A New Way Forward for Wealth Management

CFA Magazine ◽  
2017 ◽  
Vol 28 (2) ◽  
pp. 10-10
Author(s):  
John Bowman
Keyword(s):  
Wealth Management

Evolution and Disruption in the Wealth Management Industry

2017 ◽  
Vol 19 (4) ◽  
pp. 8-13 ◽  
Author(s):  
Charlotte Beyer
Keyword(s):  
Wealth Management

scholarly journals A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Zhongcui Li ◽  
Norbert Herencsar ◽  
Bay Vo ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Financial Management ◽  
Stock Price ◽  
Short Term Memory ◽  
Prediction Algorithm ◽  
Leading Indicators ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Wealth Management

AbstractIn today’s society, investment wealth management has become a mainstream of the contemporary era. 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. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. 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. More and more scholars have developed methods of prediction from multiple angles for the stock market. 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). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). 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. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

A framework for Personalized Wealth Management exploiting Case-Based Recommender Systems

2015 ◽  
Vol 9 (1) ◽  
pp. 89-103 ◽  
Author(s):  
Cataldo Musto ◽  
Giovanni Semeraro ◽  
Marco de Gemmis ◽  
Pasquale Lops
Keyword(s):  
Recommender Systems ◽  
Wealth Management ◽  
Case Based
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