Stranded Assets: How Policy Uncertainty affects Capital, Growth, and the Environment

The paper considers stochastic environmental policy and its effects on the environment, portfolio composition, and economic growth. Capital accumulation causes pollution which is reduced by private green services and public abatement. The government subsidizes green services and taxes dirty capital albeit at a rate which may become random, causing unexpected capital write-offs. Tax jumps depend on natural degradation and environmental activism. We derive how uncertainty and political activism affect the risk premia for investors. We analyze the incentives for firms to increase the greenness of production in order to reduce political uncertainty. Stochastic taxation is shown to act as a substitute for green subsidies when uncertainty decreases in the ratio of green services to capital and agents use their green activities strategically. Tax uncertainty may trigger precautionary savings, causing additional growth and enhanced environmental deterioration.

Probability of Default Model to Estimate Ex Ante Credit Risk

A genuine measure of ex ante credit risk links borrower’s financial position with the odds of default. Comprehension of a borrower’s financial position is proxied by the derivatives of its filled financial statements, i.e., financial ratios. We identify statistically significant relationships between shortlisted financial ratios and subsequent default events and develop a probability of default (PD) model that assesses the likelihood of a borrower going into delinquency at a one-year horizon. We compare the PD model constructed against alternative measures of ex ante credit risk that are widely used in related literature on bank risk taking, i.e., credit quality groups (prudential reserve ratios) assigned to creditors by banks and the credit spreads in interest rates. We find that the PD model predicts default events more accurately at a horizon of one year compared to prudential reserve rates. We conclude that the measure of ex ante credit risk developed is feasible for estimating risk-taking behaviour by banks and analysing shifts in portfolio composition.

Market Orchestrators: The Effects of Certification on Platforms and Their Complementors

We study how a multisided platform’s decision to certify a subset of its complementors affects those complementors and ultimately the platform itself. Kiva, a microfinance platform, introduced a social performance badging program in December 2011. The badging program appears to have been beneficial to Kiva—it led to more borrowers, lenders, total funding, and amount of funding per lender. To better understand the mechanisms behind this performance increase, we study how the badging program changed the bundle of products offered by Kiva’s complementors. We find that Kiva’s certification leads badged microfinance institutions to reorient their loan portfolio composition to align with the certification and that the extent of portfolio reorientation varies across microfinance institutions, depending on underlying demand- and supply-side factors. We further show that certified microfinance institutions that do align their loan portfolios enjoy stronger demand-side benefits than do certified microfinance institutions that do not align their loan portfolios. We therefore demonstrate that platforms can influence the product offerings and performance of their complementors—and, subsequently, the performance of the ecosystem overall—through careful enactment of governance strategies, a process we call “market orchestration.”

Who am I becoming?: Understanding the ministry training portfolio as a theological practice

This article outlines a theological basis for the process of compiling a portfolio as part of a programme of ministerial formation. Such a task can often seem to the candidate rather like jumping through hoops, or gathering evidence merely for the sake of it. But I argue that it is properly understood as a theological practice, inviting reflection on who they are becoming in Christ. In philosophical literature, ‘gestures’ are understood as incomplete actions that correspond to some deeper truth. By framing the task of portfolio composition in the language of ‘gesture’, it is seen to be a vital practice in formation for public ministry, rather than merely an exercise in proving one’s readiness for ordination or licensing.

Five Facts about Beliefs and Portfolios

We study a newly designed survey administered to a large panel of wealthy retail investors. The survey elicits beliefs that are important for macroeconomics and finance, and matches respondents with administrative data on their portfolio composition, their trading activity, and their login behavior. We establish five facts inthese data. (i) Beliefs are reflected in portfolio allocations. The sensitivity of portfolios to beliefs is small on average, but varies significantly with investor wealth, attention, trading frequency, and confidence. (ii) Belief changes do not predict when investors trade, but conditional on trading, they affect both the direction and the magnitude of trades. (iii) Beliefs are mostly characterized by large and persistent individual heterogeneity. Demographic characteristics explain only asmall part of why some individuals are optimistic and some are pessimistic. (iv) Expected cash flow growth and expected returns are positively related, both within and across investors. (v) Expected returns and the subjective probability of rare disasters are negatively related, both within and across investors. These five facts provide useful guidance for the design of macro-finance models. (JEL D83, E23, G11, G12, G41, G51)

Resampled Efficient Frontier Integration for MOEAs

Mean-variance portfolio optimization is subject to estimation errors for asset returns and covariances. The search for robust solutions has been traditionally tackled using resampling strategies that offer alternatives to reference sets of returns or risk aversion parameters, which are subsequently combined. The issue with the standard method of averaging the composition of the portfolios for the same risk aversion is that, under real-world conditions, the approach might result in unfeasible solutions. In case the efficient frontiers for the different scenarios are identified using multiobjective evolutionary algorithms, it is often the case that the approach to averaging the portfolio composition cannot be used, due to differences in the number of portfolios or their spacing along the Pareto front. In this study, we introduce three alternatives to solving this problem, making resampling with standard multiobjective evolutionary algorithms under real-world constraints possible. The robustness of these approaches is experimentally tested on 15 years of market data.

Robust optimization of time series momentum portfolios

Mean-Variance Optimization (MVO) is well-known to be extremely sensitive to slight differences in the expected returns and covariances: if these measures change day to day, MVO can specify very different portfolios. Making wholesale changes in portfolio composition can cause the incremental gains to be negated by trading costs. We present a method for regularizing portfolio turnover by using the ℓ1 penalty, with the amount of penalization informed by recent historical data. We find that this method dramatically reduces turnover, while preserving the efficiency of mean-variance optimization in terms of risk-adjusted return. Factoring in reasonable estimates of transaction costs, the turnover-regularized MVO portfolio substantially outperforms a leverage-constrained MVO approach, in terms of risk-adjusted return.

Using Loan Loss Indicators by Loan Type to Sharpen the Evaluation of Banks' Loan Loss Accruals

Prior research acknowledges that the determinants, timeliness, and economic implications of banks' provisions for loan losses (PLL) vary across loan types. However, the lack of machine-readable data on PLL by loan type has precluded researchers from incorporating loan type into the evaluation of PLL beyond either controlling for or partitioning the sample on crude proxies for loan portfolio composition. We calculate PLL by loan type as the change in the allowance for loan losses by loan type, which we hand collect from Form 10-K filings, plus net charge-offs by loan type, which we obtain from regulatory filings. Using these data, we show that prior findings that banks exercise discretion over PLL to smooth earnings and increase regulatory capital are driven by commercial loans, a thin slice of banks' loan portfolios, and that commonly used measures of PLL timeliness vary substantially across loan types.

Carteiras de Black Litterman com Análises Baseadas em Redes Neurais

This paper presents an autonomous portfolio management system. Autonomous investment systems consist of a series of buy and sell rules on financial markets, which can be executed by machines, oriented to maximizing investor gains. The system uses a Neural Network approach for monitoring the market and the Black-Litterman model for portfolio composition. The ten most traded assets from the Bovespa Index are analyzed, with dedicated neural networks, which suggests future return estimates using technical indicators as input. Those estimates are inserted in the Black-Litterman model which proposes daily portfolio composition using long and short positions. The results are compared to a second autonomous trading system without the Black-Litterman approach, referred to as Benchmark. The numerical results show a great performance compared to the Benchmark, especially the risk-return ratio, captured by the Sharpe Index. Such results suggest that the use of Bayesian inference models combined with neural networks may be a good alternative in portfolio management.