Contributions published at Financial Economics (Thorsten Hens)

Security analysts tend to bias stock recommendations upward, particularly if they are affiliated with the underwriter. We analyze how investors account for such distortions. Using the NYSE Trades and Quotations database, we find that large traders adjust their trading response downward. While they exert buy pressure following strong buy recommendations, they display no reaction to buy recommendations and selling pressure following hold recommendations. This “discounting” is even more pronounced when the analyst is affiliated with the underwriter. Small traders, instead, follow recommendations literally. They exert positive pressure following both buy and strong buy recommendations and zero pressure following hold recommendations. We discuss possible explanations for the differences in trading response, including information costs and investor naiveté.

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The wealth dynamics of insurance companies strongly depends on the success of their investment strategies, but also on liquidity shocks which occur during unfavorable years, when indemnities to be paid to the clients exceed collected premia. An investment strategy that does not take liquidity shocks into account, exposes insurance companies to the risk of bankruptcy. This paper analyzes the behavior of insurance companies in an evolutionary framework. We show that an insurance company that merely satisfies regulatory constraints will eventually vanish from the market. We give a more restrictive no-bankruptcy condition for the investment strategies and we characterize trading strategies that are evolutionary stable, i.e., able to drive out any mutation. We study the existence of such strategies and the conditions under which financial and insurance markets are stable.

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The authors investigate the effects of macroeconomic announcements on the realized correlation between bond and stock returns. It was found that it is not so much the surprise component of the announcement, but the mere fact that an announcement occurs that influences the realized bond—stock correlation. The impact of macroeconomic announcements varies across the business cycle. Announcement effects are highly dependent on the sign of the realized bond—stock correlation, which has recently gone from positive to negative. Macroeconomic announcement effects on realized bond and stock volatilities are also investigated. Our results are robust across 8:30 A.M. and 10:00 A.M. announcements.

We apply the recurrent reinforcement learning method of Moody, Wu, Liao, and Saffell (1998) in the context of the strategic asset allocation computed for sample data from US, UK, Germany, and Japan. It is found that the optimal asset allocation deviates substantially from the fixed-mix rule. The investor actively times the market and he is able to outperform it consistently over the almost two decades we analyze.

We develop an algorithm to compute asset allocations for Kahneman and Tversky’s (Econometrica, 47(2), 263–291, 1979) prospect theory. An application to benchmark data as in Fama and French (Journal of Financial Economics, 47(2), 427–465, 1992) shows that the equity premium puzzle is resolved for parameter values similar to those found in the laboratory experiments of Kahneman and Tversky (Econometrica, 47(2), 263–291, 1979). While previous studies like Benartzi and Thaler (The Quarterly Journal of Economics, 110(1), 73–92, 1995), Barberis, Huang and Santos (The Quarterly Journal of Economics, 116(1), 1–53, 2001), and Grüne and Semmler (Asset prices and loss aversion, Germany, Mimeo Bielefeld University, 2005) focussed on dynamic aspects of asset pricing but only used loss aversion to explain the equity premium puzzle our paper explains the unconditional moments of asset pricing by a static two-period optimization problem. However, we incorporate asymmetric risk aversion. Our approach allows reducing the degree of loss aversion from 2.353 to 2.25, which is the value found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992) while increasing the risk aversion from 1 to 0.894, which is a slightly higher value than the 0.88 found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992). The equivalence of these parameter settings is robust to incorporating the size and the value portfolios of Fama and French (Journal of Finance, 47(2), 427–465, 1992). However, the optimal prospect theory portfolios found on this larger set of assets differ drastically from the optimal mean-variance portfolio.

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In empirical modeling, there have been two strands for pricing in the options literature, namely the parametric and nonparametric models. Often, the support for the nonparametric methods is based on a benchmark such as the Black-Scholes (BS) model with constant volatility. In this paper, we study the stochastic volatility (SV) and stochastic volatility random jump (SVJ) models as parametric benchmarks against feedforward neural network (FNN) models, a class of neural network models. Our choice for FNN models is due to their well-studied universal approximation properties of an unknown function and its partial derivatives. Since the partial derivatives of an option pricing formula are risk pricing tools, an accurate estimation of the unknown option pricing function is essential for pricing and hedging. Our findings indicate that FNN models offer themselves as robust option pricing tools, over their sophisticated parametric counterparts in predictive settings. There are two routes to explain the superiority of FNN models over the parametric models in forecast settings. These are nonnormality of return distributions and adaptive learning.

Trading pension claims would serve many purposes. Beneficiaries would be able to diversify the idiosyncratic credit risk of their plan sponsors. And systematic risk could be reallocated to comply with individual risk-return preferences. The result would be an alignment of companies' and pension fund managers' incentives to keep fund plans fully funded - in line with beneficiary interests - which would lower agency costs and costs of government bailouts of defined-benefit plans and would improve the general welfare. As an accurate valuation of pension liabilities, trading would provide a measurable yardstick for plan managers.

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This study develops a framework for dealing with stochastic reference points and endogenously selecting the reference point in reference-dependent choice theories that accounts for the joint probability distribution of the prospects and the reference point. Without accounting for the dependence structure, the endogenous reference point can deviate from the decision-maker’s optimum. Accounting for dependence, reference dependence affects choice behavior only if the reference point is (in part or in whole) exogenously fixed. In an application to well-known US investment benchmark data, investors invest in riskless T-bills rather than stocks if we ignore the dependence structure, while investing in small value stocks is optimal when we account for dependence.

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We propose an affine term structure model which accommodates nonlinearities in the drift and volatility function of the short-term interest rate. Such nonlinearities are a consequence of discrete beta-distributed regime shifts constructed on multiple thresholds. We derive iterative closed-form formula for the whole yield curve dynamics that can be estimated using a linearized Kalman filter. Fitting the model on US data, we collect empirical evidence of its potential in estimating conditional volatility and correlation across yields.