When Risk and Return are Not Enough: The Role of Loss Aversion in Private Investors' Choice of Mutual Fund Fee Structures

We analyze why investors chose funds with performance fees even if expected fees are higher than in a fund with a pure management fee. Performance fees are meant to influence performance positively but they can also lead to a higher fund risk. The expected higher performance cannot fully account for the height of the performance fees chosen in our survey study. Controlling for various other explanations, we find that loss aversion is a main driver for the propensity to chose a performance fee fund

Investors care about risk, but can't cope with volatility

Following the classical portfolio theory all an investor has to do for an optimal investment is to determine his risk attitude. This allows him to find his point on the capital market line by combining a risk-free asset with the market portfolio. We investigate the following research questions in an experimental set-up: Do private investors see a relationship between risk attitude and the amount invested risky at all and do they adjust their investments if provided with different risk levels of the risky asset? To answer these questions we ask subjects in a between subject design to allocate a certain amount between a risky and a risk-free asset. Risky assets differ between conditions, but can be transformed into each other by combining them with the risk-free asset. We find that mainly investors risk attitude, but also their risk perception, and the investment horizon are strong predictors for risk taking. Indeed, investors do not appear to be naïve, but they do something sensitive. Nevertheless, we observe a strong framing effect: investors choose almost the same allocation to the risky asset independently of changes in its risk-return profile thus ending up with significantly different volatilities. Feedback does not mitigate the framing effect. The effect is somewhat smaller for investors with a high financial literacy. Overall, people seem to use two mental accounts, one for the risk-free and one for the risky investment with the risk attitude determining the percentage allocation to the risky asset and not the chosen portfolio volatility

Improving selectivity in catalytic hydrodefluorination by limiting SNV reactivity

Catalytic hydrodefluorination of perfluoroallylbenzene with Cp2TiH in THF is unselective and yields a variety of previously unknown compounds, predominantly activated in the allylic position. Several different mechanisms have been examined in detail using solvent corrected (THF) DFT(M06-2X) calculations for the archetypal perfluorinated olefin perfluoropropene and perfluoroallylbenzene: (a) single electron transfer, (b) hydrometallation/fluoride elimination, (c) σ-bond metathesis (allylic or vinylic), and (d) nucleophilic vinylic substitution (SNV, w/o Ti–F contacts in the TS). SNV is shown to be a competitive mechanism to hydrometallation and proceeds via ionic species from which F-elimination is facile and unselective leading to low selectivity in polar solvents. Subsequent experiments show that selectivity can be increased in a non-polar solvent

Quantum-Assisted Solution Paths for the Capacitated Vehicle Routing Problem

Many relevant problems in industrial settings result in NP-hard optimization problems, such as the Capacitated Vehicle Routing Problem (CVRP) or its reduced variant, the Travelling Salesperson Problem (TSP). Even with today's most powerful classical algorithms, the CVRP is challenging to solve classically. Quantum computing may offer a way to improve the time to solution, although the question remains open as to whether Noisy Intermediate-Scale Quantum (NISQ) devices can achieve a practical advantage compared to classical heuristics. The most prominent algorithms proposed to solve combinatorial optimization problems in the NISQ era are the Quantum Approximate Optimization Algorithm (QAOA) and the more general Variational Quantum Eigensolver (VQE). However, implementing them in a way that reliably provides high-quality solutions is challenging, even for toy examples. In this work, we discuss decomposition and formulation aspects of the CVRP and propose an application-driven way to measure solution quality. Considering current hardware constraints, we reduce the CVRP to a clustering phase and a set of TSPs. For the TSP, we extensively test both QAOA and VQE and investigate the influence of various hyperparameters, such as the classical optimizer choice and strength of constraint penalization. Results of QAOA are generally of limited quality because the algorithm does not reach the energy threshold for feasible TSP solutions, even when considering various extensions such as recursive, warm-start and constraint-preserving mixer QAOA. On the other hand, the VQE reaches the energy threshold and shows a better performance. Our work outlines the obstacles to quantum-assisted solutions for real-world optimization problems and proposes perspectives on how to overcome them.Comment: Submitted to the IEEE for possible publicatio