Online risk‑based portfolio allocation on subsets of crypto assets applying a prototype‑based clustering algorithm

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

Investor＇s Portfolio Allocation by Financial Asset Classes Using Fuzzy Logic-Based Approach in Decision Support System

The paper present the fuzzy logic expert system called MADSYS for an investor＇s portfolio allocation by financial asset classes. MADSYS system will be used in the interface agent （agents） of multi-agent investment management information system. One of the principal tasks of the multi-agent system is to help an investor to make investment decisions and to provide appropriate investment proposals according to the investor＇s profile. From MADSYS depends a lot of things, namely the multi-agent investment management information system accuracy, proposed investment decisions, the right portfolio allocation of financial assets, reliability and investor satisfaction. The usage of MADSYS system in the multi-agent system makes it more intellectual, i.e. the system will be able to adjust automatically to the changing of investor profile. The MADSYS system may be tried online at the following address：www.sprendimutechnologij os.lt/webapp.

Some Financial Problems in the Light of EMM Results:Asset Pricing and Efficient Portfolio Allocation

Discussing results in asset pricing and efficient portfolio allocation,we show that mixed success and errors in these results often follow from a lack of information about the asset return distribution and wrong assumptions about its properties.Some mistakes in asset pricing come from the assumption of symmetry in return distributions.Some errors in efficient portfolio allocation follow from Markowitz’s approach when applying it to portfolio optimization of skewed asset returns.The Extended Merton model(EMM),generating skewed return distributions,demonstrates that(i)in skewed asset returns,the variance is not an adequate measure of risks and(ii)positive skewness in the asset returns comes together with a high default probability.Thus,the maximization of the mean portfolio returns and skewness with controlled variance used in mainstream papers can critically increase portfolio risks.We present the new settings of the optimal portfolio allocation problem leading to less risky efficient portfolios than the solutions suggested in all previous papers.

Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation:A Semiparametric Reproducing Kernel Hilbert Space Approach

The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structure has often been taken into account. This paper proposes a dynamic factor structure whose factor loadings are generated in reproducing kernel Hilbert space(RKHS), to capture the dynamic feature of the covariance matrix. A simulation study is carried out to demonstrate its performance. Four different conditional variance models are considered for checking the robustness of our method and solving the conditional heteroscedasticity in the empirical study. By exploring the performance among eight introduced model candidates and the market baseline, the empirical study from 2001 to 2017 shows that portfolio allocation based on this dynamic factor structure can significantly reduce the variance, i.e., the risk, of portfolio and thus outperform the market baseline and the ones based on the traditional factor model.

Asset allocation： Evidence From China

As a key part of a corporate＇s operation, Asset allocation is critical to its survival and development This paper uses Markowitz financial security portfolio theory on corporate＇s asset allocation, to derive the optimal asset allocation for an corporate in China through case study.

Efficient online portfolio simulation using dynamic moving average model and benchmark index

Online portfolio selection and simulation are some of the most important problems in several research communities,including finance,engineering,statistics,artificial intelligence,machine learning,etc.The primary aim of online portfolio selection is to determine portfolio weights in every investment period(i.e.,daily,weekly,monthly,etc.)to maximize the investor’s final wealth after the end of investment period(e.g.,1 year or longer).In this paper,we present an efficient online portfolio selection strategy that makes use of market indices and benchmark indices to take advantage of the mean reversal phenomena at minimal risks.Based on empirical studies conducted on recent historical datasets for the period 2000 to 2015 on four different stock markets(i.e.,NYSE,S&P500,DJIA,and TSX),the proposed strategy has been shown to outperform both Anticor and OLMAR—the two most prominent portfolio selection strategies in contemporary literature.