According to the new Cornell study based on the largest data set ever used, the efficiency of the mathematical tools used to predict movements in the financial market can be evaluated with Machine Learning.
Future market movements can be predicted by these mathematical tools; however, due to the huge amount of information in the market and high volatility, this is considered an extremely difficult task for researchers.
According to Maureen O'Hara - the Professor at the SC Johnson College of Business, researchers are trying to optimize the potential of machine learning techniques. They expect their current models and methods not only to be assessed for good performance but also to be expanded with the assistance of Machine Learning.
He also mentioned that because of the huge database, it is not easy to appraise these sorts of things with standard techniques but machine learning. In some cases, they can predict the movements of many contracts and pick up the patterns of how markets affect other markets thanks to the power of these microstructure features which attach to one contract.
Using Machine learning to predict market behavior
Forest machine learning algorithms were used randomly by researchers to gain more insight into the effectiveness of some of these models. They evaluated these instruments using a data set of 87 futures contracts - agreements to buy or sell assets in the future at predetermined prices.
O'Hara emphasized that all active futures contracts around the world for five years tens of millions of trades were used in their analysis as basic samples. To understand the development process of microstructure tools for less complex market settings work, they use machine learning to predict the future price process both within a contract and then collectively across contracts. They also noticed the varying performance of the variables, some did well, some didn’t.
In finance, Machine learning has been known as a “black box”- in which reams of data to predict future patterns are used without revealing how determinations occurred by applying an artificial intelligence algorithm. O'Hara wondered how to test the theories of market-moving causes, whether those theories are good or how the theory can be applied from a machine learning approach to help him understand and have the ability to build better models, even the complex one.
Unlimited access to the database for the historical market since the 1980s as well as daily increasing informative collectives. Despite the capability to generate immense power for computing along with enormous database capacity resulting in more sophisticated performance as well as better analytical results, the requirements and the expense for these datasets with their acquired computing power might be respectively outrageous for researchers.
Source: Science Daily
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