Till now in our ‘AI for Trading Series’, we have learned about the indices and ETFs, their applications in the real world and how they work on a transaction level. In this article, we will look at the risks and return properties of a collection of stocks.
For example, consider a scenario where you have done your research and have come up with a list of stocks you want to invest in. You have calculated the amount of money you have to spend and you are now ready to buy those stocks that are needed to construct your portfolio for…
Volatility is the standard deviation of probability distribution of log returns. It is the measure of the spread of this particular distribution. Volatility gives a sense of range of values, log returns are likely to fall into.
Volatility can be used for :
Calculating standard deviation of log returns of past data is known as historical volatility.
In this series, we will cover the following ways to perform time-series analysis-
The random walk hypothesis is a financial theory stating that stock market prices evolve according to a random walk and thus cannot be predicted. A Random Walk Model beleives that :
In this article, we will learn about the Exchange Traded Funds (ETFs) and how they work. To understand more about ETFs, let us first understand few drawbacks of open-ended and close-ended mutual funds. You can read more about what are open-ended and close-ended mutual funds in my post here.
The two most popular types of trading strategies are Momentum and Mean Reversion. You can read more about the Momentum Trading strategy in my post here. A Mean Reversion Trading Strategy involves betting that prices will revert back towards the mean or average, whereas, the Momentum Trading Strategy predicts prices will continue in the same direction.
A simplistic example of a mean reversion strategy is to buy a stock after it has had an unusually large fall in price. …
This series focuses on two main points:
There are some visual ways to check if a distribution is normally distributed or not:
There are three hypothesis tests that can be used to decide if a data distribution is normal:
A hypothesis is an idea for a way to profit from trading. This hypothesis goes through a several phases of rigorous testing. After coming up with a hypothesis, next comes the research phase. In the research phase, we decide what set of positions to enter, on which assets and at what times in order to get positive future returns. After this phase, comes the testing phase which decides how much money to invest in each asset, in what conditions to exit positions and what are the risks. This is called back-testing.
Learn about basic terminologies used while analyzing stocks.