reinforcement learning. The attempt to replicate the logical flow of human decision making through processing symbols became known as the “symbol processing hypothesis” (Newell, Shaw, and Simon, 1957; Newell and Simon, 1961, Gilmartin, Newell and Simon, 1976). Much of AI in the 1950s and 1960s did not focus on finance applications. In the 1960s, a substantial body of work on Bayesian statistics was being developed that would later be used in ML. Neural networks (which would become a cornerstone of deep learning) were developed in the 1960s and grew rapidly. However, due to a lack of sufficiently available electronic data and computing power, AI fell out of favour into what became known as an “AI winter” (Kaplan, 2016; FSB, 2017). The term “AI Winter” also connotes a slowdown in investment and interest. In 1973, the UK Lighthill Report ended government support for AI research. The 1980s witnessed an AI revival due to new funding and techniques. During the 1980s, Japan, the UK and the USA competed heavily in AI funding. Japan invested $400 million through the Japanese Fifth Generation Computer Project. The UK invested £350 million in the Alvey Program and DARPA spent over $1 billion on its Strategic Computing Initiative. In 1982 AI made inroads into the financial services industry when James Simons founded quantitative investment firm Renaissance Technologies7. This included the development of “expert systems” (or “knowledge systems”) which is a technique that solves problems and answers questions within a specific context. Brown, Nielson and Phillips (1990) provide an overview of integrated personal financial planning expert systems. They emphasise expert systems that use heuristics and the separation of knowledge and control as well as providing examples of expert systems that were prevalent at the time. For example, PlanPower provided tailored financial plans to individuals with incomes over $75,000.
5 The Personal Financial Planning System (PFPS) was used by Chase Lincoln First Bank and Arthur D. Little Inc. to undertake investment planning, debt planning, retirement planning, education planning, life-insurance planning, budget recommendations and income tax planning. Expert systems were also used in the stock market in what was known as “program trading.” At the time, institutional investors used program trading to capitalise on pricing disparities in the market. Finnerty and Park (1987) provide an empirical study of program trading that identifies discrepancies between stock index futures and the underlying stock index. They find the program trading strategy consistently outperforms the simple execute and hold to expiration strategy. However, program trading was often attributed to the wild market swings of the late 1980s, culminating in the 508-point drop in the Dow Jones Industrial Average (DJIA) in 1987. Chen and Liang (1989) detail PROTRADER (an expert system prototype for program trading) which is based on a learning mechanism based on parameter adjustment to several critical parameters based on market conditions.