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Optimal Rebalancing: A Scalable Solution
"... Institutional investors usually employ meanvariance analysis to determine optimal portfolio weights. Almost immediately upon implementation, however, the portfolioâ€™s weights become suboptimal as changes in asset prices cause the portfolio to drift away from the optimal targets. We apply a quadrati ..."
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Cited by 2 (0 self)
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quadratic heuristic to address the optimal rebalancing problem, and we compare it to a dynamic programming solution as well as to standard industry heuristics. The quadratic heuristic provides solutions that are remarkably close to the dynamic programming solution. Moreover, unlike the dynamic programming
Optimal Rebalancing of Binary Search Trees
, 1996
"... We give, for any reasonable function f , a scheme for rebalancing a binary search tree with amortized O(f(n)) work per update while guaranteeing a height bounded by dlog(n + 1) +1=f(n)e for all n. As a corollary, in the semidynamic case, height dlog(n+1)e can be guaranteed with amortized O(log n) w ..."
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We give, for any reasonable function f , a scheme for rebalancing a binary search tree with amortized O(f(n)) work per update while guaranteeing a height bounded by dlog(n + 1) +1=f(n)e for all n. As a corollary, in the semidynamic case, height dlog(n+1)e can be guaranteed with amortized O(log n
OPTIMAL REBALANCING OF PORTFOLIOS WITH TRANSACTION COSTS ASSUMING CONSTANT RISK AVERSION
"... Optimal rebalancing of portfolios with transaction costs assuming constant risk aversion ..."
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Optimal rebalancing of portfolios with transaction costs assuming constant risk aversion
Testing Rebalancing Strategies for StockBond Portfolios: What Is the Optimal Rebalancing Strategy?
, 2013
"... We compare the performance of different rebalancing strategies under realistic market conditions by reporting statistical significance levels. Our analysis is based on historical data from the United States, the United Kingdom, as well as Germany and comprises three different classes of rebalancing ..."
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(namely periodic, threshold, and range rebalancing). Despite crosscountry differences, we provide evidence that both excessive as well as too infrequent rebalancing lead to an inferior riskadjusted portfolio performance. Specifically, the optimal rebalancing strategy seems to be quarterly periodic
Optimal Rebalancing of Portfolio Weights under Timevarying Return Volatility.
, 2001
"... This paper considers horizon effects on portfolio weights under time varying and forecastable return volatility. The return volatility is modelled as a GARCHM, which is general enough to encompass both constant and time varying mean. The analysis confirms earlier results, namely that there are n ..."
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This paper considers horizon effects on portfolio weights under time varying and forecastable return volatility. The return volatility is modelled as a GARCHM, which is general enough to encompass both constant and time varying mean. The analysis confirms earlier results, namely that there are no horizon effects when the stochastic process, that governs asset returns, has constant mean. However, when time varying and forecastable volatility is included in the mean equation, there are horizon effects. The horizon effect arises because the ratio of mean over variance is not constant over time. We show that three features are important for the horizon effect: First, the size of the parameter on conditional volatility in the mean equation. Second, persistence in conditional volatility. Third, the asymmetry in volatility have some effect. In addition the parameter of relative risk aversion is important, the relationship between risk aversion and horizon effects is positive.
IIB Dynamic Programming................................. 6 III Optimal Rebalancing Using Dynamic Programming 6
"... 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 riskadjusted returns net of transaction costs. We then develop an optimal rebalanci ..."
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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 riskadjusted returns net of transaction costs. We then develop an optimal
GPFS: A SharedDisk File System for Large Computing Clusters
 In Proceedings of the 2002 Conference on File and Storage Technologies (FAST
, 2002
"... GPFS is IBM's parallel, shareddisk file system for cluster computers, available on the RS/6000 SP parallel supercomputer and on Linux clusters. GPFS is used on many of the largest supercomputers in the world. GPFS was built on many of the ideas that were developed in the academic community ove ..."
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Cited by 518 (3 self)
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GPFS is IBM's parallel, shareddisk file system for cluster computers, available on the RS/6000 SP parallel supercomputer and on Linux clusters. GPFS is used on many of the largest supercomputers in the world. GPFS was built on many of the ideas that were developed in the academic community over the last several years, particularly distributed locking and recovery technology. To date it has been a matter of conjecture how well these ideas scale. We have had the opportunity to test those limits in the context of a product that runs on the largest systems in existence. While in many cases existing ideas scaled well, new approaches were necessary in many key areas. This paper describes GPFS, and discusses how distributed locking and recovery techniques were extended to scale to large clusters.
Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora
, 1997
"... ..."
Are investors reluctant to realize their losses
 Journal of Finance
, 1998
"... I test the disposition effect, the tendency of investors to hold losing investments too long and sell winning investments too soon, by analyzing trading records for 10,000 accounts at a large discount brokerage house. These investors demonstrate a strong preference for realizing winners rather than ..."
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Cited by 622 (14 self)
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I test the disposition effect, the tendency of investors to hold losing investments too long and sell winning investments too soon, by analyzing trading records for 10,000 accounts at a large discount brokerage house. These investors demonstrate a strong preference for realizing winners rather than losers. Their behavior does not appear to be motivated by a desire to rebalance portfolios, or to avoid the higher trading costs of low priced stocks. Nor is it justified by subsequent portfolio performance. For taxable investments, it is suboptimal and leads to lower aftertax returns. Taxmotivated selling is most evident in December. THE TENDENCY TO HOLD LOSERS too long and sell winners too soon has been labeled the disposition effect by Shefrin and Statman ~1985!. For taxable investments the disposition effect predicts that people will behave quite differently than they would if they paid attention to tax consequences. To test the disposition effect, I obtained the trading records from 1987 through 1993 for 10,000 accounts at a large discount brokerage house. An analysis of these
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