Contributions published at Financial Engineering (Markus Leippold)

Disclosure of climate-related financial risks greatly helps investors assess companies’ preparedness for climate change. Voluntary disclosures such as those based on the recommendations of the Task Force for Climate-related Financial Disclosures (TCFD) are being hailed as an effective measure for better climate risk management. We ask whether this expectation is justified. We do so by training ClimateBERT, a deep neural language model fine-tuned based on the language model BERT. In analyzing the disclosures of TCFD-supporting firms, ClimateBERT comes to the sobering conclusion that the firms’ TCFD support is mostly cheap talk and that firms cherry-pick to report primarily non-material climate risk information.

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return.

In this thesis I investigate the inelastic market hypothesis by Gabaix and Koijen (2021), henceforth G&K. They hypothesize that, due to constrained market participants, price elasticity of demand of stock prices is smaller than commonly assumed. The consequence is that inflow and outflow in and out of the stock market have a massive price impact. Mainstream models assume asset demand to be highly elastic. In this case small changes in prices lead to a large change in demand but changes in demand would not change price by much. In those models the price is the direct result of discounted future cash flows or dividends. Market movement would only impact the prices in a small way and only short term. Arbitrageurs would instantly profit from these mispricings and thus rebalance the market. G&K propose a fundamentally di↵erent approach. They argue that the market is inelastic. The main driving force behind this are the constraints laid upon a large share of market participants. Those institution are subject to a multitude of regulations, most importantly their fixed equity share. This mandate on equity share creates additional market forces that result in this extreme price reaction. G&K make the key distinction between micro and macroeconomic elasticity. They specifically focus on the macroeconomic price elasticity that concerns the inflow of money into the stock market from outside the stock market. This is in contrast to microeconomic elasticity that is concerned with the price elasticity stemming from movements within the stock market. They find the price impact multiplier M to be around 5. This implies that a 1$ inflow into the stock market increases the aggregated value of the stock market by around 5$. This result is remarkable and would have severe impact into the fundamentals of asset pricing and the whole financial sector. It would help explain the pseudo-random behaviour of asset prices. It would also allow governments to adapt their quantitative easing approach and influence the markets more directly through stock purchase rather than through bonds. I critically discuss all the theoretical reasons given by G&K and combine it with existing literature on this topic. I especially focus on non-linear and asymmetric extensions as well as the micro foundation of these market forces. In my empirical part I use proprietary Swiss and US mutual funds data provided by Morningstar.

Investors and regulators require reliable estimates of physical climate risks for decision-making. While assessing these risks is challenging, several commercial data providers and academics have started to develop firm-level physical risk scores. We compare six physical risk scores. We find a substantial divergence between these scores, also among those based on similar methodologies. We show how this divergence could cause problems when testing whether financial markets are pricing physical risks. Hence, financial markets may not adequately account for the physical risk exposure of corporations using available risk scores. Finally, we identify key sources of uncertainty for further investigation.

Private equity is an asset class which claims to generate consistent, long-term outperformance for its investors and should be one of the critical components of any investors’ portfolio. In recent years alternative financial investment opportunities have closed the gap to traditional asset classes. This paper builds on existing literature, establishes a theoretical framework, and makes statements about performance differences and crisis resilience based on data analysis and interviews. The report finds enhanced outperformance of 3.63% p.a. over the last 20 years for private equity compared to public markets. Additionally, private equity markets show more stability and resilience during crises.

The BAB strategy exploits the failure of the CAPM and has been shown to achieve risk-adjusted returns. Based on previous studies, the performance is induced by using unconventional procedures leading to mis-hedging and overweighting small-capitalized stocks, posing implementation issues. Nevertheless, a granular approach beyond the initial computation delivers a feasible volatilitymanaged BAB strategy based on value-weighted portfolios that achieves risk-adjusted returns in the USA and Switzerland. Furthermore, relative benchmark performance measures of institutions contribute to the anomaly, while lottery demand partially explains abnormal returns conditional on volatility only in Switzerland, suggesting that the beta anomalies’ explanations can differ between markets.

Over the last two decades, enormous losses associated with the momentum strategy surged again to an all-time high since the Great Depression. This thesis examines the contribution of the Constant and Dynamic Volatility Scaling Approach to the momentum effect in an industrial composition. Their contributions suggest that momentum risks are highly variable over time and partly predictable. I provide evidence that risk-scaling strategies can approximately double the Sharpe ratio of momentum in industries. Furthermore, improvements persist during various sub-sample periods. It appears that the Constant Volatility Approach is the most efficient strategy in this setting.

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We study the impact of economic policy uncertainty on the term structure of nominal interest rates. In a general equilibrium model populated by an uncertainty-averse agent, we show that political uncertainty not only affects the yield curve and the corresponding volatility term structure but also bond risk premia carry a premium for political uncertainty. Our model simultaneously captures both the shape of the yield curve and the hump shape of yield volatilities, a stylized feature that is hard to match with a theoretical model. Our model gives rise to a set of testable predictions for which we find strong support in the data: Higher policy uncertainty leads to a significant decline in yield levels and increases bond yield volatilities. Moreover, policy uncertainty predicts future short rates and has an ambiguous effect on term premia. Finally, short (long) maturity bond risk premia respond negatively (positively) to increases in policy uncertainty.

Is the geopolitical crisis due to the Russian invasion of Ukraine likely to accelerate or retard the transition to a low-carbon economy? This column argues that stock prices reactions offer a preview of the future economic impact of the Russia-Ukraine war. These reactions suggest that the speed of transition to a low-carbon economy appears to be diverging between the US and Europe. These results obtain while controlling for ESG measures, inflation exposure, and international exposure of firms.

Die Schweizer Asset Management Branche ist für den Schweizer Finanzplatz, die Realwirtschaft und die Vorsorgewerke sehr wichtig. Sie steht aber auch vor Herausforderungen, etwa beim Marktzugang und der Nachhaltigkeit.

Factor investing with lookahead fundamental features significantly outperforms those with historical fundamental features. Motivated by this insight and the great amount of unstructured textual data about companies, we use textual features extracted from earnings call transcripts by a pre-trained FinBERT model and financial features to predict year-ahead earnings. Additionally, we construct risk-adjusted portfolios incorporating uncertainty estimates, which significantly outperform standard factor models. Specifically, our portfolio based on the RNN uncertainty-aware model with FinBERT textual features has an annualized return of 15.39% (vs. 9.43% for S&P 500 total return) and a Sharpe ratio of 1.29 (vs. 0.71). This result is robust to the control of the Fama-French five factors, and our portfolio shows a significant positive alpha of 0.32% per month.

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