Journal articles on the topic 'Optimal trading portfolio'
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Aliu, Florin, Artor Nuhiu, Besnik Krasniqi, and Fisnik Aliu. "Modeling the Optimal Portfolio: the Case of the Largest European Stock Exchanges." Comparative Economic Research. Central and Eastern Europe 23, no. 2 (June 30, 2020): 41–51. http://dx.doi.org/10.18778/1508-2008.23.11.
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Portfolio optimization is the main concern for portfolio managers. Financial securities are placed within the portfolio based on the investor’s risk tolerance. The study measures the risk-reward relationship when the number of stocks in the portfolio increases. Six diverse portfolios have been created with a different number of stocks, such as portfolios with 47 stocks, 95 stocks, 142 stocks, 190 stocks, 239 stocks, and 287 stocks. Stock prices and trading volume were collected on a weekly basis from the six largest European stock exchanges (FTSE100, CAC40, FTSE MIB, IBEX35, DAX, and MDAX). Markowitz’s (1952) diversification formula has been used to measure the risk level of the individual portfolios. The results of the study show that the diversification risk constantly decreases when we move from the portfolios with 47 stocks to the portfolios with 287 stocks. The weighted average returns increase on the portfolios with a higher number of stocks, which is contrary to the standard portfolio theories. The results of the study indicate managerial implications for financial investors that are focused exclusively on the largest European stock exchanges.
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Min, Seungki, Costis Maglaras, and Ciamac C. Moallemi. "Cross-Sectional Variation of Intraday Liquidity, Cross-Impact, and Their Effect on Portfolio Execution." Operations Research 70, no. 2 (March 2022): 830–46. http://dx.doi.org/10.1287/opre.2021.2201.
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Over the past decade, there has been a significant rise in assets managed under passive and systematic strategies. Such strategies hold and trade portfolios in a coordinated manner, often concentrating trading around the end of the trading session. Simultaneously, there has been a rise in activity from market participants that act as liquidity providers, themselves trading along portfolio directions. In “Cross-Sectional Variation of Intraday Liquidity, cross-impact, and Their Effect on Portfolio Execution,” Min, Maglaras, and Moallemi investigate the implications of these two observations, specifically exploring how the phenomenon of portfolio liquidity provision leads to cross-security impact and influences the optimal execution schedules of risk-neutral traders that seek to minimize their expected execution costs. They show that the optimized schedules deviate from the naïve approach that trades each security separately and instead, couple the trading intensity across stocks so as to benefit from the liquidity provided along attractive portfolio trading directions. Empirical analysis demonstrates that coupled optimized schedules could lower costs by as much as 15% relative to the naïve approach.
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Papantonis, Ioannis. "Cointegration-based trading: evidence on index tracking & market-neutral strategies." Managerial Finance 42, no. 5 (May 9, 2016): 449–71. http://dx.doi.org/10.1108/mf-12-2014-0318.
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Purpose – The purpose of this paper is to present an alternative approach to equity trading that is based on cointegration. If there are long-run equilibria among financial assets, a cointegration-based trading strategy can exploit profitable opportunities by capturing mean-reverting short-run deviations. Design/methodology/approach – First, the author introduces an equity indexing technique to form cointegration tracking portfolios that are able to replicate an index effectively. The author later enhances this tracking methodology in order to construct more complex portfolio-trading strategies that can be approximately market neutral. The author monitors the performance of a wide range of trading strategies under different specifications, and conducts an in-depth sensitivity analysis of the factors that affect the optimal portfolio construction. Several statistical-arbitrage tests are also carried out in order to examine whether the profitability of the cointegration-based trading strategies could indicate a market inefficiency. Findings – The author shows that under certain parameter specifications, an efficient tracking portfolio is able to produce similar patterns in terms of returns and volatility with the market. The author also finds that a successful long-short strategy of two cointegration portfolios can yield an annualized return of more than 8 percent, outperforming the benchmark and also demonstrating insignificant correlation with the market. Even though some cointegration-based pairs-trading strategies can consistently generate significant cumulative profits, yet they do not seem to converge to risk-less arbitrages, and thus the hypothesis of market efficiency cannot be rejected. Originality/value – The primary contribution of the research lies within the detailed analysis of the factors that affect the tracking-portfolio performance, thus revealing the optimal conditions that can lead to enhanced returns. Results indicate that cointegration can provide the means to successfully reproducing the risk-return profile of a benchmark and to implementing market-neutral strategies with consistent profitability. By testing for statistical arbitrage, the author also provides new evidence regarding the connection between the profit accumulation of cointegration-based pairs-trading strategies and market efficiency.
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Wang, Jiexin, Xue Han, Emily J. Huang, and Christopher Yost-Bremm. "Abnormal trading around common factor pricing models." Review of Behavioral Finance 12, no. 4 (November 8, 2019): 317–34. http://dx.doi.org/10.1108/rbf-03-2019-0038.
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Purpose The purpose of this paper is to investigate the impact of factor-based trading strategies on pricing and volume. Design/methodology/approach The authors employ a regression discontinuity approach to identify abnormalities in volume or pricing around expected portfolio changes. In addition, the authors characterize more granular effects on pricing and volume as a result of portfolio re-classification through Fama and Macbeth (1973) regressions. Findings The authors find that firms which are predicted to transfer among the factor portfolios of Fama and French (1993) exhibit strong and statistically significant short-term variation in stock price and volume. Short-term returns around the cutoff values comprising SMB and HML tend to be temporarily high if the firm is predicted to move into a long component of a factor-mimicking portfolio, and temporarily low if moving into a short component. Similar results are apparent when examining movement in and out of the 25 size and book-to-market sorted test asset portfolios. Practical implications The use of portfolio strategies formulated on the basis of sorting procedures, while once upon a time a niche market in the portfolio management industry, is now ubiquitous. The results of this study raise interesting methodological questions about the pricing implications arising from these common methodologies. Originality/value This study makes a number of contributions. First, it contributes to the idea that the publication or dissemination of trading strategies or – more generally – common portfolio sorting methods, leads to effects on pricing and volume through commonly motivated trading pressure. In other words, recipe-like discoveries of advantageous trading strategies lead to a synthetic creation of demand. Second, by noting that a lot of factor-focused trading activity begins around July and August of each year, the study relates to existing literature which documents seasonal variation in stock returns and volume. The findings raise questions about what guides institutional investors’ portfolio allocation decisions and whether these are optimal in aggregate.
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Li, Thomas Nanfeng, and Agnès Tourin. "Optimal pairs trading with time-varying volatility." International Journal of Financial Engineering 03, no. 03 (September 2016): 1650023. http://dx.doi.org/10.1142/s2424786316500237.
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In this paper, we propose a pairs trading model that incorporates a time-varying volatility of the constant elasticity of variance type. Our approach is based on stochastic control techniques; given a fixed time horizon and a portfolio of two cointegrated assets, we define the trading strategies as the portfolio weights maximizing the expected power utility from terminal wealth. We compute the optimal pairs strategies by using a finite difference method. Finally, we illustrate our results by conducting tests on historical market data at daily frequency. The parameters are estimated by the generalized method of moments.
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Giemza, Dawid. "Ranking of optimal stock portfolios determined on the basis of expected utility maximization criterion." Journal of Economics and Management 43 (2021): 154–78. http://dx.doi.org/10.22367/jem.2021.43.08.
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Aim/purpose – The aim of the paper is to rank the optimal portfolios of shares of com- panies listed on the Warsaw Stock Exchange, taking into account the investor’s propen- sity to risk. Design/methodology/approach – Investment portfolios consisting of varied number of companies selected from WIG 20 index were built. Next, the weights of equity holdings of these companies in the entire portfolio were determined, maximizing portfolio’s expected (square) utility function, and then the obtained structures were compared between investors with various levels of risk propensity. Using Hellwig’s taxonomic development measure, a ranking of optimum stock portfolios depending on the inves- tor’s risk propensity was prepared. The research analyzed quotations from 248 trading sessions. Findings – The findings indicated that whilst there are differences in the weight struc- tures of equity holdings in the entire portfolio between the investor characterized by aversion to risk at the level of γ = 10 and the investor characterized by aversion to risk at the level of γ = 100, the rankings of the constructed optimum portfolios demonstrate strong similarity. The study validated, in conformity with the literature, that with the increase in the number of equity holdings in the portfolio, the portfolio risk initially decreases and then becomes stable at a certain level. Research implications/limitations – The study used data from the past as for which there is no guarantee that they will be adequate for the future. There is sensitivity to the selection of the period from which the historic data come. When changing the period of the analyzed historic data by a small time unit it may prove that the portfolio composi- tion will become totally different. Originality/value/contribution – The paper compares the composition of optimum stock portfolios depending on the investor’s propensity to risk. Their ranking was cre- ated using the taxonomic method for this purpose. Taking advantage of this method also additional variables can be taken into account, which describe and differentiate the port- folio and they can be assigned relevant significance depending on the investor’s prefer- ences. Keywords: optimal portfolio, expected rate of return on the portfolio, portfolio standard deviation, expected utility theory, multidimensional comparative analysis. JEL Classification: G10, G11.
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Saputra, Ramadhan Dwi, and Irham Alifiandipura. "Rancangan Strategi Portofolio Optimal PT. ABC dengan Metode Single Index Model." JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN) 8, no. 1 (November 30, 2021): 58–69. http://dx.doi.org/10.31289/jkbm.v8i1.5627.
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As social insurance company of the Republic of Indonesia, PT ABC (Persero) has a captive market based on the provisions of Undang-undang in Indonesia. The company has managed funds from the government, and is one of the four insurance products in the company, into an investment portfolio. As one of the company’s revenue generator, PT ABC needs to put the fund into investment instruments that have higher returns to meet the needs of the company, one of which is through stock investment. The formation of stock portfolio is carried out through an optimum portfolio approach by using single index model method. This is a quantitative descriptive research using analysis tools and data processing by Microsoft Excel software. Portfolios are formed into several scenarios by considering the composition of the current portfolio in one of the company’s products and stock price movements in Indonesia. The data used in this study are historical data on daily stock movements for the five years of the 2014-2018 trading period and historical data on the investment portfolio for XYZ products in 2018. From this research, 2 strategic plans for forming company portfolios and 1 recommendation of stocks are produced that have good resilience during a pandemic. The results of the calculations are expected to be taken into consideration by the company in the formation of future company portfolios
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Edirisinghe, Chanaka, and Jaehwan Jeong. "Mean–Variance Portfolio Efficiency under Leverage Aversion and Trading Impact." Journal of Risk and Financial Management 15, no. 3 (February 23, 2022): 98. http://dx.doi.org/10.3390/jrfm15030098.
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This paper addresses the optimal rebalancing problem of a long–short portfolio with high net asset value under trading impact losses. The fund manager may employ leveraging as a tool to increase portfolio returns. However, to mitigate potential leverage risks, frequent rebalancing may become necessary, which leads to significant slippage losses that dampen portfolio performance ex post. We consider the problem in an integrated framework by incorporating trading impact and leverage restrictions ex ante within a mean–variance framework, where leverage control is imposed using a chance constraint. The resulting mean–variance–leverage optimization model (MVL) is non-convex, and we develop an efficient scheme to obtain the optimal portfolio. We investigate how portfolio leverage modifies the MV efficient frontier in the presence of trading impact, and highlight the significant outperformance of the proposed model relative to the standard mean–variance model. Increased target means require less restrictions on leverage, which result in higher rates of slippage losses. Our analysis supports the notion that leverage restrictions contribute to choosing high beta assets, even in the presence of trading impact.
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BELLALAH, MONDHER, and ZHEN WU. "A MODEL FOR MARKET CLOSURE AND INTERNATIONAL PORTFOLIO MANAGEMENT WITHIN INCOMPLETE INFORMATION." International Journal of Theoretical and Applied Finance 05, no. 05 (August 2002): 479–95. http://dx.doi.org/10.1142/s0219024902001559.
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This paper presents of model of market closure in the management of international portfolios. We consider an investor holding a portfolio of domestic stocks and foreign stocks who faces market closure in the management of his portfolio. The investor's portfolio is affected by the exchange rate risk and different dynamics of the underlying assets during the period of trading and non-trading. The investor must determine the optimal proportions of his wealth to allocate to domestic stocks and foreign stocks during the market open and close periods. The paper investigates the effects of opening and closing on transactions demand of domestic and foreign stocks. The transactions demand at open and close periods in the securities markets are studied in the presence of information costs using the main concepts in Merton's (1987) model of capital market equilibrium with incomplete information. Using optimal control theory, we provide a solution in the general case and propose analytic solutions for the constant relative aversion utility functions. The model can be applied to solve several problems in financial economics in the presence of market closure.
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Aljinović, Zdravka, Branka Marasović, and Tea Šestanović. "Cryptocurrency Portfolio Selection—A Multicriteria Approach." Mathematics 9, no. 14 (July 16, 2021): 1677. http://dx.doi.org/10.3390/math9141677.
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This paper proposes the PROMETHEE II based multicriteria approach for cryptocurrency portfolio selection. Such an approach allows considering a number of variables important for cryptocurrencies rather than limiting them to the commonly employed return and risk. The proposed multiobjective decision making model gives the best cryptocurrency portfolio considering the daily return, standard deviation, value-at-risk, conditional value-at-risk, volume, market capitalization and attractiveness of nine cryptocurrencies from January 2017 to February 2020. The optimal portfolios are calculated at the first of each month by taking the previous 6 months of daily data for the calculations yielding with 32 optimal portfolios in 32 successive months. The out-of-sample performances of the proposed model are compared with five commonly used optimal portfolio models, i.e., naïve portfolio, two mean-variance models (in the middle and at the end of the efficient frontier), maximum Sharpe ratio and the middle of the mean-CVaR (conditional value-at-risk) efficient frontier, based on the average return, standard deviation and VaR (value-at-risk) of the returns in the next 30 days and the return in the next trading day for all portfolios on 32 dates. The proposed model wins against all other models according to all observed indicators, with the winnings spanning from 50% up to 94%, proving the benefits of employing more criteria and the appropriate multicriteria approach in the cryptocurrency portfolio selection process.
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EWALD, CHRISTIAN-OLIVER. "OPTIMAL LOGARITHMIC UTILITY AND OPTIMAL PORTFOLIOS FOR AN INSIDER IN A STOCHASTIC VOLATILITY MARKET." International Journal of Theoretical and Applied Finance 08, no. 03 (May 2005): 301–19. http://dx.doi.org/10.1142/s0219024905003025.
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We combine methods for portfolio optimization in incomplete markets which are due to Karatzas et al. [6] with methods proposed by Nualart based on Malliavin Calculus to model insider trading within a stochastic volatility model. We compute the optimal portfolio within a certain set of insider strategies for a general stochastic volatility model but also apply the methods to explicit examples. We further discuss how the Heston model fits into this context.
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ALTAY, SÜHAN, KATIA COLANERI, and ZEHRA EKSI. "PAIRS TRADING UNDER DRIFT UNCERTAINTY AND RISK PENALIZATION." International Journal of Theoretical and Applied Finance 21, no. 07 (November 2018): 1850046. http://dx.doi.org/10.1142/s0219024918500462.
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In this work, we study a dynamic portfolio optimization problem related to pairs trading, which is an investment strategy that matches a long position in one security with a short position in another security with similar characteristics. The relationship between pairs, called a spread, is modeled by a Gaussian mean-reverting process whose drift rate is modulated by an unobservable continuous-time, finite-state Markov chain. Using the classical stochastic filtering theory, we reduce this problem with partial information to an equivalent one with full information and solve it for the logarithmic utility function, where the terminal wealth is penalized by the riskiness of the portfolio according to the realized volatility of the wealth process. We characterize optimal dollar-neutral strategies as well as optimal value functions under full and partial information and show that the certainty equivalence principle holds for the optimal portfolio strategy. Finally, we provide a numerical analysis for a toy example with a two-state Markov chain.
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GAVRISHCHAKA, VALERIY V. "BOOSTING-BASED FRAMEWORK FOR PORTFOLIO STRATEGY DISCOVERY AND OPTIMIZATION." New Mathematics and Natural Computation 02, no. 03 (November 2006): 315–30. http://dx.doi.org/10.1142/s1793005706000506.
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Increasing availability of the multi-scale market data exposes limitations of the existing quantitative models such as low accuracy of the simplified analytical and statistical frameworks as well as insufficient interpretability and stability of the best machine learning algorithms. Boosting was recently proposed as a simple and robust framework for intelligent combination of the clarity and stability of the analytical and parsimonious statistical models with the accuracy of the adaptive data-driven models. Encouraging results of the boosting application to symbolic volatility forecasting have also been reported. However, accurate forecasting does not always warrant optimal decision making that leads to acceptable performance of the portfolio strategy. In this work, a boosting-based framework for a direct trading strategy and portfolio optimization is introduced. Due to inherent adaptive control of the parameter space dimensionality, this technique can work with very large pools of base strategies and financial instruments that are usually prohibitive for other portfolio optimization frameworks. Unlike existing approaches, this framework can be effectively used for the coupled optimization of the portfolio capital/asset allocation and dynamic trading strategies. Generated portfolios of trading strategies not only exhibit stable and robust performance but also remain interpretable. Encouraging preliminary results based on real market data are presented and discussed.
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Moallemi, Ciamac C., and Mehmet Sağlam. "Dynamic Portfolio Choice with Linear Rebalancing Rules." Journal of Financial and Quantitative Analysis 52, no. 3 (June 2017): 1247–78. http://dx.doi.org/10.1017/s0022109017000345.
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We consider a broad class of dynamic portfolio optimization problems that allow for complex models of return predictability, transaction costs, trading constraints, and risk considerations. Determining an optimal policy in this general setting is almost always intractable. We propose a class of linear rebalancing rules and describe an efficient computational procedure to optimize with this class. We illustrate this method in the context of portfolio execution and show that it achieves near optimal performance. We consider another numerical example involving dynamic trading with mean-variance preferences and demonstrate that our method can result in economically large benefits.
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LEUNG, TIM, RAPHAEL YAN, and YANG ZHOU. "OPTIMAL DYNAMIC FUTURES PORTFOLIO UNDER A MULTIFACTOR GAUSSIAN FRAMEWORK." International Journal of Theoretical and Applied Finance 24, no. 05 (August 2021): 2150028. http://dx.doi.org/10.1142/s021902492150028x.
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We study the problem of dynamically trading futures in continuous time under a multifactor Gaussian framework. We present a utility maximization approach to determine the optimal futures trading strategy. This leads to the explicit solution to the Hamilton–Jacobi–Bellman (HJB) equations. We apply our stochastic framework to two-factor models, namely, the Schwartz model and Central Tendency Ornstein–Uhlenbeck (CTOU) model. We also develop a multiscale CTOU model, which has a fast mean-reverting and a slow mean-reverting factor in the spot asset price dynamics. Numerical examples are provided to illustrate the investor’s optimal positions for different futures portfolios.
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Narayanaswamy, C. R., and Herbert E. Phillips. "OPTIMAL TRADING STRATEGIES IN THE CONTEXT OF PORTFOLIO REVISION." Financial Review 21, no. 3 (August 1986): 71. http://dx.doi.org/10.1111/j.1540-6288.1986.tb00735.x.
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Redeker, Imke, and Ralf Wunderlich. "Portfolio optimization under dynamic risk constraints: Continuous vs. discrete time trading." Statistics & Risk Modeling 35, no. 1-2 (January 1, 2018): 1–21. http://dx.doi.org/10.1515/strm-2017-0001.
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AbstractWe consider an investor facing a classical portfolio problem of optimal investment in a log-Brownian stock and a fixed-interest bond, but constrained to choose portfolio and consumption strategies that reduce a dynamic shortfall risk measure. For continuous- and discrete-time financial markets we investigate the loss in expected utility of intermediate consumption and terminal wealth caused by imposing a dynamic risk constraint. We derive the dynamic programming equations for the resulting stochastic optimal control problems and solve them numerically. Our numerical results indicate that the loss of portfolio performance is not too large while the risk is notably reduced. We then investigate time discretization effects and find that the loss of portfolio performance resulting from imposing a risk constraint is typically bigger than the loss resulting from infrequent trading.
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Bakurova, Anna, Hanna Ropalo, and Elina Tereschenko. "Modeling of complex diversification for centralized pharmacy network." E3S Web of Conferences 166 (2020): 09003. http://dx.doi.org/10.1051/e3sconf/202016609003.
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The risk management of a centralized pharmacy network identifies the research object. The paper proposes a strategy of complex diversification for the pharmacy network. By constructing portfolio models for complex diversification and solving relevant multicriteria problems, multiple pareto-optimal portfolios have been found for successive risk management. Based on the fundamentals of Markowitz portfolio theory and multicriteria optimization, this paper builds four models of the optimal portfolios for centralized pharmacy network. In contrast to the classic two-criteria model (risk minimization while maximizing income), our models have been introduced to maximize entropy, which enhances the diversification effect. Matlab software has been developed for solving multicriteria problems. Model verification was performed on real data provided by one of the pharmacy networks. The modeling results will be useful for automating the business processes of any trading network, managing risk, analysing loyalty programs to improve the effectiveness of their operations.
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Leung, Tim, and Yang Zhou. "Optimal dynamic futures portfolio in a regime-switching market framework." International Journal of Financial Engineering 06, no. 04 (December 2019): 1950034. http://dx.doi.org/10.1142/s2424786319500348.
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We study the problem of dynamically trading futures in a regime-switching market. Modeling the underlying asset price as a Markov-modulated diffusion process, we present a utility maximization approach to determine the optimal futures trading strategy. This leads to the analysis of the associated system of Hamilton–Jacobi–Bellman (HJB) equations, which are reduced to a system of linear ODEs. We apply our stochastic framework to two models, namely, the Regime-Switching Geometric Brownian Motion (RS-GBM) model and Regime-Switching Exponential Ornstein–Uhlenbeck (RS-XOU) model. Numerical examples are provided to illustrate the investor’s optimal futures positions and portfolio value across market regimes.
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Hou, Xinru, Xinsheng Xu, and Haibin Chen. "Optimal Ordering Policy for Supply Option Contract with Spot Market." Mathematical Problems in Engineering 2020 (December 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/6672088.
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This paper considers the procurement mechanism with two supply channels, namely, an option contract purchase and a spot market. For the mechanism, under the stochastic demand and the stochastic spot price, we consider the portfolio procurement with the spot trading liquidity and the option speculation respectively. To maximize the buyer’s profit, we establish two optimal portfolio procurement strategy models for those two scenarios. Based on the buyer’s cost-benefit analysis, we present a solution method to each model and provide an optimal ordering policy to the buyer. By the obtained results, we analyze the role of the spot trading liquidity and option speculation in a buyer’s expected profit. Some numerical experiments are presented to show the validity of the formulated models.
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Guéant, Olivier, Jean-Michel Lasry, and Jiang Pu. "A Convex Duality Method for Optimal Liquidation with Participation Constraints." Market Microstructure and Liquidity 01, no. 01 (June 2015): 1550002. http://dx.doi.org/10.1142/s2382626615500021.
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In spite of the growing consideration for optimal execution in the financial mathematics literature, numerical approximations of optimal trading curves are almost never discussed. In this paper, we present a numerical method to approximate the optimal strategy of a trader willing to unwind a large portfolio. The method we propose is very general as it can be applied to multi-asset portfolios with any form of execution costs, including a bid-ask spread component, even when participation constraints are imposed. Our method, based on convex duality, only requires Hamiltonian functions to have C1,1 regularity while classical methods require additional regularity and cannot be applied to all cases found in practice.
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Terentiev, Oleksandr, Tatyana Prosiankina-Zharova, Volodymyr Savastiyanov, Valerii Lakhno, and Vira Kolmakova. "The Features of Building a Portfolio of Trading Strategies Using the SAS OPTMODEL Procedure." Computation 9, no. 7 (July 6, 2021): 77. http://dx.doi.org/10.3390/computation9070077.
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The article describes the original information technology of the algorithmic trading, designed to solve the problem of forming the optimal portfolio of trade strategies. The methodology of robust optimization, using the Ledoit–Wolf shrinkage method for obtaining stable estimates of the covariance matrix of algorithmic strategies, was used for the formation of a portfolio of trade strategies. The corresponding software was implemented by SAS OPTMODEL Procedure. The paper deals with a portfolio of trade strategies built for highly-profitable, but also highly risky financial tools—cryptocurrencies. Available bitcoin assets were divided into a corresponding proportion for each of the recommended portfolio strategies, and during the selected period (one calendar month) were used for this research. The portfolio of trade strategies is rebuilt at the end of the period (every month) based on the results of trade during the period, in accordance with the conditions of risk minimizing or income maximizing. Trading strategies work in parallel, being in a state of waiting for a relevant trading signal. Strategies can be changed by moving the parameters in accordance with the current state of the financial market, removed if ineffective, and replaced where necessary. The efficiency of using a robust decision-making method in the context of uncertainty regarding cryptocurrency trading was confirmed by the results of real trading for the Bitcoin/Dollar pair. Implementation of the offered information technology in electronic trading systems will allow risk reduction as a result of making incorrect decisions or delays in making decisions in a systemic trading.
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Labbé, Chantal, and Andrew J. Heunis. "Convex duality in constrained mean-variance portfolio optimization." Advances in Applied Probability 39, no. 01 (March 2007): 77–104. http://dx.doi.org/10.1017/s0001867800001610.
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We apply conjugate duality to establish the existence of optimal portfolios in an asset-allocation problem, with the goal of minimizing the variance of the final wealth which results from trading over a fixed, finite horizon in a continuous-time, complete market, subject to the constraints that the expected final wealth equal a specified target value and the portfolio of the investor (defined by the dollar amount invested in each stock) take values in a given closed, convex set. The asset prices are modelled by Itô processes, for which the market parameters are random processes adapted to the information filtration available to the investor. We synthesize a dual optimization problem and establish a set of optimality relations, similar to the Euler-Lagrange and transversality relations of calculus of variations, giving necessary and sufficient conditions for the given optimization problem and its dual to each have a solution, with zero duality gap. We then solve these relations, to establish the existence of an optimal portfolio.
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Labbé, Chantal, and Andrew J. Heunis. "Convex duality in constrained mean-variance portfolio optimization." Advances in Applied Probability 39, no. 1 (March 2007): 77–104. http://dx.doi.org/10.1239/aap/1175266470.
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We apply conjugate duality to establish the existence of optimal portfolios in an asset-allocation problem, with the goal of minimizing the variance of the final wealth which results from trading over a fixed, finite horizon in a continuous-time, complete market, subject to the constraints that the expected final wealth equal a specified target value and the portfolio of the investor (defined by the dollar amount invested in each stock) take values in a given closed, convex set. The asset prices are modelled by Itô processes, for which the market parameters are random processes adapted to the information filtration available to the investor. We synthesize a dual optimization problem and establish a set of optimality relations, similar to the Euler-Lagrange and transversality relations of calculus of variations, giving necessary and sufficient conditions for the given optimization problem and its dual to each have a solution, with zero duality gap. We then solve these relations, to establish the existence of an optimal portfolio.
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Ameliana Yunus, Yana. "Comparison of Sharia Stock Prices and Trading Volumes Before and During COVID-19." Golden Ratio of Finance Management 1, no. 1 (March 15, 2021): 13–24. http://dx.doi.org/10.52970/grfm.v1i1.111.
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Before making an investment, entrepreneurs or investors must consider the benefits and financial risks obtained. So, investors need to take action in investing, meaning that investors need to form a portfolio by selecting several assets so that financial risk can be minimized without reducing the expected. The COVID-19 pandemic has significantly impacted the economy, especially investors, informing an optimal portfolio. This study aims to determine the optimal portfolio formation during the COVID-19 pandemic. In this study measurement, we used variables in the form of stock prices and stock trading volumes before and during COVID-19 pandemic. This study shows a comparison, but not so significant, between stock prices before and during the pandemic. Based on the survey conducted, the following results were found, i.e., first, shows an insignificant difference between prices before and after the rights issue announcement. The stock trading volume indicates a significant difference between the stock trading volume before and after the rights issue; trading volume increases after the information of the rights issue. By implementing companies affected by COVID-19 pandemic, we can watch the prices that occur around the announcement date. Investors can make a reason about their investments in shares of issuers affected by COVID-19 pandemic.
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Yoshida, Yuji. "A Dynamic Risk Allocation of Value-at-Risks with Portfolios." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 7 (November 20, 2012): 800–806. http://dx.doi.org/10.20965/jaciii.2012.p0800.
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A mathematical dynamic portfolio model with uncertainty is discussed by use of value-at-risks. The risk criterion is composed by the sum of unexpected shortterm risks which occur suddenly in each period. By dynamic programming approach, we derive an optimality condition for the optimal value-at-risk portfolio in a stochastic decision process. It is shown that the optimal value-at-risk is a solution of the optimality equation under a reasonable assumption, and an optimal trading strategy is obtained from the equation. A numerical example is given to illustrate our idea.
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Lim, Qing Yang Eddy, Qi Cao, and Chai Quek. "Dynamic portfolio rebalancing through reinforcement learning." Neural Computing and Applications 34, no. 9 (December 27, 2021): 7125–39. http://dx.doi.org/10.1007/s00521-021-06853-3.
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AbstractPortfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebalancing of portfolios with vigorous risk through an RL agent. This is done while accounting for market conditions, asset diversifications, risk and returns in the global financial market. Studies have been performed in this paper to explore four types of methods with variations in fully portfolio rebalancing and gradual portfolio rebalancing, which combine with and without the use of the Long Short-Term Memory (LSTM) model to predict stock prices for adjusting the technical indicator centring. Performances of the four methods have been evaluated and compared using three constructed financial portfolios, including one portfolio with global market index assets with different risk levels, and two portfolios with uncorrelated stock assets from different sectors and risk levels. Observed from the experiment results, the proposed RL agent for gradual portfolio rebalancing with the LSTM model on price prediction outperforms the other three methods, as well as returns of individual assets in these three portfolios. The improvements of the returns using the RL agent for gradual rebalancing with prediction model are achieved at about 27.9–93.4% over those of the full rebalancing without prediction model. It has demonstrated the ability to dynamically adjust portfolio compositions according to the market trends, risks and returns of the global indices and stock assets.
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SERVA, MAURIZIO. "OPTIMAL LAG IN DYNAMICAL INVESTMENTS." International Journal of Theoretical and Applied Finance 02, no. 04 (October 1999): 471–81. http://dx.doi.org/10.1142/s0219024999000236.
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A portfolio of different stocks and a risk-less security whose composition is dynamically maintained stable by trading shares at any time step leads to a growth of the capital with a nonrandom rate. This is the key for the theory of optimal-growth investment formulated by Kelly. In presence of transaction costs, the optimal composition changes and, more important, it turns out that the frequency of transactions must be reduced. This simple observation leads to the definition of an optimal lag between two rearrangement of the portfolio. This idea is tested against an investment in a risky asset and a risk-less one. The price of the first is proportional to NYSE composite index while the price of the second grows according to the American Discount Rate.
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Guryanova, Lidiya, and Natalia Chernova. "Metals futures market: a comparative analysis of investment and arbitrage strategies." Development Management 17, no. 4 (March 3, 2020): 42–54. http://dx.doi.org/10.21511/dm.17(4).2019.04.
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The article deals with the application of optimal portfolio theory and pair trading theory on the metals futures market. Advantages of the futures market over the spot market include relatively small initial price, low transaction costs, and high volatility. The main aim of the study is to explore the potential of both strategies for effective trading. The following financial instruments were chosen as the inputs of the models: futures on industrial metals (aluminum, copper, nickel, zinc, lead, tin), futures on precious metals (gold and silver). When building the optimal portfolio, it was decided to include Dow Jones Index futures and S&P Index futures among metals. This is because these instruments are extremely volatile and may play the role of a hedge in the portfolio. A drawdown indicator was used to assess the effectiveness of each strategy. The results show that both strategies can be applied on the real-life market. The final choice will depend on the level of risk taking by investors and the desired value of return.
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Xing, Haipeng. "A Singular Stochastic Control Approach for Optimal Pairs Trading with Proportional Transaction Costs." Journal of Risk and Financial Management 15, no. 4 (March 23, 2022): 147. http://dx.doi.org/10.3390/jrfm15040147.
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Optimal trading strategies for pairs trading have been studied by models that try to find either optimal shares of stocks by assuming no transaction costs or optimal timing of trading fixed numbers of shares of stocks with transaction costs. To find optimal strategies that determine optimally both trade times and number of shares in a pairs trading process, we use a singular stochastic control approach to study an optimal pairs trading problem with proportional transaction costs. Assuming a cointegrated relationship for a pair of stock log-prices, we consider a portfolio optimization problem that involves dynamic trading strategies with proportional transaction costs. We show that the value function of the control problem is the unique viscosity solution of a nonlinear quasi-variational inequality, which is equivalent to a free boundary problem for the singular stochastic control value function. We then develop a discrete time dynamic programming algorithm to compute the transaction regions, and show the convergence of the discretization scheme. We illustrate our approach with numerical examples and discuss the impact of different parameters on transaction regions. We study the out-of-sample performance in an empirical study that consists of six pairs of U.S. stocks selected from different industry sectors, and demonstrate the efficiency of the optimal strategy.
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Anghelache, Constantin, Mădălina-Gabriela Anghel, and Ștefan Virgil Iacob. "Econometric Model Used in the Portfolio Optimization over Several Periods." Economic Insights – Trends and Challenges 2021, no. 1 (2021): 31–41. http://dx.doi.org/10.51865/eitc.2021.01.04.
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"The classic problem of optimizing a portfolio can be extended to a multi-stage programming problem. The purpose of the multi-period portfolio optimization problem is to determine the optimal portfolio for a certain finite time horizon. In a multi-period model in which investors are allowed to change the composition of the portfolio, it is essential to take into account trading costs, a solution in this regard being the use of tree-type scenarios. The study undertaken by the authors considered the construction of a portfolio optimization model in case there is a certain constraint on returns. ARMA type processes were used to model the conditional mean equation."
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GASSIAT, PAUL, HUYÊN PHAM, and MIHAI SÎRBU. "OPTIMAL INVESTMENT ON FINITE HORIZON WITH RANDOM DISCRETE ORDER FLOW IN ILLIQUID MARKETS." International Journal of Theoretical and Applied Finance 14, no. 01 (February 2011): 17–40. http://dx.doi.org/10.1142/s0219024911006243.
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We study the problem of optimal portfolio selection in an illiquid market with discrete order flow. In this market, bids and offers are not available at any time but trading occurs more frequently near a terminal horizon. The investor can observe and trade the risky asset only at exogenous random times corresponding to the order flow given by an inhomogenous Poisson process. By using a direct dynamic programming approach, we first derive and solve the fixed point dynamic programming equation satisfied by the value function, and then perform a verification argument which provides the existence and characterization of optimal trading strategies. We prove the convergence of the optimal performance, when the deterministic intensity of the order flow approaches infinity at any time, to the optimal expected utility for an investor trading continuously in a perfectly liquid market model with no-short sale constraints.
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BROADIE, MARK, and WEIWEI SHEN. "HIGH-DIMENSIONAL PORTFOLIO OPTIMIZATION WITH TRANSACTION COSTS." International Journal of Theoretical and Applied Finance 19, no. 04 (May 25, 2016): 1650025. http://dx.doi.org/10.1142/s0219024916500254.
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This paper studies Merton’s portfolio optimization problem with proportional transaction costs in a discrete-time finite horizon. Facing short-sale and borrowing constraints, investors have access to a risk-free asset and multiple risky assets whose returns follow a multivariate geometric Brownian motion. Lower and upper bounds for optimal solutions up to the problem with 20 risky assets and 40 investment periods are computed. Three lower bounds are proposed: the value function optimization (VF), the hyper-sphere and the hyper-cube policy parameterizations (HS and HC). VF attacks the conundrums in traditional value function iteration for high-dimensional dynamic programs with continuous decision and state spaces. HS and HC respectively approximate the geometry of the trading policy in the high-dimensional state space by two surfaces. To evaluate lower bounds, two new upper bounds are provided via a duality method based on a new auxiliary problem (OMG and OMG2). Compared with existing methods across various suites of parameters, new methods lucidly show superiority. The three lower bound methods always achieve higher utilities, HS and HC cut run times by a factor of 100, and OMG and OMG2 mostly provide tighter upper bounds. In addition, how the no-trading region characterizing the optimal policy deforms when short-sale and borrowing constraints bind is investigated.
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Loewenstein, Mark. "On optimal portfolio trading strategies for an investor facing transactions costs in a continuous trading market." Journal of Mathematical Economics 33, no. 2 (March 2000): 209–28. http://dx.doi.org/10.1016/s0304-4068(99)00013-0.
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Setyowati, Ery Indah, and Husnurrosyidah Husnurrosyidah. "CAPM, INDEKS TUNGGAL DAN TREYNOR SEBAGAI ANALISIS PORTOFOLIO PADA SAHAM SYARIAH." KEUNIS 9, no. 1 (February 27, 2021): 63. http://dx.doi.org/10.32497/keunis.v9i1.2222.
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<em>This study aims to analyze the optimal portfolio of stocks using a single index model and the Capital Asset Pricing Model (CAPM) in making investment decisions as well as the expected profit and risk of the optimal portfolio formed on Islamic stocks in the Indonesian Sharia Stock Index (ISSI) on the Indonesia Stock Exchange. 2016-2020 period. This research design is descriptive quantitative research. The study population was all stocks that were consistently included in the Indonesian Sharia Stock Index (ISSI), amounting to 207 stocks. The number of samples of this study was 136 stocks using the Slovin method. The results show that there are 54 stocks that meet the criteria for optimal portfolio formation. The optimal portfolio of ISSI index stocks has a portfolio return rate of 21.95% and a portfolio risk of 10.49%. The portfolio performance based on the Treynor index shows that the best of the 54 stocks is PTSP shares amounting to 32.73% of the trading sector. While the results in determining investment decisions using the Capital Asset Pricing Model (CAPM) method and 136 company shares, there are 65 undervalued stocks, and 71 stocks are overvalued.</em><p> </p>
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Chen, An-Sing, and Che-Ming Yang. "Optimal statistical arbitrage trading of Berkshire Hathaway stock and its replicating portfolio." PLOS ONE 16, no. 1 (January 15, 2021): e0244541. http://dx.doi.org/10.1371/journal.pone.0244541.
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In this paper, we make use of the replicating asset for statistical arbitrage trading, where the replicating asset is constructed by a portfolio that mimics the returns from a factor model. Using the replicating asset in the context of statistical arbitrage has never been done before in the literature. A novel optimal statistical arbitrage trading model is applied, and we derive the average transaction length and return for the Berkshire A stock and its replicating asset. The results show that the statistical arbitrage method proposed by Bertram (2010) is profitable by using the replicating asset. We also compute the average returns under different transaction costs. For the statistical arbitrage using the replicating asset of the factor model, average annual returns were at least 33%. Robustness is examined with the S&P500. Our results can provide hedge fund managers with a new technique for conducting statistical arbitrage.
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Schroeder, Pascal, Imed Kacem, and Günter Schmidt. "Optimal online algorithms for the portfolio selection problem, bi-directional trading and -search with interrelated prices." RAIRO - Operations Research 53, no. 2 (April 2019): 559–76. http://dx.doi.org/10.1051/ro/2018064.
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In this work we investigate the portfolio selection problem (P1) and bi-directional trading (P2) when prices are interrelated. Zhang et al. (J. Comb. Optim. 23 (2012) 159–166) provided the algorithm UND which solves one variant of P2. We are interested in solutions which are optimal from a worst-case perspective. For P1, we prove the worst-case input sequence and derive the algorithm optimal portfolio for interrelated prices (OPIP). We then prove the competitive ratio and optimality. We use the idea of OPIP to solve P2 and derive the algorithm called optimal conversion for interrelated prices (OCIP). Using OCIP, we also design optimal online algorithms for bi-directional search (P3) called bi-directional UND (BUND) and optimal online search for unknown relative price bounds (RUN). We run numerical experiments and conclude that OPIP and OCIP perform well compared to other algorithms even if prices do not behave adverse.
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BIAGINI, FRANCESCA, and BERNT ØKSENDAL. "MINIMAL VARIANCE HEDGING FOR INSIDER TRADING." International Journal of Theoretical and Applied Finance 09, no. 08 (December 2006): 1351–75. http://dx.doi.org/10.1142/s0219024906003998.
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In this paper, we first study the problem of minimal hedging for an insider trader in incomplete markets. We use the forward integral in order to model the insider portfolio and consider a general larger filtration. We characterize the optimal strategy in terms of a martingale condition. In the second part we focus on a problem of mean-variance hedging where the insider tries to minimize the variance of his wealth at time T given that this wealth has a fixed expected value A. We solve this problem for an initial enlargement of filtration by providing an explicit solution.
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Schroder, Mark, and Costis Skiadas. "Optimal lifetime consumption-portfolio strategies under trading constraints and generalized recursive preferences." Stochastic Processes and their Applications 108, no. 2 (December 2003): 155–202. http://dx.doi.org/10.1016/j.spa.2003.09.001.
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Wang, Hui, Haocheng Xu, and Wenhui Zhao. "Optimal Trading Decision-Making of Power Supply Chain under Renewable Portfolio Standards." Energy Engineering 118, no. 5 (2021): 1375–94. http://dx.doi.org/10.32604/ee.2021.014641.
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Guo, Hongyu. "A Comparative Study of the Functions of Gold and Bit coin in the Financial Industry." BCP Business & Management 18 (April 13, 2022): 264–75. http://dx.doi.org/10.54691/bcpbm.v18i.564.
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With the development of society, gold and "digital gold" Bit coin have been incorporated into the portfolio by investors. This paper will help traders predict the price of them, and plan the optimal portfolio strategy, so as to maximize the return and minimize the risk cost We have developed a model that uses only the past stream of daily prices to date to determine each day if the trader should buy, hold, or sell their assets in their portfolio. We will start with $1000 on 9/11/2016. We will use the five-year trading period, from 9/11/2016 to 9/10/2021. On each trading day, the trader will have a portfolio consisting of cash, gold, and bitcoin [C, G, B] in U.S. dollars, troy ounces, and bitcoins, respectively. The initial state is [1000, 0, 0]. The commission for each transaction (purchase or sale) costs α% of the amount traded. Assume α gold = 1% and α bitcoin = 2%. There is no cost to hold an asset. Note that bitcoin can be traded every day, but gold is only traded on days the market is open, as reflected in the pricing data files LBMA-GOLD.csv and BCHAIN-MKPRU.csv. Our model accounts for this trading schedule. To develop our model, we may only use the data in the two spreadsheets: LBMA-GOLD.csv and BCHAIN-MKPRU.csv.
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HEYNE, GREGOR, MICHAEL KUPPER, and LUDOVIC TANGPI. "PORTFOLIO OPTIMIZATION UNDER NONLINEAR UTILITY." International Journal of Theoretical and Applied Finance 19, no. 05 (July 29, 2016): 1650029. http://dx.doi.org/10.1142/s0219024916500291.
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This paper studies the utility maximization problem of an agent with nontrivial endowment, and whose preferences are modeled by the maximal subsolution of a backward stochastic differential equation (BSDE). We prove existence of an optimal trading strategy and relate our existence result to the existence of a maximal subsolution to a controlled decoupled forward–BSDE (FBSDE). Using BSDE duality, we show that the utility maximization problem can be seen as a robust control problem admitting a saddle point if the generator of the BSDE additionally satisfies a specific growth condition. We show by convex duality that any saddle point of the robust control problem agrees with a primal and a dual optimizer of the utility maximization problem, and can be characterized in terms of a BSDE solution.
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Yang, Sung-Jin, Min-Ku Lee, and Jeong-Hoon Kim. "Portfolio optimization under the stochastic elasticity of variance." Stochastics and Dynamics 14, no. 03 (May 29, 2014): 1350024. http://dx.doi.org/10.1142/s021949371350024x.
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Based on the observation that the elasticity of variance of risky assets is randomly varying around a constant, we take an underlying asset model in which the averaged constant elasticity of variance is perturbed by a small fast fluctuating process and study the Merton type portfolio optimization problem using dynamic programming as well as asymptotic expansions. The Hamilton–Jacobi–Bellman equation for each of the power and exponential utility functions leads to an optimal trading strategy as a perturbation around the well known one. We reveal the impact of both the constant elasticity of variance upon the Merton investment optimal control under the Black–Scholes model and the stochastic elasticity of variance upon the investment optimal control under the constant elasticity of variance model. The concavity of the investment policy with respect to the excess return is characteristic of a market economy with the constant or stochastic elasticity of variance.
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Kuzmanovic, Marija, Dragana Makajic-Nikolic, and Nebojsa Nikolic. "Preference Based Portfolio for Private Investors: Discrete Choice Analysis Approach." Mathematics 8, no. 1 (December 24, 2019): 30. http://dx.doi.org/10.3390/math8010030.
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Behavioral finance literature shows that in addition to Markowitz’s rate of return and risk, private investors consider various other stock features. This paper discusses the problem of determining investors’ preferences for portfolio selection criteria, as well as the problem of optimal portfolio determination from the investors’ point of view. The study primarily focuses on private investors who are interested in one-time investments rather than stock trading. We use a discrete choice analysis and hierarchical Bayes method to measure individual investors’ preferences, and a logit model to determine individual shares of preferences. We treat the share of preferences as the share of certain stocks in an optimal portfolio. The proposed methodology is illustrated by the example of companies whose stocks are traded on the Belgrade Stock Exchange. We measure respondents’ preferences for companies, preferences for return rates, riskiness of stocks, and dividend rates. The results of comparing the performance of the resulting portfolio with the efficient frontier obtained using Markowitz’s portfolio theory indicate its high efficiency, thus validating the proposed approach.
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Nunnalina, Radna, Rofiqab Wabdab, and Yanuar Bacbtiar. "KOMPOSISI PORTOFOLIO OPTIMAL ANTAR SEKTOR SAHAM DI PT. BURSA EFEK JAKARTA." EKUITAS (Jurnal Ekonomi dan Keuangan) 6, no. 1 (December 8, 2016): 79. http://dx.doi.org/10.24034/j25485024.y2002.v6.i1.1947.
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This research aims to describe optimal composition among share sector at Jakarta Stock Exchange (JSX). This research used share return data from the most active weekly trading company at JSX This research used Mean Variance Model (M-V Model) to show the optimal invesment diversification (portfolio) among share sectors, so it can help investors in making decisions concerning invesments at JSX. Looking at the optimal composition of share, investors can minimize the risk of capital and maximize their return from capital investment result at Jakarta Stock Exchange.
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Oprisor, Razvan, and Roy Kwon. "Multi-Period Portfolio Optimization with Investor Views under Regime Switching." Journal of Risk and Financial Management 14, no. 1 (December 23, 2020): 3. http://dx.doi.org/10.3390/jrfm14010003.
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We propose a novel multi-period trading model that allows portfolio managers to perform optimal portfolio allocation while incorporating their interpretable investment views. This model’s significant advantage is its intuitive and reactive design that incorporates the latest asset return regimes to quantitatively solve managers’ question: how certain should one be that a given investment view is occurring? First, we describe a framework for multi-period portfolio allocation formulated as a convex optimization problem that trades off expected return, risk and transaction costs. Using a framework borrowed from model predictive control introduced by Boyd et al., we employ optimization to plan a sequence of trades using forecasts of future quantities, only the first set being executed. Multi-period trading lends itself to dynamic readjustment of the portfolio when gaining new information. Second, we use the Black-Litterman model to combine investment views specified in a simple linear combination based format with the market portfolio. A data-driven method to adjust the confidence in the manager’s views by comparing them to dynamically updated regime-switching forecasts is proposed. Our contribution is to incorporate both multi-period trading and interpretable investment views into one framework and offer a novel method of using regime-switching to determine each view’s confidence. This method replaces portfolio managers’ need to provide estimated confidence levels for their views, substituting them with a dynamic quantitative approach. The framework is reactive, tractable and tested on 15 years of daily historical data. In a numerical example, this method’s benefits are found to deliver higher excess returns for the same degree of risk in both the case when an investment view proves to be correct, but, more notably, also the case when a view proves to be incorrect. To facilitate ease of use and future research, we also developed an open-source software library that replicates our results.
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Chang, Mou-Hsiung. "Hereditary Portfolio Optimization with Taxes and Fixed Plus Proportional Transaction Costs—Part II." Journal of Applied Mathematics and Stochastic Analysis 2007 (January 3, 2007): 1–25. http://dx.doi.org/10.1155/2007/40149.
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This paper is the continuation of the paper entitled “Hereditary portfolio optimization with taxes and fixed plus proportional transaction costs I” that treats an infinite-time horizon hereditary portfolio optimization problem in a market that consists of one savings account and one stock account. Within the solvency region, the investor is allowed to consume from the savings account and can make transactions between the two assets subject to paying capital-gain taxes as well as a fixed plus proportional transaction cost. The investor is to seek an optimal consumption-trading strategy in order to maximize the expected utility from the total discounted consumption. The portfolio optimization problem is formulated as an infinite dimensional stochastic classical impulse control problem due to the hereditary nature of the stock price dynamics and inventories. This paper contains the verification theorem for the optimal strategy. It also proves that the value function is a viscosity solution of the QVHJBI.
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Korn, Ralf, and Anke Wiese. "Optimal Investment and Bounded Ruin Probability: Constant Portfolio Strategies and Mean-variance Analysis." ASTIN Bulletin 38, no. 02 (November 2008): 423–40. http://dx.doi.org/10.2143/ast.38.2.2033348.
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We study the continuous-time portfolio optimization problem of an insurer. The wealth of the insurer is given by a classical risk process plus gains from trading in a risky asset, modelled by a geometric Brownian motion. The insurer is not only interested in maximizing the expected utility of wealth but is also concerned about the ruin probability. We thus investigate the problem of optimizing the expected utility for a bounded ruin probability. The corresponding optimal strategy in various special classes of possible investment strategies will be calculated. For means of comparison we also calculate the related mean-variance optimal strategies.
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Korn, Ralf, and Anke Wiese. "Optimal Investment and Bounded Ruin Probability: Constant Portfolio Strategies and Mean-variance Analysis." ASTIN Bulletin 38, no. 2 (November 2008): 423–40. http://dx.doi.org/10.1017/s0515036100015233.
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We study the continuous-time portfolio optimization problem of an insurer. The wealth of the insurer is given by a classical risk process plus gains from trading in a risky asset, modelled by a geometric Brownian motion. The insurer is not only interested in maximizing the expected utility of wealth but is also concerned about the ruin probability. We thus investigate the problem of optimizing the expected utility for a bounded ruin probability. The corresponding optimal strategy in various special classes of possible investment strategies will be calculated. For means of comparison we also calculate the related mean-variance optimal strategies.
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Wildan, Rachmat, Noer Azam Achsani, and Bagus Sartono. "Evaluation of Optimal Stock Portfolio Performance by Grouping Issuers Based on Stock Price Movements." International Journal of Research and Review 9, no. 3 (March 16, 2022): 295–307. http://dx.doi.org/10.52403/ijrr.20220333.
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Stock investment has a high risk, one of which is caused by changes in stock prices that occur in trading on the stock exchange every day, thus affecting the level of stock returns which also changes. Investors will face uncertainty in making choices and evaluating stock performance in the future because the returns obtained are uncertain and depend on the risks that affect it. Risk in investing can be reduced by investing in various types of stocks by forming an optimal stock portfolio. These problems are solved in this study by taking a calculation approach in selecting stocks and determining the optimal portfolio with the advantage of a single index model that can explain the relationship between the returns of each stock and the market index returns to calculate the variance of a stock. portfolio. There are 4 stocks with the largest excess return to beta, namely MEGA, JECC, UNIC, and KICI stocks. Performance measurement in this study was carried out using the Treynor Index, because this index used systematic risk as measured by Beta. Keywords: Single Index Model, Treynor, Dynamic Time Wraping.