Odd lot trading and earnings announcements

This study investigates odd lot trading, both trades and orders, around quarterly earnings announcements to determine whether odd lot traders are informed regarding the information contained in earnings announcements. We find pre-announcement odd lot order imbalances are not positively correlated with post-announcement returns and odd lot traders do not earn excess returns. Portfolios long stocks highly bought by odd lot traders in the pre-announcement period and short stocks highly sold by odd lot traders do not outperform the market. We conclude that odd lot traders are not in possession of earnings announcement information prior to its release to the public.     

Using real options theory to explain patterns in the valuation of research and development expenditures

Real options theory posits that the value of the firm is a combination of the value generated by the assets in place and the value of the option to invest in the future. It is based on the idea that many decisions are difficult to reverse, and valuing the outcome of these decisions is more complicated than estimating the present value of future cash flows. R&D activities often generate real options due to the nature of these activities, and examining the valuation of R&D expenditures through the lens of real options theory can help explain differing results documented in both the R&D and value relevance of earnings and book value literatures. Numerous studies have documented that the stock market positively values R&D expenditures; however, recent work has raised questions about whether this positive relation occurs across firms reporting both profits and losses. Consistent with real options theory, I find that the negative coefficient on the R&D expenditures of profitable firms documented by prior studies only exists for low growth firms. In addition, for all R&D firms experiencing high sales growth, the market places a lower value on assets in place and a higher value on R&D expenditures.     

Does corporate diversification reduce value in high technology firms?

We find that firm value is reduced via industrial diversification and this reduction in value depends upon a firm’s technology intensity. We consider that asymmetric information problems are more severe in technology intensive industries and find that high tech industry firms present distinctly larger value reduction when compared to low tech industry firms. The negative valuation effect is greater for firms that have a relatively larger amount of intangible assets and greater R&D capital. We determine that our findings are robust to different estimation methods and alternative excess value measures.     

Behavioral portfolio selection and optimization: an application to international stocks

The behavioral approach of decision making has emerged as a diversified solution in the presence of risk and uncertainty. Using the popular cumulative prospect theory as an objective function for portfolio selection, this study implements the classical mean–variance model to compare the portfolio performance of high behavioral stocks with that of stocks with lower behavioral values. Based on a sample of 37 international stocks over the period from October 1998 to November 2017, empirical results from D-vine pair copula GARCH-GEV indicate that the portfolio of high behavioral prospect stocks outperforms the portfolio of stocks with low behavioral scores. This finding may suggest that portfolios with high behavioral values coincide with rational efficiency sets.     

Risk measurement distortion: an improved model of return smoothing

We examine the effects of smoothed hedge fund returns on standard deviation, skewness, and kurtosis of return and on correlation of returns using a MA(2)-GARCH(1,1)-skewed-t representation instead of the traditional MA(2) model employed in the literature. We present evidence that our proposed representation is more consistent with the behavior of hedge fund returns than the traditional MA(2) representation and that the traditional method tends to overstate the degree of smoothing observed in hedge fund returns. We examine methods for correcting the distortive effects of smoothing using our representation.     

Mean–variance and mean–semivariance portfolio selection: a multivariate nonparametric approach

While univariate nonparametric estimation methods have been developed for estimating returns in mean-downside risk portfolio optimization, the problem of handling possible cross-correlations in a vector of asset returns has not been addressed in portfolio selection. We present a novel multivariate nonparametric portfolio optimization procedure using kernel-based estimators of the conditional mean and the conditional median. The method accounts for the covariance structure information from the full set of returns. We also provide two computational algorithms to implement the estimators. Via the analysis of 24 French stock market returns, we evaluate the in-sample and out-of-sample performance of both portfolio selection algorithms against optimal portfolios selected by classical and univariate nonparametric methods for three highly different time periods and different levels of expected return. By allowing for cross-correlations among returns, our results suggest that the proposed multivariate nonparametric method is a useful extension of standard univariate nonparametric portfolio selection approaches.     

Are financial constraints of corporate activist investors perceived negatively?

This paper shows that financial constraints of corporate activist investors are negatively perceived by the market. By conducting an event study on a sample of 561 Schedule 13(D) filings disclosed by US corporations in the years 1996–2016, abnormal share price reactions in the [?10, \(+\)3] event window are about 10.8% lower for targets of financially constrained corporate investors. The average abnormal return for all targets is equal to 13.4%. This positive market response suggests that activism results in actual value improvement for the target. Yet, our analyses show that value improvements crucially depend on the investor’s access to external financing.     

Aufdeckung von Versicherungsbetrug bei Kfz-Schäden mit Hilfe des Benford-Tests

The German Insurance Association estimates a yearly amount of damage of € 1.5?bn to German motor vehicle insurance companies because of systematic fraud by insurance holders. It is supposed that about 10% of submitted claim applications contain manipulated data, therefore insurance companies are forced to complete a detailed and cost intensive case-by-case review of each single application. An alternative method to detect fraud in empiric data is the method of digital analysis based on Benford’s law. The Benford method uses a mathematical law of specific logarithmic distribution attributes of first digits. According to this approach, the data of a Benford set confirm with the expected digit distribution, if the data is not manipulated, whereas fraudulent interventions lead to a deviation from Benford’s law. Hence, until now there has not been any investigation whether the Benford method can also be applied on insurance data. The present article analyses a dataset consisting of more than 120,000 damage claim applications to answer this question as well as to identify the impact of specific characteristics on the probability of fraud contained in claim applications, such as the repair of the vehicle in a franchised or an independent workshop, the vehicle brand or the examination by insurance companies experts. Indeed it could be shown that Benford’s Law is only applicable on second digits of insurance data, but delivers very strong results here: All results of the considered characteristics could be verified by plausible arguments. For this reason insurance companies can benefit from making use of the Benford method to identify those claim applications with a high probability of fraud, which should then be reviewed in more detail so that resources can be allocated in a much more cost efficient way.     

Der neue Standard zur bilanziellen Abbildung von Versicherungsverträgen (IFRS 17) – Eine kritische Analyse der Auswirkungen auf die Versicherungsbranche

The new standard for the accounting of insurance contracts (IFRS 17) will entail substantial changes for the insurance industry. In the following article the new standard is critically analyzed. First and foremost the coming valuation model, the so-called building block approach, is presented which will be the basis for all insurance contracts within the scope of IFRS 17. (For certain insurance contracts, especially those with direct participation features, or for less complex or short-term insurance contracts, there are some modifications.) To be more precise, IFRS 17 introduces an enterprise-specific valuation approach that is grounded on the so-called fulfilment value. This fulfilment value is determined by four separate building blocks (fulfilment-cashflow, discount rate, risk margin and contractual service margin), which will be addressed in detail. Finally, major changes in performed accounting practices that insurance enterprises are confronted with and will have to adapt to in their financial statements and accounts are pointed out.     

Katastrophenanleihen als Risikotransfer-Instrument für Industrieunternehmen

All over the world an increase in natural catastrophes and resulting damages can be observed for entire economies as well as for individual industrial enterprises. This trend leads to extraordinary expensive traditional natural catastrophe insurance or even a lack of insurance capacities. Due to the increased threat resulting from natural catastrophes and the inefficiencies of traditional insurance solutions, it will be analyzed in the following whether catastrophe bonds (cat bonds), as an alternative risk transfer instrument, should play a role in the risk management portfolio of industrial companies. It will be discussed whether catastrophe bonds are possible risk transfer instruments for industrial companies and whether certain basic prerequisites for a confirmation of the suitability are recognizable. In a further step, the design possibilities for cat bonds will be presented and the most suitable design alternatives for industrial companies as issuers of cat bonds will be elaborated. In addition, a critical comparison between the usage of traditional insurance solutions and cat bonds by industrial companies in order to protect themselves against natural catastrophes will be conducted. The result is a first orientation and a general guideline for dealing with catastrophe bonds as part of the operational risk management for industrial companies.