Quantitative Investing with a Focus on Low-Risk Stocks

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1 Topic 1: Quantitative Investing with a Focus on Low-Risk Stocks Jan Bauer According to Schmielewski and Stoyanov (2017), there are at least two ways how low-volatility portfolios can be constructed: The portfolio can be obtained by (1) minimizing portfolio variance subject to some weight constraints or (2) a two-step process in which stocks are ranked by a risk-related criterion (e.g., historical volatility or beta) and then low-volatility stocks are selected and weighted on an ad-hoc basis using equal weights or cap weights. The authors propose a new variant of the ranking approach (2) that is based on the tail risk measure Value at Risk (VaR) instead of the volatility or beta. You compare the performance of the defensive portfolios, which are constructed using the methods described above, based on US stocks (CRSP data can be used). A market capitalization weighted index and a 1/n portfolio serve as benchmarks. You study the impact of weight constraints for the traditional minimum-variance approach (1). Furthermore, it should be tested whether the method used to calculate the Value at Risk makes a difference; therefore, parametric, non-parametric and/or semi-parametric estimation methods could be used. Additionally, you could consider another tail risk measure for the ranking approach (2), for example the Expected Shortfall. The (committed) student could also consider the Conservative Formula described by van Vliet and Blitz (2018) as an additional low-risk strategy. The composition of the different low-risk portfolios could be compared over time (e.g. allocation to sectors) and transactions cost could be included. Finally, you could check the exposure of the strategies to the Fama-French factors. A programming tool should be used. We recommend Matlab (licenses provided). : Albrecht, P., & Huggenberger, M. (2015). Finanzrisikomanagement. Schaefer Poeschel. Sections 2 and 3. Albrecht, P., & Maurer, R. (2016). Investment- und Risikomanagement: Modelle, Methoden, Anwendungen (4. Auflage). Schäffer Poeschel. Sections and 6F.3. Baker, M., Bradley, B., & Wurgler, J. (2011). Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly. Financial Analysts Journal, 67(1), Clarke, R. G., de Silva, H., & Thorley, S. (2006). Minimum-Variance Portfolios in the U.S. Equity Market. The Journal of Portfolio Management, 33(1),

2 Master Thesis Topics Fall Semester 2018/2019 Page 2 DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies, 22(5), Scherer, B. (2011). A note on the returns from minimum variance investing. Journal of Empirical Finance, 18, Schmielewski, F., & Stoyanov, S. (2017). Defensive portfolio construction based on extreme value at risk. The Journal of Portfolio Management, 43(3), van Vliet, P., & Blitz, D. (2018). The Conservative Formula: Quantitative Investing Made Easy. The Journal of Portfolio Management, (Summer), Topic 2: EBA Bank Stress Tests and Borrower Discrimination The recent financial crisis led to heightened regulatory attention on banks, especially on the largest financial institutions. One particular set of regulatory actions are the European Bank Authorities (EBA) Stress Tests carried out in 2009, 2010, 2011, 2014, 2016 and These tests aim to assess the resilience of financial institutions to adverse market developments and improve overall financial stability. The tests should ensure that large banks have sufficient capital to remain viable and continue lending even in for example another crisis. However, the Stress tests might have (unintended) consequences on the supply of credit on the intensive margin as banks shift from more to less risky borrowers. This raises the question whether stress test banks reduce lending to a specific group of borrowers as compared to banks that are not part of the stress test? Moreover, it is important to understand whether this effect differs among different types of banks? Using loan-level data form Dealscan as well as bank financial information (SNL/Bankscope) the thesis aims at answering these questions in a European context, Acharya and Steffen (2015): The greatest carry trade ever? Understanding eurozone bank risk, Journal of Financial Economics 115, Acharya et al. (2018): Lending Implications of U.S. Bank Stress Tests: Cost or Benefits?, Journal of Financial Intermediation, forthcoming. Ivashina (2009): Asymmetric information effects on loan spreads, Journal of Financial Economics 92, Ivashina and Scharfstein (2010): Bank lending during the financial crisis of 2008, Journal of Financial Economics 97,

3 Master Thesis Topics Fall Semester 2018/2019 Page 3 Topic 3: Systemic Risk in Europe Systemic risk may be defined as the propensity of a financial institution to be undercapitalized when the financial system as a whole is undercapitalized. Widespread failures and losses of financial institutions can impose an externality on the rest of the economy, and the global financial crisis of provides ample evidence of the importance of containing this risk. However, current financial regulations, such as Basel capital requirements, are designed to limit each (or representative) institution s risk seen in isolation; they are not sufficiently focused on systemic risk even though systemic risk is often the rationale provided for such regulation. As a result, while individual risks may be properly dealt with in normal times, the system itself remains, or in some cases is induced to be, fragile and vulnerable to large macroeconomic shocks. Therefore, it is of utmost importance to quantify how much systemic risk a single financial institutions poses to the financial system as a whole. One popular market-based indicator of the exposure of a financial institution to systemic risk is the marginal expected shortfall (MES). The MES of an institution can be defined as its expected equity loss when the market itself is in its left tail. The goal of this paper is to apply a measure for systemic risk to the European financial sector and analyze its development among different countries and sectors. Finally, drivers of systemic risk of financial institutions should be examined and put in the context of current financial regulation. Acharya et al. (2017): Measuring Systemic Risk, The Review of Financial Studies, Vol. 30 (1), Acharya, Engle and Richardson (2012): Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks, American Economic Review, Vol. 102 (3), Brownlees and Engle (2016): SRISK: A conditional capital shortfall measure of systemic risk, The Review of Financial Studies, Vol. 30 (1), Engle, Jondeau and Rockinger (2015): Systemic Risk in Europe, Review of Finance 19, Hartmann-Wendels, Pfingsten and Weber (2015): Bankbetriebslehre, Springer Gabler, 6. Auflage, Teil F. Idier, Lamé and Mésonniert (2013): How useful ist he marginal expeted shortfall for the measurement of systemic exposure? A practical assessment, ECB Working Paper Series No

4 Master Thesis Topics Fall Semester 2018/2019 Page 4 Topic 4: Too-Big-To-Fail and the Introduction of the Single Resolution Mechanism In the years leading up to the 2008 financial crisis, banks around the world expanded their balance sheets to increase profitability in an environment of cheap funding. Access to international funding made it possible for individual banks and overall banking systems to reach enormous size relative to their countries GDP. Up to this day, Europe s twelve largest banks make up more than 50% of European banking sector in terms of total assets. These large banks have become so intertwined with their countries real economy that they would inflict massive damages (e.g. on employment and output) if one of these banks would go bankrupt and were not to be rescued by their local government. Consequently, these banks might anticipate a government s willingness to rescue them, which provides a state subsidy for these financial institutions through the creation of implicit guarantees. As a consequence, banks deemed too-big-to-fail (TBTF) could benefit from favorable funding conditions where investors require a lower risk-adjusted return as they expect that some of the losses are absorbed by the government and consequently by the taxpayer. The problem of dealing with TBTF financial institutions is not a new one in financial policy, but the severity of the global economic and financial crisis that started in 2007 has put a spotlight on it like never before. One way to eliminate the potential funding cost advantage for TBTF banks was the introduction of the Single Resolution Mechanism (SRM) establishing uniform rules and procedures for the resolution of failed financial institutions. The goal of this paper is to analyze whether large banks profit from lower funding costs as compared to smaller banks and whether the SRM was successful in reducing or even eliminating this advantage. The examination should be performed using a factor-model approach. Adrian, Friedman and Muir (2016): The Cost of Capital in the Financial Sector, CEPR Working Paper, No Fama and French (1993): Common risk factors in the returns on stocks and bonds, Journal of Financial Economics, Vol. 33 (1), 3-56, Flannery (2010): What to Do about TBTF, Federal Reserve Bank of Atlanta Working Paper. Gandhi and Lustig (2015): Size Anomalies in U.S. Bank Stock Returns, The Journal of Finance, Vol. 70 (2), Steinruecke (2018): Are European banks still too-big-to-fail? The impact of government interventions and regulatory reform on bailout expectations in the EU, SSRN Working Paper.

5 Master Thesis Topics Fall Semester 2018/2019 Page 5 Topic 5: Capital Requirements and the Low-Risk Anomaly in Europe The financial crisis has led to stricter bank capital requirements that were implemented in Europe via Basel III. Setting these requirements requires balancing many costs and benefits, both social and private. One important private cost might be the impact on banks funding costs: all else equal, making regulated banks less risky may actually raise their cost of capital with consequent implications for investment, growth, and the development of a shadow banking sector. How can this be? Making equity less risky brings an overlooked low-risk anomaly into play: within the stock market, historical returns and thus realized costs of equity are higher, not lower, for less risky equity. As any such anomaly is much weaker in the debt markets, Modigliani-Miller capital structure irrelevance fails (even absent other frictions), and banks weighted average cost of capital becomes inversely related to leverage. The goal of this paper is to investigate the relation between bank equity returns and bank leverage as well as the existence of the low-risk anomaly in Europe. Admati et al. (2013): Fallacies, irrelevant facts, and myths in the discussion on capital regulation: why bank equity is not expensive, SSRN Working Paper. Ang et al. (2006): The Corss-Section of Volatilty and Expected Returns, The Journal of Finance, Vol. 61 (1), Baker, Bradley and Wurgler (2011): Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly, Financial Analysts Journal, Vol. 67 (1), Baker and Wurgler (2013): Do Strict Capital Requirements Raise the Cost of Capital? Banking Regulation and the Low Risk Anomaly, NBER Working Paper No Baker and Wurgler (2015): Do Strict Capital Requirements Raise the Cost of Capital? Bank Regulation, Capital Structure, and the Low-Risk Anomaly, American Economic Review, Vol 105 (5), Kashyap, Stein and Hanson (2010): An Analysis of the Impact of `Substantially Heightened Capital Requirements on Large Financial Institutions, Booth School of Business, University of Chicago, mimeo 2.