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Given a set of securities or assets it is of interest to find an optimal way of investing in these assets. What is optimal has to specified. The objective is to optimize the return consistent with the specified objective. When there are several assets it is unlikely all the assets will increase if they are correlated. It is necessary to diversify one’s assets for a secure return. To deal with the different assets a combination of the assets should be considered with constraints as needed. One approach is the Markowitz mean-variance model where the mean variance is minimized including constraints. In this paper neural networks and machine learning are used to extend the ways of dealing with portfolio asset allocation. Portfolio selection problem in an efficient way. The use of heuristic algorithms in this case is imperative. In the past some heuristic methods based mainly on evolutionary algorithms, tabu search and simulated annealing have been developed. The purpose of this paper is to consider a particular neural network model, the Hopfield network, which has been used to solve some other optimisation problems and apply it here to the portfolio selection problem, comparing the new results to those obtained with previous heuristic algorithms. Although great success has been achieved for portfolio analysis with the birth of Markowitz model, the demand for timely decision making has significantly increased especially in recent years with the advancement of high frequency trading (HFT), which combines powerful computing servers and the fastest Internet connection to trade at extremely high speeds. This demand poses new challenges to portfolio solvers for real-time processing in the face of time-varying parameters. Neural networks, as one of the most powerful machine learning tools has seen great progress in recent years for financial data analysis and signal processing ([1], [14]). Using computational methods, e.g., machine learning and data analytics, to empower conventional finance is becoming a trend widely adopted in leading investment companies ([3]). 

Abstract To enable real-time control of next-generation active structures during shock events, there is a need to identify the start of a shock event within microseconds of its initiation. The delayed classification of a shock event may cause damage to the system that could have been prevented with assumed next-generation active control mechanisms. Addressing the challenge of ultra-low latency shock event classification requires utilizing prior information on normal behaviors (i.e., the system under vibrational loading) to identify abnormalities that can be classified as features of a shock event. The purpose of changepoint shock classification is to automatically recognize when a structure of interest behaves differently than expected in some measurable way. In this work, we analyze two different methods for shock classification using changepoint methodologies. We study the use of adaptive cumulative summation and expectation maximization algorithms in this work. Each method presents advantages and disadvantages for different scenarios. This study aims to derive features (streams of time series data) for the changepoint algorithms and revise the changepoint models to be used in real-time robust shock event detection. In this work, a printed circuit board under continuous vibrations before, during, and after a shock event is used to investigate the proposed methodologies. The printed circuit board is monitored with an accelerometer that is used to monitor both the vibrational and shock state of the system. The vibrational response of the system consists of accelerations up to 20 m/s2, while the shock event consists of loadings up to 2,000 m/s2. This work showed that the CUSUM algorithm is fairly effective at identifying the shock state in data but generates many false positives during normal behavior times, with no false positives post-shock, indicating accurate shock state detection despite early errors. In contrast, the Expectation Maximization (EM) algorithm shows improved performance by correctly predicting no shock in the initial phase and accurately identifying the onset of the shock state. It occasionally misclassifies shocked points as normal due to its change point identification process. Compared to CUSUM, EM has fewer false positives before the shock and similar performance during and after the shock event. Future research efforts will focus on developing online versions of these algorithms, which can identify system states with a minimum number of errors. The limitations of the system and its robustness to noise are discussed.