How To Study The Stock Market

How To Study The Stock Market

How To Study The Stock Market : Given the quantitative revolution that has integrated technology into the finance industry (i.e. AI and algorithm-driven investing), there is now overwhelming evidence to prove that methodological investing outperforms human investing.

We can see this through methodological investing’s mitigation of asymmetric information and human bias, as well as by the execution of high frequency trading (as seen through the example of Renaissance Technologies’ Medallion Fund). 

Methodological investing, unlike human investing, mitigates the investor’s susceptibility to asymmetric information.

With so many variables factoring into asset prices (reference Fama’s efficient markets hypothesis), it is impossible to track the wide range of constantly updating financial and economic data in order to monitor and predict stock price movements without artificial intelligence and algorithms.

Cameron Winklevoss & Tyler Winklevoss Talk with Rebellion Research CEO Alexander Fleiss on Bitcoin’s Future & Gemini Exchange

Today’s financial analysts who continue to do so without the newest AI technologies spend their valuable time executing administrative tasks that are prone to human error; AI would eliminate these issues.

By engaging with big data and developing AI investing models, firms put themselves at an advantage to not only track, sort, and analyze data in order to make better investment decisions, but also stay cost-effective. 

As Bill Maris, a former managing partner at Google Ventures once said, when you “have access to the world’s largest datasets…it would be foolish to just go out and make gut investments.” 

Algorithm-driven investing also eliminates the role of human bias that cannot be controlled for in human investing. Human traders are prone to stress, as well as emotional and psychological reactions, while trading, resulting in panic selling when stock prices dip in the short run and leading to financial losses for their firms. Other traders may be overly risk averse or vice versa, and make instinctive and irrational trade decisions.

Employing mathematical and statistical analysis to develop and execute a trading algorithm removes these human reactions and biases from the trading process and thus prevents these irrational trades from occurring. Algorithm-based investing can consolidate the risk-level of the investor, the ideas of financial economic theories (e.g. Modern Portfolio Theory, Capital Asset Portfolio Management, Fama French Three Factor Model, etc.), and current financial and economic data to build and execute the optimal investment strategy to produce higher returns for clients. 

Additionally, AI-based trading firms retain the unique ability of partaking in high frequency trading in order to take advantage of arbitrage opportunities. High frequency trading firms’ profits are dependent on having lower latency between financial centers than their competitors, and they must track the most recent price movements in key markets to adjust their trading activity.

Humans simply cannot process information, such as changes in stock prices within nanoseconds, as quickly as machines. The use of computer algorithms allows firms to execute profitable, high-volume trades through strategies like electronic front running, rebate arbitrage, and slow-market arbitrage. Innovations in satellite technology (as opposed to the fiber-optic

cables, microwave towers, and lasers that are currently in use) will only further advance the field of high frequency trading by cutting latency, which will increase profits for firms that have developed algorithm-driven investment technologies. 

Firms that were early to employ algorithm-driven investing, like Renaissance Technologies, have historically outperformed their competitors (e.g. Buffett’s Berkshire Hathaway, Cohen’s SAC, Dalio’s Pure Alpha), who either lacked or lagged behind in their AI technology.

The founder of Renaissance, Jim Simons, is considered by the WSJ to be the “most successful moneymaker in the history of modern finance,” as his Medallion Fund boasts an average annual return of 66%, and as of 2020, a 76% annual return. Thus, with historical empirical data, methodological investing is undeniably superior and the future of finance.

How To Study The Stock Market Written by Vivian Fang

AI Investing with New York’s Leading AI Firm – Rebellion Research

Physics Nobel Prize Winner MIT Prof Frank Wilczek Talks With Rebellion Research CEO Alexander Fleiss on String Theory, Gravitation, Newton & Big Bang
Nasdaq Head of Ai, JPM Head of Ai, Point72 Head of Data Solutions Discuss Ai with Alexander Fleiss