Dynamic Investment Strategies Research Articles

Analysis on the impact of dynamic innovation investment strategy of green supply chain enabled by blockchain.

The emergence of the green supply chain represents a natural evolution from the traditional model. However, this transition has created trust concerns in operational processes. Fortunately, blockchain technology offers a promising solution to address this issue and help businesses overcome related obstacles. As artificial intelligence and blockchain continue to advance, enterprises are increasingly exploring opportunities for green innovation investments, although the optimal timing for successful product innovation can be difficult to predict. The effects of successful innovation on eco-friendly supply chains are analyzed through various factors such as optimal investment strategy, level of blockchain technology, and overall system profit. Differential game theory is used to determine the most effective approach across three alliance modes: horizontal cooperative, non-cooperative, and vertical cooperative. Additionally, the impact of innovation uncertainty on member strategies and alliance selection is thoroughly examined. According to the results, predicting the likelihood of innovation realization can influence decision makers to prioritize current profits. Both horizontal and vertical cooperative alliance models can lead to Pareto improvements in total system profit, both before and after innovation success. However, the vertical cooperative alliance model proves to be more effective, especially at higher realization rates. Green suppliers stand to benefit from the vertical cooperative alliance model, as it can enhance their innovative investment strategy, while platform cooperation does not significantly affect their strategy. Platforms, on the other hand, can benefit from the vertical cooperative alliance model, as it can promote their innovative investment strategy and level of blockchain technology.

Dynamic investment strategies for a folk sports tourism destination under uncertain demand

More research should shed light on discovering the optimal investment strategy for folk sports tourism destination (FSTD) projects. Therefore, in this paper, we develop a dynamic game model of FSTD considering the dynamic characteristics of FSTD investment, the mode of division of labor and cooperation between public and private operators, and the uncertainty of consumer demand. Public capital is responsible for constructing infrastructures such as venues, and private capital is responsible for services such as catering and accommodation. To promote the development of the FSTD project, the higher-level government subsidizes public investment. Consumer demand for the program is affected by factors such as the size of the two types of capital, the price and quality of services, and demand uncertainty. The study finds that the subsidy leads to an increase in the quantity of public investment and consumption demand, but private sector investment and the prices of both public and private projects are unaffected by the subsidy; the public sector's net return varies in an inverted U-shape with the rate of subsidy, but the private sector's net return rises monotonically. Demand disturbances widen the gap in the net returns of operators between the subsidized and unsubsidized scenarios.

Dynamic Connectedness and Investment Strategies between Commodities and ESG Stocks: Evidence from India

The study investigates the connectedness between commodities and ESG stocks of India using the extended joint connectedness approach. The study found a time-varying relationship between commodities and ESG stocks. It also discovered that there is a low spillover between the two. However, the total connectedness increased during the Russia-Ukraine war but remained low. The study found that crude oil and natural gas act as net transmitters, while ESG stocks and gold act as net receivers. ESG stocks are negatively connected with gold and have a low degree of positive correlation with crude oil and natural gas. Therefore, portfolio diversification opportunities exist between commodities and ESG stocks. The study exhibits that investors may derive significant benefits by adjusting their portfolios based on the optimum weights provided by the portfolio construction technique. The study provides valuable insights for portfolio managers, investors, and policymakers.

Across-time risk-aware strategies for outperforming a benchmark

We propose a novel objective function for constructing dynamic investment strategies with the goal of outperforming an investment benchmark at multiple points of evaluation during the investment time horizon. The proposed objective is intuitive, easy to parameterize, and directly targets a favorable tracking difference of the actively managed portfolio relative to the benchmark. Under stylized assumptions, we derive closed-form optimal investment strategies to guide the intuition in more realistic settings. In the case of discrete rebalancing with investment constraints, optimal strategies are obtained using a neural network-based numerical approach that does not rely on dynamic programming techniques. Compared to the targeting of a favorable tracking difference relative to the benchmark only at some fixed time horizon, our results show that the proposed objective offers a number of advantages: (i) The associated optimal strategies exhibit potentially more attractive asset allocation profiles, in that less extreme positions in individual assets are taken early in the investment time horizon, while achieving a similar terminal terminal wealth distribution. (ii) Across-time risk awareness leads to more robust performance and a higher probability of benchmark outperformance during the investment horizon in out-of-sample testing. The resulting strategies therefore exhibit desirable characteristics for active portfolio managers with periodic reporting requirements.

Retracted: Dynamic Index Optimal Investment Strategy Based on Stochastic Differential Equations in Financial Market Options

Retracted: Dynamic Index Optimal Investment Strategy Based on Stochastic Differential Equations in Financial Market Options

Dynamic volatility spillovers and investment strategies between crude oil, new energy, and resource related sectors

Promoting green and low-carbon development with new energy, and resource related sectors is an important way for mankind to deal with global climate change. With the marketization of new energy, the public and regulators have paid extensive attention to market relevance and risk contagion in China. In order to analyze the risk contagion mechanism, this paper examines the dynamic conditional correlation and the volatility spillover effect between crude oil, new energy, and resource related sectors. In addition, we compared the optimal diversification strategy by calculating hedging ratios and portfolio weights. The main results are as follows：First, new energy and resource related sectors have high dynamic correlation and the volatility spillover effect is significant. Second, from the perspective of net spillover index, new energy is the largest net volatility transmitter and WTI is the largest net volatility receiver. Third, when new energy is a long (short) position, the hedging efficiency of the portfolio with new energy vehicles using the hedging ratio strategy is the greatest. And when new energy is long, taking a short position in new energy vehicles is the most effective hedging strategy, but the most expensive hedging strategies are found in resource related sectors.

Beating a Benchmark: Dynamic Programming May Not Be the Right Numerical Approach

We analyze dynamic investment strategies for benchmark outperformance using two widely used objectives of practical interest to investors: (i) maximizing the information ratio (IR), and (ii) obtaining a favorable tracking difference (cumulative outperformance) relative to the benchmark. In the case of the tracking difference, we propose a simple and intuitive objective function based on the quadratic deviation (QD) from an elevated benchmark. In order to gain some intuition about these strategies, we provide closed-form solutions for the controls under idealized assumptions. For more realistic cases, we represent the control using a neural network (NN) and directly solve a sampled optimization problem, which approximates the original optimal stochastic control formulation. Unlike the typical approach based on dynamic programming (DP), e.g., reinforcement learning, solving the sampled optimization with an NN as a control avoids computing conditional expectations and leads to an optimization problem with a small number of variables. In addition, our NN parameter size is independent of the number of portfolio rebalancing times. Under some assumptions, we prove that a traditional DP approach results in a high-dimensional problem, whereas directly solving for the control without using DP yields a low-dimensional problem. Our analytical and numerical results illustrate that, compared with IR-optimal strategies with the same expected value of terminal wealth, the QD-optimal investment strategies result in comparatively more diversified asset allocations during certain periods of the investment time horizon.

Brazilian REITs: Are They an Opportunity for Diversification and Performance?

We study the dynamic diversification benefits of Brazilian real estate investment trusts (BR-REITs) by analyzing their complex dynamic relationship with stock and government bond indices. We estimate the assets’ conditional variance–covariance matrix using a vector autoregressive multivariate dynamic conditional correlation GARCH model with Student’s t distribution (VAR-DCCt MGARCH) and a global minimum variance portfolio. We find that BR-REITs provide strong risk-adjusted performance and persistent diversification benefits for Brazilian investors. Furthermore, these diversification benefits are not impacted by the market performance of stocks and bonds. Nevertheless, our results show that the BR-REITs’ conditional correlations with stocks and bonds are dynamic; thus, BR-REIT investors should employ dynamic investment strategies that account for time variation in REITs’ diversification benefits. Our results suggest that Brazilian investors, particularly long-term investors, would secure significant performance and diversification benefits, lowering their portfolios’ short-term volatility, by investing in BR-REITs.

Application of improved meta-heuristic algorithms for green preservation technology management to optimize dynamical investments and replenishment strategies

In this study, an application of green preservation technology to obtain optimal pricing and replenishment policies is developed. The decreasing value in waste is considered along with retailer dynamic promotional and green preservation technology investments under a price-promotion model with ramp-type demand. To maximize total profits, the study aims to clean the environment while simultaneously optimizing selling price, replenishment schedule, order quantity, green preservation technology investment, and dynamic investment rate. Pontryagin’s maximum principle is adopted to obtain the optimal dynamic investment rate for waste reduction. Additionally, simulated annealing, particle swarm optimization, and BAT algorithms are individually applied to obtain an optimal decision for waste reduction via green preservation technology. Results showed that the investment rate in promotion is significantly affected by price changes and that the investment in green preservation technology is affected by the nature of the product. Extensive computational experiments are performed to validate the proposed model. Under Ramp-type demand, price differentiation is more rewarding than the uniform pricing of the retailer. Results demonstrate the significant role of dynamic investment strategy. It has invested more based on the low-price sensitivity in the second period, and the optimal investment follows a reverse trend.

Dynamic pricing and investment for fashion products under reference effect

AbstractIn this study, we discuss on the dynamic pricing and investment strategy for fashion products which decay in value, such as fashion clothes. The products are sold by an enterprise under a monopoly market in a finite horizon. Due to the obsolescence of fashion products, their fashion level decreases over time. Obviously, the selling price has a negative effect on demand, while the fashion level has a positive effect. Moreover, we introduce a reference value, which is used to indicate consumers' requirements for fashion level. We assume that investment on products performance or goodwill can reduce consumers' requirements for fashion level, an optimal control model is established. To maximize the enterprise's total profit, we obtain the trajectories of the optimal pricing and investment strategy by utilizing the Pontryagin's maximum principle. The optimal strategy is to gradually lower the selling price and then slightly raise it at the end of the sales period and reduce investment as products become obsolete. Afterwards, we provide a numerical example to illustrate our conclusions. Through the sensitivity analysis of the main parameters, we capture the influence of the parameters on an optimal decision. Moreover, some management implications are given.

Proposed Model of a Dynamic Investment Portfolio with an Adaptive Strategy

This article covers a set of models and methods of portfolio investment which help adapt modern economic and mathematical instruments of portfolio investment to the current financial market situation. The main hypotheses serve as a basis for the adaptive dynamic investment portfolio. The experimental analysis shows that the adaptive dynamic investment strategy is more beneficial than classical approaches. The advantage of the adaptive strategy is that it is based on forecast data, whereas classical strategies focus only on historical data.

Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market

This paper proposes a long short-term memory (LSTM) neural network model to predict daily stock price movements based on asset pricing factors (i.e., the five factors proposed by Fama and French, and the short-term momentum factor). Based on three independent experiments, we systematically evaluate the explanatory power and the predictive power of the LSTM model by employing 3316 A-share listed companies in the Shanghai and Shenzhen stock exchanges from the in-sample period January 1, 2008 to December 31, 2019. Furthermore, we propose a four-step approach to dynamically update the underlying stocks in different portfolios based on the empirical findings. All portfolios are simulated using out-of-sample data (i.e., from January 1, 2020, to May 31, 2021) to avoid look-ahead bias. The trading results suggest that our dynamic investment strategies are superior to the benchmark index and are able to generate significant returns with relatively low risks.

Tactical asset allocation using the Kalman filter

ABSTRACT Tactical asset allocation (TAA) is a dynamic investment strategy which seeks actively to adjust fund allocation to a variety of asset classes by systematically exploiting inefficiencies and temporary imbalances in equilibrium values. This approach contrasts with strategic asset allocation (SAA) in which a long-term investment view target allocation is established using a combination of target return and risk tolerance. Asset returns are forecasted using the Capital Asset Pricing Model (CAPM), complemented with results obtained from the Kalman filter. Performance of TAA and SAA approaches are compared using several diagnostic metrics. The TAA approach outperforms its SAA counterpart for most of these metrics for the period under consideration, showing some potential benefits of using this approach.

Dynamic investment strategies and leadership in product innovation

We study the inter-temporally optimal innovation strategies of incumbent manufacturing firms that compete in an established market and can extend their product line through product innovation. Firms invest in production capacity and R&D knowledge stock, where the R&D knowledge stock and the current R&D investment determine the hazard rate of innovation. Our findings show that the firms’ optimal R&D strategies are driven by a subtle interplay between the relative positions of their R&D knowledge stocks and their current relative positions on the established market. First, we find that under symmetric investment costs the knowledge leader should spend more on R&D than the knowledge laggard only if it has a substantially smaller market share on the established market. If the knowledge leader’s market share is sufficiently large, its optimal investment in R&D is so small that its innovation rate is lower than the knowledge laggard’s. Second, optimal investment in R&D knowledge is negatively affected by the opponent’s production capacity on the established market if the competitor has not innovated yet. However, we find that this effect is reversed after the competitor has successfully introduced the new product on the submarket. Third, the manufacturing firm with higher costs of adjusting production capacity for the established product has a higher incentive to engage in product innovation and might even achieve a higher total discounted profit than its more efficient competitor.

Dynamic Investment Strategy Based on Nonlinear Programming

<p><span style="font-family: 'Times New Roman';">Aiming at the trading problem of gold and bitcoin in the financial market, this paper establishes a trading strategy based on KDJ and MACD indicators, and establishes an effective frontier curve model based on the change of mean variance to determine the investment ratio, and uses Lagrange multiplier method to maximize the trader's return rate.</span></p>

Modelling and filtering for dynamic investment in the precious-metals market

ABSTRACT Taking financial positions on precious metals could serve as risk management mechanism against both inflation and economic downturn. The demand for precious metals depends on their consumption- and investment-asset role. We consider the accurate and efficient price modelling of gold, silver, platinum and palladium under a hidden Markov model (HMM) multivariate framework. The interplay of various computational implementation aspects is underscored. Recursive model parameter estimates are generated, which are then utilized to test several dynamic investment strategies. We consider the pure-switching, mixed-switching and mean-variance strategies. The pure switching strategy outperforms the other two strategies 61.76% of the time. Benchmarking against the asset-only strategy and within the 25-year data examined in our study, investing in palladium is found to be better than the above-mentioned strategies. A general mean-variance strategy for any number of assets is also presented in conjunction with a filtering-based parameter estimation for any number of HMM states.

Leading macroeconomic indicators for a dynamic investment strategy

In this paper a long-term portfolio optimization model is developed, through the use of economic indicators (CLI and BCI). In this way, an investment portfolio will adjust to the movements of the business cycle, mitigating its risk in the event of possible downturns. The proposed model was tested in Mexico between 1998-2021. The active strategy makes investments in fixed income (Certificados de la Tesorería, CETES) and the market index (Índice de Precios y Cotizaciones, IPC), through operations of 25 % of the capital in monthly decisions. The dynamic investment strategy outperforms market index by 4.3 % in the period analyzed (differences in annual geometric return). In that period, only 5 % of the annual returns of the active strategy were negative, compared to 25.8 % in the market index.

Maximize Expected Profits by Dynamic After-Sales Service Investment Strategy Based on Word-of-Mouth Marketing in Social Network Shopping

This paper discusses how word-of-mouth marketing affects the profits of product sales in social network-based shopping under good after-sales service. First, a new word-of-mouth communication model based on silent evaluation, positive evaluation, and negative evaluation is proposed. Second, we use the way of increasing after-sales service to achieve high praise and thereby maximize the expected profits. Thus, the proportion control problem of after-sales service investment is modeled as an optimal control problem. Third, the existence of optimal control is proved, and an optimal control strategy for dynamic proportion of after-sales service investment is proposed. Fourth, through data simulation of different real-world networks, it is verified that the expected profits under the dynamic after-sales service strategy is higher than that under any uniform control strategy. Finally, sensitivity analysis is performed to explore how different parameters affect the expected profits.

Optimization of Grid Dynamic Investment Strategy for Urban Distribution System Considering GIS Information

Optimization of Grid Dynamic Investment Strategy for Urban Distribution System Considering GIS Information

Matching-game approach for green technology investment strategies in a supply chain under environmental regulations

Facing increasingly severe environmental problems, green technology (GT) innovation has become an effective way to achieve the sustainable development of firms. Many manufacturers often choose and invest in appropriate GTs from GT-suppliers to improve the environmental impact of production. But others abandon GTs because they are too expensive to invest in. This paper thereby studies the dynamic investment strategy of GT in a manufacturer-supplier supply chain and explores the optimal government subsidy incentive and its impact on investment and sustainable production decisions. Firstly, an original two-sided matching game model (including two-sided matching analysis and Nash equilibrium analysis) is presented to study the matching process between a manufacturer with a limited investment budget and a GT-supplier with GTs and to determine the optimal GT investment-production strategy. Then, a government-manufacturer Stackelberg game model integrating differential game is constructed to determine the optimal government subsidy strategy and to investigate the effect of government subsidy on investment-production decisions. The results show that stable matching between the manufacturer and GT-supplier can be obtained. Numerical simulations verify the feasibility and rationality of the proposed matching game mechanism and demonstrate that GT investment profit is higher than non-investment profit, and subsidy profit is better than non-subsidy profit. The increase in the carbon price leads to an increase in the GT investment intensity. The increase in the carbon price can cause an increase in product greenness and green demand. The contributions of this paper are in providing an infrastructure for studying how managers can obtain the optimal GT investment-production strategy in the supply chain, and how the government formulates the optimal subsidy strategy to stimulate managers’ GT innovation behavior.