Portfolio Blending via Thompson Sampling

1 Introduction

The modern portfolio theory framework pioneered by [Markowitz, 1952] has been instrumental in developing and understanding financial markets and investment decision making. Thus far its mean-variance paradigm remains the pervasive formulation of portfolio choice problems in both academia and industry [Brandt, 2010; Kolm et al., 2014]. Its increasing popularity among pension funds, mutual funds and 401(k) plans has called for thorough understanding and careful implementing. Generally, the mean-variance framework formalizes the concept of return-risk tradeoff that investors should consider return and risk together to determine the allocation of funds among investment alternatives. In particular, it suggests that among available portfolios that achieve a particular return objective, investors should invest the portfolio with the smallest variance. All other portfolios are "inefficient" in terms of having a higher variance representing a higher risk. However, due to the hurdle of accurately estimating involved parameters, the mean-variance portfolio often performs poorly in out-of-sample settings [Broadie, 1993].

On the other hand, the concept of blending portfolios arising from different investment perspectives to construct a new portfolio can be traced back to the ingenious two-fund separation theorem by [Tobin, 1958]. In the mean-variance framework, the two-fund separation theorem states that the efficient portfolio can be considered as a linear combination of two portfolios. Given the unsatisfactory out-of-sample performance of the mean-variance portfolio, the two-fund separation theorem naturally brings us the opportunity of blending portfolios to achieve better performance than the meanvariance portfolio and other heuristic strategies. However, as the pivotal drivers of performance, blending coefficients that characterize the combination of portfolios demands a systematic and comprehensive way to determine.

Meanwhile, the massive amounts of data in the financial industry spark the use of advanced data analysis tools to implement online portfolio strategies. As machine learning algorithms have shown extreme efficiency in the automated process of large datasets, over years researchers have made significant efforts of designing real time data stream based portfolio strategies [Blum and Kalai, 1999; Cover and Ordentlich, 1996; Borodin et al., 2004; Agarwal et al., 2006; Li and Hoi, 2012; Shen et al., 2014; Shen and Wang, 2015]. Illustration over a wide range of online portfolio strategies may be found in the survey by [Li and Hoi, 2014], and the references therein.

In this paper, we address the conundrum of appropriately determining blending coefficients of portfolios in an online setting by a machine learning algorithm. We believe that it is a step in the development of exploiting machine learning algorithms for portfolio choice problems. In particular, we first construct three basis portfolios in finance prepared for blending and formulate the portfolio blending problem into a Thompson sampling problem. Then we model blending coefficients as probabilities of choosing basis portfolios and rest on Bayes decision rules to update the distribution characterizing those probabilities. With two sets of different basis portfolios, we design two blended portfolios accordingly. To justify their performance from various angles, we employ a suite of standard finance metrics consisting of Sharpe ratios, volatility, and maximum drawdowns. Our extensive empirical studies and comparisons of the two blended portfolios with seven competing strategies over five real-world market datasets conspicuously illustrate the superiority of the proposed Thompson sampling based blending algorithm.

2 Background and RelatedWork

In this section, we briefly discuss two topics, i.e., Thompson sampling and portfolio blending. The former covers a short history, the current advance, and the formulation in a bandit setting of Thompson sampling; the latter comprises of the discussion about the two-fund theorem with shrinkage rules and representative work.

2.1 Thompson Sampling

As a heuristic solution to the well-known explorationexploitation problem, Thompson sampling was first induced by [Thompson, 1933] in the early 1930's. Surprisingly, unlike other probability matching methods, such as Bayes decision rules, Thompson sampling remained unpopular for an extremely long time in the research community. Recently, Thompson sampling has been revisited by many researchers and successfully applied to various machine learning problems, such as reinforcement learning [Granmo, 2010], online advertising [Graepel et al., 2010] and Markov decision processes [Strens, 2000]. In particular, for multi-armed bandit learning problems, a recent empirical study shows that Thompson sampling is a highly promising strategy of addressing the exploration-exploitation tradeoff [Chapelle and Li, 2011]. Despite of its simplicity, Thompson sampling achieves comparable performance with competing methods such as upper confidence bound (UCB) and ✏-greedy methods. In addition, although in contrast with UCB [Auer et al., 2002] Thompson sampling lacks strong theoretical guarantees on the regret, recent studies have shown that it converges asymptoticly in the bandit learning context [Granmo, 2010; Agrawal and Goyal, 2012; Gopalan et al., 2014]. Also, the role of risk in bandit learning has started to be acknowledged and studied [Sani et al., 2012; Shen et al., 2015]. We briefly describe the Thompson sampling algorithm below.

Consider a set of actions A and a reward r. In each round, a player chooses an action ↵ 2 A and then receives the corresponding reward r 2 R following a probability distribution that depends on the issued action. The player attempts to determine a policy that can generate an action set ¶↵1↪ ▷ ▷ ▷ ↪ ↵k↪ ▷ ▷ ▷ ↪ ↵m♦ that creates the maximum cumulative reward after playing m rounds. In a Bayesian setting, the set of past observations D that consists of ¶(↵1↪ r1)↪ ▷ ▷ ▷ ↪ (↵k↪ rk)♦ is modeled as a parametric likelihood function P(r♣↵↪ ✓) with a set of parameters ✓. By assuming a prior distribution P(✓) on those parameters, the posterior distribution is given by P(✓♣D) / Qk P(rk♣↵k↪ ✓)P(✓). Denoting by ✓⇤ the set of unknown true parameters, the optimal action at time tk is determined by maximizing the expected reward, i.e., ↵⇤k = argmax↵k E(rk♣↵k↪ ✓⇤ ). However, since ✓⇤ is unknown, by randomly selecting an action according to its probability of being optimal, the action is chosen with probability:

where I is the indicator function. The implementation of Thompson sampling strategy can be realized by samplings, which is straightforward in many applications including multi-armed bandit problems. Briefly, in each round, the set of parameters ✓ is sampled from the posterior P(✓♣D) and the action ↵k are chosen to maximize E(rk♣↵k↪ ✓). A detailed description of Thompson sampling research may be found in [Russo and Van Roy, 2014].

2.2 Portfolio Blending

Although blending portfolios to construct a better performing portfolio sounds naive, the observed empirical results have demonstrated its superiority [DeMiguel et al., 2009]. Theoretically, the portfolio structure induced by Tobin's two-fund theorem implies that the two-fund theorem falls under the rubric of applying shrinkage directly to the portfolio weights. Since shrinkage estimators mitigate estimation error by introducing bias, the approach of blending portfolios provides a pathway to improving the mean-variance portfolio.

While the effectiveness of blending disparate portfolios varies by virtue of the specified shrinking target, they can often outperform the mean-variance portfolio and other heuristic portfolios [Meucci, 2009]. In particular, [Kan and Zhou, 2007] propose a three-fund blending portfolio to further improve the models based on Bayes-Stein shrinkage estimators [Jorion, 1986]. They include the third fund as to diminish the adverse impact of estimation error in terms of hedging the estimation risk embedded in the first two funds. [Tu and Zhou, 2011] consider optimally blending the equallyweighted portfolio with the mean-variance portfolio or with their early proposed three-fund blending portfolio. They calibrate the blending coefficients under the assumption of independent and identically distributed (i.i.d.) normal returns by maximizing investors' expected utility. Their results show that their four-fund blending portfolio outperforms the mean-variance portfolio but not always performs as well as the equally-weighted portfolio. Recently, [DeMiguel et al., 2013] attack the similar problem as [Tu and Zhou, 2011] by testing more economic criteria for coefficient calibration. Their results show the variance minimization criterion is most robust. Furthermore, among numerous approaches to improving the performance of the mean-variance portfolio, many of them essentially share the concept of portfolio blending in different forms [Jorion, 1986; Ledoit and Wolf, 2008]. A more comprehensive review of those variants of the meanvariance portfolio may be referred to [Kolm et al., 2014].

3 Methodology

In this section, we first introduce the notations and finance terms used in this paper. Then we discuss three basis portfolios for blending, formulate the problem of portfolio blending into a Bernoulli bandit problem, and calibrate the blending coefficients by Thompson sampling. Finally, we summarize the proposed algorithm.

3.1 Notations

In a self-financing, discrete-time and finite-horizon investment environment, we denote a series of trading periods as tk = k∆t, k = 0↪ ▷ ▷ ▷ ↪m, where ∆t represents one week or one month, depending on the rebalance interval. For simplicity, we use k for short as the index to indicate the trading period at time tk hereafter. From time tk-1 to tk the gross return vector of n risky assets accessible to investors is denoted as Rk = (Rk↪1↪ ▷ ▷ ▷ ↪ Rk↪i↪ ▷ ▷ ▷ ↪ Rk↪n)>. The gross return Rk↪i for the i-th asset is computed as Rk↪i = Sk↪i◁Sk-1↪i, where Sk↪i and Sk-1↪i represent the prices of the i-th asset at time tk and tk-1, respectively.

Denote by !k = (!k↪1↪ ▷ ▷ ▷ ↪ !k↪i↪ ▷ ▷ ▷ ↪ !k↪n)> the vector of the portfolio weights reflecting the investment decision at time tk. The i-th element of !k specifies the invested percentage of wealth in the i-th asset. We assume the sum of all the portfolio weights equals one, i.e., !>k 1 = Pni=1 !k↪i = 1, where 1 is a column vector with ones as its entities. If !k↪i > 0, it indicates that investors take a long position of the i-th asset. In contrast, !k↪i < 0 indicates a short sale of the i-th asset, where investors liquidate the borrowed i-th asset to invest other assets. If the price of the borrowed asset rebounds, investors will suffer from a loss. The maximum loss for a long position will be the total amount of invested wealth and the maximum loss of a short sale position could be infinity theoretically. Given gross returns and portfolio weights, we can compute the realized portfolio before-cost net return θk from time tk-1 to tk as θk = R>k !k - 1.

3.2 Basis Portfolios

In our study, we focus on three basis portfolios for blending, i.e., the equally-weighted, the value-weighted and the minimum-variance portfolios. Those portfolios are standard in finance and easy to compute from data.

Equally-weighted portfolio (EW): EW simply ignores all data information and distributes the investment equally among all the assets:

Value-weighted portfolio (VW): As a passive market mimicking strategy, VW is calculated by:

where ◦ denotes the Hadamard product of two vectors. VW assigns a weight to each asset equal to its market capitalization divided by the total market capitalization of all the assets at each rebalancing time.

Minimum-variance portfolio (MV): Denote by ⌃k the covariance matrix of the n asset returns Rk at time tk. MV as a variant of the mean-variance portfolio is computed by:

3.3 Portfolio Blending with Thompson Sampling

After obtaining the weights of basis portfolios, we take a linear combination to construct the blending portfolios. In particular, we blend the equally-weighted and the minimumvariance portfolios as:

and blend the value-weighted and the minimum-variance portfolios as:

where 0  δk  1 is the blending coefficient acting as the main driver of the performance after determining the basis portfolios. Intuitively, given a dynamic trading environment, an optimal blending should perform at least as well as any individual strategy. In this paper, we make the sequential decision on the blending coefficient δk by applying Thompson sampling to a Bernoulli bandit problem, as discussed below.

First, we consider the blending coefficient δk as the probability of choosing the minimum-variance portfolio !MV k . Intuitively, the blending portfolio can be read as the expectation of different portfolios if the blending coefficients are the corresponding probabilities of choosing those portfolios. If basis portfolios are constructed according to different projections of future market conditions, the blending coefficient δk acting as a probability captures the market view of investors. For example, if investors lack information to create sophisticated strategies, they may rely more on EW, i.e., put more weight on !EW k . Next, we assume the probability of choosing MV follows a Beta distribution with parameters a and b, i.e., δk ⇠ Beta(a↪ b). The Beta distribution with the support (0↪ 1) has the probability density function as f(x; a↪ b) / xa-1 (1 - x)b-1 with parameters a > 0 and b > 0 and the mean a◁(a + b). Given the probability density function f(x; a↪ b), the higher the a and b the tighter is the concentration around its mean. The Beta distribution is advantageous for Bernoulli rewards because if the prior is a Beta(a↪ b) distribution, after observing a Bernoulli test, the posterior distribution is Beta(a + 1↪ b) or Beta(a↪ b + 1), depending upon whether the test offers a success or a failure. In the limit of a !1, investors will be certain to select this portfolio; in contrast, if b ! 1 investors will surely not invest in this portfolio.

Further, to design our Bernoulli test, we set up a benchmark blending portfolio with its blending coefficient equal to the mean of the Beta(a↪ b) distribution i.e., ¯ δk = a◁(a + b). Therefore, the corresponding benchmark portfolios are:

where we use !EW (VW) k for short to represent the portfolio weight vector !EW k or !VW k . We then sample one ˜ δk from the Beta(a↪ b) distribution and construct the testing blending portfolios as:

After observing the gross return yield by the rebalancing, we call it a success or a failure based on:

Algorithm 1 Portfolio Blending via Thompson Sampling

• 1: Inputs: m, n, R-⌧+1↪ ▷ ▷ ▷ ↪Rm, ⌧
• 3: Compute the equally-weighted portfolio !EW k ;
• 2: for k = 1 !mdo
• 4: Compute the value-weighted portfolio !VW k by (3);
• 5: Estimate the covariance matrix of asset returns ⌃k by ¶Rk-⌧ ↪ ▷ ▷ ▷ ↪Rk-1♦ and compute the minimumvariance portfolio !MV k by (4);
• 6: Initialize the Beta distribution by a = 1 and b = 1;
• 7: for j = 1 ! ⌧ do
• 8: Compute the benchmark blending coefficient ¯ δj ;
• 9: Construct the benchmark portfolio ¯!EM (VM) j by (7);
• 10: Sample one ˜ δj from the Beta(a↪ b) distribution;
• 11: Construct the testing portfolio ˜!EM (VM) j by (8);
• 12: Compare the testing and benchmark portfolios according to (9) by using Rj-⌧ ;
• 13: Update a and b:
• 14: if Success then
• 15: a = a + 1;
• 16: else
• 17: b = b + 1;
• 18: Compute the optimal blending coefficient δ⇤ k by (10);
• 19: Construct the proposed TS-EM portfolio and the TSVM portfolio !TS-EM (TS-VM) k by (11);

20: Output:

The series of portfolios !TS-EM (TS-VM) k and the portfolio before-coast net returns θTS-EM (TS-VM) k for k = 1↪ ▷ ▷ ▷ ↪m.

Specifically, if R>k ˜!EM (VM) k > R>k ¯!EM (VM) k and ˜ δk > ¯ δk or R>k ˜!EM (VM) k < R>k ¯!EM (VM) k and ˜ δk < ¯ δk, we call it a success because investors have made a wise decision about the overweight or the underweight on MV. Otherwise, we call it a failure because investors have made an inadvisable bet on the weight. A success suggests updating the parameters such that in the next round of rebalance investors should have a higher probability of choosing MV, and vice versa. 1

Furthermore, similar to the steps in [Agrawal and Goyal, 2012], we apply Thompson sampling to implementing the distribution updating step. We start with the initial prior as Beta(1↪ 1) and ⌧ periods of historical data. Given no information about the performance of portfolios, Beta(1↪ 1), i.e., a standard uniform distribution, is reasonable to investors. At each rebalance time, investors construct the aforementioned Bernoulli test, observe a success or a failure thereafter, and correspondingly update the posterior distribution. After the training period with ⌧ rebalances, the algorithm ends up with the updated distribution as Beta(1 + a⌧ ↪ 1 + b⌧ ), by assuming investors have encountered a⌧ successes and b⌧ failures.

Finally, we determine the blending coefficient as the mean of the most updated distribution as:

Namely, the proposed Thompson sampling based equallyweighted and minimum-variance blending portfolio (TS- EM) and the value-weighted and minimum-variance blending portfolio (TS-VM) read:

1 If R>k ˜!EM (VM) k = R>k ¯!EM (VM) k , we do not update the parameters; if ˜ δk = ¯ δk, we simply re-sample from the Beta distribution.

Accordingly, the realized portfolio before-cost net return θk from time tk-1 to tk will be

On the one hand, while surpassing either EW or MV has been shown arduous, the proposed TS-EM portfolio aims to perform at least as well as EW and MV via the new blending algorithm. On the other hand, by incorporating market trend information in VW and risk control mechanism in MV, the proposed TS-VM portfolio attempts to exploit the interplay of VW and MV, thereby constructing a superior blending portfolio. In addition, we estimate the covariance matrix ⌃k by a factor model [Fan et al., 2008] based on the historical data in sliding windows with the size of ⌧ training data. Algorithm 1 succinctly summarizes the detailed procedure of constructing these two blending portfolios.

4 Experiments

In this section, we perform empirical studies to evaluate the proposed portfolio blending algorithm. We first describe the experimental settings, including a brief introduction of the testing benchmarks and the evaluation metrics. Then we will report the results and compare with seven state-of-theart competing portfolio strategies.

4.1 Data

To fairly appraise the new method, following [DeMiguel et al., 2009; Shen et al., 2014] in our experiments we choose five datasets from two distinct classes of benchmarks that represent both academic standards and real-world market datasets.

Fama and French datasets (FF) [Fama and French, 1992]: As standard evaluation protocols and oft-adopted testbeds in the finance community, the FF datasets are constructed portfolios of broad financial segments of the U.S. stock market. The datasets at the monthly frequency spanning a period of forty years have an extensive coverage to asset classes. Real-world market datasets [Shen et al., 2015]: The real-world datasets including ETF139 and EQ181 are crawled from Yahoo! Finance on a weekly basis from 2008 to 2012. The ETF139 dataset consists of 139 exchange-traded funds that are traded like stocks in the U.S. market. Not only do they offer investors more flexibility and channels to the market, but also they have the advantages on taxes and interests of the investment over mutual funds. The EQ181 dataset contains individual equities from the large-cap segment of the Russell 200 index that covers 63% of total market capitalization. After removing those stocks with missing historical data from the start of our testing periods, we finally collect a total of 181 U.S. stocks to form the EQ181 dataset.

We summarize those two groups of benchmarks in Table 1. They essentially embody different perspectives for performance assessment. On the one hand, the FF25, FF48 and FF100 datasets underline the long-term performance since the forty-year spanning would introduce limited selection bias and performance manipulation. On the other hand, the ETF139 and EQ181 datasets emphasize the robustness with respect to the higher trading frequency and the vicissitude market environment after the recent financial crisis in 2007.

4.2 Competing Portfolios

To comprehensively evaluate the performance of the two proposed portfolios, we consider seven state-of-the-art competing portfolios: (a) Equally-weighted portfolio (EW): EW in equation (2) has been shown to outperform 14 sophisticated models across seven empirical datasets as well as one simulated dataset at monthly frequency of 2000 years [DeMiguel et al., 2009]. Thus, EW is commonly suggested to serve as the first obvious but challenging benchmark in portfolio research. (b) Value-weighted portfolio (VW): While VW in equation (3) forms a passive portfolio, most active mutual fund managers have the difficulty of outperforming passive benchmarks such as the market even before netting out fees [Fama and French, 2010]. (c) Minimum-variance portfolio (MV): MV in equation (4) has consistently shown robust performance in different market conditions [Jagannathan and Ma, 2003]. (d) Two-fund portfolio by [Tu and Zhou, 2011] (TZT): TZT blends the traditional mean-variance and the EW portfolios to achieve both estimation error reduction and wealth growth. (e) Three-fund portfolio by [Kan and Zhou, 2007] (KZT): KZT encompasses the risk-free, the mean-variance and MV portfolios to diminish the inherent estimation error in the mean-variance portfolio by blending its alike variant. (f) Four-fund portfolio by [Tu and Zhou, 2011] (TZF): TZF is formed by mixing the KZT and the EW portfolios. Their study shows it performs comparably with EW in some special cases and better in general. (g) Online moving average reversion based portfolio by [Li and Hoi, 2012] (MAR): MAR developed by machine learning researchers has been shown to outperform 12 portfolio strategies across five datasets.

In sum, the first three strategies, i.e., EW, VW and MV, have been the common baselines for portfolio research in finance. They have been broadly adopted as the touchstones of portfolio performance. They also represent the special cases of blending with fixed blending coefficients. The next three portfolios, i.e., TZT, KZT and TZF, are well recognized as important portfolio blending strategies so far. They reflect the up-to-date efforts of researchers on portfolio blending.

4.3 Performance Metrics

We employ the "rolling-horizon" settings suggested in [DeMiguel et al., 2009]. Specifically, the sliding windows with the size of ⌧ = 120 months or ⌧ = 200 weeks of training data are used to construct portfolios for the subsequent month or week. 2 We compute the out-of-sample performance of the portfolios by the following standard criteria in finance [Brandt, 2010]: (i) Sharpe ratios; (ii) volatility, and (iii) maximum drawdowns. In addition, we incorporate the information of the turnover of each strategy through deducting the return by a proportional transaction cost [Broadie and Shen, 2016]. We set a cost factor c equal to 50 basis points per transaction to obviate inflated return from large turnovers, as suggested in [DeMiguel et al., 2009].

First, the Sharpe ratio (SR), which measures the reward-torisk ratio of a portfolio strategy, is computed as the portfolio return normalized by its standard deviation:

where the mean of portfolio after-cost net return ˆθ and the corresponding standard deviation ˆσ are computed as

where ˜θk = θk(1 - ck!k+ - !kk1) denotes the after-cost net return from time tk-1 to tk, !k+ represents the portfolio weight vector before rebalancing at tk+1 and k · k1 denotes l1-norm. SR heightens the significance of gauging portfolio performance with the dual consideration of risk and return.

Second, the volatility is a quantitative risk measure of investment. The calculation of the portfolio volatility relates to the standard deviation of returns ˆσ by (14). To compare strategies based on different rebalancing frequencies, we compute the annualized volatility by pHˆσ with H the total number of rebalancing times each year. In our experiments, we set H = 12 and H = 52 for monthly and weekly rebalances, respectively.

Third, we report the maximum drawdown (MDD) for each strategy [Magdon-Ismail and Atiya, 2004]. The maximum drawdown is defined as the maximum drop of the cumulative wealth from its running maximum over a period of time:

where the drawdown Mk -Wk is defined as the drop of the wealth from its running maximum Mk:

2 The study in [DeMiguel et al., 2009] shows portfolio performance generally does not vary considerably by using longer than five years of monthly data.

where the after-cost cumulative wealth Wk is computed by Wk = Qkj=1 !>j ˜θj. Since large drawdowns inevitably lead to fund redemptions, MDD has been the top-one risk measure for money management professionals.

To further quantify the statistical significance of the difference in SR between two comparing portfolios, we also report the p-values under the corresponding SR results. To compute the p-values for the case of non-i.i.d. returns, we adopt the studentized circular block bootstrapping methodology in [Ledoit and Wolf, 2008]. In particular, we set the EW portfolio as the benchmark with 1000 bootstrap resamples, 95% significance level, and a block with the size of 5.

4.4 Results

Table 2 presents the overall performance of the compared nine portfolios across the tested five benchmarks. In particular, we report the Sharpe ratios, the volatility and the maximum drawdowns for all portfolios to comprehensively evaluate performance with the emphasis on the tradeoff between return and risk. In most testing cases, the two proposed blending portfolios clearly outperform both the challenging baselines circulated in financial research, i.e., EW and VW, and representative blending strategies, i.e, TZT, KZT and TZF. We observe that the new portfolios consistently produce the highest risk-adjust return across all the benchmarks with statistical significance. In addition, they often yield lower investment risks than the other three compared blending strategies, reflected by the smaller volatility and maximum drawdowns. Even in some cases, our blending portfolios generate lower risk than the MV strategy whose sole objective is investment risk minimization. Those observations echo with the intrinsic design of our algorithm in calibrating blending coefficients for portfolios with moderate risk according to portfolio gross return. Further, the proposed strategies demonstrate statistically significant better performance with a noticeable effect size than their basis portfolios in risk and return evaluation metrics. As the performance of the blending portfolios stems from the tradeoff between the gains from the blending coefficient and the losses from the estimation errors in estimating that additional parameter, we interpret those positive findings in performance as the evidence supporting the new algorithm.

In summary, our blending strategies formed by two sets of basis portfolios have embedded careful risk control mechanism and market dynamics. Therefore, in most of testing cases our methods can generate superior performance, i.e., higher risk-adjusted returns, lower volatility and drawdown risks, and outperform individual basis portfolios as well as other representative blending portfolios.

5 Conclusions and Discussions

In this paper, we develop a machine learning algorithm of viably blending portfolios from different investment principles to generate robust and high-quality portfolio strategies. Through casting the question of determining blending coefficients into a Bernoulli bandit problem, we implement Thompson sampling to obtain optimal blending portfolios. Two blended portfolios with different basis portfolios consistently outperform seven highly competitive strategies across five datasets. Our results not only address the "1◁n" portfolio challenge [DeMiguel et al., 2009] but also demonstrate the insights of adapting portfolio strategies to accommodate parameter estimation errors. In our future work, we will extend the current blending algorithm for multiple portfolios by Dirichlet distribution [Silverthorn and Miikkulainen, 2010].