors will not be rewarded for bearing idiosyncratic risks as such risks are diversifiable. In the paper Economic Forces and the Stock Market 1986, the authors introduced an attribution model to identify the specific systematic economic forces influencing the market. They first defined and examined five classic factors from previous research papers: Industrial Production, Unanticipated Inflation, Change in Expected Inflation, Risk Premia, and The Term Structure. By adding in new factors, the Market Indices, Consumptions and Oil Prices, one by one, they examined the significant contribution of each factor to the stock return. The paper concluded that the stock returns are exposed to the systematic economic news, and they are priced with respect to their risk exposure. Also, the significant factors can be identified by simply adopting their model. Driven by such motivation, we conduct an attribution analysis based on the general framework of their model to further prove the importance of the economic factors and identify the specific identity of significant factors. ...

Does Sustainability Generate Better Financial Performance? Review, Meta-analysis, and Propositions ArXiv ID: ssrn-3708495 “View on arXiv” Authors: Unknown Abstract Sustainability in business and ESG (environmental, social, and governance) in finance have exploded in popularity among researchers and practitioners. We survey Keywords: ESG (Environmental, Social, and Governance), Sustainable Finance, Asset Pricing, Portfolio Management, Literature Review, Multi-Asset Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on large-scale meta-analysis of existing studies rather than novel mathematical modeling, yet demonstrates high empirical rigor through systematic review of 1,141 papers and providing public replication data and methodology. flowchart TD A["Research Goal:<br>Does Sustainability Improve Financial Performance?"] B["Methodology:<br>Systematic Review & Meta-Analysis"] C["Data Inputs:<br>Existing Studies on ESG & Returns"] D["Computational Process:<br>Aggregation & Bias Correction"] E["Outcome 1: Positive<br>ESG-Return Relationship"] F["Outcome 2: Risk-Based<br>Explanations Dominate"] G["Proposition:<br>ESG as Risk Factor in Asset Pricing"] A --> B B --> C C --> D D --> E D --> F E & F --> G

A Backtesting Protocol in the Era of Machine Learning ArXiv ID: ssrn-3275654 “View on arXiv” Authors: Unknown Abstract Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, th Keywords: Machine Learning, Investment Management, Quantitative Finance, Asset Pricing, Algorithmic Trading Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper focuses on a research protocol for backtesting and data mining, with moderate empirical rigor involving practical concerns like overfitting and data scarcity, but lacks advanced mathematical derivations, centering instead on statistical concepts and real-world data challenges. flowchart TD A["Research Goal: Develop robust backtesting protocol for ML in finance"] --> B["Data: Cross-sectional stock data & fundamental features"] B --> C["Methodology: ML pipelines with walk-forward validation"] C --> D["Computation: Model training, hyperparameter tuning, & signal generation"] D --> E["Risk Controls: Transaction costs, liquidity constraints, & overfitting tests"] E --> F["Key Outcomes: Generalizable signals & realistic performance metrics"] F --> G["Implication: ML requires rigorous validation to avoid false discoveries"]