Firm Reputation, Innovation and Employee Startups

2019 ◽  
Vol 130 (627) ◽  
pp. 822-851
Author(s):  
Ján Zábojník
Keyword(s):  
Stochastic Dominance ◽  
Repeated Game ◽  
Second Order ◽  
Firm Reputation ◽  
Relational Contract ◽  
Order Stochastic Dominance ◽  
Extreme Distribution ◽  
Pareto Efficient ◽  
The Creation ◽  
Second Order Stochastic Dominance

Abstract This article studies how a firm’s reputation for rewarding innovative employees affects innovation and startup creation. In any Pareto-efficient equilibrium of the repeated game, low-value innovations are developed in-house, while high-value innovations are developed in startups. When distributions of ideas are ordered by simple cases of first- or second-order stochastic dominance, the firm has a preference for an extreme distribution. The article also characterises the optimal relational contract and workers’ incentives to invest in innovation. The model’s predictions are consistent with a broad set of observed regularities regarding the creation of employee startups.

Visualizing the Variance of a Random Variable

2011 ◽  
Vol 18 (01) ◽  
pp. 71-85
Author(s):  
Fabrizio Cacciafesta
Keyword(s):  
Stochastic Dominance ◽  
Mean Absolute Error ◽  
Random Variable ◽  
Absolute Error ◽  
Second Order ◽  
Taylor Formula ◽  
Order Stochastic Dominance ◽  
The Mean ◽  
Second Order Stochastic Dominance ◽  
Options Theory

We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

scholarly journals Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Q. H. Zhai ◽  
T. Ye ◽  
M. X. Huang ◽  
S. L. Feng ◽  
H. Li
Keyword(s):  
Portfolio Optimization ◽  
Optimization Algorithm ◽  
Stochastic Dominance ◽  
Optimization Model ◽  
Second Order ◽  
Whale Optimization Algorithm ◽  
Order Stochastic Dominance ◽  
Whale Optimization ◽  
Portfolio Optimization Model ◽  
Second Order Stochastic Dominance

In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints.

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