Using AI -- Avoiding Financial Losses and Making Financial Gains

The ability to predict in a market economy is equal to being able to generate wealth by avoiding financial losses and making financial gains. It would offer a significant and lucrative competitive edge over other market participants.

Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Sentiment analysis is an area where structured and unstructured data is analyzed to generate useful behavioral insights leading to improved performance.

Nowadays, many investment banks and hedge funds are trying to utilize the sentiments of investors to help make better predictions about the financial market.The challenge is to transform [market] information into an increase in the value of their asset holdings, that is, capture the ever-elusive alpha. Where and how can the firms innovate to obtain such alpha?

Market predictive AI using Sentimental Analysis

Our market-predictive AI platform is a viable solution that may bring about a much higher degree of confidence on comprehension of market-movements based on gaining insights into human psychology at a macro level through text-mining of the, now widely available, textual resources on the Internet at a virtually real-time pace.

The holy grail for investors — and one being frantically searched for by technologists, entrepreneurs, and investors — is to find a way to program machines to decipher social media (or more accurately, unstructured text) and structure a trading system around it. Through text mining of news, microblogs, and online search results (Google, Wikipedia), massive amounts of data are distilled into information. This information is then used to construct actionable strategies for (i) trading, (ii) fund management and (iii) risk control.

Technical And Fundamental Analysis

In general, the predictive measures are divided into technical or fundamental analyses. They are differentiated based on their input data, with historic market data to be used for the former and any other kind of information or news about the country, society, company, etc. for the latter. Most of the research in the past has been done on technical analysis approaches, mainly due to the availability of quantitative historic market data and the general desire among traders for technical quantitative methods. Fundamental data is more challenging to use as input, especially when it is unstructured.

We utilize sentiment signals gleaned from unstructured data, in addition to structured transactional data (like past prices, historical earnings, and dividends), in our sophisticated machine learning models for algorithmic trading. Our hybrid-models based on the sum of the best of the two worlds (technical and fundamental) produce exceptional results.

Gateway to powerful data Analytics

We use Deep Learning to extract the incredible information that is buried in Big Data. We open the gateway to powerful data analytics using Deep Learning and sentimental analysis. In contrast to data mining approaches with its shallow learning process, our Deep Learning algorithms transform inputs through more layers. The Hidden layers in Deep Learning are generally used to extract features or data representations. This hierarchical learning process in Deep Learning provides the opportunity to find word semantics and relations. These attributes make our Deep Learning one of the most desirable models for sentiment analysis. For the state-of-the-art sentimental analysis on a large corpus, we use bi-directional LSTMs with word level CNNs.