The Experts below are selected from a list of 4641 Experts worldwide ranked by ideXlab platform

Soichiro Takahashi - One of the best experts on this subject based on the ideXlab platform.

  • creating investment scheme with state space modeling
    2017
    Co-Authors: Masafumi Nakano, Akihiko Takahashi, Soichiro Takahashi
    Abstract:

    This paper proposes a unified approach to creating high performance Portfolios with various desirable properties for investors. Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as achieving target mean-variance Portfolios or Sharpe ratios, and generating alphas (additional returns) over benchmark indexes.More concretely, in state space modeling to financial time-series data, we can apply the system model to representing Portfolio Weight processes with various constraints, as well as the standard underlying state variables such as volatility processes. Moreover, we may formulate the observation model to stand for target value processes with non-linear functions of observed and latent variables.Numerical experiments demonstrate the effectiveness of our methodology through alpha-creation against S&P 500 futures, and substantial improvement of the performance on mean-variance Portfolios.

  • creating investment scheme with state space modeling
    2017
    Co-Authors: Masafumi Nakano, Akihiko Takahashi, Soichiro Takahashi
    Abstract:

    This paper proposes a unified approach to creating investment strategies with various desirable properties for investors. Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as generating additional returns over benchmark stock indexes or achieving target risk-adjusted returns. Our state space models with particle filtering algorithm are employed to develop expert systems for investment strategies in highly complex financial markets. More concretely, in our state space framework, we apply a system model to representing Portfolio Weight processes with various constraints, as well as the standard underlying state variables such as volatility processes. Further, we formulate an observation model to stand for target value processes with non-linear functions of observed and latent variables. Numerical experiments demonstrate the effectiveness of our methodology through creating excess returns over S&P 500 and generating investment Portfolios with fine risk-return profiles.

  • creating investment scheme with state space modeling
    CIRJE F-Series, 2017
    Co-Authors: Masafumi Nakano, Akihiko Takahashi, Soichiro Takahashi
    Abstract:

    This paper proposes a unified approach to creating investment strategies with various desirable properties for investors. Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as achieving target mean-variance Portfolios or Sharpe ratios, and generating alphas (additional returns) over benchmark indexes. More concretely, in state space modeling to financial time-series data, we can apply the system model to representing Portfolio Weight processes with various constraints, as well as the standard underlying state variables such as volatility processes. Moreover, we may formulate the observation model to stand for target value processes with non-linear functions of observed and latent variables. Numerical experiments demonstrate the effectiveness of our methodology through alpha-creation against S&P 500 futures, and substantial improvement of the performance on mean-variance Portfolios.

Ross B Barmish - One of the best experts on this subject based on the ideXlab platform.

  • rebalancing frequency considerations for kelly optimal stock Portfolios in a control theoretic framework
    Conference on Decision and Control, 2018
    Co-Authors: Chunghan Hsieh, John A Gubner, Ross B Barmish
    Abstract:

    In this paper, motivated by the celebrated work of Kelly, we consider the problem of Portfolio Weight selection to maximize expected logarithmic growth of a trader's account. Going beyond existing literature, our focal point here is the rebalancing frequency which we include as an additional parameter in the maximization. The problem is first set up in a control-theoretic framework, and then, the main question we address is as follows: In the absence of transaction costs, does high-frequency trading always lead to the best performance? Related to this question is our prior work on Kelly betting which examines the impact of making a wager and letting it ride. Our prior results indicate that it is often the case that there are no performance benefits associated with high-frequency trading. In the present paper, we generalize the analysis from the single-asset case to a Portfolio with multiple risky assets. We show that if there is an asset satisfying a certain dominance condition, then an optimal Portfolio consists of this asset alone; i.e., if the trader puts “all eggs in one basket,” performance becomes a constant function of rebalancing frequency. Said another way, the problem of rebalancing is rendered moot. The paper also includes simulations which address practical considerations associated with real stock prices vis-a-vis the dominance condition.

  • rebalancing frequency considerations for kelly optimal stock Portfolios in a control theoretic framework
    2018
    Co-Authors: Chunghan Hsieh, John A Gubner, Ross B Barmish
    Abstract:

    In this paper, motivated by the celebrated work of Kelly, we consider the problem of Portfolio Weight selection to maximize expected logarithmic growth. Going beyond existing literature, our focal point here is the rebalancing frequency which we include as an additional parameter in our analysis. The problem is first set in a control-theoretic framework, and then, the main question we address is as follows: In the absence of transaction costs, does high-frequency trading always lead to the best performance? Related to this is our prior work on betting, also in the Kelly context, which examines the impact of making a wager and letting it ride. Our results on betting frequency can be interpreted in the context of Weight selection for a two-asset Portfolio consisting of one risky asset and one riskless asset. With regard to the question above, our prior results indicate that it is often the case that there are no performance benefits associated with high-frequency trading. In the present paper, we generalize the analysis to Portfolios with multiple risky assets. We show that if there is an asset satisfying a new condition which we call dominance, then an optimal Portfolio consists of this asset alone; i.e., the trader has "all eggs in one basket" and performance becomes a constant function of rebalancing frequency. Said another way, the problem of rebalancing is rendered moot. The paper also includes simulations which address practical considerations associated with real stock prices and the dominant asset condition.

Richard Roll - One of the best experts on this subject based on the ideXlab platform.

  • seeking alpha it s a bad guideline for Portfolio optimization
    The Journal of Portfolio Management, 2016
    Co-Authors: Moshe Levy, Richard Roll
    Abstract:

    Alpha is the most popular measure for evaluating the performance of both individual assets and funds. The alpha of an asset with respect to a given benchmark Portfolio measures the change in the Portfolio’s Sharpe ratio driven by a marginal increase in the asset’s Portfolio Weight. Thus, alpha indicates which assets should be marginally over- or underWeighted relative to the benchmark Weights, and by how much. In this article, the authors show that alpha is actually an ineffective guideline for Portfolio optimization. The reason is that alpha only measures the effects of infinitesimal changes in the Portfolio Weights. For small but finite changes, which are those relevant to investors, the optimal Weight adjustments are almost unrelated to the alphas. In fact, in many cases the optimal adjustment is in the opposite direction of alpha—it may be optimal to reduce the Weight of an asset with a positive alpha, and vice versa. Rather than employing alphas as a guideline, the authors argue that investors can do much better by using direct optimization with the desired constraint on the distance from the benchmark Portfolio Weights.

  • seeking alpha it s a bad guideline for Portfolio optimization
    Social Science Research Network, 2015
    Co-Authors: Moshe Levy, Richard Roll
    Abstract:

    Alpha is the most popular measure for evaluating the performance of both individual assets and funds. The alpha of an asset with respect to a given benchmark Portfolio measures the change in the Portfolio’s Sharpe ratio driven by a marginal increase in the asset’s Portfolio Weight. Thus, alpha indicates which assets should be marginally over/underWeighted relative to the benchmark Weights, and by how much. This study shows that alpha is actually a bad guideline for Portfolio optimization. The reason is that alpha only measures the effects of infinitesimal changes in the Portfolio Weights. For small but finite changes, which are those relevant to investors, the optimal Weight adjustments are almost unrelated to the alphas. In fact, in many cases the optimal adjustment is in the opposite direction of alpha – it may be optimal to reduce the Weight of an asset with a positive alpha, and vice versa. Rather than employing alphas as a guideline, one can do much better by direct optimization with the desired constraint on the distance from the benchmark.

Masafumi Nakano - One of the best experts on this subject based on the ideXlab platform.

  • creating investment scheme with state space modeling
    2017
    Co-Authors: Masafumi Nakano, Akihiko Takahashi, Soichiro Takahashi
    Abstract:

    This paper proposes a unified approach to creating high performance Portfolios with various desirable properties for investors. Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as achieving target mean-variance Portfolios or Sharpe ratios, and generating alphas (additional returns) over benchmark indexes.More concretely, in state space modeling to financial time-series data, we can apply the system model to representing Portfolio Weight processes with various constraints, as well as the standard underlying state variables such as volatility processes. Moreover, we may formulate the observation model to stand for target value processes with non-linear functions of observed and latent variables.Numerical experiments demonstrate the effectiveness of our methodology through alpha-creation against S&P 500 futures, and substantial improvement of the performance on mean-variance Portfolios.

  • creating investment scheme with state space modeling
    2017
    Co-Authors: Masafumi Nakano, Akihiko Takahashi, Soichiro Takahashi
    Abstract:

    This paper proposes a unified approach to creating investment strategies with various desirable properties for investors. Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as generating additional returns over benchmark stock indexes or achieving target risk-adjusted returns. Our state space models with particle filtering algorithm are employed to develop expert systems for investment strategies in highly complex financial markets. More concretely, in our state space framework, we apply a system model to representing Portfolio Weight processes with various constraints, as well as the standard underlying state variables such as volatility processes. Further, we formulate an observation model to stand for target value processes with non-linear functions of observed and latent variables. Numerical experiments demonstrate the effectiveness of our methodology through creating excess returns over S&P 500 and generating investment Portfolios with fine risk-return profiles.

  • creating investment scheme with state space modeling
    CIRJE F-Series, 2017
    Co-Authors: Masafumi Nakano, Akihiko Takahashi, Soichiro Takahashi
    Abstract:

    This paper proposes a unified approach to creating investment strategies with various desirable properties for investors. Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as achieving target mean-variance Portfolios or Sharpe ratios, and generating alphas (additional returns) over benchmark indexes. More concretely, in state space modeling to financial time-series data, we can apply the system model to representing Portfolio Weight processes with various constraints, as well as the standard underlying state variables such as volatility processes. Moreover, we may formulate the observation model to stand for target value processes with non-linear functions of observed and latent variables. Numerical experiments demonstrate the effectiveness of our methodology through alpha-creation against S&P 500 futures, and substantial improvement of the performance on mean-variance Portfolios.

Chunghan Hsieh - One of the best experts on this subject based on the ideXlab platform.

  • rebalancing frequency considerations for kelly optimal stock Portfolios in a control theoretic framework
    Conference on Decision and Control, 2018
    Co-Authors: Chunghan Hsieh, John A Gubner, Ross B Barmish
    Abstract:

    In this paper, motivated by the celebrated work of Kelly, we consider the problem of Portfolio Weight selection to maximize expected logarithmic growth of a trader's account. Going beyond existing literature, our focal point here is the rebalancing frequency which we include as an additional parameter in the maximization. The problem is first set up in a control-theoretic framework, and then, the main question we address is as follows: In the absence of transaction costs, does high-frequency trading always lead to the best performance? Related to this question is our prior work on Kelly betting which examines the impact of making a wager and letting it ride. Our prior results indicate that it is often the case that there are no performance benefits associated with high-frequency trading. In the present paper, we generalize the analysis from the single-asset case to a Portfolio with multiple risky assets. We show that if there is an asset satisfying a certain dominance condition, then an optimal Portfolio consists of this asset alone; i.e., if the trader puts “all eggs in one basket,” performance becomes a constant function of rebalancing frequency. Said another way, the problem of rebalancing is rendered moot. The paper also includes simulations which address practical considerations associated with real stock prices vis-a-vis the dominance condition.

  • rebalancing frequency considerations for kelly optimal stock Portfolios in a control theoretic framework
    2018
    Co-Authors: Chunghan Hsieh, John A Gubner, Ross B Barmish
    Abstract:

    In this paper, motivated by the celebrated work of Kelly, we consider the problem of Portfolio Weight selection to maximize expected logarithmic growth. Going beyond existing literature, our focal point here is the rebalancing frequency which we include as an additional parameter in our analysis. The problem is first set in a control-theoretic framework, and then, the main question we address is as follows: In the absence of transaction costs, does high-frequency trading always lead to the best performance? Related to this is our prior work on betting, also in the Kelly context, which examines the impact of making a wager and letting it ride. Our results on betting frequency can be interpreted in the context of Weight selection for a two-asset Portfolio consisting of one risky asset and one riskless asset. With regard to the question above, our prior results indicate that it is often the case that there are no performance benefits associated with high-frequency trading. In the present paper, we generalize the analysis to Portfolios with multiple risky assets. We show that if there is an asset satisfying a new condition which we call dominance, then an optimal Portfolio consists of this asset alone; i.e., the trader has "all eggs in one basket" and performance becomes a constant function of rebalancing frequency. Said another way, the problem of rebalancing is rendered moot. The paper also includes simulations which address practical considerations associated with real stock prices and the dominant asset condition.