An adaptively managed dynamic portfolio selection model using a time-varying investment target according to the market forecast

Abstract

In this paper, we propose an adaptive investment strategy (AIS) based on a dynamic portfolio selection model (DPSM) that uses a time-varying investment target according to the market forecast. The DPSM allows for flexible investments, setting relatively aggressive investment targets when market growth is expected and relatively conservative targets when the market is expected to be less attractive. The model further allows investments to be liquidated into risk-free assets when the market forecast is pessimistic. By dynamically determining the investment target, the DPSM allows construction of portfolios that are more responsive to market changes, while eliminating the possibility of the model becoming infeasible under certain market conditions. When the proposed DPSM is implemented in real-life investment scenarios using the AIS, the portfolio is rebalanced according to a predefined rebalancing cycle and the model's input parameters are estimated on each rebalancing date using an exponentially weighted moving average (EWMA) estimator. To evaluate the performance of the proposed approach, a 7-year investment experiment was conducted using historical stock returns data from 10 different stock markets around the world. Performance was assessed and compared using diverse measures. Superior performance was achieved using the AIS proposed herein compared with various benchmark approaches for all performance measures. In addition, we identified a converse relationship between the average trading volume of a market and the value of the weighting parameter prescribed to the EWMA estimator, which maximizes cumulative returns in each market.

Financing method for real estate and infrastructure development using Markowitz’s portfolio selection model and the Monte Carlo simulation

“…This study verified that Markowitz's model could be applied to stock issues for the development of real estate and infrastructure. The result is similar to those of previous studies (Gottschlich and Hinz, 2014;Jung and Kim, 2015) verifying the usefulness of Markowitz's portfolio selection model for improving the earnings rate. In the following section, Markowitz's model is compared with the hybrid model of Markowitz's model and MCS.…”

“…Gottschlich and Hinz (2014) put forward a method to improve the earnings rate of a stock investment using Markowitz's portfolio model. Jung and Kim (2015) used Markowitz's model to improve the investment earnings rate considering the time variance of the market. These previous studies that measured project performance and reduced the possible risk are different from the present study, as one objective of this study is to propose a method for financing a large PF project.…”

“…These previous studies that measured project performance and reduced the possible risk are different from the present study, as one objective of this study is to propose a method for financing a large PF project. Among the previous studies on improving the earnings rate of investment, Gottschlich and Hinz (2014) and Jung and Kim (2015) used Markowitz's model similar to this study, but their proposed method did not target stocks related to a PF project because these studies did not focus on financing.…”

“…Markowitz's model (Equation ( 1)) is a method to choose diversified investments by optimal combination of earnings rate and risk based on annualized return, variance and covariance of stocks, where N is the number of stocks that can be included in the portfolio, σ ij is the covariance based on the earnings rate of stocks i and j≠i, w i is the proportion of money invested in stock i in the portfolio (i ¼ 1, 2, …, N ), w j is the proportion of money invested in stock j in the portfolio ( j ¼ 1, 2, …, N ; j≠i), μ j is the average earnings rate of stock j ( j ¼ 1, 2, …, N ), σ jj is the variance based on earnings rate of stock j ( j ¼ 1, 2, …, N ) and K is the minimum expected rate of return required by the investor ( Jung and Kim, 2015):…”

Computational approaches and data analytics in financial services: A literature review

“…The same also applies to models that incorporate transaction costs and other real features (Glen, 2011;Jobst, Horniman, Lucas, & Mitra, 2001;Lobo, Fazel, & Boyd, 2007). In such cases, algorithms (heuristics and metaheuristics) that lead to approximate optimal solutions in reasonable time, have become very popular and have been used a variety of different settings (Ertenlice & Kalayci, 2018;Maringer, 2005 (Bjrk, Murgoci, & Zhou, 2012;Bo & Capponi, 2016;Calafiore, 2008;Çelikyurt & € Ozekici, 2007;Jung & Kim, 2015;Pfister, Utz, & Wimmer, 2015), fuzzy models (Gupta, Mehlawat, & Saxena, 2008;Vercher & Bermudez, 2013;Xu, He, Chen, & Zhang, 2015), robust optimization (Ban, Karoui, & Lim, 2018;Bertsimas & Sim, 2004;Fabozzi, Kolm, Pachamanova, & Focardi, 2007;Goldfarb & Iyengar, 2003;Kim, Kim, Ahn, & Fabozzi, 2012;, and network models (Boginski, Butenko, & Pardalos, 2005;Guo, Zhang, & Tian, 2018;Kalyagin, Koldanov, Koldanov, Pardalos, & Zamaraev, 2014;Kalyagin, Koldanov, Koldanov, & Pardalos, 2017;Kalyagin, Pardalos, & Rassias, 2014;Kocheturov, Batsyn, & Pardalos, 2014;Koldanov, Koldanov, Kalyagin, & Pardalos, 2013;Zhao, Li, & Cai, 2016).…”