High Risk Portfolios Research Articles

Political risks, excess and carry trade returns in global markets

We re-examine the relationship between exchange rates and sovereign risk and provide evidence that sovereign risk affects advanced economies and emerging markets differently. Also, double-sorting 34 currencies from developed and emerging economies into different portfolios based on the level of macro risk and political risk, we provide evidence that local determinants of sovereign risk are priced in the FX markets. Local political risk in particular seems to have become an important carry trade risk factor in the post-2007 financial crisis era. High macro risk portfolios in the presence of high political risk lead to higher excess returns. Our results are robust across country categories and currency regimes, with stronger results for emerging economies. This is one of the few papers to examine how the local components of sovereign risk, rather than the systematic risk component, drive currency carry trade returns. We contribute a fresh perspective that supports the risk-based view on the UIP puzzle.

Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach

The economic onslaught of the COVID-19 pandemic has compromised the risk management of financial institutions. The consequences related to such an unprecedented situation are difficult to foresee with certainty using traditional methods. The regulatory credit loss attached to defaulted mortgages, so-called expected loss best estimate (ELBE), is forecasted using a machine learning technique. The projection of two ELBEs for 2022 and their comparison are presented. One accounts for the outbreak's impact, and the other presumes the nonexistence of the pandemic. Then, it is concluded that the referred crisis surely adversely affects said high-risk portfolios. The proposed method has excellent performance and may serve to estimate future expected and unexpected losses amidst any event of extraordinary magnitude.

Perpetual know your customer: A new approach to addressing customer due diligence

Perpetual Know Your Customer (pKYC) represents a new and alternative approach to the traditional customer due diligence process currently practised at many regulated financial institutions (FIs) today. FIs adopt a periodic approach in identifying anti-money laundering risks within their customer base as part of a government regulated compliance process. Conventional KYC processes can be ineffective and costly in managing compliance risks, with cumbersome and complex customer onboarding processes. Increasingly, FIs see value in adopting pKYC approaches either alongside or as a replacement for their current processes. This article describes pKYC and how it differs from traditional KYC processes; the benefits and challenges to adopting pKYC; and the right use cases within an FI. pKYC is a continual approach to customer due diligence, potentially replacing traditional forms of customer onboarding. This alternative approach, while it increases operational costs within an FI, does provide benefits in terms of reduced compliance risk exposure. This reduced exposure is achieved as a result of adapting continual review processes leveraging current customer and external reference data during the review process. This article finds that not all FIs can necessarily benefit from pKYC despite the inherent advantages. FIs need to consider their current KYC process that would be appropriate for their line of business. Lines of business that have high risk portfolios and volumes are typically the best candidates for adopting pKYC, compared to FIs that have relatively static and smaller customer bases. The article also provides the reader with a framework for understanding pKYC in the context of adopting such an approach in their FIs so as to make a more informed decision.

The Asymmetric Impact of Funding Liquidity Risk on the Volatility of Stock Portfolios during the COVID-19 Crisis

In this study, we identify economic transmission channels through which changes in funding liquidity conditions in interbank markets asymmetrically affect volatilities of stock portfolios during the COVID-19 crisis. For the purpose of this study, the quantile regression approach is utilized. Controlling for macroeconomic factors, we document that volatilities of high-risk portfolios increase more in response to a deterioration in funding liquidity conditions compared to less risky portfolios. More importantly, this increase intensifies in high-volatility periods of high-risk portfolios, which implies the impact is stronger during uncertain economic environments, such as the one caused by the COVID-19 outbreak.

Corporate social irresponsibility and portfolio performance: A cross-national study

This study examines the impact of reputational risk, measured by corporate social irresponsibility (CSI) ratings, on shareholder abnormal returns. Based on 7368 non-financial companies from 42 countries during 2007–2017, we find that long-short portfolios (buying no reputation risk and selling high reputation risk portfolios) earn significantly positive abnormal returns. The cross-national results indicate that the long-short portfolio returns are more pronounced (i) in the emerging market segment than in the developed market segment, (ii) in civil law jurisdictions than in their common law peers, (iii) within nations with higher confidence in corporations and, (iv) within nations with higher institutional trust.

Impact of Credit Risk on Momentum and Contrarian Strategies: Evidence from South Asian Markets

We examine the profitability of the momentum and contrarian strategies in three South Asian markets, i.e., Bangladesh, India, and Pakistan. We also analyze, whether credit risk influences momentum and contrarian return for these markets from 2008 to 2014. We use default risk that relates to non-payments of debts by firms as a measure of credit risk. For that purpose, we use distance to default (DD) by Kealhofer, McQuown, and Vasicek (KMV) model as a proxy of credit risk. We calculate the credit risk and form the momentum and contrarian strategies of the firms based on high, medium, and low risk. We find that in all three markets, the momentum and contrarian returns are significant for medium and high credit risk portfolios and no momentum and contrarian returns for low credit risk portfolios.

Corporate Social Irresponsibility and Portfolio Performance: A Cross-National Study

This study examines the impact of reputational risk, measured by corporate social irresponsibility (CSI) ratings, on shareholder abnormal returns. Based on 7,368 non-financial companies from 42 countries during 2007-2017, we find that long-short portfolios (buying no reputation risk and selling high reputation risk portfolios) earn significantly positive abnormal returns. The cross-national results indicate that the long-short portfolio returns are more pronounced (i) in the emerging market segment than in the developed market segment, (ii) in civil law jurisdictions than in their common law peers, (iii) within nations with higher confidence in corporations and, (iv) within nations with higher institutional trust.

DO THE FAMA AND FRENCH FIVE-FACTOR MODEL FORECAST WELL USING ANN?

Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily, Artificial Neural Networks (ANN) is a flexible machine learning tool which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and French five-factor model. We construct ANN’s based on the composite factors of the FF5F model to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons full sample period. In stage two, the study uses this best combination to forecast the model under 48-months rolling window analysis. In-sample and out-sample comparisons, regression, and goodness of fit test and actual and predicted values of the stock returns of our ANN model reveal that the proposed model accurately predicts the one-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than mid and low beta portfolios and the methodology will significantly improve the return on investment of the investors.

Equity Solvency Capital Requirements - What Institutional Regulation Can Learn from Private Investor Regulation

Solvency II has one standard equity solvency capital requirement for type 1 or developed market stocks (39 percent) and one for type 2 or emerging market stocks (49 percent). As such, differences in financial economic risk of stock portfolios within developed or emerging markets do not influence solvency requirements. This encourages risk-seeking behavior by insurance companies, and could sustain or even create structural mispricing in the cross-section of stock returns. We argue to improve Solvency II regulation by aligning it with more sophisticated European regulation that is already in place for mutual funds. Specifically, we propose to multiply the standard solvency charge of 39 percent with the ratio of equity portfolio volatility to broad equity market volatility. This ratio will be above one for more risky portfolios and below one for less risky portfolios, meaning that high-risk stock portfolios require more solvency capital than the market, while low-risk stock portfolios require less. Our approach encompasses the existing distinction between emerging and developed markets, and reduces geography to just one of many potential sources of risk that should be recognized. The proposed approach gives better incentives to institutional investors, contributes to market efficiency, and is much less prone to regulatory arbitrage than the existing approach.

Expected discounted penalty function for a phase-type risk model with stochastic return on investment and random observation periods

PurposeThe purpose of this paper is to build a phase-type risk model with stochastic return on investment and random observation periods to characterize the ruin quantities under which the insurance company may take effective investment strategies to avoid bankruptcy.Design/methodology/approachBy the Markov property and Ito’s formula, this paper derives the integro-differential equations in which the interclaim times follow a phase-type distribution. Using the sinc method, this paper obtains the approximate solutions of the expected discounted penalty function. The numerical examples are given to verify the robustness of the proposed sinc method.FindingsThis paper discloses the relationship between the investment strategy and initial surplus level. The insurance company with a high initial surplus level prefers high risk portfolios to earn more profit. Contrarily, the insurance company would invest low risk portfolios to avoid bankruptcy. In addition, this paper shows that a short observation period would bring higher ruin probability.Originality/valueThe risk model is distinct in that a phase-type risk model is constructed with stochastic return on investment and random observation periods. These considerations in the risk model are in sharp contrast to the setting in which the stochastic return on investment is observed continuously. In practice, the insurance company only can periodically observe the surplus level to check the balance of the book. This setting, therefore, is difficult to adopt. This paper develops a sinc method to solve the approximate solutions of the expected discounted penalty function.

Returns to Low Risk Investment Strategy

The paper studies the low risk anomaly in the Indian market using entire National Stock Exchange (NSE) as sample from January 2001 to June 2016. It provides evidence that low risk portfolio sorted for total risk, systematic risk as well as unsystematic risk individually for the large cap, mid cap, small cap and the entire NSE universe give higher returns to the investor as compared to high risk portfolio. The difference of returns from low risk portfolio versus high risk portfolio is positive as well as economically and statistically significant for all the risk measures. The results also prove that low risk portfolio investing strategy returns outperform the benchmark portfolio. Using either total volatility, idiosyncratic volatility or beta as a risk measure in stocks, the low risk portfolio gives higher returns even after controlling for the well-known size, value and momentum factors. The excess returns are the highest for low risk portfolio sorted for volatility of large cap stocks. Most of the low risk portfolios consists of growth and winner stocks. In conclusion, the low risk portfolio investment strategy is independent of size and gives positive excess returns as compared to high risk portfolio in the Indian stock market.

How Many Factors? Does Adding Momentum and Volatility Improve Performance

This paper considers whether adding two established anomalies, momentum and low volatility, will improve our understanding of asset pricing beyond the FF5 model. We do this by considering whether these factors provide economic, as opposed to statistical, significance within the asset pricing model. We measure economic significance in two ways, first, we consider whether the factor coefficient signs and values on the factors are economically meaningful, for example, do the coefficients distinguish between high and low risk portfolios. Second, we consider an out-of-sample trading rule based on expected returns derived from each asset pricing model. Our results suggest that the momentum and volatility factors provide no additional information over the FF5 model. Moreover, it is not clear that the FF5 model itself provides a noticeable improvement over the FF3 model. Of note, the momentum and low volatility factors exhibit limited statistical significance, have similar coefficients across high and low values of different anomalies and big and small firm portfolios. The trading performance of a seven-factor model, while reasonable itself, is worse than both the FF3 and FF5 models. Furthermore, based on the trading results, the FF5 model provides no noticeable contribution over the FF3 model, the latter of which could be regarded as preferred.

Fuzzy Decision Theory Based Metaheuristic Portfolio Optimization and Active Rebalancing Using Interval Type-2 Fuzzy Sets

This paper discusses a hitherto unexplored problem of fuzzy portfolio optimization with its tripartite stages of portfolio optimization, market scenario forecasting, and portfolio rebalancing. The portfolio optimization phase, which determines the original portfolio to be invested in, deals with the multiobjectives of maximizing its diversification ratio and its expected portfolio return, subject to the nonlinear constraints of risk budgeting and other investor preferential constraints. The complex problem model necessarily depends on metaheuristics to arrive at the optimal portfolio. The market scenario forecasting phase, where the investor desires to generate future market scenarios to handle the uncertainty in the markets while attempting to rebalance the portfolio, adopts a strategically refined Monte Carlo simulation to generate close-to-real future market scenarios. The last stage of portfolio rebalancing, which is crucial to the investor, employs fuzzy decision theory based metaheuristics using Interval Type-2 fuzzy sets, to cleverly exploit the uncertainty modeled by the market scenarios generated, to arrive at the ultimate optimal rebalanced portfolio. In the absence of reported work for a complex problem of such a nature and scale, two metaheuristic strategies chosen from two different genres of evolutionary algorithms, viz., multiobjective differential evolution and multiobjective evolution strategy, have been strategically evolved to solve the multistaged problem, for comparison of results. The experimental studies have been undertaken over a high-risk portfolio of BSE 200 index (March 1999–March 2009, Bombay Stock Exchange, India). Extensive simulations including data envelopment analysis have been undertaken to analyze the performance and robustness of the solution strategies.

Investor sentiment and economic forces

Economic theory suggests that pervasive factors should be priced in the cross-section of stock returns. However, our evidence shows that portfolios with higher risk exposure do not earn higher returns. More importantly, our evidence shows a striking two-regime pattern for all 10 macro-related factors: high-risk portfolios earn significantly higher returns than low-risk portfolios following low-sentiment periods, whereas the exact opposite occurs following high-sentiment periods. These findings are consistent with a setting in which market-wide sentiment is combined with short-sale impediments and sentiment-driven investors undermine the traditional risk-return tradeoff, especially during high-sentiment periods.

A STUDY OF CAPM TESTING IN INDIAN CONTEXT

In this paper, it is checked whether Capital Assets Pricing Model (CAPM) holds for Indian stock markets or not by studying the excess return-beta relationship for the period ranging from April 2010 to March 2015 for Indian market. If CAPM holds then intercept should equals to zero and the slope should equal the excess returns on the market portfolio. To make results more reliable and effective portfolios have been created on the basis of betas obtained from time-series regression. This study founds CAPM does not holds for high risk portfolio, whereas excess return- beta relationship is well captured by CAPM for medium and low risk portfolios for the sample period. The findings of this paper might help investors for their funds and in taking better informed decisions.

Another Take on Real Estate's Role in Mixed-Asset Portfolio Allocations

This article examines real estate's role in institutional mixed-asset portfolios using both private- and public-real estate indices, as a means of examining varying real estate-related risk/return opportunities. In so doing, this article also examines the effects of: (1) increasing the investment horizon, (2) placing constraints on the maximum allocation to any one asset class, and (3) varying the risk preferences of investors. The empirical results suggest—using infinite-horizon returns and all of the caveats that accompany such a perspective—that real estate allocations of approximately 10–15% of the mixed-asset portfolio represent an upper bound for most investors. For those investors preferring low-risk portfolios, (unlevered) private real estate is the vehicle serving this allocation preference; for those investors preferring high-risk portfolios, public real estate (with its embedded leverage of 40–50%) is the vehicle serving this allocation preference—with such vehicles serving as substitutes for a variety of noncore real estate strategies. In some sense, the distinction between private and public real estate is more about the use of leverage. For those investors preferring moderate-risk portfolios, an intermediate-leverage approach seems optimal.

Delegated portfolio management and diversification

This article analyses the potential diversification benefits available to high-net-worth investors utilizing multiple portfolio managers. We show that enlisting seven actively managed portfolios from multiple portfolio managers generate significant benefits in terms of risk reduction, and, interestingly, diversification benefits are shown to be larger for low-risk portfolios compared to high-risk portfolios.

Risk-Return Trade-off in Emerging Markets: Evidence from Dhaka Stock Exchange Bangladesh

This paper attempts to measure the risk and return relationship in Dhaka Stock Exchange (DSE). The study reports a statistically significant positive relationship between risk and return both at the individual security level and at the portfolio level, confirming the theoretical predictions and empirical findings on this issue in developed markets. Although portfolio risk and returns are found to be significantly positively related in general, some inconsistencies were revealed in the context of relative risk for high risk portfolios, suggesting the existence of some anomalies or mispricing in high risk assets. These findings have important implications for investment decisions at the DSE in that the investors may be able to create profitable investment strategies using the mispricing information.

Stock price effects of asset securitization: The case of liquidity facility providers

Abstract We analyze the effects of asset securitization announcements on the market value of banks involved in the transaction as liquidity facility providers. Based on a unique sample of 97 European securitization transactions undertaken between 2002 and 2010, we find that abnormal returns occur around the announcement dates, and that they are positively related to the respective bank's liquidity. Moreover, abnormal returns tend to be negative for transactions involving high-risk portfolios, and they have decreased significantly after the global financial crisis. Our results suggest that providing liquidity facilities in securitization transactions is considered value relevant information by equity investors, and that bank management may even be able to infer the likely sign of the market reaction based on the respective bank's characteristics, and current business environment.

The Limits to Arbitrage and the Low-Volatility Anomaly

The authors found that over 1963–2010, the existence and trading efficacy of the low-volatility stock anomaly were more limited than widely believed. For example, they found no anomalous returns for equal-weighted long–short (low-risk minus high-risk) portfolios and that alpha is largely eliminated when omitting low-priced stocks from value-weighted long–short portfolios. Furthermore, performance of long–short portfolios was significantly reduced by high transaction costs, reflecting the finding that the abnormal returns were concentrated among low-liquidity and smaller stocks. Amplifying liquidity needs, the anomalous excess returns quickly reversed, requiring frequent rebalancing. The authors’ findings have meaningful implications for implementing low-risk equity portfolio strategies.