The Experts below are selected from a list of 11787 Experts worldwide ranked by ideXlab platform
Charlotte Christiansen - One of the best experts on this subject based on the ideXlab platform.
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Mean Reversion in US and International Short Rates
The North American Journal of Economics and Finance, 2010Co-Authors: Charlotte ChristiansenAbstract:We extend the CKLS one factor short rate model to include nonlinear Mean Reversion in a new way. We allow for extreme value Mean Reversion by including the smallest short rate during the previous year in the Mean equation. The US short rate is found to exhibit extreme value Mean Reversion. The evidence of Mean Reversion varies across the short rates in the US and five other major markets (Canada, Germany, Japan, Switzerland, and the UK).
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Mean Reversion in US and International Short Rates
2008Co-Authors: Charlotte ChristiansenAbstract:In this paper we extend the CKLS one factor short rate model to include extreme value nonlinear Mean Reversion. Similarly to a recent stock market study, we include the smallest short rate during the previous year in the Mean equation. We investigate the US and five other major markets (Canada, Germany, Japan, Switzerland, and the UK). There is extreme value Mean Reversion in the US short rate. For Japan there is both linear and nonlinear Mean Reversion. For the remaining short rates there is no evidence of Mean Reversion.
Vivekanand Gopalkrishnan - One of the best experts on this subject based on the ideXlab platform.
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confidence weighted Mean Reversion strategy for online portfolio selection
ACM Transactions on Knowledge Discovery From Data, 2013Co-Authors: Bin Li, Peilin Zhao, Vivekanand GopalkrishnanAbstract:Online portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing online portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the Mean Reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online portfolio selection strategy named Confidence Weighted Mean Reversion (CWMR). Inspired by the Mean Reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR models the portfolio vector as a Gaussian distribution, and sequentially updates the distribution by following the Mean Reversion trading principle. CWMR’s closed-form updates clearly reflect the Mean Reversion trading idea. We also present several variants of CWMR algorithms, including a CWMR mixture algorithm that is theoretical universal. Empirically, CWMR strategy is able to effectively exploit the power of Mean Reversion for online portfolio selection. Extensive experiments on various real markets show that the proposed strategy is superior to the state-of-the-art techniques. The experimental testbed including source codes and data sets is available online.
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pamr passive aggressive Mean Reversion strategy for portfolio selection
Machine Learning, 2012Co-Authors: Bin Li, Peilin Zhao, Vivekanand GopalkrishnanAbstract:This article proposes a novel online portfolio selection strategy named "Passive Aggressive Mean Reversion" (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the Mean Reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the Mean Reversion property of markets. By analyzing PAMR's update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the Mean Reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art portfolio selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications. The experimental testbed including source codes and data sets is available at http://www.cais.ntu.edu.sg/~chhoi/PAMR/ .
Bin Li - One of the best experts on this subject based on the ideXlab platform.
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confidence weighted Mean Reversion strategy for online portfolio selection
ACM Transactions on Knowledge Discovery From Data, 2013Co-Authors: Bin Li, Peilin Zhao, Vivekanand GopalkrishnanAbstract:Online portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing online portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the Mean Reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online portfolio selection strategy named Confidence Weighted Mean Reversion (CWMR). Inspired by the Mean Reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR models the portfolio vector as a Gaussian distribution, and sequentially updates the distribution by following the Mean Reversion trading principle. CWMR’s closed-form updates clearly reflect the Mean Reversion trading idea. We also present several variants of CWMR algorithms, including a CWMR mixture algorithm that is theoretical universal. Empirically, CWMR strategy is able to effectively exploit the power of Mean Reversion for online portfolio selection. Extensive experiments on various real markets show that the proposed strategy is superior to the state-of-the-art techniques. The experimental testbed including source codes and data sets is available online.
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pamr passive aggressive Mean Reversion strategy for portfolio selection
Machine Learning, 2012Co-Authors: Bin Li, Peilin Zhao, Vivekanand GopalkrishnanAbstract:This article proposes a novel online portfolio selection strategy named "Passive Aggressive Mean Reversion" (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the Mean Reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the Mean Reversion property of markets. By analyzing PAMR's update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the Mean Reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art portfolio selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications. The experimental testbed including source codes and data sets is available at http://www.cais.ntu.edu.sg/~chhoi/PAMR/ .
Laura Spierdijk - One of the best experts on this subject based on the ideXlab platform.
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Mean Reversion in international stock markets an empirical analysis of the 20th century
Journal of International Money and Finance, 2012Co-Authors: Laura Spierdijk, J A Bikker, Pieter Van Den HoekAbstract:Abstract This paper analyzes Mean Reversion in the stock markets of 18 OECD countries during the years 1900–2009. In this period it takes stock prices about 18.5 years, on average, to absorb half of a shock. However, using a rolling-window approach we establish large fluctuations in the speed of Mean Reversion over time. The highest Mean Reversion speed is found for the period including the Great Depression and the start of World War II. Furthermore, the early years of the Cold War and the period containing the Oil Crisis of 1973, the Energy Crisis of 1979 and Black Monday in 1987 are also characterized by relatively fast Mean Reversion. We document half-lives ranging between 2.0 and 22.6 years. Our results suggest that the speed at which stocks revert to their fundamental value is higher in periods of high economic uncertainty, caused by major economic and political events.
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Mean Reversion in Stock Prices: Implications for Long-Term Investors
SSRN Electronic Journal, 2012Co-Authors: Laura Spierdijk, Jacob A. BikkerAbstract:This paper discusses the implications of Mean Reversion in stock prices for longterm investors such as pension funds. We start with a general definition of a Meanreverting price process and explain how Mean Reversion in stock prices is related to Mean Reversion in stock returns. Subsequently, we show that Mean Reversion makes stocks less risky for investors with long investment horizons. Next, we consider a Mean-variance efficient investor and show how Mean Reversion in stock prices affects such an investor’s optimal portfolio weights. Finally, we discuss the implications of our findings for the investment decisions of long-term investors.
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Mean Reversion in international stock markets: An empirical analysis of the 20th century
Journal of International Money and Finance, 2012Co-Authors: Laura Spierdijk, Jacob A. Bikker, Pieter Van Den HoekAbstract:This paper analyzes Mean Reversion in international stock markets during the period 1900-2008, using annual data. Our panel of stock indexes in seventeen developed countries, covering a time span of more than a century, allows us to analyze in detail the dynamics of the Mean-Reversion process. In the period 1900-2008 it takes stock prices about 13.8 years, on average, to absorb half of a shock. However, using a rolling-window approach we establish large fluctuations in the speed of Mean Reversion over time. The highest Mean Reversion speed is found for the period including the Great Depression and the start of World War II. Furthermore, the early years of the Cold War and the period covering the Oil Crisis of 1973, the Energy Crisis of 1979 and Black Monday in 1987 are also characterized by relatively fast Mean Reversion. Overall, we document half-lives ranging from a minimum of 2.1 years to a maximum of 23.8 years. In a substantial number of time periods no significant Mean Reversion is found at all, which underlines the fact that the choice of data sample contributes substantially to the evidence in favour of Mean Reversion. Our results suggest that the speed at which stocks revert to their fundamental value is higher in periods of high economic uncertainty, caused by major economic and political events.
Mukul Pal - One of the best experts on this subject based on the ideXlab platform.
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Markov and the Mean Reversion Framework
SSRN Electronic Journal, 2015Co-Authors: Mukul PalAbstract:Natural systems witness Reversion and divergence simultaneously across different periods of time. This paper tests the performance proxy as mentioned in a previous paper on the ‘Mean Reversion Framework’ for Markov’s transition probabilities. The framework exhibits a stable pattern when tested for STOXX 50, SP value, growth and core exhibit a consistency in growth and decay pattern. Both value and growth exhibit persistence compared to the core bin and tends to decay slowly. While the core bins show a symmetric decay across other bins. Such a probabilistic behavior in group components leads the author to believe that the Mean Reversion framework is indeed converging and diverging leading to a robust expression of a stock market system. The framework could work across data sets from various domains, confirming the proposed universality of the Mean Reversion framework.
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Mean Reversion Framework
SSRN Electronic Journal, 2015Co-Authors: Mukul PalAbstract:The original work by Galton on Mean Reversion in 1886 emphasized relative before absolute, talked about the relation of the variable with the sample average, pointed out the balance between convergence and divergence and showcased cross-domain expression of Mean Reversion. Though Mean Reversion as an idea has been in the open domain for 130 years, there has been no attempt to extend the Galtonian definition of natural systems into a framework that could allow for better understanding and functioning of natural systems and also explain the failures of Reversion. Any proxy that expresses Galtonian Reversion should be simple, relative and universal. This paper takes a stock market case and defines a framework that builds on the Galtonian explanation of a natural system and incorporates the idea of relative ranking, relative average, balancing forces of convergence and divergence, and the universal workability of the framework across domains.
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Mean Reversion Indexing
SSRN Electronic Journal, 2012Co-Authors: Mukul PalAbstract:In their 1985 paper ‘Does the stock market overeact?', DeBondt and Thaler explained the idea of Mean Reversion and how it leads to the Loser’s portfolio of 3 years outperforming the Winner’s portfolio of the same time. Based on Mean Reversion, this paper illustrates a new stock selection and trend determining approach. The paper uses an innovative approach to convert price performance data into non price ranking data, which is positively tested for Mean Reversion and stationarity.
