Research articles for the 2020-11-15

A Mixed-Method Landscape Analysis of SME-focused B2B Platforms in Germany
Tina Krell,Fabian Braesemann,Fabian Stephany,Nicolas Friederici,Philip Meier

Digital platforms offer vast potential for increased value creation and innovation, especially through cross-organizational data sharing. It appears that SMEs in Germany are currently hesitant or unable to create their own platforms. To get a holistic overview of the structure of the German SME-focused platform landscape (that is platforms that are led by or targeting SMEs), we applied a mixed method approach of traditional desk research and a quantitative analysis. The study identified large geographical disparity along the borders of the new and old German federal states, and overall fewer platform ventures by SMEs, rather than large companies and startups. Platform ventures for SMEs are more likely set up as partnerships. We indicate that high capital intensity might be a reason for that.

Asset Allocation via Machine Learning and Applications to Equity Portfolio Management
Qing Yang,Zhenning Hong,Ruyan Tian,Tingting Ye,Liangliang Zhang

In this paper, we document a novel machine learning based bottom-up approach for static and dynamic portfolio optimization on, potentially, a large number of assets. The methodology overcomes many major difficulties arising in current optimization schemes. For example, we no longer need to compute the covariance matrix and its inverse for mean-variance optimization, therefore the method is immune from the estimation error on this quantity. Moreover, no explicit calls of optimization routines are needed. Applications to a bottom-up mean-variance-skewness-kurtosis or CRRA (Constant Relative Risk Aversion) optimization with short-sale portfolio constraints in both simulation and real market (China A-shares and U.S. equity markets) environments are studied and shown to perform very well.

Capital Structure & Corporate Finance Techniques to Maximize Firm Value & Investor Returns
Burgess, Nicholas
A firm’s capital structure describes the way in which a company is financed and how its free cash flows or net profits are distributed to investors. In what follows we review how capital structure can be organized to maximize firm value and investor returns.Firstly we look at fundamental theory from corporate finance, namely the Modigliani and Miller propositions. The assumptions from this theory describe capital structure features that can be manipulated to benefit corporations and investors. Secondly we review trade-off theory and how corporations can exploit the tax advantages from issuing debt without exposing themselves excessively to bankruptcy risks. We discuss how firms use trade-off theory to optimize their capital structure to maximize firm value. This is a cyclical activity that is greatly influenced by the state of the economy, the company’s beta and financial health.Finally we present two case studies where we firstly create a replicating portfolio to demonstrate Modigliani and Miller capital structure irrelevance and secondly show how debt financing can increase firm value and equity beta.

Geographic Loan Diversification and Bank Risk: A Cross-Country Analysis
Le, Tu
This study investigates the geographic loan expansion on bank risk using the aggregate data of 53 countries from 2005 to 2016 using the system generalized method of moments proposed by Arellano and Bover (1995). Our findings show that global expansion tends to increase bank insolvency and reduce bank adjusted-risk-performance. Our findings further indicate loans distributed to advanced markets tend to reduce bank stability while the proportion of loans to other emerging markets and developing countries may have the potential to improve bank solvency and risk-adjusted-performance. As diversification is seen as a necessary strategy to diversify bank risks, bank managers should put more attention to emerging markets.

Keynes' Measuring Rod for Investment Policy
Woods, J.E.
The Keynes Archive at King’s College, Cambridge contains a short, undated Note, A Measuring Rod for Investment Policy, most likely drafted in 1935â€"6, in which Keynes proposed using a Fixed Interest Index for performance measurement purposes. Starting from the position that the ultimate objective of normal institutional investment was to purchase a reasonably secure annual income, he considered the investment problem of an institution that held a nonâ€"Fixed Interest portfolio with the aim of making a profitable switch to Fixed Interest subsequently by being able to buy a larger annual income. Keynes’ proposed criterion being terminal capital value, the switch would be made if the nonâ€"Fixed Interest portfolio had the higher market value at the decision date. In this paper, we discuss Keynes’ proposal, identifying conditions under which a consistent conclusion can be drawn, illustrating the discussion using data on two major UK asset market Indices (the Over 15 Year Gilts and the Allâ€"Share) over the period 1990â€"2019.

Learning in a Small/Big World
Benson Tsz Kin Leung

Savage (1972) lays down the foundation of Bayesian decision theory, but asserts that it is not applicable in big worlds where the environment is complex. Using the theory of finite automaton to model belief formation, this paper studies the characteristics of optimal learning behavior in small and big worlds, where the complexity of the environment is low and high, respectively, relative to the cognitive ability of the decision maker. Confirming Savage's claim, optimal learning behavior is closed to Bayesian in small worlds but significantly different in big worlds. In addition, I show that in big worlds, the optimal learning behavior could exhibit a wide range of well-documented non-Bayesian learning behavior, including the use of heuristic, correlation neglect, persistent over-confidence, inattentive learning, and other behaviors of model simplification or misspecification. These results establish a clear and testable relationship between the prominence of non-Bayesian learning behavior, complexity and cognitive ability.

Population Growth, Building, and Banking
Antoniades, Adonis
US counties with higher population growth prior to 2000, experienced higher growth in residential construction in the run-up to the financial crisis of 2007-08. Banks operating in these counties shifted their loan portfolios more aggressively towards construction loans and away from other non-real estate loans. Although shifts towards construction lending that followed local construction activity did not lead to higher loan default rates, more aggressive shifts did.

Shadow economy and populism-risk and uncertainty factors for establishing low-carbon economy of Balkan countries-case study for Bulgaria
Shteryo Nozharov,Nina Nikolova

The main purpose of the current publication is to formulate a scenario model for analysis of the opportunities for low-carbon economy establishment in the countries with transition economies.The model studies risk factors such as shadow economy level and populism based on the implementation and development of Balkan countries economic policy and at the same time shows future climate changes tendencies and uncertainties of climate models.A transdisciplinary approach is implemented in the study. Climate change perception and understanding about low-carbon economy are examined through the public opinion and analysis of mass-media publications.The results of the research are important in order to clarify the multicultural divergences as a factor for risk and uncertainty in the implementation process of the policy for climate change.In this way geographical aspects of risk and uncertainty, which are not only related to the economic development of the relevant countries, could be brought out.

Simulation of the drawdown and its duration in L\'{e}vy models via stick-breaking Gaussian approximation
Jorge González Cázares,Aleksandar Mijatović

We develop a computational method for expected functionals of the drawdown and its duration in exponential L\'evy models. It is based on a novel simulation algorithm for the joint law of the state, supremum and time the supremum is attained of the Gaussian approximation of a general L\'evy process. We bound the bias for various locally Lipschitz and discontinuous payoffs arising in applications and analyse the computational complexities of the corresponding Monte Carlo and multilevel Monte Carlo estimators. Monte Carlo methods for L\'evy processes (using Gaussian approximation) have been analysed for Lipschitz payoffs, in which case the computational complexity of our algorithm is up to two orders of magnitude smaller when the jump activity is high. At the core of our approach are bounds on certain Wasserstein distances, obtained via the novel SBG coupling between a L\'evy process and its Gaussian approximation. Numerical performance, based on the implementation in the dedicated GitHub repository, exhibits a good agreement with our theoretical bounds.

Stochastic stability of agglomeration patterns in an urban retail model
Minoru Osawa,Takashi Akamatsu,Yosuke Kogure

We consider a model of urban spatial structure proposed by Harris and Wilson (Environment and Planning A, 1978). The model consists of fast dynamics, which represent spatial interactions between locations by the entropy-maximizing principle, and slow dynamics, which represent the evolution of the spatial distribution of local factors that facilitate such spatial interactions. One known limitation of the Harris and Wilson model is that it can have multiple locally stable equilibria, leading to a dependence of predictions on the initial state. To overcome this, we employ equilibrium refinement by stochastic stability. We build on the fact that the model is a large-population potential game and that stochastically stable states in a potential game correspond to global potential maximizers. Unlike local stability under deterministic dynamics, the stochastic stability approach allows a unique and unambiguous prediction for urban spatial configurations. We show that, in the most likely spatial configuration, the number of retail agglomerations decreases either when shopping costs for consumers decrease or when the strength of agglomerative effects increases.

Technical Indicators and Realized Volatility: What is the Source of Predictability?
Souropanis, Ioannis,Vivian, Andrew
Forecasting Realized Volatility (RV) is of paramount importance for both academics and practitioners. During recent decades, academic literature has made substantial progress both in terms of methods and predictors under consideration. Despite the popularity of technical indicators, there has been only scarce reference to the effectiveness of this group of predictors in forecasting RV. This paper examines the out-of-sample forecasting performance of technical indicators for S&P500 RV. Moreover, we shed light on the sources of forecasting ability by identifying their impact on the Good and Bad components of RV, as well as, their ability to forecast different frequency components. Technical indicators perform especially strongly for forecasting the short frequency signal of both Good and Bad RV and this drives the overall strong performance of the frequency decomposition. Thus our results suggest that frequency decomposition is more important than sign (Good/Bad) decomposition when forecasting RV.We extend our experiment by accounting for timing effects in the positive performance; specifically, we measure the performance during recessions/expansions, high and low VIX periods, we consider different out-of-sample periods, conduct different sub-sample regressions and alternative forecasting horizons. There is consistent outperformance from using technical indicators across the different conditions considered and in particular we find that technical indicators enhance their predictive power during crisis periods. The results imply that the role of technical indicators in RV forecasting should be taken into consideration by practitioners and policymakers.

The Uncertain Shape of Grey Swans: Extreme Value Theory with Uncertain Threshold
Hamidreza Arian,Hossein Poorvasei,Azin Sharifi,Shiva Zamani

Extreme Value Theory (EVT) is one of the most commonly used approaches in finance for measuring the downside risk of investment portfolios, especially during financial crises. In this paper, we propose a novel approach based on EVT called Uncertain EVT to improve its forecast accuracy and capture the statistical characteristics of risk beyond the EVT threshold. In our framework, the extreme risk threshold, which is commonly assumed a constant, is a dynamic random variable. More precisely, we model and calibrate the EVT threshold by a state-dependent hidden variable, called Break-Even Risk Threshold (BRT), as a function of both risk and ambiguity. We will show that when EVT approach is combined with the unobservable BRT process, the Uncertain EVT's predicted VaR can foresee the risk of large financial losses, outperforms the original EVT approach out-of-sample, and is competitive to well-known VaR models when back-tested for validity and predictability.

Weak Identification in Discrete Choice Models
David T. Frazier,Eric Renault,Lina Zhang,Xueyan Zhao

We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in many commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that if the null hypothesis that identification is weak can be rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo analysis compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of conventionally applied, i.e., linear, weak identification tests in this context. We compare our testing approach to those commonly applied in the literature within two empirical examples: married women labor force participation, and US food aid and civil conflicts.