Contributions published at Financial Economics (Thorsten Hens)

This paper studies how the investors' attitude towards earnings surprises affects the managers' incentives to manipulate earnings in an intertemporal context, where the consensus forecast of the analysts is not exogenously given but determined by the strategic interaction between the analysts and the managers. Our analysis shows that given the asymmetric investors' reaction to earnings surprises, managers have strong incentives to manipulate earnings. In dependence on their time preferences, managers may choose to manipulate the earnings in order to match the consensus forecasts. In this equilibrium, rational investors are systematically fooled. Assuming that managers' preferences are equally distributed in the economy, we also derive conclusions on how the absolute level of manipulation in the economy changes with the investors' preferences, the managers' compensation package and the earnings guidance they may provide to analysts.

Empirical investigations of analysts forecast surveys concerning earnings realizations find significant time varying biases usually attributed to the analysts liability to cognitive limitations. For example, a positive autocorrelation of analysts forecast errors is commonly explained by analysts underreaction. In this paper we develop a random dynamical system describing the evolution of analysts forecasts and firms prices and show that managerial guidance is capable to explain such inefficiencies in the analysts forecasting behavior. This result is well supported by empirical tests. In particular, we find that the managers of growth firms guide stronger than the managers of value firms, which allows further conclusions on the precision and efficiency of earnings forecasts released for value and growth stocks in line with the literature.

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This paper studies a centralized market with idiosyncratic uncertainty and money as a medium of exchange from a theoretical as well as an experimental perspective. In our model, prices are fixed and markets are cleared by rationing. We prove the existence of stationary monetary equilibria and of an optimum quantity of money. The rational solution of our model, which is based on the assumption of individual rationality and rational expectations, is compared with actual behavior in a laboratory experiment. The theoretical results are strongly supported by this experiment.

Managers' information disclosure to firm's outsiders plays an essential role for mitigating information asymmetry and agency problems. The main objective of this thesis is to analyze the managers' reporting incentives in a broader context, while considering the preferences of behavioural investors and the active role of financial analysts as target setters in particular. Further, this thesis aims to study the optimal disclosure policy of different firms in the form of guidance and to analyze its influence on the efficiency of analysts’ earnings forecasts. Overall, this thesis contributes to the broad research on behavioural corporate finance studying the determinants and consequences of managers' decisions when managers and (or) investors suffer cognitive biases and (or) have behavioural preferences. The analysis focuses on the investors' preferences as described in the prospect theory of Kahneman and Tversky (1979) and neglects any cognitive biases that might lead to irrational decisions. The contribution of this thesis is threefold. First, this thesis contributes to the empirical literature on the relevance of thresholds by showing that reported performance, particularly around the zero target, influences the market value of a firm and in particular the investors' perception of the value generated by intangibles such as R&D investments. Second, the thesis extends the theoretical literature on earnings manipulation by analyzing the managers' reporting incentives in an inter-temporal strategic game with the analysts where the managers' payoff is determined by investors using the analysts' consensus forecast as a target when evaluating earnings reports. Finally, instead of adapting the view that agents suffer some cognitive limitations, this thesis contributes to the literature that seeks economic explanations for the analysts' underreaction by showing empirically that managerial guidance is capable to explain such inefficiencies in the analysts' forecasting behavior.

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The prospect theory of Kahneman and Tversky (in Econometrica 47(2), 263–291, 1979) and the cumulative prospect theory of Tversky and Kahneman (in J. Risk uncertainty 5, 297–323, 1992) are descriptive models for decision making that summarize several violations of the expected utility theory. This paper gives a survey of applications of prospect theory to the portfolio choice problem and the implications for asset pricing. We demonstrate that prospect theory (and similarly cumulative prospect theory) has to be re-modelled if one wants to apply it to portfolio selection. We suggest replacing the piecewise power value function of Tversky and Kahneman (in J. Risk uncertainty 5, 297–323, 1992) with a piecewise negative exponential value function. This latter functional form is still compatible with laboratory experiments but it has the following advantages over and above Tversky and Kahneman’s piecewise power function: 1. The Bernoulli Paradox does not arise for lotteries with finite expected value. 2. No infinite leverage/robustness problem arises. 3. CAPM-equilibria with heterogeneous investors and prospect utility do exist. 4. It is able to simultaneously resolve the following asset pricing puzzles: the equity premium, the value and the size puzzle. In contrast to the piecewise power value function it is able to explain the disposition effect. Resolving these problems of prospect theory we show how it can be combined with mean–variance portfolio theory.

This paper applies a non-parametric modular neural network (MNN) model to price the S&P500 European call options. The modules are defined based on time to maturity and moneyness of the options. The option price function of interest is homogenous of degree one with respect to the underlying index price and the strike price. We find that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint), relative to the Black-Scholes model. This improvement is found to be an increasing function of the number of modules used in an MNN.

In this paper we suggest a behavioral foundation for the reward-risk approach to portfolio selection based on prospect theory. We identify sufficient conditions for two-fund separation in reward-risk models in general, and for the behavioral reward-risk model in particular. It is shown that a prospect theory investor with piecewise-power function satisfies two-fund separation if the reference point is the risk-free rate, while two-fund separation fails if the reference point is higher than the risk-free rate. We derive a multiple-account version of the behavioral reward-risk model and we perform an empirical analysis on U.S. data to show that this model explains the asset allocation puzzle.

This paper provides new empirical evidence for intraday scaling behavior of stock market returns utilizing a 5 min stock market index (the Dow Jones Industrial Average) from the New York Stock Exchange. It is shown that the return series has a multifractal nature during the day. In addition, we show that after a financial “earthquake”, aftershocks in the market follow a power law, analogous to Omori's law. Our findings indicate that the moments of the return distribution scale nonlinearly across time scales and accordingly, volatility scaling is nonlinear under such a data generating mechanism.

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How do consumers choose from a menu of contracts? We analyze a novel dataset from three U.S. health clubs with information on both the contractual choice and the day-to-day attendance decisions of 7,752 members over three years. The observed consumer behavior is difficult to reconcile with standard preferences and beliefs. First, members who choose a contract with a flat monthly fee of over \$70 attend on average 4.3 times per month. They pay a price per expected visit of more than \$17, even though they could pay \$10 per visit using a 10-visit pass. On average, these users forgo savings of \$600 during their membership. Second, consumers who choose a monthly contract are 17 percent more likely to stay enrolled beyond one year than users committing for a year. This is surprising because monthly members pay higher fees for the option to cancel each month. We also document cancellation delays and attendance expectations, among other findings. Leading explanations for our findings are overconfidence about future self-control or about future efficiency. Overconfident agents overestimate attendance as well as the cancellation probability of automatically renewed contracts. Our results suggest that making inferences from observed contract choice under the rational expectation hypothesis can lead to biases in the estimation of consumer preferences.

An algorithm for estimating Lyapunov exponents of an unknown dynamical system is designed. The algorithm estimates not only the largest but all Lyapunov exponents of the unknown system. The estimation is carried out by a multivariate feedforward network estimation technique. We focus our attention on deterministic as well as noisy system estimation. The performance of the algorithm is very satisfactory in the presence of noise as well as with limited number of observations.

This paper develops a wavelet (spectral) approach to testing the presence of a unit root in a stochastic process. The wavelet approach is appealing, since it is based directly on the different behavior of the spectra of a unit root process and that of a short memory stationary process. By decomposing the variance (energy) of the underlying process into the variance (energy) of its low frequency components and that of its high frequency components via the discrete wavelet transformation (DWT), we design unit root tests which have substantial power against near unit root alternatives. Since DWT is an energy preserving transformation and able to disbalance energy across high and low frequency components of a series, it is possible to isolate the most persistent component of a series in a small number of scaling coefficients. Our tests utilize the wavelet coefficients of the coarsest scale. We generalize our unit root tests to residual based tests for cointegration and to the maximum overlap DWT (MODWT), demonstrate their size and power properties through Monte Carlo simulations, and apply them to financial time series.

We develop a dynamic framework to identify aggregate market fears ahead of a major market crash through the skewness premium of European options. Our methodology is based on measuring the distribution of a skewness premium through a q-Gaussian density and a maximum entropy principle. Our findings indicate that the October 19th, 1987 crash was predictable from the study of the skewness premium of deepest out-of-the-money options about two months prior to the crash.

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