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This paper considers investors who are looking to maximize their probability of remaining solvent throughout their lifetime by using an algorithm that aims to optimize their investment allocation strategy and optimize their tax strategy for withdrawal allocations between tax deferred accounts (TDAs), Roth accounts, and taxable stock and bond accounts. This optimization works with stochastic investment returns and stochastic mortality, extending and combining different investment and tax-efficiency paradigms. We find that optimizing the investment strategy has a much larger impact on the investor remaining solvent than optimizing the tax strategy. This result is key to effectively optimizing both strategies simultaneously. This optimized investment strategy soundly beats a standard target date fund strategy, and the novel optimized tax strategy displays optimal desired properties suggested by non-stochastic tax optimization research. [ABSTRACT FROM AUTHOR]

The smart meters installed at the customer premises are one of the main apparatuses promoting the modernization of the distribution systems. These devices collect a huge amount of data, demanding the development of analytic techniques to transform these data into useful information. In addition, by proposing new applications to data supplied by smart meters, more value is added to this equipment, allowing higher return on the associated investments. Utilities can have a business case by increasing their operational efficiency if smart meters are used, for instance, to identify non-technical losses, which represent an important cause of revenue losses. This paper presents a new data analytic technique for detection and location of non-technical losses caused by illegal connections of loads to distribution systems in the presence of smart meters. The data analytic technique relies on bad data analysis, similar to the ones used in state estimation methods, developed specifically for this application. A real 34-bus low voltage system is used to illustrate the main concepts of the proposed algorithm. Systematic tests are also conducted on a real 1682-bus distribution system to evaluate the method performance considering electricity theft caused by medium and low voltage customers. [ABSTRACT FROM AUTHOR]

In this paper, a probabilistic form of the portfolio selection problem is established in which the uncertainty of risky assets is considered through a probabilistic optimization problem. To this end, by taking seven portfolios of Shanghai stock as a case study, the mean and standard deviation of daily return values are calculated based on five years of real data. The optimal values corresponding to each random case were then stored as a comprehensive database of system responses. Then, by sorting the resulting optimal values from best to worst, the exceedance probabilities of return and risk rankings were calculated for each portfolio and presented in the form of probabilistic pie charts. The results showed that the portfolio with the highest deterministic rate of return has the highest probability of getting the best return ranking as well. However, since the probability of risk in all cases was calculated, the probability of each portfolio to place in the lower rankings (i.e., ranking 2–7) could also be discussed. Additionally, to check the convergence of the model, the probability values calculated by the Monte Carlo method against the sample size were plotted to ensure the accuracy of the final answer. Eventually, by generating Gaussian random noise and importing it into the model input, probability changes were calculated to assess the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The risk parity solution to the asset allocation problem yields portfolios where the risk contribution from each asset is made equal. We consider a generalized approach to this problem. First, we set an objective that seeks to maximize the portfolio expected return while minimizing portfolio risk. Second, we relax the risk parity condition and instead bound the risk dispersion of the constituents within a predefined limit. This allows an investor to prescribe a desired risk dispersion range, yielding a portfolio with an optimal risk–return profile that is still well-diversified from a risk-based standpoint. We add robustness to our framework by introducing an ellipsoidal uncertainty structure around our estimated asset expected returns to mitigate estimation error. Our proposed framework does not impose any restrictions on short selling. A limitation of risk parity is that allowing of short sales leads to a non-convex problem. However, we propose an approach that relaxes our generalized risk parity model into a convex semidefinite program. We proceed to tighten this relaxation sequentially through the alternating direction method of multipliers. This procedure iterates between the convex optimization problem and the non-convex problem with a rank constraint. In addition, we can exploit this structure to solve the non-convex problem analytically and efficiently during every iteration. Numerical results suggest that this algorithm converges to a higher quality optimal solution when compared to the competing non-convex problem, and can also yield a higher ex post risk-adjusted rate of return. [ABSTRACT FROM AUTHOR]

In order to effectively improve the accuracy of related analysis models in the application of government risk investment, a government risk investment prediction model based on fuzzy clustering discrete algorithm is put forward in this paper. First of all, government risk investment problem is analyzed. Based on Markowitz theory, the general government risk investment model is considered, and the market value constraint and the upper bound constraint are combined to improve the government risk investment model and obtain the mixed constraint government risk investment model. Secondly, the fuzzy clustering discrete algorithm is introduced in the analysis process of government venture investment model, and it is used to solve the mixed constraint analysis model of government venture investment. In addition, to further improve the performance of discrete algorithm based on fuzzy clustering in the model solving process, automatic contraction and expansion of factors is used to carry out adaptive learning of related parameters based fuzzy clustering discrete algorithm, and improve the convergence of the algorithm. Finally, the simulation experiments on some stock samples of investment sector show that the algorithm in this paper can obtain more ideal government venture investment schemes, so as to reduce investment risk and obtain greater investment returns. [ABSTRACT FROM AUTHOR]

With the increasing number of population and the rising demand for electricity, providing safe and secure energy to consumers is getting more and more important. Adding dispersed products to the distribution network is one of the key factors in achieving this goal. However, factors such as the amount of investment and the return on the investment on one side, and the power grid conditions, such as loss rates, voltage profiles, reliability, and maintenance costs, on the other hand, make it more vital to provide optimal annual planning methods concerning network development. Accordingly, in this paper, a multilevel method is presented for optimal network power expansion planning based on the binary dragonfly optimization algorithm, taking into account the distributed generation. The proposed objective function involves the minimization of the cost of investment, operation, repair, and the cost of reliability for the development of the network. The effectiveness of the proposed model to solve the multiyear network expansion planning problem is illustrated by applying them on the 33-bus distribution network and comparing the acquired results with the results of other solution methods such as GA, PSO, and TS. [ABSTRACT FROM AUTHOR]

This research has two objectives: (1) to model and analyze the momentum effect and (2) to propose a portfolio-reconstruction algorithm that uses the momentum effect to obtain excess return. The momentum effect tends to be present in the stock market and describes the phenomenon whereby rising (declining) stocks tend to continue to rise (decline). However, because existing research does not separate momentum effects from stock price fluctuations, it is not always possible to obtain an excess return when working with an unknown dataset that contains a momentum effect. In this research, we define a new externalforce momentum-effect (EFME) model based on bias in stock price rises (declines). We prepared an artificial stock dataset that contained this momentum effect and constructed a portfolio with the proposed algorithm. Then, we analyzed the relationship between the EFME model and excess return and verify that excess return is obtained. Additionally, we confirmed that the proposed method yields higher excess return than the existing method when applied to artificial and real stock datasets. [ABSTRACT FROM AUTHOR]

Abstract: Aims: To inform the transferability of tobacco control‐related economic evidence to resource‐poor countries. Methods: We ran a univariate sensitivity analysis on a return on investment (ROI) model, the European study on Quantifying Utility of Investment in Protection from Tobacco model (EQUIPTMOD), to identify key input values to which the ROI estimates were sensitive. The EQUIPTMOD used a Markov‐based state transition model to estimate the ROI of several tobacco control interventions in five European countries (England, Germany, Spain, Hungary and the Netherlands). Base case ROI estimates were obtained through average values of model inputs (throughout the five countries), which were then replaced one at a time with country‐specific values. Tornado diagrams were used to evaluate the significance of sensitivity, defined as a ≥ 10% difference in ROI estimates from the base case estimates. Results: The ROI estimates were sensitive to 18 (of 46) input values. Examples of model inputs to which ROI estimates were sensitive included: smoking rate, costs of smoking‐related diseases (e.g. lung cancer) and general population attributes. Conclusion: Countries that have limited research time and other resources can adapt EQUIPTMOD to their own settings by choosing to collect data on a small number of model inputs. EQUIPTMOD can therefore facilitate transfer of tobacco control related economic evidence to new jurisdictions. [ABSTRACT FROM AUTHOR]

The success of data stream mining techniques has allowed decision makers to analyze their data in multiple domains, ranging from monitoring network intrusion to financial markets analysis and online sales transactions exploration. Specifically, online ensembles that construct accurate models against drifting data streams have been developed. Recently, there has been a surge in interest in mobile (or so-called pocket) data stream mining, aiming to construct near real-time models for data stream mining applications that run on mobile devices. In such a setting, it follows that the computational resources are limited and that there is a need to adapt analytics to map the resource usage requirements. Consequently, the resultant models should not only be highly accurate, but they should also adapt swiftly to changes. In addition, the data mining techniques should be fast, scalable, and efficient in terms of resource allocation. It then becomes important to consider Return on Investment (ROI) issues such as storage requirements and memory utilization. This paper introduces the Adaptive Ensemble Size (AES) algorithm, an extension of the Online Bagging method, to address these issues. Our AES method dynamically adapts the sizes of ensembles, based on ROI usage patterns. We illustrate our approach by analyzing the performances against both synthetic and real-world data streams. The results, when comparing our AES algorithm with the state-of-the-art, indicate that we are able to obtain a high Return on Investment (ROI) and to swiftly adapt to change, without compromising on the predictive accuracy. [ABSTRACT FROM AUTHOR]

Optimal asset replacement is analysed under non-exponential technological improvement, whereby the asset capital cost and the operational cost reduce for newer assets, and the relative rate of this decrease varies with time. We establish that the optimal lifetime of sequentially replaced assets increases if the relative rate of technological change decreases, and converse. Next, the two-cycle replacement algorithm is employed to take the unequal asset lifetime into account. The algorithm is adjusted to the case of several discrete estimates of changing asset costs and demonstrates good performance in various scenarios of improving technology. [ABSTRACT FROM AUTHOR]

This paper presents the performance of seven portfolios created using clustering analysis techniques to sort out assets into categories and then applying classical optimization inside every cluster to select best assets inside each asset category. The proposed clustering algorithms are tested constructing portfolios and measuring their performances over a two month dataset of 1-minute asset returns from a sample of 175 assets of the Russell 1000® index. A three-week sliding window is used for model calibration, leaving an out of sample period of five weeks for testing. Model calibration is done weekly. Three different rebalancing periods are tested: every 1, 2 and 4 hours. The results show that all clustering algorithms produce more stable portfolios with similar volatility. In this sense, the portfolios volatilities generated by the clustering algorithms are smaller when compare to the portfolio obtained using classical Mean-Variance Optimization (MVO) over all the dataset. Hierarchical clustering algorithms achieve the best financial performance obtaining an adequate trade-off between accumulated financial returns and the risk-adjusted measure, Omega Ratio, during the out of sample testing period. [ABSTRACT FROM AUTHOR]

This paper assesses the feasibility of three-phase pulse-width modulated (PWM) three-level voltage-source converters (VSC), namely neutral-point-clamped (3L-NPC) and T-type (3L-T) as alternatives to two-level converter (2L-VSC) for low-voltage multimegawatt renewable energy grid-connected converter applications. For this purpose, a novel design algorithm that takes switching frequency, capacity factor, modulation index, PWM scheme(s), and converter topology(ies) as input is unveiled to provide the design steps and performance benchmark for the specified topologies and modulation methods. Design of the LCL filter is also addressed, and equivalent output ripple performance criteria is used to benchmark the converter performances for a set of selected filter parameters. Main contribution of this algorithm is that it covers cost and operating factors of the design during the hardware design phase by introducing/modifying the terms of total cost of ownership (TCO) and return on investment (ROI) time considering the major energy markets. The design is detailed for 1-MVA system, and the results are summarized for 0.5 and 2-MVA systems. The study shows that 3L topologies perform more efficiently than 2L-VSC (T-type being the most prominent—with the shortest ROI), whereas the difference becomes much more prominent at the multimegawatt range, provided that all three yield equivalent outputs fulfilling the stringent grid codes. [ABSTRACT FROM PUBLISHER]

This article proposes a novel stochastic volatility (SV) model that draws from the existing literature on autoregressive SV models, aggregation of autoregressive processes, and Bayesian nonparametric modeling to create a SV model that can capture long-range dependence. The volatility process is assumed to be the aggregate of autoregressive processes, where the distribution of the autoregressive coefficients is modeled using a flexible Bayesian approach. The model provides insight into the dynamic properties of the volatility. An efficient algorithm is defined which uses recently proposed adaptive Monte Carlo methods. The proposed model is applied to the daily returns of stocks. [ABSTRACT FROM PUBLISHER]

Energy storage technologies all have different characteristics that make them suitable to particular purposes. A hybrid energy storage (HES) arrangement can take advantage of these differences. An example of this is a compressed-air energy storage plant with a flywheel mounted on the same shaft as the motor/generator and turbo machinery, where the flywheel would be used for high-frequency transactions with the grid while the compressed air store, with lower efficiency but also lower cost per unit of energy storage, could be used for longer term storage. This paper reports an algorithm which can be used to find the maximum arbitrage value achievable with a given HES plant for a given set of electricity prices. This is used in an optimisation routine to find the configuration of hybrid flywheel/compressed air plant that maximises potential arbitrage value. With realistic costs and efficiencies, it is found that the maximum annual return on investment is 0.29%, corresponding to a plant with 10% of its total energy storage capacity in the form of flywheel and the rest as compressed air. [ABSTRACT FROM AUTHOR]

The article discusses the algorithms for generating an investment portfolio, a set of securities owned by a legal entity or a natural person. It mentions the required modeling method in preparation of such portfolio. It notes the need to consider the value from the growth trend designated by investor as the period of building the investment. It also emphasizes the need to combine assets with different rates of return, risk, the relationship between the assets and the dynamics of their return.

Content is highly appealing to marketers, but does not always have the same impact on prospects as is hoped for. This article argues that marketers need to consider adopting the opposite of business as usual in order to achieve the desired standout. [ABSTRACT FROM AUTHOR]

Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called "continuous trend labeling" is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data. [ABSTRACT FROM AUTHOR]

The study of the quality of hospital emergency services is based on analyzing a set of indicators such as the average time of first medical attention, the average time spent in the emergency department, degree of completion of the medical report and others. In this paper, an analysis is presented of one of the quality indicators: the rate of return of patients to the emergency service less than 72 h from their discharge. The objective of the analysis was to know the variables that influence the rate of return and which prediction model is the best. In order to do this, the data of the activity of the emergency service of a hospital of a reference population of 290,000 inhabitants were analyzed, and prediction models were created for the binary objective variable (rate of return to emergencies) using the logistic regression techniques, neural networks, random forest, gradient boosting and assembly models. Each of the models was analyzed and the result shows that the best model is achieved through a neural network with activation function tanh, algorithm levmar and three nodes in the hidden layer. This model obtains the lowest mean squared error (MSE) and the best area under the curve (AUC) with respect to the rest of the models used. [ABSTRACT FROM AUTHOR]

In order to realize the understanding of expected returns after issuance of blockchain new currency initial coin offerings (ICO) and maximize investment returns, in this study, the Semantic Orientation Pointwise Mutual Information (SO-PMI) algorithm is used to create a customer emotional dictionary of blockchain new currency, and collect users' online comments based on blockchain currency before ICO. The Support Vector Machine (SVM) algorithm is used to construct an evaluation model, analyze and judge users' comments, make accurate prediction of the expected return of ICO issuing new currency, improve investment operations, and maximize the return of investment. The results show that the combination of the SO-PMI and SVM algorithms can accurately evaluate the price after the issuance of new currency, and then realize the judgment of expected return and obtain the expected return of investment. It can be seen that the combination of algorithms based on data-mining technology is applied to the study of the expected return of new currency issuance in blockchain, which achieves the goal of revenue anticipation and greatly reduces the investment risk of new currency issuance in blockchain. [ABSTRACT FROM AUTHOR]