scholarly journals Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms

2020 ◽  
Vol 12 (3) ◽  
pp. 849 ◽  
Author(s):  
Wonbin Ahn ◽  
Hee Soo Lee ◽  
Hosun Ryou ◽  
Kyong Joo Oh
Keyword(s):  
Genetic Algorithms ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Financial Time Series ◽  
Threshold Value ◽  
Exchange Traded Funds ◽  
Instability Index ◽  
Allocation Model ◽  
Risk Parity ◽  
One Year

There has been a growing demand for portfolio management using robo-advisors, and hence, research on the automation of portfolio composition has been increasing. In this study, we propose a model that automates the portfolio structure by using the instability index of the financial time series and genetic algorithms (GAs). We use the instability index to filter the investment assets and optimize the threshold value used as a filtering criterion by applying a GA. For an empirical analysis, we use stocks, bonds, commodities exchange traded funds (ETFs), and exchange rate. We compare the performance of our model with that of risk parity and mean-variance models and find our model has better performance. Several additional experiments with our model using various internal parameters are conducted, and the proposed model with a one-month test period after one year of learning is found to provide the highest Sharpe ratio.

The Black-Litterman Model for Portfolio Optimization on Vietnam Stock Market

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 99-111
Author(s):  
Bao Quoc Ta ◽  
Thao Vuong
Keyword(s):  
Stock Market ◽  
Portfolio Optimization ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Market Equilibrium ◽  
Management Model ◽  
Optimal Portfolios ◽  
Allocation Model ◽  
Allocation Process ◽  
Allocation Decisions

The Black-Litterman asset allocation model is an extended portfolio management model to construct optimal portfolios by combining the market equilibrium with investor views into asset allocation decisions. In this paper we apply Black-Litterman model for portfolio optimization on Vietnames stock market. We chose ARIMA methodology utilized in financial econonometrics to predict the views of investor which are used as inputs of the Black-Litterman asset allocation process to find optimal portfolio and weights.

scholarly journals Factor investing and asset allocation strategies: a comparison of factor versus sector optimization

2021 ◽  
Author(s):  
Wolfgang Bessler ◽  
Georgi Taushanov ◽  
Dominik Wolff
Keyword(s):  
Asset Allocation ◽  
Factor Versus ◽  
Minimum Variance ◽  
Superior Performance ◽  
Relative Advantage ◽  
Allocation Model ◽  
Risk Parity ◽  
And Performance ◽  
Mean Variance ◽  
Allocation Strategies

AbstractGiven the tremendous growth of factor allocation strategies in active and passive fund management, we investigate whether factor or sector asset allocation strategies provide investors with a superior performance. Our focus is on comparing factor versus sector allocations as some recent empirical evidence indicates the dominance of sector over country portfolios. We analyze the performance and performance differences of sector and factor portfolios for various weighting and portfolio optimization approaches, including “equal-weighting” (1/N), “risk parity,” minimum-variance, mean-variance, Bayes–Stein and Black–Litterman. We employ a sample-based approach in which the sample moments are the input parameters for the allocation model. For the period from May 2007 to November 2020, our results clearly reveal that, over longer investment horizons, factor portfolios provide relative superior performances. For shorter periods, however, we observe time-varying and alternating performance dominances as the relative advantage of one over the other strategy depends on the economic cycle. One important insight is that during “normal” times factor portfolios clearly dominate sector portfolios, whereas during crisis periods sector portfolios are superior offering better diversification opportunities.

scholarly journals Make the Best from Comparing Conventional and Islamic Asset Classes: A Design of an All-Seasons Combined Portfolio

2021 ◽  
Vol 14 (10) ◽  
pp. 484
Author(s):  
Andrea Delle Foglie ◽  
Gianni Pola
Keyword(s):  
Portfolio Management ◽  
Asset Allocation ◽  
Strategic Asset Allocation ◽  
Risk Parity ◽  
Asset Classes ◽  
Asset Allocations

This paper aims to contribute to the existing literature in portfolio management and strategy by investigating the performance, diversification, and hedging benefits arising from integrating Sharia-compliant stocks into a conventional portfolio. Thus, this paper tests the performance of a Combined Portfolio, resulting from the combination of conventional Islamic instruments, covering different macroeconomic scenarios in the last decade (2010–2020). The strategic asset allocation was designed following the Global Macro Anima (GMA) strategy, solving a risk-parity optimisation problem using a specifically developed MATLAB™ algorithm. The findings will contribute to answering the question related to the possibility of including alternative instruments to increase diversification with hedging benefits by building asset allocations that perform well across different macroeconomic scenarios.

Pravda Asset Management

2017 ◽  
pp. 1-9
Author(s):  
Richard B. Evans
Keyword(s):  
Portfolio Management ◽  
Asset Allocation ◽  
Asset Management ◽  
Expected Returns ◽  
Allocation Model ◽  
Mba Students ◽  
Tactical Asset Allocation ◽  
New Employee ◽  
Valuation Ratios ◽  
Allocation Decisions

This case examines the importance of forecasting expected returns in asset allocation decisions. Although the case is targeted to MBA students in an investments or portfolio management course, it is also appropriate for an advanced undergraduate course. It is written from the perspective of a new employee at a small investment management firm that was surprised by the market crash of 2008 and subsequent market rebound in 2009. Students must analyze the ability of simple valuation ratios to forecast returns and will use a smoothed price-to-earnings ratio to forecast future returns. In addition, students must use their regression results to form a simple (two-asset) tactical asset-allocation strategy to better understand the importance of forecasting expected returns for asset-allocation decisions and how such forecasts could be used to form a simple tactical asset-allocation model.

The study of mixed assets allocation based on Black–Litterman model

2021 ◽  
pp. 2150022
Author(s):  
Jianwu Lin ◽  
Mengwei Tang ◽  
Jiachang Wang ◽  
Ping He
Keyword(s):  
Stock Market ◽  
Asset Allocation ◽  
Private Equity ◽  
Equity Market ◽  
Research Topic ◽  
Allocation Model ◽  
Quantitative Models ◽  
Challenging Research ◽  
Qualitative Models ◽  
New Type

With Private Funds having a new type of license for asset allocation practice in China, comprehensive asset allocation cross private equity and stock market has received more attention. However, most of the studies focus more on the stock market, and asset allocation models for private equity market that are mainly made based on experience. Thus, the joint allocation of assets crosses both markets making it a challenging research topic. This paper introduces the Black–Litterman model into the private equity market, realizing the transition from qualitative models to quantitative models. It lays a solid quantitative ground for the mixed asset allocation model in both the markets.

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