ANN is a method of simulating human thinking. Especially, an artificial neural network (ANN) which is very important in social life, such as completing some signal processing or pattern recognition function, speech recognition, constructing expert system, making robot and so on. With the continuous development of information technology, the application of machine learning is becoming more and more extensive. The model’s success depends mainly on The validity of these assumptions. It is worth noting that people design features subjectively, and models based on technical analysis are generally based on some assumptions of the market framework. They all assume that the future price trend is the result of historical behavior. However, the key part of many forecasting methods is the extraction of features. Establishing a model of the relationship between future price trends and historical behavior, and use the sample in historical market trends to predict future price. The survival probability of chromosomes is determined according to the profit fitness evaluation, and the better combination is found. Secondly, the genetic algorithm is used in the possible stock permutation and combination to perform the random number generation, selection, exchange, mutation, etc. First of all, taking the stock portfolio as the research object, genetic programming is used to derive a more accurate prediction function. Among them, the stochastic forest algorithm is used to build the stock model based on the historical price information for the trend prediction in the process of stock investment. As we all know, traditional methods include linear discriminant analysis, statistical methods, random forests, quadratic discriminant analysis, evolutionary computation algorithms, logistic regression and genetic algorithm. Therefore, the stock’s methods forecasting is widely spread. Researchers and investors are trying to find opportunities to gain profit in stocks. Even great in part, it depends on the influence of investors’ psychological games and other micro-factors. Especially in stocks, because fluctuations in stock prices are influenced by many factors, including economic trends, economic cycles, economic structure, and other macro factors, as well as industry development, listed companies’ financial quality, and other factors. To seek greater benefits from it, generations of scholars and investors continue to explore the secrets and develop many prediction methods. Financial markets are trading financial instruments, such as bonds, savings certificates, stock, etc. So financing refers to the process of economic operation both supply and demand of funds use various financial instruments to adjust the capital surplus of activity. The capital market includes money as well as the market, namely the financial market. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly. It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. This new method is aptly named stock sequence array convolutional LSTM (SACLSTM).
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According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). More and more scholars have developed methods of prediction from multiple angles for the stock market. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. In today’s society, investment wealth management has become a mainstream of the contemporary era.
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