We all agree that financial markets are at the heart of our modern economy and no doubt that they provide an important avenue for the sale and purchases of assets such as bonds, stocks, foreign exchange, and derivatives. However, to make profits from such markets, investors should study the market’s complicated and highly volatile environment. Let’s consider the stock market as an example, to profit from such a market you need to take into account all the possible political, economical and even environmental (like a pandemic) factors that affect the market movement and that makes trading a very difficult task for a human to do, that’s why in the past decade many efforts have been made to automatically generate successful deals in trading financial assets by designing adaptive systems that take advantage of markets while reducing the risk.
What is the problem with supervised models?
The majority of these adaptive systems were relying on Supervised Learning, which in essence train a predictive model on historical data to forecast the trend direction, but regardless of their popularity these supervised methods suffered from different limitations which led to sub-optimal results.
The main reason for supervised model limitations in financial markets is that trading financial assets is not only a process of predicting the future price as most supervised models do, but it also involves many other aspects that should be considered, such as the risk involved, where the supervised model seeks minimization of prediction error (maximization of return ) regardless of the risk, which it’s not in the interest of the investor, also exogenous constraints (e.g., lack of liquidity and transaction costs) are not being considered at all in most cases. Besides, financial markets data is extremely noisy, hence employing an algorithm with enormous learning capacity such as **_Neural Networks _**in such an environment will mostly lead to overfitting.
The aforementioned drawbacks of _Supervised Learning _can be tackledby using Reinforcement Learning.
#stock-market #reinforcement-learning #finance #machine-learning
