Why Does Machine learning Perform Better in Casinos Than Wall Street
When people discuss machine learning, they can’t bypass AI. Machine learning is the application of AI, where the computer utilizes mathematical models based on data inputs to learn without human-provided directions.
Machine Learning models for casinos far outperform those designed for investing! Why?
Currently, AI and machine learning are used in casinos to curb cheating, improve marketing, and optimize operations. When detecting fraud in physical casinos, on average, only 36% of the patrons can identify players who experience gaming problems on-site and online. Physical casinos have security advantages over online casinos. In Macau, some major casinos have employed technological measures, such as sensor-based poker chips and baccarat tables, concealed cameras, and facial biometrics, to record the gambling activities of the players.
With their behaviors tracked through ultra HD cameras and RFID-enabled tables and chips. Their unique data becomes transferred to the central database server. Where individual profiles become created. Machine learning analyzes how the betting outcomes correlate with the retrieved data, thereby detecting fraudulent activities. Concerning online casinos, the processes are more accessible and less expensive: the players’ gambling data is already available! And the data becomes analyzed to gradually identify players who manipulate the odds in their favor. And eventually, their accounts are flagged or suspended.
Casinos also need marketing strategies to help them retain customers. Obtaining a new player is far more costly than keeping an existing player. Utilizing machine learning models, casinos are capable of identifying individuals who are not likely to return and the logic behind the casinos’ churn rates. Moreover, by analyzing the betting behaviors of players, casinos can provide suitable and customized deals to target players, thereby decreasing advertising and marketing costs.
Machine learning can also optimize the operational efficiencies of casinos.
Usually, a casino only has one floor with multiple sectors based on the types and characteristics of games. Thus, it is crucial that casinos rationally assign limited space to different activities. With the help of machine learning, the management is able to gain valuable insights from the players’ overall demands for gaming machines, table games, and random number games. Then the casinos can determine how to arrange games on the casino floor and schedule staff working hours based on budgets.
In Wall Street, AI and machine learning demand have become more technical. There are mainly two ways to employ them: as a tool to drive AI-automated investing like the AI Powered Equity ETF (AIEQ) and HSBC Holdings Plc’s AI Powered US Equity Index (AiPEX), or as a tool assist companies in algorithm trading, risk management, and wealth management.
The AIEQ and AiPEX use IBM Watson as technology backup, including IBM’s portfolio of AI-powered business-ready tools, applications, and solutions. The AIEQ claims that it employs IBM Watson to equal a team of 1,000 research analysts, traders, and quants. However, the fund has underperformed and failed to pique investors’ interests. Since its inception on October 18, 2017, it has generated approximately 22.31% cumulative return and annual volatility of around 24.52%. In comparison, the Vanguard S&P 500 ETF’s return in the same period is about 54.98% and an annual volatility of 18.16%. AiPEX, on the other hand, also underachieves. Beginning on August 9, 2019, AiPEX’s three-year annualized return is -1.00%, and it returns less than the market.
The other way of utilizing machine learning seems more promising.
In algorithm trading, machine learning becomes extensively used. Where complex formulas, mathematical models, and human oversight combine to determine whether to buy or sell securities over the market. Institutional investors make profits through deep learning and high-frequency trading technology.
For the processes of risk management, the company and market risk data fetched. And then analyzed through machine learning, where supervised or unsupervised learning models become applied. Potential risky outliers become detected and targeted during the risk modeling and advised on risk management strategies.
In wealth management, machine learning has proven its success in asset allocation. The Robo-advisors, which are AI-powered investment platforms. Help investors to automate the wealth management process. They can build up portfolios and choose active or passive investment options for the clients and then utilize machine learning to adjust the proportions of different assets in the portfolio according to the market conditions.
When comparing the performances of machine learning in casinos and Wall Street, it largely depends on how individuals employ machine learning.
Data determines the outcomes of machine learning and thus decides the performance. The data inputs for casinos are straightforward. Where players’ gambling behaviors, outcomes, and demands become recorded and analyzed. The casino data required by machine learning also, not significantly influenced by external conditions. Compared to the data inputs utilized by Wall Street. Market news, government policies, political situations, and other information can easily affect the data employed by the Wall Street machine learning algorithms. Sometimes, the algorithms even fail to capture the changes in data.
In the last short squeeze of GameStop, Wall Street perceived GameStop as a declining company and assumed it was heading to failure. However, Reddit users were confident about GameStop’s future and decided to buy the stock. The stock price skyrocketed and made the short-selling hedge funds suffer, pressuring them to buy back GameStop shares and causing them to lose about $19.75 billion on January 29, 2021. This incident was tough to predict by the machine learning models. Because it happened so fast. And there were no remarkable changes in GameStop that could become captured. Machine learning algorithms identified the initial increase in GameStop’s stock prices as an anomaly. Thus, by comparing the machine learning performances in casinos and Wall Street, the casinos’ performances should be better than those in Wall Street.