scholarly journals Trading Cryptocurrencies Using Second Order Stochastic Dominance

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2861
Author(s):  
Gil Cohen
Keyword(s):  
Stochastic Dominance ◽  
Second Order ◽  
Trading System ◽  
Simple Strategy ◽  
Order Stochastic Dominance ◽  
Daily Data ◽  
Net Profit ◽  
Price Trends ◽  
Second Order Stochastic Dominance

This research is the first attempt to customize a trading system that is based on second order stochastic dominance (SSD) to five known cryptocurrencies’ daily data: Bitcoin, Ethereum, XRP, Binance Coin, and Cardano. Results show that our system can predict price trends of cryptocurrencies, trade them profitably, and in most cases outperform the buy and hold (B&H) simple strategy. Our system’s best performance was achieved trading XRP, Binance Coin, Ethereum, and Bitcoin. Although our system has also generated a positive net profit (NP) for Cardano, it failed to outperform the B&H strategy. For all currencies, the system better predicted long trends than short trends.

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.

Sign in / Sign up

Export Citation Format

Share Document