An artificial neural network-based stock trading system using technical analysis and big data framework

ACM SouthEast Regional Conference (2017 : Kennesaw; United States)In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural net- work model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance. Copyright 2017 ACM.Assoc. for Computing Machinery (ACM

Deep learning for financial applications: A survey

Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field. © 2020 Elsevier B.V

Association between Vitamin D Levels and Nonalcoholic Fatty Liver Disease: Potential Confounding Variables

Nonalcoholic fatty liver disease (NAFLD), historically considered to be the hepatic component of the metabolic syndrome, is a spectrum of fat-associated liver conditions, in the absence of secondary causes, that may progress to nonalcoholic steatohepatitis (NASH), fibrosis, and cirrhosis. Disease progression is closely associated with body weight or fatness, dyslipidemia, insulin resistance, oxidative stress, and inflammation. Recently, vitamin D deficiency has been linked to the pathogenesis and severity of NAFLD because of vitamin D "pleiotropic" functions, with roles in immune modulation, cell differentiation and proliferation, and regulation of inflammation. Indeed, several studies have reported an association between vitamin D and NAFLD/NASH. However, other studies have failed to find an association. Therefore, we sought to critically review the current evidence on the association between vitamin D deficiency and NAFLD/NASH, and to analyze and discuss some key variables that may interfere with this evaluation, such as host-, environment-, and heritability-related factors regulating vitamin D synthesis and metabolism; definitions of deficient or optimal vitamin D status with respect to skeletal and nonskeletal outcomes including NAFLD/NASH; methods of measuring 25(OH)D; and methods of diagnosing NAFLD as well as quantifying adiposity, the cardinal link between vitamin D deficiency and NAFLD