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Author: Raj Sau Publisher: ISBN: 9781267768278 Category : Languages : en Pages : 98
Book Description
In this thesis we study the performance of a re-balanced portfolio strategy relative to the market portfolio in the presence of transaction costs. The strategy involves re-balancing to fixed weights at regular time steps. We consider an equity market with m Stocks. Our goal is to compare the asymptotic growth rate of such strategies to the market. With the application of an Ergodic theorem, we show that the problem can be transformed to computing the expectation of a functional of the market weightsof the stocks. Expressing the gain in the re-balanced portfolio over the market portfolio as a functional of the market weights, we derive the condition under which the growth rate of the rebalanced strategy beats that of the market portfolio. We also show a method to compute the maximum transaction cost that can be paid in order for the rebalanced portfolio to beat the market portfolio. We discuss the result in the context of the Volatility-Stabilized model and the Geometric Brownian Motion model. In the secondpart of the thesis, we define the optimal re-balancing portfolio that maximizes the growth rate within the class of such fixed weight re-balancing strategies. We study the relationship of the optimum portfolio and optimal growth rate to the re-balancing time step and transaction cost coefficient. Finally, we look at the performance of such re-balancing strategies on real data sets obtained from Yahoo Finance. The study indicates that for small trading time steps, the re-balancing strategy under performs compared to the market in the presence of transaction costs.
Author: Raj Sau Publisher: ISBN: 9781267768278 Category : Languages : en Pages : 98
Book Description
In this thesis we study the performance of a re-balanced portfolio strategy relative to the market portfolio in the presence of transaction costs. The strategy involves re-balancing to fixed weights at regular time steps. We consider an equity market with m Stocks. Our goal is to compare the asymptotic growth rate of such strategies to the market. With the application of an Ergodic theorem, we show that the problem can be transformed to computing the expectation of a functional of the market weightsof the stocks. Expressing the gain in the re-balanced portfolio over the market portfolio as a functional of the market weights, we derive the condition under which the growth rate of the rebalanced strategy beats that of the market portfolio. We also show a method to compute the maximum transaction cost that can be paid in order for the rebalanced portfolio to beat the market portfolio. We discuss the result in the context of the Volatility-Stabilized model and the Geometric Brownian Motion model. In the secondpart of the thesis, we define the optimal re-balancing portfolio that maximizes the growth rate within the class of such fixed weight re-balancing strategies. We study the relationship of the optimum portfolio and optimal growth rate to the re-balancing time step and transaction cost coefficient. Finally, we look at the performance of such re-balancing strategies on real data sets obtained from Yahoo Finance. The study indicates that for small trading time steps, the re-balancing strategy under performs compared to the market in the presence of transaction costs.
Author: Anthony W. Lynch Publisher: ISBN: Category : Languages : en Pages : 65
Book Description
We consider the impact of transaction costs on the portfolio decisions of a long-lived agent with isoelastic preferences. In particular, we focus on how portfolio choice, rebalancing frequency and average cost incurred change over the lifecycle are affected by return predictability. Two types of costs are evaluated: proportional to the change in the holding of the risky asset and a fixed fraction of portfolio value. We find that realistic transaction costs can materially affect rebalancing behavior, creating no-trade regions that widen near the investor's terminal date. At the same time, realistic proportional and fixed costs have little effect on the midpoint of the no-trade region, unless liquidation costs differ across assets. Return predictability calibrated to U.S. stock returns is found to have large effects on rebalancing behavior relative to independent and identically distributed (i.i.d.) returns with the same unconditional distribution. For example, return predictability causes rebalancing frequency to increase, and cost incurred to increase by an order of magnitude, at all points in the investor's life. No-trade regions early in life are wider when returns are predictable than when they are not. Finally, we find that the nature of the return predictability, including the presence or not of return heteroscedasticity, can have large effects on rebalancing behavior.
Author: Mark Kritzman Publisher: ISBN: Category : Languages : en Pages : 23
Book Description
Institutional investors usually employ mean-variance analysis to determine optimal portfolio weights. Almost immediately upon implementation, however, the portfolio's weights become sub-optimal as changes in asset prices cause the portfolio to drift away from the optimal targets. In an idealized world without transaction costs investors would rebalance continually to the optimal weights. In the presence of transaction costs investors must balance the cost of sub-optimality with the cost of restoring the optimal weights. We apply a quadratic heuristic to address the asset weight drift problem, and we compare it to a dynamic programming solution as well as to standard industry heuristics. Our tests reveal that the quadratic heuristic provides solutions that are remarkably close to the dynamic programming solutions for those cases in which dynamic programming is feasible and far superior to solutions based on standard industry heuristics. In the case of five assets, in fact, it performs better than dynamic programming due to approximations required to implement the dynamic programming algorithm. Moreover, unlike the dynamic programming solution, the quadratic heuristic is scalable to as many as several hundreds assets.
Author: Bin Li Publisher: CRC Press ISBN: 1482249642 Category : Business & Economics Languages : en Pages : 227
Book Description
With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.
Author: Jeremy Dale Myers Publisher: ISBN: Category : Languages : en Pages : 88
Book Description
In the financial world, many quantitative investment managers have developed sophisticated statistical techniques to generate signals about expected returns from previous market data. However, the manner in which they apply this information to rebalancing their portfolios is often ad-hoc, trading off between rebalancing their assets into an allocation that generates the greatest expected return based on the generated signals and the incurred transaction costs that the reallocation will require. In this thesis, we develop an approximation to our investor's true value function which incorporates both return predictability and transaction costs. By optimizing our approximate value function at each time step, we will generate a portfolio strategy that closely emulates the optimal portfolio strategy, which is based on the true value function. In order to determine the optimal set of parameters for our approximate function which will generate the best overall portfolio performance, we develop a simulation-based method. Our computational implementation is verified against well-known base cases. We determine the optimal parameters for our approximate function in the single stock and bond case. In addition, we determine a confidence level on our simulation results. Our approximate function gives us useful insight into the optimal portfolio allocation in complex higher dimensional cases. Our function derivation and simulation methodology extend easily to portfolio allocation in higher dimensional cases, and we implement the modifications required to run these simulations. Simple cases are tested and more complex tests are specified for testing when appropriate dedicated computing resources are available.
Author: David T. Brown Publisher: ISBN: Category : Languages : en Pages : 24
Book Description
Portfolio rebalancing strategies involve trading tracking error off against the transaction costs of frequent trading to avoid tracking error. Existing analytical work derives optimal rebalancing strategies that result in minimal expected transactions required to achieve a given level of tracking error. Employing the strategies described in the literature can obtain the same level of tracking error as naiuml;ve strategies often observed in practice with much lower transaction costs. We show that further (and substantial) reductions in expected transaction costs can be obtained by using derivatives to synthetically rebalance a portfolio. However, the design of an efficient synthetic rebalancing program is complicated. We show the key elements of the design of an efficient synthetic rebalancing program. Finally, we show how a rebalancing strategy should be designed when a portfolio experiences cash inflows and outflows.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
In this thesis we extend the Markowitz Mean-Variance model to a rebalancing portfolio optimization problem incorporating realistic considerations such as transaction costs and a risk-free asset with short-selling allowed, and we apply the Tabu Search (TS) heuristic to solve practical portfolio problems. First of all, we propose a biobjective portfolio optimization model which we expect to yield a portfolio equilibrium by combining the two objectives: maximize the portfolioââ'¬â"¢s expected return and minimize its risk. For realistic portfolio problems we consider the multi-objective portfolio optimization models incorporating the risk-free asset and its short-selling and nonlinear transaction costs based on a single-period and a rebalancing portfolio optimization problem. Especially, to solve the rebalancing portfolio problem, we develop an adaptive, advanced TS algorithm having an evolutionary neighborhood structure, and we solve the problem with an iterative folding back procedure in the decision tree structure. Computational studies are performed with a risk-free asset and the number of risky assets to be 5, 10, 12, and 15 for both the single-period and rebalancing portfolio problems.
Author: Walter Sun Publisher: ISBN: Category : Languages : en Pages : 25
Book Description
Institutional fund managers generally rebalance using ad hoc methods such as calendar basis or tolerance band triggers. We propose a different framework that quantifies the cost of a rebalancing strategy in terms of risk-adjusted returns net of transaction costs. We then develop an optimal rebalancing strategy that actively seeks to minimize that cost. We use certainty equivalents and the transaction costs associated with a policy to define a cost-to-go function, and we minimize this expected cost-to-go using dynamic programming. We apply Monte Carlo simulations to demonstrate that our method outperforms traditional rebalancing strategies like monthly, quarterly, annual, and 5% tolerance rebalancing. We also show the robustness of our method to model error by performing sensitivity analyses.
Author: Raj Sau
Author: Raj Sau
Author: Wendell Helms Fleming
Author: Anthony W. Lynch
Author: Mark Kritzman
Author: Bin Li
Author: Jeremy Dale Myers
Author: David T. Brown
Author: 
Author: Walter Sun
Author: Ajay Subramanian Aiyer