How machine learning AI will shape the future of investing

Technology and artificial intelligence continue to progress at an exponential rate. Even though AI develops pretty quickly these days, it is generally recognized to be in its infant stage. Artificial general intelligence (AGI), or strong AI, is more comparable to human intelligence, but they are still decades away from it.
The current levels of AI still have the potential to influence numerous areas of human civilization. Machine learning is one of the best tools available at this stage of R&D. It also has the potential to transform crucial areas, such as finance, investing, and research.

Machine learning

Coined by Arthur Samuel in 1959, term machine learning is a subset of artificial intelligence that applies algorithms and statistical techniques to large sets of data to analyze and make predictions, or learn, without being explicitly programmed to do so. Some notable milestones in machine learning AI development can be found in competitive strategy gaming:
  • 1997: IBM Deep Blue defeats world chess champion Garry Kasparov
  • 2011: IBM Watson defeats Jeopardy! winners Brad Rutter and Ken Jennings
  • 2016: Google DeepMind AlphaGo defeats world go champion Lee Sedol
  • Next: Google DeepMind StarCraft II
We have been working on utilizing machine learning AI, and now it is possible to develop and use more efficient strategies. This can potentially lead to investment strategy improvements as well. Some iterations of these programs can also manage and execute trades on behalf of investors, and others are able to collect and analyze the overall market sentiment and events.

Quantitative analysis in machine learning

Using existing quantitative analysis techniques, AI applications can process and organize data much faster than humans. By sorting through larger datasets, machine learning AI can find strong candidates for companies, stocks, or funds while identifying weaknesses in others. With specific goals and defined outcomes, these tools can be used to guide traders and investors.
They can assist with portfolio management, guide you toward positions with higher potential, and greatly minimize risk. Different perspective from AI could also correlate or present valuable data that might have been overlooked or gone unnoticed by people. Additionally, investors may be impacted by biased information that can impair their judgment, so using a program to analyze the current positions may help traders make better decisions in the long run.

Algorithmic trading and bots

Trading and algorithm bots are applications built to execute trades based on certain criteria and market factors, either more efficiently than humans or instead of them. With user input for preferred strategies or to identify market regimes, these bots can react to the market and use algorithms to produce more favorable results and profitable trades, or protect assets and positions, if the market behaves unfavorably.
This is one of the examples where AI can certainly perform better than humans in terms of speed and optimal decision-making. However, it’s recommended that the move from supervised to unsupervised learning should be gradual when automating one’s trading (especially given the sensitive nature of digital funds and assets). Machine learning and trading bots may react appropriately within their parameters, but market conditions can change, and thus unpredictable events may occur.

Deep learning and market sentiment

Machine learning AI can also be used to evaluate the sentiment, mood, or perspectives of markets, as well as other traders or analysts. By collecting and analyzing secondary data such as news posts, social media activity, and even search engine metrics, these applications can provide insights into the emotional aspects of the market.
AI bots can be used to crawl websites and other online services and compile profiles or perspectives on the market or specific assets, funds, or investments. Also, it’s worth noting that this kind of research is more experimental and is strongly speculative; it has the potential to provide better insight into other human traders and investors during specific market periods. Although this information is not currently a primary source of decision-making, it is extremely valuable to the informed investors.

Considerations

By using machine learning, AI can correctly provide numerous benefits to traders and investors. While circumstances are always different, the possibility to manage better positions, make faster and more equitable trades, and recognize the overall atmosphere of the market, becomes a reality.
  • Using quantitative analysis techniques can provide a different perspective on the positions, stocks, and funds in a dataset. Future performance is not guaranteed, but more favorable conditions can be filtered.
  • Trading and algorithm bots can execute general trading strategies set by their owner, which can increase the decision-making and reaction speed of trades, although human supervision or intervention may still be necessary to oversee the accuracy of the bots and strategies.
  • Bots and machine learning AI can also be used to gather and crawl data on social networks or other news platforms. While these reviews or perspectives on the market do not represent the actual market data, they can reflect some of the overall sentiment or mood of the investors and analysts.
But don’t worry about AI taking over your job just yet. Much like autonomous driving, machine learning AI is not ready to fully replace people in markets or finance. So far, it is clear that the time will come when applications and AI will far exceed the efficiency of human traders and investors. For now, there’s no doubt that these quickly evolving digital tools can enhance and improve the performance of people and funds in markets and finance. However, it is not clear how long it may take to see these technological advancements become the norm.

Comments

  1. Thanks For Sharing Excellent Blog. Machine Learning is steadily moving away from abstractions and engaging more in business problem solving with support from AI and Deep Learning. With Big Data making its way back to mainstream business activities, now smart (ML) algorithms can simply use massive loads of both static and dynamic data to continuously learn and improve for enhanced performance. Pridesys IT Ltd

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