Fractals in trade duration: capturing long-range dependence and heavy tailedness in modeling trade duration

Several studies that have investigated a few stocks have found that the spacing between consecutive financial transactions (referred to as trade duration) tend to exhibit long-range dependence, heavy tailedness, and clustering. In this study, we empirically investigate whether a larger sample of stocks exhibit those characteristics. We do so by comparing goodness of fit in modeling trade duration data for stable distribution and fractional stable noise based on a procedure applying bootstrap methods developed by the authors with several alternative distributional assumptions in modeling trade duration data. The empirical results suggest that the autoregressive conditional duration model with stable distribution fits better than other combinations, while fractional stable noise itself fits better for the time series of trade duration. Our result is consistent with the general findings in the literature that trade duration is informative and that short trade durations move prices more than long trade duration. In addition, our result confirms the advantage of fractal models in the study of roughness in trade duration and provides some evidence for duration dependence. S. Rachev’s research was supported by grants from the Division of Mathematical, Life and Physical Science, College of Letters and Science, University of California, Santa Barbara, and the Deutschen Forschungsgemeinschaft. W. Sun’s research was supported by grants from the Deutschen Forschungsgemeinschaft. P.S. Kalev’s research was supported with a NCG grant from the Faculty of Business and Economics, Monash University. Data are supplied by Securities Industry Research Center of Asia-Pacific (SIRCA) on behalf of Reuters. The first draft of this paper was presented at the International Conference on High Frequency Finance 2006; the authors would like to thank the conference participants for their valuable comments.     

Liquidity,default, taxes,and yields on municipal bonds

We examine the effects of liquidity, default and personal taxes on the relative yields of Treasuries and municipals using a generalized model with liquidity risk. The municipal yield model includes liquidity as a state factor. Using a unique transaction dataset, we estimate the liquidity risk of municipals and its effect on bond yields. Empirical evidence shows that municipal bond yields are strongly affected by all three factors. The effects of default and liquidity risk on municipal yields increase with maturity and credit risk. Liquidity premium accounts for about 9–13% of municipal yields for AAA bonds, 9–15% for AA/A bonds and 8–19% for BBB bonds. A substantial portion of the maturity spread between long- and short-maturity municipal bonds is attributed to the liquidity premium. Ignoring the liquidity risk effect thus results in a severe underestimation of municipal bond yields. Conditional on the effects of default and liquidity risk, we obtain implicit tax rates very close to the statutory tax rates of high-income individuals and institutional investors. Furthermore, these implicit income tax rates are quite stable across bonds of different maturities. Results show that including liquidity risk in the municipal bond pricing model helps explain the muni puzzle.     

Performance hypothesis testing with the Sharpe ratio: The case of hedge funds

As recent research highlights that the Sharpe ratio has a decision theoretic foundation even in the case of asymmetric or fat-tailed excess returns and thus is adequate even for the evaluation of hedge funds, this note provides the first Sharpe ratio based performance analysis of the hedge fund market. Furthermore, it addresses the important practical question whether the choice of hypothesis test used to statistically compare Sharpe ratios can influence an investor’s hedge fund selection process. Our key findings are as follows: (i) Only a small fraction of hedge funds in our large dataset can significantly outperform passive investments in corresponding hedge fund indices. (ii) Especially in the presence of autocorrelated or skewed excess returns, the traditional test of Jobson and Korkie, 1981, Memmel, 2003 tends to overstate the number of significant outperformers and thus provides potentially misleading information for investors. Decision makers are advised to use the bootstrap test of Ledoit and Wolf (2008) allowing robust and more reliable inference.     

Common information asymmetry factors in syndicated loan structures

This paper provides a comprehensive study of the syndicate structure and its relationship to information asymmetry and loan spread by using principal component analysis on a large set of 40 structure-related variables. A total of six structure components are identified and related to syndicate quality, syndicate members’ heterogeneity or share concentration, lead arranger’s characteristics, lead lender’s or syndicate’s location, lender–borrower relationships, and lead institution type. In multivariate settings, all six components are significant determinants of loan spread, either directly or indirectly through their impact on other components. Lead share retention, previous lender–borrower relationships and syndicate quality are shown to be bilaterally related to loan spread. Structure components differ regionally, which can provide an explanation for the European pricing discount observed in the literature. An Asian discount is observed and cannot be explained by structure differences.     

Preparing for higher inflation: Portfolio solutions using U.S. equities

Investors have always been interested in reducing inflation risk in their portfolios. However, investors face different types of inflation than those measured by the Consumer Price Index (CPI). Moreover, different asset classes can be used to hedge portfolio inflation. In this paper, we show how individual equities can be used to construct equity portfolios sensitive to customized inflation targets. We illustrate portfolios for three types of inflation: US headline CPI, Forbes Cost of Living Extremely Well Index, and the US Medical Care Price Index. We also show how alternative weighting schemes, such as minimum volatility and maximum inflation beta, can be used to construct inflation‐hedged portfolios.     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

Does uncertainty matter for loan charge-offs?

Using a stylized real options model, we show that discretion over the timing of charging off a non-performing loan could be economically justified when collateral values are uncertain and there is a chance of loan recovery. The implied hypothesis of an “uncertainty dependence” aspect in loan charge-offs is empirically tested and validated using a panel of European banks. A welfare-maximizing regulator might want to let banks pursue such discretionary loan charge-off behavior, with the problem of distinguishing it from alternative capital management and income smoothing objectives, while transparency-seeking accounting standards setters would presumably not.     

The perceived fairness of performance evaluation: The role of uncertainty

This paper investigates the relationship between performance evaluation procedures and managerial perceptions of procedural justice. We examine two metric and two process characteristics. Metric characteristics are the diversity of metrics used by superiors and their reliance on outcome vs. effort metrics. Regarding process characteristics, we analyse the amount of subordinate's voice in the performance evaluation process, and the extent of formalization of the process. Using uncertainty management theory, we argue that justice effects of performance evaluation procedures may not be direct but are instead conditional on the amount of task uncertainty managers face in the their job context as well as on managers’ tolerance for ambiguity. Using a sample of 178 managers from the banking industry, we find that all four performance evaluation characteristics are related to justice perceptions, yet their effect depends on the level of task uncertainty and tolerance for ambiguity. These findings explain some inconsistencies in extant studies on fairness of performance evaluation procedures.