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, as of today. 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.
Technology is and has always been a crucial part of finance. From the first promissory notes (banknotes) in the Netherlands and China, there was a race with counterfeiters that parasitically undermined trust. As in political communication, technology is the message, rather than merely "a tool": when it comes to money, trust is not just instrumental, it is fundamental. With cashless payments being the norm and social media platforms weαving an additional layer of involvement in our social data web – Amazon, Google, Facebook, Apple – Artificial Intelligence (AI) is already in our wallets, business, and financial affairs. In a non-western setting, one may refer to the Chinese "social rating" system, which allows the state to value and evaluate social behaviour patterns, creating a link to individual credit rating.
The Hong Kong Fintech Week was staged at the Hong Kong Convention and Exhibition Centre, and saw HKUST faculty engaging in lively discussions with senior executives and business leaders on the digital future of the financial services industry. The audience heard three important talks on related themes. First, Professor Mike So, from the Business School's Department of Information Systems, Business Statistics & Operations Management, spoke on the "Use of Smart Data in High Frequency Finance and Risk Management". He began by describing how high-frequency stock trading works. "Once there is a profitable signal from the market, an algorithm is used by high-speed computers to perform trades."
Somewhat overshadowed in recent years by China's rise as a regional and global power, Japan still extends considerable influence as a world leader in various fields, including investment, policy management and trade, according to experts who gathered at a Tokyo conference earlier this week. But at the same time, Japan lags behind other countries in incorporating into everyday life technological innovations, such as artificial intelligence and financial technology to improve efficiency, they said. "We want to talk about Japan as a role model, not Japan following, not Japan copying, but Japan actually and constructively led by the young generation," said Jesper Koll, who heads Wisdom Tree Japan KK, a Tokyo-based exchange-traded fund sponsor, in the opening session of the annual G1 Global Conference organized at Globis University on Sunday. Experts shared insights in a session titled "Can Japan be a Role Model for Global Economic Prosperity and Stability?" Hiromichi Mizuno, chief investment officer at Government Pension Investment Fund said the country can show leadership in the field of investment by educating on responsible investment, a concept that gained traction after the fall of Lehman Brothers in 2008 that triggered the global financial crisis, exemplifying the long-term failure of a capitalism that emphasizes the single-minded pursuit of short-term corporate profits.