Читать книгу Artificial Intelligence for Asset Management and Investment - Al Naqvi - Страница 18

In late 1990s, sitting at a Borders bookstore, I picked up two books on neural networks: (1) Neural Networks for Financial Forecasting; and (2) Neural Networks in the Capital Markets (Refenes, 1995; Gately, 1996). For that time, the books offered amazing insights into how to use neural networks in investment operations. I still have those books, and I keep them to remind me that decades before machine learning gained hyped status, financial services firms, and especially quant departments, were using it to create value.

Machine learning was the ultimate tool of the quants. Quants came from different backgrounds and expertise—for example, mathematics, physics, and econometrics. Their orientation and strategies deployed were different. Everyone wanted a shot at what they thought possible. Everyone had a dream and a method to achieve that dream. Everyone wanted to prove that they had cracked the code of market mysteries. Like gold miners or searchers from gold rush times, every quant had his or her own pans, pickaxes, and shovels. Since quants brought different methodologies and approaches to achieve alpha, firms viewed such separation as achieving diversification. Splitting into silos was encouraged because it was thought that the diversity in strategies would create a portfolio outcome where the average results would turn out to be favorable. The incentives were easier to manage since they could be easily aligned with the performance. For years, this style of research and investment continued. Even today, in many firms, this is still the dominant model. Despite the perceived benefit of diversification, such a partition has many undesirable effects:

1 Machine learning was viewed as the domain of quants and was not integrated in other functions in a firm;

2 Within the quant zone in a firm, capabilities stayed siloed and inaccessible by external parties;

3 Each quant turned into a small team of experts who all maintained their own view of the world, data, algorithms, and strategies;

4 Since various subprocesses of machine learning require specialized capabilities, the talent spread unevenly across the firm, provided low opportunity to learn from each other, and it was impossible to streamline operations or build an assembly line of capabilities. As can be expected, this structure kept the costs high;

5 During major scandals or regulatory inquiries, as in the Great Recession, firms had to deal with the criticism that they were betting on both sides of the market or selling products while betting against your own products; and

6 No corporate level strategy of intelligent automation or artificial intelligence materialized.