How does the choice of Value-at-Risk estimator influence asset allocation decisions?

Considering the growing need for managing financial risk, Value-at-Risk (VaR) prediction and portfolio optimisation with a focus on VaR have taken up an important role in banking and finance. Motivated by recent results showing that the choice of VaR estimator does not crucially influence decision-making in certain practical applications (e.g. in investment rankings), this study analyses the important question of how asset allocation decisions are affected when alternative VaR estimation methodologies are used. Focusing on the most popular, successful and conceptually different conditional VaR estimation techniques (i.e. historical simulation, peak over threshold method and quantile regression) and the flexible portfolio model of Campbell et al. [J. Banking Finance. 2001, 25(9), 1789–1804], we show in an empirical example and in a simulation study that these methods tend to deliver similar asset weights. In other words, optimal portfolio allocations appear to be not very sensitive to the choice of VaR estimator. This finding, which is robust in a variety of distributional environments and pre-whitening settings, supports the notion that, depending on the specific application, simple standard methods (i.e. historical simulation) used by many commercial banks do not necessarily have to be replaced by more complex approaches (based on, e.g. extreme value theory).     

How much cash does your company need?

In late 2001, the directors of Pfizer asked that very question. And with good reason. After its 2000 merger with rival Warner-Lambert, the New York-based pharmaceutical giant found itself sitting on a net cash position of $8 billion, which seemed extraordinarily conservative for a company whose products generated $30 billion in revenues. Most large companies with revenues that healthy would increase leverage, thereby unlocking tremendous value for shareholders. But knowledge-intensive companies like Pfizer, this author argues, are in a class apart. Because their largely intangible assets (like R&D) are highly volatile and cannot easily be valued, they are more vulnerable to financial distress than are firms with a preponderance of tangible assets. To insure against that risk, they need to maintain large positive cash balances. These companies' decisions to run large cash balances is one of the key reasons their shares sustain consistent premiums. Only by investing in their intangible assets can knowledge-based companies hope to preserve the value of those assets. A company that finds itself unable to do so because unfavorable market conditions reduce its operating cash flows will see its share price suffer almost as much as if it were to default on its debts. By the same token, with the right balance sheet, knowledge companies can profitably insure against the risk of failing to sustain value-added investments in difficult times. An optimal capital structure that calls for significant cash balances is certainly at odds with the results of a traditional capital structure analysis, the author demonstrates, but it explains the financial policies of many well-run companies, from Pfizer to Intel to ChevronTexaco.     

How to build a factor portfolio: Does the allocation strategy matter?

Factor-based allocation embraces the idea of factors, as opposed to asset classes, as the ultimate building blocks of investment portfolios. We examine whether there is a superior way of combining factors in a portfolio and provide a comparison of factor-based allocation strategies within a multiple testing framework. Factor-based allocation is profitable beyond exploiting genuine risk premia, even when applying multiple testing corrections. Investment portfolios can be efficiently diversified using factor-based allocation strategies, as demonstrated by robust economic performance over various economic scenarios. The naïve equally weighted factor portfolio, albeit simple and cost-efficient, cannot be outperformed by more sophisticated allocation strategies.     

How much does tax erode fund excess returns?

We model the tax drag from active fund management based on reported monthly holdings of active equity funds. Tax drag erodes 65 percent of the 0.74 percent excess return in Broad Market funds, but only 21 percent of the 1.80 percent excess return in Small-Cap funds for Australian superannuation (pension) fund investors. Tax drag varies with investment style; market state, which is most detrimental during bull markets; and fund turnover. For high-income individual investors, tax drag is exacerbated to the extent that active management only generates meaningful after-tax excess return for Small-Cap funds of certain styles.     

Asset allocation decisions in retirement accounts: an all-or-nothing proposition?

An examination of survey responses about Individual Retirement Account (IRA) holdings reveals that individuals often take all-or-nothing approaches in their decisions to diversify across the asset categories of cash, bonds, and equity. Two thirds of survey respondents put their entire IRA holdings into a single asset category. A surprisingly large proportion of funds is held in cash, while only a minimal amount is invested in bonds. These findings also contrast with those of Bodie and Crane’s (1997) examination of TIAA-CREF participants, which is heavily weighted with individuals holding fixed income annuities. Our results suggest that there is a compelling need for risk education for investors.     

Industry growth and capital allocation:: does having a market- or bank-based system matter?

Are market-based or bank-based financial systems better at financing the expansion of industries that depend heavily on external finance, facilitating the formation of new establishments, and improving the efficiency of capital allocation across industries? We find evidence for neither the market-based nor the bank-based hypothesis. While legal system efficiency and overall financial development boost industry growth, new establishment formation, and efficient capital allocation, having a bank-based or market-based system per se does not seem to matter much.     

Asset allocation and liquidity breakdowns: what if your broker does not answer the phone?

This paper analyzes the portfolio decision of an investor facing the threat of illiquidity. In a continuous-time setting, the efficiency loss due to illiquidity is addressed and quantified. For a logarithmic investor, we solve the portfolio problem explicitly. We show that the efficiency loss for a logarithmic investor with 30 years until the investment horizon is a significant 22.7% of current wealth if the illiquidity part of the model is calibrated to the Japanese data of the aftermath of WWII. For general utility functions, an explicit solution does not seem to be available. However, under a mild growth condition on the utility function, we show that the value function of a model in which only finitely many liquidity breakdowns can occur converges uniformly to the value function of a model with infinitely many breakdowns if the number of possible breakdowns goes to infinity. Furthermore, we show how the optimal security demands of the model with finitely many breakdowns can be used to approximate the solution of the model with infinitely many breakdowns. These results are illustrated for an investor with a power utility function.     

Conditional portfolio allocation: Does aggregate market liquidity matter?

This paper investigates how aggregate liquidity influences optimal portfolio allocations across various US characteristic portfolios. We consider short-term allocation problems, with single and multiple risky assets, and use the nonparametric approach of Brandt (1999) to directly express optimal portfolio weights as functions of aggregate liquidity shocks. We find, first, that the effect of aggregate liquidity is positive and decreasing with the investment horizon. Second, at daily and weekly horizons, this effect is weaker on allocations in large stocks and gets stronger as we move toward small stocks, regardless of the other stock characteristics, suggesting that liquidity is the main concern of very short-term investors. Third, conditional allocations in risky assets decrease and exhibit shifts toward more liquid assets as aggregate liquidity worsens. Overall, conditioning on aggregate liquidity yields empirical results that are consistent with the so-called flight-to-safety and flight-to-liquidity episodes. Finally, we propose a simple tactical investment strategy and show how aggregate liquidity information can be exploited to enhance the out-of-sample performance of long-term strategies.     

UK brokers' characteristics: does size matter?

This study provides one of the first insights into how UK brokers' institutional characteristics may impact on the forecasting performance of their financial analysts. The study focuses on brokerage house size and finds it to be a significant factor explaining cross sectional variation in forecasting performance. This is consistent with evidence from several recent US studies (Jacob et al. 1997; Clement, 1999). It is likely that this broker-size effect reflects the resources (human, IT) available to brokers' analysts to support them in their activities. It may also reflect larger brokers' superior access to company managers and information. However, this broker-size effect appears to be significant only for forecasts made at horizons of one year or less. The sign of the earnings change being predicted also has a significant impact: for observations where earnings changes are negative, the broker-size effect is larger than for positive changes, though the effect is significant for both cases. In addition, the form of the model employed here suggests diminishing marginal returns to broker size. More generally, this study reiterates the importance of controlling for the most commonly cited explanatory variables for forecast accuracy, and there is evidence that the heavy industry sectors may be more difficult to forecast, echoing the conclusions of UK studies from the 1980s.     

Continuous disclosure compliance: does corporate governance matter?

Our study investigates the quality of firms’ continuous disclosure compliance during mandatory continuous disclosure reform, and whether the compliance quality is impacted by corporate governance, using the New Zealand market as the setting. We use a novel coding of different categories of disclosures (non‐routine, non‐procedural and internal), which represents the extent of proprietary insider information inherent in disclosures, to evaluate firms’ compliance quality. Our findings provide evidence that firms’ compliance quality improved after the reform, and this improvement is inconsistently impacted by corporate governance. Our findings provide important implications for regulators in their quest for a superior disclosure regime.     

How much should portfolios shrink?

This paper develops a portfolio model that penalizes the deviation from a reference portfolio. The proposed model renders a robust portfolio that performs superior under parameter uncertainty. Penalizing the deviation also improves the performance of existing shrinkage portfolio models that are suboptimal due to model parameter uncertainty. The equal-weight portfolio turns out to be a better reference portfolio than the currently holding portfolio even in the presence of transaction costs. A data-driven method for determining the degree of penalization is offered. Comprehensive simulation and empirical studies suggest that the proposed model significantly outperforms various existing models.     

Trustee reputation in securitization: When does it matter?

We consider the role of trustees–who are nominated to protect the interests of investors–in securitization pricing and whether investors rely on them to mitigate risks. In particular, we examine the effect of trustee reputation on initial yield spreads of European mortgage‐backed security (MBS) issuances between 1999 and the first half of 2007. We find that engaging reputable trustees led to lower spreads during the credit boom period prior to the 2007–2009 financial crisis. Our findings suggest that trustees’ reputation was considered by investors to be more important when risk assessment became more challenging.     

Asset restructuring strategies in bank acquisitions: Does distance between dealing partners matter?

One of the most lively-debated effects of banking acquisitions is the change in lending and asset allocation of the target bank in favour of transaction-based products, at the expense of small and informationally opaque borrowers. These changes may be the result of two distinct restructuring strategies pursued by the acquirer with respect to the asset portfolio of the acquired bank: a cleaning strategy (CS), in which the acquirer makes a clean sweep of all the negative net present value activities in the portfolio of the acquired bank, and a portfolio strategy (PS), in which the acquiring bank permanently changes the portfolio allocation of the acquired bank. In this paper we focus on Italian bank acquisitions and test which asset restructuring strategy was predominantly pursued over the period 1997–2003. Moreover, we distinguish acquisitions according to their geographic diversifying character and to the physical and cultural distances that separate acquiring from acquired banks. When we look at the mean value, we do not find clear evidence of a primacy either of CSs or PSs. When we separate in-market from out-of-market bank acquisitions, however, results show that the CSs prevail only in the former type of deals, while in the latter the portfolio of acquired banks is subject to PSs. Finally, we find that differences in asset restructuring strategies can be explained by differences in corporate culture and the workplace environment of the dealing partners.     

How much discretion for risk regulators?

We analyze the regulation of firms that undertake socially risky activities but can reduce the probability of an accident inflicted on third parties by carrying out non verifiable effort. Congress delegates regulation to an agency, although these two bodies may have different preferences toward the industry. The optimal level of discretion left to the agency results from the following trade‐off: the agency can tailor discretionary policies to its expert knowledge about potential harm, but it implements policies that are too “pro‐industry.” The agency should be given full discretion when the firm is solvent; partial discretion is preferred otherwise. We then investigate how this trade‐off changes as the political and economic landscapes are modified.     

Why does IPO volume fluctuate so much?

IPO volume fluctuates substantially over time. This paper compares the extent to which the aggregate capital demands of private firms, the adverse-selection costs of issuing equity, and the level of investor optimism can explain these fluctuations. Empirical tests include both aggregate and industry-level time-series regressions using proxies for the above factors and an analysis of the relation between post-IPO stock returns and IPO volume. Results indicate that firms’ demands for capital and investor sentiment are important determinants of IPO volume, in both statistical and economic terms. Adverse-selection costs are also statistically significant, but their economic effect appears small.     

Infrastructure spillovers and strategic interaction: does the size matter?

We set up a model in which the residents of two neighboring municipalities use the services provided by public infrastructures located in both jurisdictions. The outcome is that municipalities strategically interact when investing in infrastructures, with the small municipality reacting more to the expenditure of its neighbor than the big one. This theoretical prediction is tested by estimating the determinants of the stock of public infrastructures of the municipalities belonging to the Autonomous Province of Trento in Italy. By introducing the classical spatial lag-error component, we find that municipalities positively react to an increase in infrastructures by their neighbors, but the effect vanishes above a given population threshold. Such a result is confirmed when we exploit the exogenous variation in the neighbors’ stock of infrastructures induced by a strong flood that occurred in the Province of Trento in 2000.     

Mergers in the bond rating industry: does rating provider matter?

I examine whether investors favour bond ratings from established global agencies by analyzing the market response to Standard and Poor’s (S&P) acquisition of the Canadian Bond Rating Service (CBRS). As a result of the acquisition, CBRS ratings were completely eliminated and replaced with ratings from S&P. While little reaction was apparent for bonds, the stocks of firms with CBRS ratings responded positively to the acquisition announcement. Small firms and those with little institutional ownership experienced the greatest benefit. The findings suggest that ratings from S&P may increase the exposure of foreign firms to international investors.     

Does the choice of estimator matter when forecasting returns?

While the literature concerned with the predictability of stock returns is huge, surprisingly little is known when it comes to role of the choice of estimator of the predictive regression. Ideally, the choice of estimator should be rooted in the salient features of the data. In case of predictive regressions of returns there are at least three such features; (i) returns are heteroskedastic, (ii) predictors are persistent, and (iii) regression errors are correlated with predictor innovations. In this paper we examine if the accounting of these features in the estimation process has any bearing on our ability to forecast future returns. The results suggest that it does.