Research articles for the 2019-05-21
A Liquidity-Based Explanation of Convertible Arbitrage Alphas
SSRN
We examine the extent to which excess returns from convertible arbitrage represent positive returns to managers to exploiting pricing inefficiencies versus compensation for exposure to systematic risk factors. Initial empirical tests show that when we exclude liquidity risk as a factor, a good portion of abnormal returns to convertible bond strategies appears to be driven both by overpricing of the underlying equity and apparent underpricing of convertible bonds. However, when we include the effects of liquidity, abnormal returns to convertible bond arbitrage essentially disappear and only remain localized in convertible debt trading closer to the issuance date.
SSRN
We examine the extent to which excess returns from convertible arbitrage represent positive returns to managers to exploiting pricing inefficiencies versus compensation for exposure to systematic risk factors. Initial empirical tests show that when we exclude liquidity risk as a factor, a good portion of abnormal returns to convertible bond strategies appears to be driven both by overpricing of the underlying equity and apparent underpricing of convertible bonds. However, when we include the effects of liquidity, abnormal returns to convertible bond arbitrage essentially disappear and only remain localized in convertible debt trading closer to the issuance date.
A Simple Approach to Pricing American Options Under the Heston Stochastic Volatility Model
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In a recent paper, NBZ [2010] present a multidimensional transform for generating path-independent trees for pricing American options under low dimensional stochastic volatility models. For this class of models, this approach has higher accuracy than the GARCH tree method of Ritchken and Trevor [1999], and is computationally more efficient than the Monte Carlo regression method of Longstaff and Schwartz [2001] as well as the lattice method of Leisen [2000]. In this paper, we give an explicit demonstration of the NBZ transform using the specific example of the Heston [1993] stochastic volatility model. This approach obtains highly accurate American option prices within a fraction of a second using the control variate method.
SSRN
In a recent paper, NBZ [2010] present a multidimensional transform for generating path-independent trees for pricing American options under low dimensional stochastic volatility models. For this class of models, this approach has higher accuracy than the GARCH tree method of Ritchken and Trevor [1999], and is computationally more efficient than the Monte Carlo regression method of Longstaff and Schwartz [2001] as well as the lattice method of Leisen [2000]. In this paper, we give an explicit demonstration of the NBZ transform using the specific example of the Heston [1993] stochastic volatility model. This approach obtains highly accurate American option prices within a fraction of a second using the control variate method.
An Optimal Dividend Problem with Capital Injections over a Finite Horizon
arXiv
arXiv
In this paper we propose and solve an optimal dividend problem with capital injections over a finite time horizon. The surplus dynamics obeys a linearly controlled drifted Brownian motion that is reflected at the origin, dividends give rise to time-dependent instantaneous marginal profits, whereas capital injections are subject to time-dependent instantaneous marginal costs. The aim is to maximize the sum of a liquidation value at terminal time and of the total expected profits from dividends, net of the total expected costs for capital injections. Inspired by the study of El Karoui and Karatzas (1989) on reflected follower problems, we relate the optimal dividend problem with capital injections to an optimal stopping problem for a drifted Brownian motion that is absorbed at the origin. We show that whenever the optimal stopping rule is triggered by a time-dependent boundary, the value function of the optimal stopping problem gives the derivative of the value function of the optimal dividend problem. Moreover, the optimal dividend strategy is also triggered by the moving boundary of the associated stopping problem. The properties of this boundary are then investigated in a case study in which instantaneous marginal profits and costs from dividends and capital injections are constants discounted at a constant rate.
Are SME Loans Less Risky than Regulatory Capital Requirements Suggest?
SSRN
Our paper addresses firm size as a driver of systematic credit risk in loans to small and medium-sized enterprises (SMEs). Key contributions are the use of a unique data set of SME lending by over 400 German banks and relating systematic risk to the size dependence of regulatory capital requirements. What sets our sample apart is its comprehensive coverage of the particularly rich and well-developed credit market for SMEs in Germany. We estimate asset correlations as the key measure of systematic risk from historical default rates. Our results suggest that systematic risk tends to increase with firm size, conditional on the respective rating category. We also compare the size of this effect with the capital relief that has been granted in Basel II for SMEs relative to large firms. Our asset correlation estimates suggest a significantly larger relative difference from the corresponding values for large firms than reflected in the regulatory capital requirements in two cases: first, for SME loans in the corporate portfolio of the Internal Ratings-Based Approach and, second, for SME loans treated under the revised standardized approach of Basel II.
SSRN
Our paper addresses firm size as a driver of systematic credit risk in loans to small and medium-sized enterprises (SMEs). Key contributions are the use of a unique data set of SME lending by over 400 German banks and relating systematic risk to the size dependence of regulatory capital requirements. What sets our sample apart is its comprehensive coverage of the particularly rich and well-developed credit market for SMEs in Germany. We estimate asset correlations as the key measure of systematic risk from historical default rates. Our results suggest that systematic risk tends to increase with firm size, conditional on the respective rating category. We also compare the size of this effect with the capital relief that has been granted in Basel II for SMEs relative to large firms. Our asset correlation estimates suggest a significantly larger relative difference from the corresponding values for large firms than reflected in the regulatory capital requirements in two cases: first, for SME loans in the corporate portfolio of the Internal Ratings-Based Approach and, second, for SME loans treated under the revised standardized approach of Basel II.
Bankruptcy Prediction Models and the Cost of Debt
SSRN
Financial institutions and academic researchers utilize bankruptcy prediction models to assess distress risk. However, predicting default can be problematic since (i) few firms actually experience default in any one year, (ii) the lag between practical and actual default can vary significantly, (iii) firms can strategically default, (iv) firms can rework their obligations outside of bankruptcy, and (v) default frequency varies significantly over economic life cycles. Thus, relying on bankruptcy data alone to calibrate and validate these models can be problematic. We take a simpler approach by relying on the firm’s cost of debt as a market proxy for distress risk. We then assess the validity of four widely used bankruptcy models including two accounting-based models (Altman’s, 1968; Ohlson’s, 1980), one reduced form model (Campbell, Hilscher, and Szilagyi, 2010) and one structural distance to default model (Merton, 1974). We find dramatically different assessment of risk based on the models used. The Campbell, Hilscher, and Szilagyi (2010) model has the most explanatory power on the cost of debt followed by the Merton model. The accounting based approaches of Altman (1968)’s Z-Score and Ohlson (1980)’s O-Score are highly ineffective. We caution researchers when using Z- and O-Scores and recommend the use of Campbell, Hilscher, and Szilagyi model to measure distress risk. We also demonstrate the problems of not controlling for industry and time variation in any of these measures.
SSRN
Financial institutions and academic researchers utilize bankruptcy prediction models to assess distress risk. However, predicting default can be problematic since (i) few firms actually experience default in any one year, (ii) the lag between practical and actual default can vary significantly, (iii) firms can strategically default, (iv) firms can rework their obligations outside of bankruptcy, and (v) default frequency varies significantly over economic life cycles. Thus, relying on bankruptcy data alone to calibrate and validate these models can be problematic. We take a simpler approach by relying on the firm’s cost of debt as a market proxy for distress risk. We then assess the validity of four widely used bankruptcy models including two accounting-based models (Altman’s, 1968; Ohlson’s, 1980), one reduced form model (Campbell, Hilscher, and Szilagyi, 2010) and one structural distance to default model (Merton, 1974). We find dramatically different assessment of risk based on the models used. The Campbell, Hilscher, and Szilagyi (2010) model has the most explanatory power on the cost of debt followed by the Merton model. The accounting based approaches of Altman (1968)’s Z-Score and Ohlson (1980)’s O-Score are highly ineffective. We caution researchers when using Z- and O-Scores and recommend the use of Campbell, Hilscher, and Szilagyi model to measure distress risk. We also demonstrate the problems of not controlling for industry and time variation in any of these measures.
Capital Financing Under Information Asymmetry: Debt Underpricing as a New Signaling Approach
SSRN
In this study, we develop a static signaling game model to examine the problem of a firm raising funds externally to finance an investment project when there is an information asymmetry between the firm and its outside investors, and the life-span of the investment project is very short. Different from other capital financing models, we also allow the firm to choose the selling price of the securities issued, to explore the possibility of underpricing as a signaling tactic. We provide an efficient procedure for identifying all possible perfect bayesian equilibria (PBEs) resulting in our model, and fully characterize the optimal financing strategy adopted by the firm under various scenarios. Our study finds that when the cost of bankruptcy is low, a high-quality firm cannot signal its type to investors through its choice of financial instrument (equity or debt). The firm must instead sell its debt security at a discounted price (known as debt underpricing) to make the signaling feasible. We also show this signaling strategy to be optimal when the degree of information asymmetry is sufficiently large and the cost of bankruptcy sufficiently low. This corroborates with the empirical studies carried out in previous studies. Our work also reveals that it is still optimal for a firm to issue risky debt even when the information asymmetry cannot be resolved, due to the low information cost embedded in the debt contract. We explore how the debt underpricing improves the investment inefficiency caused by the problem of information asymmetry. We compare different financing strategies in revealing information of a firm, with the aim of determining an optimal strategy.
SSRN
In this study, we develop a static signaling game model to examine the problem of a firm raising funds externally to finance an investment project when there is an information asymmetry between the firm and its outside investors, and the life-span of the investment project is very short. Different from other capital financing models, we also allow the firm to choose the selling price of the securities issued, to explore the possibility of underpricing as a signaling tactic. We provide an efficient procedure for identifying all possible perfect bayesian equilibria (PBEs) resulting in our model, and fully characterize the optimal financing strategy adopted by the firm under various scenarios. Our study finds that when the cost of bankruptcy is low, a high-quality firm cannot signal its type to investors through its choice of financial instrument (equity or debt). The firm must instead sell its debt security at a discounted price (known as debt underpricing) to make the signaling feasible. We also show this signaling strategy to be optimal when the degree of information asymmetry is sufficiently large and the cost of bankruptcy sufficiently low. This corroborates with the empirical studies carried out in previous studies. Our work also reveals that it is still optimal for a firm to issue risky debt even when the information asymmetry cannot be resolved, due to the low information cost embedded in the debt contract. We explore how the debt underpricing improves the investment inefficiency caused by the problem of information asymmetry. We compare different financing strategies in revealing information of a firm, with the aim of determining an optimal strategy.
Explaining Debt Recovery Using an Endogenous Bankruptcy Model
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Drawing on a large sample of defaulted corporate debt from 1996 to 2007, we find that the debt recovery estimated using the Leland-Toft endogenous bankruptcy model has strong explanatory power on the debt recovery observed in the market. Our results hold after firm characteristics, industry distress, and macroeconomic conditions are taken into account. In addition, we find that both agency problems and heterogeneous bankruptcy costs weaken the explanatory power of the model. Our study suggests structural models that incorporate the role of managers in endogenously determining the bankruptcy boundary provide statistical power in explaining cross-sectional variation of corporate debt recovery.
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Drawing on a large sample of defaulted corporate debt from 1996 to 2007, we find that the debt recovery estimated using the Leland-Toft endogenous bankruptcy model has strong explanatory power on the debt recovery observed in the market. Our results hold after firm characteristics, industry distress, and macroeconomic conditions are taken into account. In addition, we find that both agency problems and heterogeneous bankruptcy costs weaken the explanatory power of the model. Our study suggests structural models that incorporate the role of managers in endogenously determining the bankruptcy boundary provide statistical power in explaining cross-sectional variation of corporate debt recovery.
Fallen Angels and Price Pressure
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We examine price pressure in a setting where trades occur because of regulations. Our study of fallen angel bond sales by insurance companies shows that price pressure is not significantly different from zero when information effects are absent. Our results confirm the prediction of several theoretical models that sellers will benefit from a higher price when they are able to separate themselves out to dealers as uninformed. We find that insurers do not attempt to hide their trades by selling bonds before they are downgraded, consistent with following a strategy of sunshine trading, as in Admati and Pfleiderer (1991).
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We examine price pressure in a setting where trades occur because of regulations. Our study of fallen angel bond sales by insurance companies shows that price pressure is not significantly different from zero when information effects are absent. Our results confirm the prediction of several theoretical models that sellers will benefit from a higher price when they are able to separate themselves out to dealers as uninformed. We find that insurers do not attempt to hide their trades by selling bonds before they are downgraded, consistent with following a strategy of sunshine trading, as in Admati and Pfleiderer (1991).
Gains from Active Bond Portfolio Management Strategies
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The belief that excess returns can be achieved by correctly timing changes in yields and/or yield spreads motivates active bond portfolio management strategies. Given the rich literature linking yield spread patterns to both the business cycle and changes in short-term interest rates, we motivate and demonstrate the efficacy of simple spread-trading strategies tied to both. Using thirty-four years of fixed income returns, we demonstrate that straightforward rules would have led to superior risk-adjusted performance relative to standard fixed-income benchmarks. Furthermore, the strategies tied to short-maturity interest rates are based on the use of past information only.
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The belief that excess returns can be achieved by correctly timing changes in yields and/or yield spreads motivates active bond portfolio management strategies. Given the rich literature linking yield spread patterns to both the business cycle and changes in short-term interest rates, we motivate and demonstrate the efficacy of simple spread-trading strategies tied to both. Using thirty-four years of fixed income returns, we demonstrate that straightforward rules would have led to superior risk-adjusted performance relative to standard fixed-income benchmarks. Furthermore, the strategies tied to short-maturity interest rates are based on the use of past information only.
Gambling Traps
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I propose a dynamic general equilibrium model in which strategic interactions between banks and depositors may lead to endogenous bank fragility and slow recovery from crises. When banks' investment decisions are not contractible, depositors form expectations about bank risk-taking and demand a return on deposits according to their risk. This creates strategic complementarities and possibly multiple equilibria: in response to an increase in funding costs, banks may optimally choose to pursue risky portfolios that undermine their solvency prospects. In a bad equilibrium, high funding costs hinder the accumulation of bank net worth and lead to a "gambling trap" with a persistent drop in investment and output. I bring the model to bear on the European sovereign debt crisis, in the course of which under-capitalized banks in default-risky countries experienced an increase in funding costs and raised their holdings of domestic government debt. The model is quantified using Portuguese data and accounts for macroeconomic dynamics in Portugal in 2010-2016. Policy interventions face a trade-off between alleviating banks' funding conditions and strengthening risk-taking incentives. Liquidity provision to banks may perpetuate gambling traps when not targeted. Targeted interventions have the capacity to eliminate adverse equilibria.
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I propose a dynamic general equilibrium model in which strategic interactions between banks and depositors may lead to endogenous bank fragility and slow recovery from crises. When banks' investment decisions are not contractible, depositors form expectations about bank risk-taking and demand a return on deposits according to their risk. This creates strategic complementarities and possibly multiple equilibria: in response to an increase in funding costs, banks may optimally choose to pursue risky portfolios that undermine their solvency prospects. In a bad equilibrium, high funding costs hinder the accumulation of bank net worth and lead to a "gambling trap" with a persistent drop in investment and output. I bring the model to bear on the European sovereign debt crisis, in the course of which under-capitalized banks in default-risky countries experienced an increase in funding costs and raised their holdings of domestic government debt. The model is quantified using Portuguese data and accounts for macroeconomic dynamics in Portugal in 2010-2016. Policy interventions face a trade-off between alleviating banks' funding conditions and strengthening risk-taking incentives. Liquidity provision to banks may perpetuate gambling traps when not targeted. Targeted interventions have the capacity to eliminate adverse equilibria.
Investor Overreaction to Earnings Surprises and Post-Earnings-Announcement Reversals
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Prior literature suggests that the market underreacts to the positive correlation in a typical firm’s seasonal earnings changes, which leads to a post-earnings-announcement drift (PEAD) in prices. We examine the market reaction for a distinct set of firms whose seasonal earnings changes are uncorrelated and show that the market incorrectly assumes that the earnings changes of these firms are positively correlated. We also document that positive (negative) seasonal earnings changes in the current quarter are associated with negative (positive) abnormal returns in the following quarter. Thus, we observe a reversal of abnormal returns, consistent with a systematic overreaction to earnings, rather than the previously documented PEAD. Additional analysis indicates that financial analysts similarly overestimate the autocorrelation of these firms, although to a lesser extent. We also find that the magnitude of overestimation and the subsequent price reversal are inversely related to the richness of the information environment. Our results challenge the notion that investors recognize but consistently underestimate earnings correlation and provide a new perspective on the inability of prices to fully reflect the implications of current earnings for future earnings. That is, we show that investors predictably overestimate correlation when it is lacking but underestimate it when it is present.
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Prior literature suggests that the market underreacts to the positive correlation in a typical firm’s seasonal earnings changes, which leads to a post-earnings-announcement drift (PEAD) in prices. We examine the market reaction for a distinct set of firms whose seasonal earnings changes are uncorrelated and show that the market incorrectly assumes that the earnings changes of these firms are positively correlated. We also document that positive (negative) seasonal earnings changes in the current quarter are associated with negative (positive) abnormal returns in the following quarter. Thus, we observe a reversal of abnormal returns, consistent with a systematic overreaction to earnings, rather than the previously documented PEAD. Additional analysis indicates that financial analysts similarly overestimate the autocorrelation of these firms, although to a lesser extent. We also find that the magnitude of overestimation and the subsequent price reversal are inversely related to the richness of the information environment. Our results challenge the notion that investors recognize but consistently underestimate earnings correlation and provide a new perspective on the inability of prices to fully reflect the implications of current earnings for future earnings. That is, we show that investors predictably overestimate correlation when it is lacking but underestimate it when it is present.
Leverage and Closed End Bond Funds
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The performance of a closed end bond fund is based on the returns of an underlying portfolio of bonds. This paper uses a structural model to assess the impact of leverage on the expected return and riskiness of a closed end bond fund. We use the model to explore the role of leverage during the financial crisis. Our model indicates that during the worst extremes of the financial crisis the debt of closed end bond funds had virtually no default risk and that the funding problems stemmed from a defective funding vehicle.
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The performance of a closed end bond fund is based on the returns of an underlying portfolio of bonds. This paper uses a structural model to assess the impact of leverage on the expected return and riskiness of a closed end bond fund. We use the model to explore the role of leverage during the financial crisis. Our model indicates that during the worst extremes of the financial crisis the debt of closed end bond funds had virtually no default risk and that the funding problems stemmed from a defective funding vehicle.
Liquidity Biases in TRACE
SSRN
The transactions database TRACE is rapidly becoming the standard data source for empirical research on US corporate bonds. This paper is the first to thoroughly discuss the assumptions needed to clean the disseminated TRACE data and to suggest that different filters should be used depending upon the application. 7.7% of all reports in TRACE are errors and in some cases up to 18% of the reports should be deleted. Failing to correct for these errors will bias popular liquidity measures towards a more liquid market. The median bias for the daily turnover will be 7.4% and for a quarter of the bonds the Amihud price impact measure will be underestimated by at least 14.6%. Further, calculating these two measures on the same data sample would potentially bias one of them.
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The transactions database TRACE is rapidly becoming the standard data source for empirical research on US corporate bonds. This paper is the first to thoroughly discuss the assumptions needed to clean the disseminated TRACE data and to suggest that different filters should be used depending upon the application. 7.7% of all reports in TRACE are errors and in some cases up to 18% of the reports should be deleted. Failing to correct for these errors will bias popular liquidity measures towards a more liquid market. The median bias for the daily turnover will be 7.4% and for a quarter of the bonds the Amihud price impact measure will be underestimated by at least 14.6%. Further, calculating these two measures on the same data sample would potentially bias one of them.
Liquidity Options
SSRN
The financial crisis of 2007-2009 highlighted the importance of liquidity to many investors. University endowment funds, for example, were forced to sell publicly traded securities at substantially depressed values in order to meet funding commitments to private investments. Hedge funds engaged in fire sales of publicly traded securities to meet margin calls from lenders and redemption demands from clients. And financial institutions faced with capital calls sold assets at substantial discounts to their government guaranteed values. Investors could guard against these capital calls by maintaining a reserve of highly liquid securities, but this protection would require them to sacrifice the expected return premium of less liquid investments. We propose, as an alternative, that investors consider purchasing liquidity options to meet unscheduled capital calls. We describe how to structure and price liquidity options, and we demonstrate how the price discovery process of liquid securities determines the fair value of illiquid securities.
SSRN
The financial crisis of 2007-2009 highlighted the importance of liquidity to many investors. University endowment funds, for example, were forced to sell publicly traded securities at substantially depressed values in order to meet funding commitments to private investments. Hedge funds engaged in fire sales of publicly traded securities to meet margin calls from lenders and redemption demands from clients. And financial institutions faced with capital calls sold assets at substantial discounts to their government guaranteed values. Investors could guard against these capital calls by maintaining a reserve of highly liquid securities, but this protection would require them to sacrifice the expected return premium of less liquid investments. We propose, as an alternative, that investors consider purchasing liquidity options to meet unscheduled capital calls. We describe how to structure and price liquidity options, and we demonstrate how the price discovery process of liquid securities determines the fair value of illiquid securities.
Maintaining a Reputation for Consistently Beating Earnings Expectations and 
the Slippery Slope to Earnings Manipulation
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This paper investigates whether maintaining a reputation for consistently beating analysts’ earnings expectations can motivate executives to move from “within GAAP†earnings management to “outside of GAAP†earnings manipulation. We analyze firms subject to SEC enforcement actions and find that these firms consistently beat analysts’ quarterly earnings forecasts in the three years prior to the manipulation period and continue to do so by smaller “beats†during the manipulation period. We find that manipulating firms beat expectations around 86 percent of the time in the twelve quarters prior to the manipulation period (versus 75 percent for control firms) and that manipulation often ends with a miss in expectations. We document that executives of manipulating firms face strong stock market and CEO pressure to perform. Prior to the manipulation period, these firms have high analyst optimism, growing institutional interest, and high market valuations, along with powerful CEOs. Further, we find that maintaining a reputation for beating expectations is more important than CEO overconfidence and is incremental to CEO equity incentives for explaining manipulation. Our results suggest that pressure to maintain a reputation for beating analysts’ expectations can encourage aggressive accounting and, ultimately, earnings manipulation.
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This paper investigates whether maintaining a reputation for consistently beating analysts’ earnings expectations can motivate executives to move from “within GAAP†earnings management to “outside of GAAP†earnings manipulation. We analyze firms subject to SEC enforcement actions and find that these firms consistently beat analysts’ quarterly earnings forecasts in the three years prior to the manipulation period and continue to do so by smaller “beats†during the manipulation period. We find that manipulating firms beat expectations around 86 percent of the time in the twelve quarters prior to the manipulation period (versus 75 percent for control firms) and that manipulation often ends with a miss in expectations. We document that executives of manipulating firms face strong stock market and CEO pressure to perform. Prior to the manipulation period, these firms have high analyst optimism, growing institutional interest, and high market valuations, along with powerful CEOs. Further, we find that maintaining a reputation for beating expectations is more important than CEO overconfidence and is incremental to CEO equity incentives for explaining manipulation. Our results suggest that pressure to maintain a reputation for beating analysts’ expectations can encourage aggressive accounting and, ultimately, earnings manipulation.
Mean-Variance Efficiency of Optimal Power and Logarithmic Utility Portfolios
arXiv
arXiv
We derive new results related to the portfolio choice problem for power and logarithmic utilities. Assuming that the portfolio returns follow an approximate log-normal distribution, the closed-form expressions of the optimal portfolio weights are obtained for both utility functions. Moreover, we prove that both optimal portfolios belong to the set of mean-variance feasible portfolios and establish necessary and sufficient conditions such that they are mean-variance efficient. Furthermore, an application to the stock market is presented and the behavior of the optimal portfolio is discussed for different values of the relative risk aversion coefficient. It turns out that the assumption of log-normality does not seem to be a strong restriction.
Measuring the Credit Risk of Synthetic CDOs with CDS-Implied Ratings
SSRN
In this paper, we demonstrate how CDS-implied ratings for corporate reference names, together with an analytic CDO ratings model, can be used to derive CDS-implied tranche ratings for corporate synthetic CDOs (CSOs). It is an experiment in which we change one key variable, the ratings of the portfolio of reference entities, while holding other data and model assumptions constant and measure how tranche ratings perform. We find that CDS-implied tranche ratings lead changes in Moody's ratings, more accurately rank order default losses by rating, and exhibit higher loss prediction accuracy ratios for the riskiest tranches.
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In this paper, we demonstrate how CDS-implied ratings for corporate reference names, together with an analytic CDO ratings model, can be used to derive CDS-implied tranche ratings for corporate synthetic CDOs (CSOs). It is an experiment in which we change one key variable, the ratings of the portfolio of reference entities, while holding other data and model assumptions constant and measure how tranche ratings perform. We find that CDS-implied tranche ratings lead changes in Moody's ratings, more accurately rank order default losses by rating, and exhibit higher loss prediction accuracy ratios for the riskiest tranches.
Neuroeconomics of Asset-Price Bubbles: Neuroimaging and Digital Technology for the Prediction and Prevention of Major Bubbles
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Asset-price bubbles challenge the explanatory and predictive power of standard economic theory, so neuroeconomic measures should be explored as potential tools for improving the predictive power of standard theory. This exploration is begun by reviewing results from functional magnetic resonance imaging (fMRI) studies of lab asset-price bubbles and herding behavior (i.e., following others' decisions). These results are consistent with a neuroeconomics-based hypothesis of asset-price bubbles. In this view, decision making during bubble or non-bubble periods of financial-market activity is driven by, respectively, evolutionarily ancient or new neurocircuitry. Neuroimaging studies that test this or other neuroeconomics-based hypotheses of asset-price bubbles may yield a bubble-related biomarker (e.g., low trade-related lateral neocortical activity associated with traders’ herding-based decisions). Wearable functional near-infrared spectroscopy (fNIRS) technology could determine the prevalence of such a biomarker among financial-market participants, thereby enabling the real-time detection of an emerging bubble. Mechanisms are described by which this early-warning signal could be exploited in self-regulatory or government-administered policies for financial-system stabilization. Digital technology may offer an even more readily achievable alternative to neuroimaging (e.g., behavioral precursors to price bubbles may be identified in analyses of investors’ interactions with online asset-trading platforms). In summary, neuroimaging- or digital technology-based financial-system regulation may be useful for distinguishing bubbles from non-bubble periods and preventing major asset-price bubbles.
SSRN
Asset-price bubbles challenge the explanatory and predictive power of standard economic theory, so neuroeconomic measures should be explored as potential tools for improving the predictive power of standard theory. This exploration is begun by reviewing results from functional magnetic resonance imaging (fMRI) studies of lab asset-price bubbles and herding behavior (i.e., following others' decisions). These results are consistent with a neuroeconomics-based hypothesis of asset-price bubbles. In this view, decision making during bubble or non-bubble periods of financial-market activity is driven by, respectively, evolutionarily ancient or new neurocircuitry. Neuroimaging studies that test this or other neuroeconomics-based hypotheses of asset-price bubbles may yield a bubble-related biomarker (e.g., low trade-related lateral neocortical activity associated with traders’ herding-based decisions). Wearable functional near-infrared spectroscopy (fNIRS) technology could determine the prevalence of such a biomarker among financial-market participants, thereby enabling the real-time detection of an emerging bubble. Mechanisms are described by which this early-warning signal could be exploited in self-regulatory or government-administered policies for financial-system stabilization. Digital technology may offer an even more readily achievable alternative to neuroimaging (e.g., behavioral precursors to price bubbles may be identified in analyses of investors’ interactions with online asset-trading platforms). In summary, neuroimaging- or digital technology-based financial-system regulation may be useful for distinguishing bubbles from non-bubble periods and preventing major asset-price bubbles.
New Essentials of Economic Theory III. Economic Applications
RePEC
This paper presents applications of our theory to description of particular economic problems. We give all definitions and equations in Part I and II of our work. Here we argue propagation of small perturbations of economic variables and transactions on economic space. We show that small perturbations may follow wave equations that have parallels to propagation of sound waves and surface waves in fluids. We underline that nature of economic waves is completely different from waves in physical fluids but parallels between them may be useful for their studies. Wave generation, propagation and interactions are the most general properties of any complex system. Descriptions of economic waves on economic space fill existing gap in economic modeling. Usage of economic space allows distribute agents by their risk ratings as coordinates. Agents on economic space cover economic domain bounded by minimum and maximum risk grades. Change of risk ratings of agents due to their economic activity, economic processes or other factors induce flows of economic variables, transactions and expectations. Borders of economic domain cause fluctuations of economic flows and mean risks and these fluctuations describe business cycles. For example fluctuations of credit flows model credit cycles, investment flows model investment cycles and etc. Further we model assets price disturbances as consequences of relations between transactions and expectations. As last economic sample we argue classical Black-Scholes-Merton option pricing model and discuss problems those arise from modeling on economic space.
RePEC
This paper presents applications of our theory to description of particular economic problems. We give all definitions and equations in Part I and II of our work. Here we argue propagation of small perturbations of economic variables and transactions on economic space. We show that small perturbations may follow wave equations that have parallels to propagation of sound waves and surface waves in fluids. We underline that nature of economic waves is completely different from waves in physical fluids but parallels between them may be useful for their studies. Wave generation, propagation and interactions are the most general properties of any complex system. Descriptions of economic waves on economic space fill existing gap in economic modeling. Usage of economic space allows distribute agents by their risk ratings as coordinates. Agents on economic space cover economic domain bounded by minimum and maximum risk grades. Change of risk ratings of agents due to their economic activity, economic processes or other factors induce flows of economic variables, transactions and expectations. Borders of economic domain cause fluctuations of economic flows and mean risks and these fluctuations describe business cycles. For example fluctuations of credit flows model credit cycles, investment flows model investment cycles and etc. Further we model assets price disturbances as consequences of relations between transactions and expectations. As last economic sample we argue classical Black-Scholes-Merton option pricing model and discuss problems those arise from modeling on economic space.
Non-Default Component of Sovereign Emerging Market Yield Spreads and Its Determinants: Evidence from Credit Default Swap Market
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In this paper, I show that a sizable component of emerging market sovereign yield spreads is due to factors other than default risk such as liquidity. I estimate the non-default component of the yield spreads as the basis between the actual credit default swap (CDS) premium and the hypothetical CDS premium implied by emerging market bond yields. On average, the basis is large and positive for speculative grade bonds and slightly negative for investment grade bonds. This large positive basis for speculative grade bonds support the existence of speculation in the CDS market when the underlying's credit quality is bad. I study the effects of bond liquidity, liquidity in the CDS market, equity market performance and macroeconomic variables on the non-default component of the emerging market yield spreads. I show that bond liquidity has a significant and positive effect on the CDS-bond basis of investment grade bonds. The results suggest that the liquid bonds of investment grade bonds are more expensive relative to the prices implied their CDS premiums. However, the results are somewhat mixed and even contrary for the speculative grade bond sample.
SSRN
In this paper, I show that a sizable component of emerging market sovereign yield spreads is due to factors other than default risk such as liquidity. I estimate the non-default component of the yield spreads as the basis between the actual credit default swap (CDS) premium and the hypothetical CDS premium implied by emerging market bond yields. On average, the basis is large and positive for speculative grade bonds and slightly negative for investment grade bonds. This large positive basis for speculative grade bonds support the existence of speculation in the CDS market when the underlying's credit quality is bad. I study the effects of bond liquidity, liquidity in the CDS market, equity market performance and macroeconomic variables on the non-default component of the emerging market yield spreads. I show that bond liquidity has a significant and positive effect on the CDS-bond basis of investment grade bonds. The results suggest that the liquid bonds of investment grade bonds are more expensive relative to the prices implied their CDS premiums. However, the results are somewhat mixed and even contrary for the speculative grade bond sample.
Portfolio Optimization in the Presence of Extreme Risks: A Pareto-Dirichlet Approach
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We construct a new mean variance kurtosis portfolio optimization (MVK) that combines the Pareto improvement method with Dirichlet simulations. We argue that skewness is not the appropriate indicator for modeling extreme risks and that kurtosis is a better choice. We show that the existing portfolio optimization methods with high order moments can misclassify inefficient portfolios as efficient, while our method detects the actual efficient set. For its implementation, we use Dirichlet simulations to approximate the feasible portfolio set. We introduce a generalized Sharpe ratio, which combines the variance and kurtosis into a single risk indicator. We show that mean variance optimization (MV) is a subcase of MVK and that MVK efficient portfolios always outperform MV efficient portfolios in terms of a generalized Sharpe ratio. We also examine the robustness of the Dirichlet efficient frontier.
SSRN
We construct a new mean variance kurtosis portfolio optimization (MVK) that combines the Pareto improvement method with Dirichlet simulations. We argue that skewness is not the appropriate indicator for modeling extreme risks and that kurtosis is a better choice. We show that the existing portfolio optimization methods with high order moments can misclassify inefficient portfolios as efficient, while our method detects the actual efficient set. For its implementation, we use Dirichlet simulations to approximate the feasible portfolio set. We introduce a generalized Sharpe ratio, which combines the variance and kurtosis into a single risk indicator. We show that mean variance optimization (MV) is a subcase of MVK and that MVK efficient portfolios always outperform MV efficient portfolios in terms of a generalized Sharpe ratio. We also examine the robustness of the Dirichlet efficient frontier.
Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning
arXiv
arXiv
At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.
Strategic Default on First and Second Lien Mortgages During the Financial Crisis
SSRN
Strategic default behavior suggests that the default process is not only a matter of inability to pay. Economic costs and benefits affect the incidence and timing of defaults. As with prior research, the authors find that people default strategically as their home value falls below the mortgage value (exercise the put option to default on their first mortgage). While some of these homeowners default on both first mortgages and second lien home equity lines, a large portion of the delinquent borrowers have kept their second lien current during the recent financial crisis. These second liens, which are current but stand behind a seriously delinquent first mortgage, are subject to a high risk of default. On the other hand, relatively few borrowers default on their second liens while remaining current on their first. This paper explores the strategic factors that may affect borrower decisions to default on first vs. second lien mortgages. The authors find that borrowers are more likely to remain current on their second lien if it is a home equity line of credit (HELOC) as compared to a closed-end home equity loan. Moreover, the size of the unused line of credit is an important factor. Interestingly, they find evidence that the various mortgage loss mitigation programs also play a role in providing incentives for homeowners to default on their first mortgages.
SSRN
Strategic default behavior suggests that the default process is not only a matter of inability to pay. Economic costs and benefits affect the incidence and timing of defaults. As with prior research, the authors find that people default strategically as their home value falls below the mortgage value (exercise the put option to default on their first mortgage). While some of these homeowners default on both first mortgages and second lien home equity lines, a large portion of the delinquent borrowers have kept their second lien current during the recent financial crisis. These second liens, which are current but stand behind a seriously delinquent first mortgage, are subject to a high risk of default. On the other hand, relatively few borrowers default on their second liens while remaining current on their first. This paper explores the strategic factors that may affect borrower decisions to default on first vs. second lien mortgages. The authors find that borrowers are more likely to remain current on their second lien if it is a home equity line of credit (HELOC) as compared to a closed-end home equity loan. Moreover, the size of the unused line of credit is an important factor. Interestingly, they find evidence that the various mortgage loss mitigation programs also play a role in providing incentives for homeowners to default on their first mortgages.
Testing Sharpe ratio: luck or skill?
arXiv
arXiv
Sharpe ratio (sometimes also referred to as information ratio) is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the (excess) net return over the strategy standard deviation. However, the elements to compute the Sharpe ratio, namely, the expected returns and the volatilities are unknown numbers and need to be estimated statistically. This means that the Sharpe ratio used by funds is likely to be error prone because of statistical estimation errors. In this paper, we provide various tests to measure the quality of the Sharpe ratios. By quality, we are aiming at measuring whether a manager was indeed lucky of skillful. The test assesses this through the statistical significance of the Sharpe ratio. We not only look at the traditional Sharpe ratio but also compute a modified Sharpe insensitive to used Capital. We provide various statistical tests that can be used to precisely quantify the fact that the Sharpe is statistically significant. We illustrate in particular the number of trades for a given Sharpe level that provides statistical significance as well as the impact of auto-correlation by providing reference tables that provides the minimum required Sharpe ratio for a given time period and correlation. We also provide for a Sharpe ratio of 0.5, 1.0, 1.5 and 2.0 the skill percentage given the auto-correlation level.
The Credit Default Swap Market's Determinants
SSRN
This paper explores the ability of variables suggested by structural models to explain variation in CDS spread changes. Using monthly changes in CDS spreads for 333 firms from January, 2001-March, 2006, I find that these variables are able to explain thirty percent of the variation in CDS spread changes. A rating-based CDS index that accounts for both credit risk and overall market conditions is the single best predictor of CDS spread changes. Leverage and volatility, however, are also key determinants, as these two variables can explain almost half of the explained variation in monthly CDS spread changes.
SSRN
This paper explores the ability of variables suggested by structural models to explain variation in CDS spread changes. Using monthly changes in CDS spreads for 333 firms from January, 2001-March, 2006, I find that these variables are able to explain thirty percent of the variation in CDS spread changes. A rating-based CDS index that accounts for both credit risk and overall market conditions is the single best predictor of CDS spread changes. Leverage and volatility, however, are also key determinants, as these two variables can explain almost half of the explained variation in monthly CDS spread changes.
The Impact of Equity Misvaluation on Predictive Accuracy of Bankruptcy Models
SSRN
This paper examines the impact of equity misvaluation on the predictive accuracy of bankruptcy models. We find that structural bankruptcy prediction models are not affected by misvaluation. However, for hazard models, forecasting accuracy for properly-valued firms is greater than for misvalued firms and model forecasting accuracy improves significantly if model coefficients vary with misvaluation. Our results show the importance of taking stock market misvaluation into account when forecasting bankruptcies using hazard models.
SSRN
This paper examines the impact of equity misvaluation on the predictive accuracy of bankruptcy models. We find that structural bankruptcy prediction models are not affected by misvaluation. However, for hazard models, forecasting accuracy for properly-valued firms is greater than for misvalued firms and model forecasting accuracy improves significantly if model coefficients vary with misvaluation. Our results show the importance of taking stock market misvaluation into account when forecasting bankruptcies using hazard models.
