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ML holds immense potential to offset the risk factor in stock market trading. Here's how.
FREMONT, CA: When it comes to financial trading, each decision can have a significant impact in terms of losses or gains. As a result, firms have deployed relevant business intelligence (BI) solutions that can analyze and process massive structured and unstructured data. Trading has always relied on data analysis. With the emergence of technologies such as big data and machine learning (ML), traders are getting better at identifying trading opportunities while reducing trading costs. ML and big data together are complementing each other and extracting smarter insights that will facilitate better trading.
Conventionally, experienced human minds have analyzed volumes of companies’ annual reports while the analysis tools were mostly limited to spreadsheets. However, with the exponential growth in data and in the current times, it is nearly impossible for a human to analyze those massive volumes of data manually. Further, the rapid rise in globalization has lead to an increased sense of interdependence among the stocks. Thus, a slight human error that could have been insignificant in terms of consequences can result in massive losses at present times. ML can be useful incorporation in this aspect.
ML and data analytics can offer unmatched results in investment and trading procedures. In trade execution, the aim of utilizing ML is to accomplish a trade order at the best price within the applicable constraints. Depending on the parameters, ML algorithms comprehend the trade information in real-time while optimizing, routing, and executing them to minimize slippages and costs.
Natural language processing (NLP) is the other useful area where AI can offer invaluable contributions to analyze and draw insights from unstructured data sets. It can also be crucial in determining the strategies depending on the sentiment analysis of trading.
Information discovery has found immense scope as ML algorithms can access various datasets. Data discovery is useful in many ways, such as estimating the sales at supermarkets even before the quarterly report is prepared. In the above use cases, ML and data analytics offer a significant advantage over the conventional approach that required human involvement. There are other added advantages that accompany ML’s ability to access and analyze diversified data sets. For instance, ML-equipped data analytics results in better diversification and risk management capabilities, reduce trading costs, allows better signal generations, and many others.
Another important consideration that comes in the present times is the external factors that influence the financial analysis. External factors such as economic and social trends within the political environment, economy, consumer behavior, and preferences must all be considered if they have the potential to impact the prices of a particular stock or stock prices in a particular industry. Big data analytics can be useful incorporations into the predictive models. Investors can utilize this information to minimize the risks associated with trading.
Despite the massive ML innovations in the field of data analytics in trading, the technology is still in its initial stage. Potentially, the use cases of the technology have immense potential for the traders in the future. It can also enable computers to make financial decisions, and get better by learning from past experiences, arrive at better investment decisions.