Machine Learning and Artificial Intelligence in Finance

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Updated: Nov 11, 2022
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In this investigative report paper we’ll present an overview of finance, what it looks like today, give some examples of the emerging markets in finance and outline the general trends and tendencies. Then, we’ll describe what trading is, how it is done and list some of the biggest trading firms; go through an overview of ML and AI and present some examples of how they are mostly used in today’s markets. After that, we’ll dive into the influences of AI & ML in trading and the impact they’re providing

To explore the current state of ML/AI in Trading we’ll present some case studies of the firms using advanced technologies in trading; give an overview of things like Algorithmic Trading is and how some funds benefit from it; present the current trends in ML and AI, their functions for trading and finance; then outline how profitable and successful these methods are.

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We’ll also outline other potential applications of ML and AI in Finance and their future applications and address a game theory of what could happen when all trading firms use ML and AI.


Machine learning and artificial intelligence are giving several financial firms, especially in trading, a competitive advantage. We propose to investigate exactly how both ML and AI techniques make firms more profitable and to predict the future of their uses in trading and other areas of finance. We believe that in the short run, ML and AI will make firms profitable, but in the long run, profits will shrink as more firms introduce these technologies.

Reports have shown that hedge funds already using these technologies significantly outperform generalized bureaucratic hedge funds. They’re incredibly helpful tools for any human navigating the decision-making processes, especially when involved with investments and risk assessment. The impact of human emotions on trading decisions is often the greatest hindrance to outperformance. Algorithms and computers make decisions and execute trades faster than any human can, -and do so free from the influence of emotions.- However, do all fund managers want to give full control of decision making to computers? Especially when it comes to very large fund assets?

In this report we’ll dive into those questions as well as provide our recommendations and vision for the future of financial firms.

Overview of Finance and Trading

What is finance and what does it look like today?

Finance is the study of how money management and the process of acquiring the needed funds. It encompasses the oversight, creation and study of money, banking, credit, investments, assets and liabilities that make up financial systems. Many of the basic concepts in finance come from micro and macroeconomic theories. One of the most fundamental theories is the time value of money, which essentially states that a dollar today is worth more than a dollar in the future.

Finance is also defined as a branch of economics concerned with ??‹resource allocation and resource management, acquisition and investment, as well as a way to raise money through the issuance and sale of debt and/or equity.

Since individuals, businesses and government entities all need funding to operate, the field is often separated into three main sub-categories: personal finance, corporate finance and public (government) finance.

Personal Finance

It involves analyzing an individual’s or a family’s current financial position, and formulating strategies for future needs within financial constraints. Personal finance is a very personal activity that depends largely on one’s earnings, living requirements, goals and individual desires.

For example, individuals need to save for retirement expenses, which means investing enough money along the way to properly fund their long-term plans. This type of financial management decision falls under personal finance.

Personal finance includes the purchasing of financial products, like credit cards, insurance, mortgages and various types of investments. Banking is also considered a part of personal finance, including checking and savings accounts as well as online or mobile payment services like PayPal and Venmo.

Corporate Finance

It consists of the financial activities related to running a corporation, usually with a division or department set up to oversee the financial activities.

For example, a large company may have to decide whether to raise additional funds through a bond issue or stock offering. Investment banks may advise the firm on such considerations and help them market the securities.

Startups may receive capital from angel investors or venture capitalists in exchange for a percentage of ownership. If a company thrives and decides to go public, it will issue shares on a stock exchange in an initial public offering (IPO) to raise cash.

Another instance could be a company that is trying to budget their capital and make decisions on what projects to finance and what projects to put on hold in order to grow the company. These types of decisions fall under corporate finance.

Public Finance

It includes tax, spending, budgeting and debt issuance policies that all affect how a government pays for the services it provides to the public.

The federal government helps prevent market failure by overseeing the allocation of resources, distribution of income and stabilization of the economy. Regular funding is secured mostly through taxation. Borrowing from banks, insurance companies and other governments also help finance the government.

In addition to managing money for its day-to-day operations, a government body also has larger social responsibilities. Its goals include attaining an equitable distribution of income for its citizens and enacting policies that lead to a stable economy.

How finance looks like today? Today, finance is involving through online and Mobile Banking, automation as well as development of AI for investment decisions.

Online Banking

It’s what gives you the ability to manage money online with your mobile device or computer. There’s no need to visit a bank branch, and you can do what you need to do when it’s most convenient for you.


Increases productivity; Automation is not supplanting humans with computers and machines. It’s about eradicating stagnant processes that workers employ daily, so as to improve performance and efficiency.


The development of AI/ML involves feeding an algorithm with historical financial data samples. The data samples consist of variables called predictors, as well as a target variable, which is the expected outcome. The algorithm learns to use the predictor variables to predict the target variable, such as stock price.

Examples of emerging markets and general trends in finance.

In developing/emerging countries, people spend a significant portion of the time and resources that should go into growing a business simply trying to transact with your bank. But even as transportation infrastructure remains relatively underdeveloped, digital infrastructure is advancing rapidly. Utilizing digital technologies, more convenient and flexible ways to conduct banking are emerging as we speak. Most of the emerging countries people have access to cell phones and data as ever before, myself being a citizen ofanemergingcountry. Whyshouldn’ttheybeabletomanageyouraccountusingonlya mobile phone? Instead of spending the day traveling to a branch, why shouldn’t they be able repay their loan by visiting a banking agent at the corner store in their town or village?

Due to Fintech innovation, the rapid expansion of mobile networks is making financial services more accessible, even for the most excluded. Because of this convergence, we are now able to integrate digital technologies into new and existing services, so that more people in emerging countries and elsewhere are now able to access crucial financial services without ever setting foot in a bank branch.

As emerging markets demand more financial flexibility, banks and microfinance institutions will need to adapt quickly to survive. Digital financial services are becoming the norm, they keep getting better.

Trends shaping financial services can be classify in 4 keys:

Financial Inclusion is Advancing, But Access Isn’t Everything

A World Bank study showed that new technologies are making financial services available at unprecedented levels ??“ especially for low-income people in emerging markets; the very people who have traditionally been excluded from the formal banking system.

Since 2014, 515 million people have gained access to financial services. Of the 1.7 billion adults who remain unbanked, a full two-thirds now have a mobile phone. That means there’s a real opportunity to close the remaining gaps in financial inclusion by providing mobile financial services.

However, we should remember that inclusion is not a panacea. Financial services need to be more than available, they need to be responsible and impactful.

The global gap in savings is one area where more intentional action is needed. While the Findex shows tremendous progress in access to financial services overall, the percentage of savers in emerging markets has actually declined in recent years. In 2014, 53 percent of adults in emerging markets reported saving or setting aside money during the past 12 months, whereas only 43 percent reported doing so in 2017.

Financial services providers need to do a better job of expanding financial literacy to prevent over-indebtedness.

Emerging markets want banking, not Banks

Technology and big data have irrevocably disrupted traditional banks. The microfinance sector, though successful in reaching low-income people with credit and savings products, is not immune to these changes.

Therefore, a financial services model that depends on transaction fees to be profitable, relies on armies of loan officers to reach customers and requires customers to physically travel to bank branches, is unsustainable and outdated. The future of finance is a world where payments, remittances and transfers will be made free of charge, and these capabilities will be accessible to customers anywhere and anytime they need them. Given the prevalence of digital technologies, it’s no longer viable for a small business owner to close up shop for the afternoon to travel to a physical bank branch and make a deposit.

Credit will increasingly become commoditized in emerging markets as more providers enter the space. Customers are becoming increasingly savvy, putting the onus on banks and financial service providers to create products that meet the needs of the customers. Credit scoring will become more accurate; risk will be better managed; and people will have greater access to appropriately-priced responsible financial services. Mobile Network Operators (MNOs) will flood emerging markets with expensive but convenient $10 loans.

Viability will depend on adaptability

Financial institutions of all stripes must adapt to survive in today’s evolving emerging-market landscape. This means integrating digital technologies into their existing services. Next, it means innovating new technologies to develop the financial services of the future.

Recently in Guatemala for example, FINCA Impact Finance, a FinTech company piloted a psychometric credit scoring, an innovative method that involves having potential borrowers fill in an interactive questionnaire via tablet. And in Tanzania, they piloted credit scoring based on mobile phone usage, using metrics like the number of calls individuals make to determine their connection to the community. So far, the information gleaned from these initial interactions have been accurate predictors of customer behavior and ability to repay loans, and are examples of how we can take traditional metrics and conduct them in a lighter-touch, more efficient way.

People, Not Products

While technology is key to surviving and thriving in today’s rapidly-evolving landscape, one important factor hasn’t changed. This industry is about people. Digital solutions to maximize reach and efficiency are necessary for any financial institution to remain viable, but they don’t substitute for personal, trust-based relationships.

Banking customers in emerging market are waiting for a new generation of financial service providers

What is trading? How is it done? What are the biggest trading firms?

To “trade” in the jargon of the financial markets means to buy and sell. It is an active action of buying and selling stocks, commodities, currency or other instruments, with the goal of generating returns that outperform buy-and-hold investing. While investors may be content with a 10% to 15% annual return, traders might seek a 10% return each month. Trading profits are generated through buying at a lower price and selling at a higher price within a relatively short period of time. The reverse is also true: trading profits are made by selling at a higher price and buying to cover at a lower price to profit in falling markets. Where buy-and-hold investors wait out less profitable positions, traders must make profits (or take losses) within a specified period of time, and often use a protective stop loss order to automatically close out losing positions at a predetermined price level. Traders often employ technical analysis tools, such as moving average and stochastic oscillators to find high-probability trading setups.

How trading is done? There are two ways:

  • Exchange floor trade
  • You tell your broker to buy 100 shares of Acme Kumquats at market.
  • Your broker’s order department sends the order to its floor clerk on the
  • The floor clerk alerts one of the firm’s floor traders, who finds another floor trader willing to sell 100 shares of Acme Kumquats. This is easier than it sounds because the floor trader knows which floor traders make markets in particular stocks.
  • The two agree on a price and complete the deal. The notification process goes back up the line and your broker calls you back with the final price. The process may take a few minutes or longer depending on the stock and the market. A few days later, you will receive the confirmation notice in the mail.

Electronic trades

In this fast-moving world, some people are wondering how long a human-based system like the NYSE can continue to provide the level of service necessary. The NYSE handles a small percentage of its volume electronically, while its rival NASDAQ is completely electronic.


Barclays, JP Morgan, Citigroup, Goldman Sachs.

Overview of ML and AI

Machine Learning

It is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions. So in layman’s terms ML is the science of getting computers to act without being explicitly programmed.

There are many different types of machine learning algorithms, with hundreds published each day, and they’re typically grouped by either learning style (i.e. supervised learning, unsupervised learning, semi-supervised learning) or by similarity in form or function(i.e. classification, regression, decision tree, clustering, deep learning, etc.). Regardless of learning style or function, all combinations of machine learning algorithms consist of the following:

  • Representation (a set of classifiers or the language that a computer understands)
  • Evaluation (aka objective/scoring function)
  • Optimization (search method; often the highest-scoring classifier, for example; there are both off-the-shelf and custom optimization methods used)


It is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

ML and AI in Trading

Algorithmic trading

  • Trade execution algorithms, which break up trades into smaller orders to minimize the impact on the stock price. An example of this is a Volume Weighted Average Price (VWAP) strategy
  • Strategy implementation algorithms which make trades based on signals from real-time market data. Examples of this are trend-based strategies that involve moving averages, channel breakouts, price level movements and other technical indicators.
  • Stealth/gaming algorithms that are geared towards detecting and taking advantage of price movements caused by large trades and/or other algorithm strategies.
  • Arbitrage Opportunities. An example would be where a stock may trade on two separate markets for two different prices and the difference in price can be captured by selling the higher-priced stock and buying the lower priced stock.
  • There are multiple strategies which use Machine Learning to optimize algorithms, including linear regressions, neural networks, deep learning, support vector machines, and naive Bayes, to name a few. And well-known funds such as Citadel, Renaissance Technologies, Bridgewater Associates and Two Sigma Investments are pursuing Machine Learning strategies as part of their investment approach.


  • AI/Machine Learning hedge funds have outperformed the average global hedge fund for all years excluding 2012.
  • Barring 2011 and 2014, returns for AI/Machine Learning hedge funds have outpaced those for traditional CTA/managed futures strategies while underperforming systematic trend following strategies only for the year 2014 when the latter realized strong gains from short energy futures.
  • Over both the five, three and two year annualized period, AI/Machine Learning hedge funds have outperformed both traditional quants and the average global hedge fund delivering annualized gains of 7.35%, 9.57%, and 10.56% respectively over these periods.
  • AI/Machine Learning hedge funds have also posted better risk-adjusted returns over the last two and three year annualized periods compared to all peers depicted in the table below, with Sharpe ratios of 1.51 and 1.53 over both periods respectively.
  • While returns have been more volatile compared to the average hedge fund (compare with Eurekahedge Hedge Fund Index), AI/Machine Learning funds have posted considerably lower annualized volatilities compared with systematic trend following strategies.
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Machine Learning and Artificial Intelligence in Finance. (2019, Feb 20). Retrieved from