Optimizing the terminal wealth under partial information: The drift process as a continuous time Markov chain

Sass, Jörn; Haussmann, U. G.

doi:10.1007/s00780-004-0132-9

Cited by 156 publications

(155 citation statements)

References 22 publications

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“…Researchers, such as Honda (2003), Sass and Haussmann (2004), Taksar and Zeng (2007), and Erlwein et al (2009), proved that the regime-switching variables made significant improvements to portfolio selection models. The RSM is not identical to the HMM introduced by Baum and Petrie (1966).…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Model for Stock Trading

Nguyen

2018

IJFS

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Hidden Markov model (HMM) is a statistical signal prediction model, which has been widely used to predict economic regimes and stock prices. In this paper, we introduce the application of HMM in trading stocks (with S&P 500 index being an example) based on the stock price predictions. The procedure starts by using four criteria, including the Akaike information, the Bayesian information, the Hannan Quinn information, and the Bozdogan Consistent Akaike Information, in order to determine an optimal number of states for the HMM. The selected four-state HMM is then used to predict monthly closing prices of the S&P 500 index. For this work, the out-of-sample R 2 OS , and some other error estimators are used to test the HMM predictions against the historical average model. Finally, both the HMM and the historical average model are used to trade the S&P 500. The obtained results clearly prove that the HMM outperforms this traditional method in predicting and trading stocks.

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Section: Introductionmentioning

confidence: 99%

Hidden Markov Model for Stock Trading

Nguyen

2018

IJFS

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“…The mathematical basis of this approach is the martingale method for stochastic optimal control which was pioneered by Rishel [37], Duncan and Varaiya [38,39], and Davis [40]. The martingale approach has been used to discuss optimal asset allocation problems in some filtered financial models (see, e.g., Sass and Haussmann [15], Korn et al [23], and Siu [24] and the relevant references therein). In this section, the classical convex dual arguments in, for example, Karatzas and Shreve [8], Pham [10], and Cvitanic and Karatzas [7], will be used.…”

Section: Martingale Approach For Asset Allocationmentioning

confidence: 99%

A Stochastic Flows Approach for Asset Allocation with Hidden Economic Environment

Siu

2015

International Journal of Stochastic Analysis

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An optimal asset allocation problem for a quite general class of utility functions is discussed in a simple two-state Markovian regimeswitching model, where the appreciation rate of a risky share changes over time according to the state of a hidden economy. As usual, standard filtering theory is used to transform a financial model with hidden information into one with complete information, where a martingale approach is applied to discuss the optimal asset allocation problem. Using a martingale representation coupled with stochastic flows of diffeomorphisms for the filtering equation, the integrand in the martingale representation is identified which gives rise to an optimal portfolio strategy under some differentiability conditions.

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“…It is well-known (see for example [11,17] that the F-semimartingale decomposition of X (h) is given by…”

Section: Portfolio Optimization Under Partial Information With Expertmentioning

confidence: 99%

Portfolio Optimization Under Partial Information With Expert Opinions

Frey

Gabih

Wunderlich

2012

Int. J. Theor. Appl. Finan.

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This paper investigates optimal portfolio strategies in a market with partial information on the drift. The drift is modelled as a function of a continuous-time Markov chain with finitely many states which is not directly observable. Information on the drift is obtained from the observation of stock prices. Moreover, expert opinions in the form of signals at random discrete time points are included in the analysis. We derive the filtering equation for the return process and incorporate the filter into the state variables of the optimization problem. This problem is studied with dynamic programming methods. In particular, we propose a policy improvement method to obtain computable approximations of the optimal strategy. Numerical results are presented at the end.

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Optimizing the terminal wealth under partial information: The drift process as a continuous time Markov chain

Cited by 156 publications

References 22 publications

Hidden Markov Model for Stock Trading

Hidden Markov Model for Stock Trading

A Stochastic Flows Approach for Asset Allocation with Hidden Economic Environment

Portfolio Optimization Under Partial Information With Expert Opinions

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