This article was written by David Shabotinsky, a Financial Analyst at I Know First, and enrolled at the undergraduate Finance program at the Interdisciplinary Center, Herzliya.

Machine Learning Stock Market

Summary

Background behind Machine Learning and Deep Learning

Strategic adaption of AI and Machine Learning in a business environment

Why financial firms need Deep Learning techniques to remain competitive

Finance firm’s adoption of GPUs into operational activities

Background

Today, as a result of globalization and other technological advancements many businesses are finding their margins ever shrinking as they enter pricing wars in red ocean markets. Though finding niche markets may be a solution for few, businesses that would like to stay competitive and revert their margin to grow again must adapt to the new technical trend of AI. Machine Learning, is a branch of Artificial Intelligence, which uses mass computing power and advanced algorithms to analyze data and perform human functions.

Machine Learning has developed exponentially over the past half a century but dates back to early uses classical statistical inferences during the 18th century. Then, smaller data sets were used to develop statistical inferences. Today, as a result of big data, scientists have developed new techniques to better analyze the large quantities of data with better quality. During the 1930s and 1940s, early adapters of Artificial Intelligence, such as Alan Turing, had set forth in motion the process of using neural networks which evolved into what we know has Machine Learning today. Due to a lack real technological advancements over the next forty years, Machine Learning only began to break out in the late 1970s and 1980s. Technologies such as the steam engine, a higher usage of electricity, and modern computers had scientist and business begin to adapt and unlock the value of Machine Learning and later Deep Learning techniques.

What is Machine Learning?

Machine Learning in general is algorithmic based and utilizing the concept of self-learning, being able to learn from large amounts of data (without relying on specific rules). Thanks to cheap computer power and the digitalization era, scientists were able to adapt this with more ease. Though scientists have made tremendous strides in the development of machine learning and AI, the more big data grows, and an increasing in complexity, the demand for machine learning to analyze this large volume of data increases every day.

In the mid-200-s, Fei-Fei Li began developing a program that identities the visual elements of any picture with a high degree of accuracy. He had developed this by inputting data sets of various images till his program began developing its own rules and use those to identify similar pictures. In this way, it is similar to how the neurons in our brains identify objects as well. Big firms such as Google and IBM have as well developed similar to programs and have even competed in popular games around the world. IBM’s Watson was able to beat the world’s best Jeopardy players in 2011, and Google developed Alpha Go to beat the 2500-year-old Chinese game called GO.

Implementing Machine Learning in Business Strategies

Overall, Machine Learning can add significant competitive advantages to businesses’’ strategies if implemented correctly, especially in the financial industry. Originally, the inputs that Machine Learning carried were for structured data, Deep Learning Techniques were adapted so that businesses could begin to analyze unstructured data. In the financial industry, for these programs to accurately analyze the data with high-quality outputs, they need to be able to adapt to unstructured data. Though Machine Learning is used across the most business, even General Electric, the oldest firm of the Dow Jones, it is especially prevalent in the financial industry with banking as it can not only help a business grow its margins, but empower consumers as well, creating a win-win scenario. To learn about the background of Deep Learning, read this article on I Know First.

(Source: An executive’s guide to machine learning)

In order to for C-level executives at large firms to successfully utilize Machine Learning and Deep Learning, they must begin with a strategy as a focus point as McKinsey explains. If a firm simply adapts a specific program without setting aside a specific goal for it or merging the program into its business strategy, there is a huge risk the firm will lose the long-term value of the program and rather be used for a short-term gain i.e. customer retention.

McKinsey explains that the method of going about adapting Machine Learning and Deep Learning is similar to how a firm would go about an M&A transaction. That is there are three cascading commitments: investigating feasible alternatives, having a ready to go strategy at the C-suite level and to use/obtain existing and expertise on the topic to guide the leaders of the firm in pursuit of the strategy. For example, having a “quant” to develop the program, and a “translator” to explain to the executives of a firm.

Deep Learning in the Financial Industry

In the financial industry, banks across Europe and America are beginning to rapidly adapt Deep Learning techniques in their business operations. They have been able to develop micro-targeted models to analyze for examples the likelihood and impact of client loan default and other credit analysis. As a result of these types of innovative tactics, banks have been able to increase sales in products at 10%, save about 20% in capital expenditure, a 20% increase in cash collection, and a 20% decline in churn. However, unlike other business types, the banks need to utilize Deep Learning, because they need to be able to take unstructured data and identify patterns and trends through that.

Deep Learning techniques have allowed banks to further excel in the adaptation of Machine Learning in many fields. For example, risk management is a primary concern of banks, whose main operational revenue is from interest rates and thus focus on securing that sum. Since their revenue models can seem static, they are very risk-averse in nature and Deep Learning tools help identify the specific audiences with varying degrees of risk. They are able to for example to deduce credit worthiness based on shopping patterns of their clients. This not only allows banks to detect which clients are likely to default but as well gives customers the ability to negotiate better terms if they are credit worthy. Additionally, fraud analytics is another key area that Deep Learning can help banks focus on. Where general credit fraud damages are estimated to be at around $16 billion, this field can greatly help banks increase their brand reputation and keep their clients’ assets safe. It as well allows these banks increase in their customer segmentation and deeply leverage social media and other new avenues to attract a new, younger client base. The banks are able to data mine and segment customers accordingly.

How GPUs Are Used By Financial Firms

Additionally, in order to obtain the best Deep Learning technology, banks are partnering with technology companies such as NVIDIA and use the technology of those firms. Banks and other investment firms are beginning to use NVIDIA’s GPU and DGX-1 technology, which are built using Deep Learning analytics. This can be mainly used for risk management of a trader’s portfolio. Instead of having to do an extensive and long research, these GPUs are able to perform data exploration and model development/scoring at a much faster pace with more accurate results (reality deviates less from the expected result). No longer do clients or quants have a data movement challenge, as they are able to deploy these models and run sophisticated data science workloads on a single database.

Furthermore, using the GPU’s and Deep Learning allows clients and institutional investors to be able to get the best price for the underlying assets that you are trading. The technology and analyze and determine the best price within milliseconds of accuracy levels. This is able to be done since the programs back test empirical evidence of the asset’s pricing to determine in real time the most optimal point in time to execute the trade. In addition, since the programs are based on Machine Learning, it is constantly evolving and becoming more intelligence with every usage.

I Know First’s Development In Machine Learning

I Know First, Ltd. is a FinTech company that brings science and math to the financial world by providing daily investment forecasts based on an advanced self-learning algorithm. Developed by CTO Lipa Roitman (PhD from Weizmann Institute – 35 years of experience), the algorithm utilizes artificial intelligence and machine learning techniques through which I Know First is able to analyze, model and predict the stock market.

Predictions are generated daily for a growing universe of over 7,000 securities, including stocks, world indices, ETFs, interest rates and more for the short, medium and long term horizons. The algorithm is applied to discover the best investment opportunities and used as a decision support system for existing investment processes or to develop systematic trading strategies. It is adaptable and scalable allowing comprehensive customized algorithmic solutions including integration of additional markets depending on clients’ needs – family offices, wealth management firms and hedge funds – as well as fund management partnerships. Furthermore, by offering top-notch technology to retail clients, I Know First also empowers private investors to identify opportunities in markets and manage their portfolios with more confidence. To read more about I Know First click here.

Conclusion

Throughout the last half-century, the development and implementation of Machine Learning has grown exponentially. Today, in order for firms to maintain their competitive edge (or develop one) they should begin to adopt Machine Learning programs into their business strategies. Since, the classic Machine Learning is used for structured data, financial service firms use Deep Learning programs, which allows them to analyze large volumes of data that is unstructured. The evidence is clear is that it not only increases their top and bottom line, but as well helps them better serve customers.