Technology Reply Financial Services has developed a Use Case based on Machine Learning technologies,
to predict the performance of stocks.
In the last decade we have experienced, abroad more than in Italy, a revolution still in progress, involving the investor and the financial intermediary: automated financial consulting.
The first financial intermediary company to focus on the automation of financial advice was Betterment, founded in 2008, which introduced the concept of robo-advisor.
Robo-advisors are a kind of financial consultants that provide financial advice or investment management online with few human intervention, from moderate to minimal.They provide digital financial advice based on mathematical formulas or algorithms executed directly by software that does not require a human advisor. The software uses its algorithms to distribute, manage, and optimize client assets.
The activities have been aimed at the creation of a model able to predict the performance of shares of listed companies, operating on financial time series and all the factors related to them.This model is addressed to financial intermediation companies (B2B) in order to support the advisory service provided to investors.
The model is based on the choices an investor can make, which are:
The price movement of a given stock can be imagined as a chronological sequence of numerical values.
For this reason, a time series is the perfect tool for the representation of the variation of the price of an action or a set of them as time passes, where time can be marked by a daily, weekly or monthly granularity.
Considering that within a time interval, which can be daily or more, the price can vary several times, it is appropriate to set a reference instant to make the observation; for example the opening or closing of the stock exchanges. Several reference instants could be chosen to then carry out an aggregation operation such as the average, for example.
Synthetic data was generated for low-to-medium level activities, while actual data was adopted for advanced activities.
A financial intermediary or financial advisor who decides to use the model presented here would, in the first instance, find it easier to make the decision to advise clients on a particular transaction than another.
The advice provided would tend to be more accurate and this would lead to greater effectiveness.
In this way, client satisfaction would tend to increase, leading to greater retention.
In addition, it would be possible to provide basic assistance to any client at any time of any day of the week, avoiding, thanks to automation, an increase in the cost of providing this service if it were to be done in the traditional way.