A P-spline based clustering approach for portfolio selection

Iorio, Carmela; Frasso, Gianluca; D’Ambrosio, Antonio; Siciliano, Roberta

doi:10.1016/j.eswa.2017.11.031

Cited by 47 publications

(21 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, since the Kelly criterion is the best strategy to use on a series of favorable bets, its use in option strategies and derivatives is also under investigation as it could result in maximizing the benefits compared with the traditional trading strategies. Last but not least, we will implement the method introduced in Iorio et al [36,37] to select stocks to be included in a Kelly portfolio through P-spline clustering of time series.…”

Section: Discussionmentioning

confidence: 99%

Practical Implementation of the Kelly Criterion: Optimal Growth Rate, Number of Trades, and Rebalancing Frequency for Equity Portfolios

Carta

Conversano

2020

Front. Appl. Math. Stat.

View full text Add to dashboard Cite

We develop a general framework to apply the Kelly criterion to the stock market data, and consequently, to portfolio optimization. Under few conditions, using Monte Carlo simulations with different scenarios we prove that the Kelly criterion beats any other approach in many aspects. In particular, it maximizes the expected growth rate and the median of the terminal wealth. We also show that, under a normal distribution of returns, the Kelly criterion has the best performance in the long run. Next, we optimize a portfolio with the Kelly criterion with no leverage and no short selling conditions and show that this portfolio lays in the mean-variance efficient frontier and has higher expected return and higher variance, although it is less diversified, respect to the tangent portfolio optimized under the Markowitz approach. Finally, we implement a dynamic strategy applied on the European stock market data and compare the results between the tangent and the optimal Kelly portfolios. In a dynamic setting, the rolling Kelly portfolio outperforms competitors particularly in the case of rebalanced portfolios optimized with a 2-years window width.

show abstract

Section: Discussionmentioning

confidence: 99%

Practical Implementation of the Kelly Criterion: Optimal Growth Rate, Number of Trades, and Rebalancing Frequency for Equity Portfolios

Carta

Conversano

2020

Front. Appl. Math. Stat.

View full text Add to dashboard Cite

show abstract

“…We consider first how the clustering approaches can be used to form portfolios of stocks (e.g., [1,3]) in the case of the first analyzed dataset containing the N = 25 stocks without missing values included in the FTSE100 Index.…”

Section: Ftse100mentioning

confidence: 99%

“…Clustering of time series data is an important tool for data analysis in different areas ranging from engineering to finance and economics. For example, through clustering methods it is possible to build portfolios of similar stocks for financial applications (for example [1][2][3]). The main clustering approaches for time series can be summarized into three main groups [4]: observation-based, feature-based, and model-based.…”

Section: Introductionmentioning

confidence: 99%

Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series

2021

View full text Add to dashboard Cite

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.

show abstract

“…They include the portfolio model based on the fuzzy goal programming, the portfolio optimization approach using the quadratic programming, the portfolio selection approach using the multiobjective stochastic programming, the adaptive neuro‐fuzzy portfolio model, the multiperiod fuzzy portfolio optimization model, and the fuzzy multiobjective higher order moment portfolio model . In addition, the portfolio approaches have been proposed based on the operational models such as the decision support portfolio selection approach, the neural network portfolio model, the value at risk portfolio model, the data envelopment analysis cross‐efficiency portfolio model, the genetic algorithm portfolio model, the mean‐semi‐entropy portfolio selection approach, and the P‐spline clustering portfolio selection model . The obvious advantages of these operational approaches are that fewer data are needed and the optimal investment ratios can be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…15 In addition, the portfolio approaches have been proposed based on the operational models such as the decision support portfolio selection approach, 16 the neural network portfolio model, 17 the value at risk portfolio model, 18 the data envelopment analysis cross-efficiency portfolio model, 19 the genetic algorithm portfolio model, 20 the mean-semi-entropy portfolio selection approach, 21 and the P-spline clustering portfolio selection model. 22 The obvious advantages of these operational approaches are that fewer data are needed and the optimal investment ratios can be obtained.…”

mentioning

confidence: 99%

Score-hesitation trade-off and portfolio selection under intuitionistic fuzzy environment

Zhou

Zhang

2018

Int J Intell Syst

View full text Add to dashboard Cite

Qualitative portfolio selection approach is a suitable technique for obtaining an optimal portfolio when quantitative data are unavailable and traditional portfolio models are ineffective. However, few studies focus on this issue. This study addresses the lack of research by defining the score‐hesitation trade‐off rule and introducing the intuitionistic fuzzy set (IFS), based on which an intuitionistic fuzzy portfolio selection (IFPS) model is proposed. The IFS is introduced because of its comprehensive consideration of preference and nonpreference, and is used to represent qualitatively evaluated information from investors and experts. Furthermore, an intuitionistic fuzzy investment scenario is established and a trisection approach is designed to distinguish three types of risk investors, based on which three corresponding IFPS models are constructed. After this, a portfolio selection process under the intuitionistic fuzzy environment is provided, and a simple example is given to show the application of the process. In addition, the investment opportunities and efficient frontier of the IFPS model are investigated to demonstrate the effectiveness of the proposed portfolio selection model. Finally, an example of calculating optimal investment ratios and selecting an optimal portfolio for four newly listed stocks in China is provided to demonstrate the feasibility and practicability of the proposed approaches.

show abstract

A P-spline based clustering approach for portfolio selection

Cited by 47 publications

References 40 publications

Practical Implementation of the Kelly Criterion: Optimal Growth Rate, Number of Trades, and Rebalancing Frequency for Equity Portfolios

Practical Implementation of the Kelly Criterion: Optimal Growth Rate, Number of Trades, and Rebalancing Frequency for Equity Portfolios

Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series

Score-hesitation trade-off and portfolio selection under intuitionistic fuzzy environment

Contact Info

Product

Resources

About