Impact of Artificial Intelligence and Machine Learning on Stock Market

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Impact of Artificial Intelligence and Machine Learning on Stock Market

Financial markets are complex to understand. Stock markets generate a huge amount of data and this data. Big banks and hedge funds are making use of artificial intelligence and machine learning algorithms to automate the process of trading. Machine learning applications have improved the everyday banking, payment systems and more powerful insight into the financial sector.
Machine Learning service is making the banking and financial sector smarter. Smarter means that they can have a better customer insight and improved customer experience. This has made a huge difference in the banking industry by increasing profits and growth. According to a survey, around 35 percent of the financial organization has deployed at least one machine learning application.
First, what is Machine Learning?
Machine Learning is the sub-branch of Artificial intelligence that enhances the learning power of the machines. Machines are fed with a huge amount of datasets that enables them to take a decision on their own without being explicitly programmed. Artificial intelligence machines require less very less human intervention for obtaining the results.
Let us discuss how machine learning algorithms are serving in finance and stock:

Trading Made Simple and Better

The AI machine learning algorithms help in implementing the rules, conditions, sentiments that makes trading simpler. In the current scenario, statisticians are building the trading algorithms. This will be replaced by machine learning algorithms that are powered by neural networks. These neural networks learn from the past behavior and get improved over time.  Data science Consulting services have become a powerful tool for the hedge funds and equity funds managers too.

Better Risk Management

As we all know that financial markets involve risks. The use of business intelligence techniques has leveraged the high frequency trading by assisting the fund managers. By taking calculated risks, the chances of getting into loss are reduced. Moreover taking calculated risks can help the financial markets to grow steadily. As ML can handle a huge amount of data at a particular time, it can analyze data much better than humans. Various data mining techniques can be used to manage the bigger equity stakes in which risk factor is high.  Also, AI machines do not involve guesses in taking decisions. This also helps in managing the risk better.

ML Supported Robo Advisors

Choosing stocks from thousands of stocks is a daunting task. The general masses are not aware of terminologies used in stock markets making hedge funding and trading difficult. Machine learning and deep learning algorithms can be used to built Robo Advisors that guides the people in high frequency trading. They can crunch huge amount of data and assist people in stock market trading. Robo advisor eliminates the guesswork in future prediction and do all the necessary market research.

Machine Learning in Banking Domain

Machine Learning software companies are helping the banking domain in the enormous number of ways. They help banking domain by providing powerful solutions to the everyday problems faced by the banking sector. Banking is all about data and data is the main pillar of banking domain. AI powered systems help banks in handling a huge amount of data efficiently. Data mining techniques and algorithms makes planning and data analysis simpler for bankers and financial experts. For example, managing account operations, transactions, liquidity in the bank etc. It has also made the process of credit and loans simpler. Moreover, AI machines generate insights and identify the loopholes in the system that are hard for humans to identify.

Fraud Identification

Online payment, online money transfer has become an integral part of our lives. But this also has given a chance for fraudsters to peek into your information and get access to your account. This seems to haunt all of us. But this is the reality. In the recent years, online fraud is gaining momentum.
Someone somewhere can have access to your account and perform illegal activities. Deep learning neural networks help in reducing the risk of fraud. It has pieces of information about your location, past payments, amount of payments etc. If at some point in time it detects some deviation or breach in your account, it immediately alarms you or the concerned authorities in the bank. In some cases freezing your bank account making further transactions impossible.

Stock Market Prediction

Investing in the stock market involves rigorous research of the market. Stock marketers have to keep themselves updated from various sources like financial magazines, newspapers and various online sources. Customers are gaining most from machine learning applications. As they can use face recognition systems to unlock the finance apps, biometrics, voice commands etc that makes the financial system secure. Let us keep a watch on how Machine Learning Applications are going to affect financial services in the upcoming years.
Moving a step ahead, one can take advice from a financial expert but this may cost a bit and there is no guarantee of accurate stock market prediction. By automating the process using business intelligence technologies, the process of trading has become simpler and effective. Machine Learning algorithms predict the trends of the market so that the fund managers can identify the market changes before they happen.

Conclusion

Financial service providers are finding new and innovative ways to serve their clients. Machine learning applications have changed the landscape of financial and stock market services. Artificial intelligence research has made the identification of high-return stocks easier. High frequent trading will witness a boost in profits and investors will be able to unlock the potential of stock markets.

About the Author

Mandeep Kaur is working as a Data Scientist in Webtunix Solutions Private Limited.
She is very enthusiastic to learn about Machine Learning and Deep Learning techniques and always expresses her knowledge to beginners who want to start their career as a Data Scientist.

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