29 citations · 78 across the 21 of their papers we have counts for
22 papers
Efficient and robust calibration of the Heston option pricing model for American options using an improved Cuckoo Search Algorithm
Stefan Haring, Ronald Hochreiter
In this paper an improved Cuckoo Search Algorithm is developed to allow for an efficient and robust calibration of the Heston option pricing model for American options. Calibration…
Computing a consensus journal meta-ranking using paired comparisons and adaptive lasso estimators
Laura Vana, Ronald Hochreiter, Kurt Hornik
In a "publish-or-perish culture", the ranking of scientific journals plays a central role in assessing performance in the current research environment. With a wide range of existin…
Computing trading strategies based on financial sentiment data using evolutionary optimization
Ronald Hochreiter
In this paper we apply evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data. The sentiment data was extracted fro…
An Evolutionary Optimization Approach to Risk Parity Portfolio Selection
Ronald Hochreiter
In this paper we present an evolutionary optimization approach to solve the risk parity portfolio selection problem. While there exist convex optimization approaches to solve this…
The Role of Emotions in Propagating Brands in Social Networks
Ronald Hochreiter, Christoph Waldhauser
A key aspect of word of mouth marketing are emotions. Emotions in texts help propagating messages in conventional advertising. In word of mouth scenarios, emotions help to engage c…
Active extension portfolio optimization with non-convex risk measures using metaheuristics
Ronald Hochreiter, Christoph Waldhauser
We consider the optimization of active extension portfolios. For this purpose, the optimization problem is rewritten as a stochastic programming model and solved using a clever mul…
Effects of Sampling Methods on Prediction Quality. The Case of Classifying Land Cover Using Decision Trees
Ronald Hochreiter, Christoph Waldhauser
Clever sampling methods can be used to improve the handling of big data and increase its usefulness. The subject of this study is remote sensing, specifically airborne laser scanni…
Modeling multi-stage decision optimization problems
Ronald Hochreiter
Multi-stage optimization under uncertainty techniques can be used to solve long-term management problems. Although many optimization modeling language extensions as well as computa…
Automated Classification of Airborne Laser Scanning Point Clouds
Christoph Waldhauser, Ronald Hochreiter, Johannes Otepka +4
Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become…
Modeling Credit Spreads Using Nonlinear Regression
Radoslava Mirkov, Thomas Maul, Ronald Hochreiter +1
The term structure of credit spreads is studied with an aim to predict its future movements. A completely new approach to tackle this problem is presented, which utilizes nonlinear…
Zombie Politics: Evolutionary Algorithms to Counteract the Spread of Negative Opinions
Ronald Hochreiter, Christoph Waldhauser
This paper is about simulating the spread of opinions in a society and about finding ways to counteract that spread. To abstract away from potentially emotionally laden opinions, w…
A Genetic Algorithm to Optimize a Tweet for Retweetability
Ronald Hochreiter, Christoph Waldhauser
Twitter is a popular microblogging platform. When users send out messages, other users have the ability to forward these messages to their own subgraph. Most research focuses on in…
An Evolutionary Approach towards Clustering Airborne Laser Scanning Data
Ronald Hochreiter, Christoph Waldhauser
In land surveying, the generation of maps was greatly simplified with the introduction of orthophotos and at a later stage with airborne LiDAR laser scanning systems. While the ori…
Revolutionary Algorithms
Ronald Hochreiter, Christoph Waldhauser
The optimization of dynamic problems is both widespread and difficult. When conducting dynamic optimization, a balance between reinitialization and computational expense has to be…
Evolutionary Optimization for Decision Making under Uncertainty
Ronald Hochreiter
Optimizing decision problems under uncertainty can be done using a variety of solution methods. Soft computing and heuristic approaches tend to be powerful for solving such problem…
An algorithm for calculating steady state probabilities of queueing systems
Stefan Hochrainer, Ronald Hochreiter, Georg Pflug
This paper presents a method for calculating steady state probabilities of queueing systems. The infinitesimal generator matrix is used to define all possible states in…
Evolving Accuracy: A Genetic Algorithm to Improve Election Night Forecasts
Ronald Hochreiter, Christoph Waldhauser
In this paper, we apply genetic algorithms to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market res…
Data Mining Cultural Aspects of Social Media Marketing
Ronald Hochreiter, Christoph Waldhauser
For marketing to function in a globalized world it must respect a diverse set of local cultures. With marketing efforts extending to social media platforms, the crossing of cultura…
A note on evolutionary stochastic portfolio optimization and probabilistic constraints
Ronald Hochreiter
In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment…
Evolutionary multi-stage financial scenario tree generation
Ronald Hochreiter
Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns…
Evolutionary estimation of a Coupled Markov Chain credit risk model
Ronald Hochreiter, David Wozabal
There exists a range of different models for estimating and simulating credit risk transitions to optimally manage credit risk portfolios and products. In this chapter we present a…
A Coupled Markov Chain Approach to Credit Risk Modeling
David Wozabal, Ronald Hochreiter
We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating tra…