4 Main Must-Know Quant Finance Concepts for Data Scientists

 

Quantitative finance includes a set of complex mathematical formulas and rules. Data scientists all over the world use quantitative financial concepts, and things like price securities and market risks are calculated using these concepts. This is the basis for quantitative engineering which builds tools and apps. Let’s simply check some major concepts and how they can be helpful in financial calculations to make useful and accurate financial predictions.

 

After a thorough research, we have come up with 4 concepts which will be explained in detail. The explanation might seem a bit technical but the important thing is that these are the important concepts to consider for the finance experts.

 

Contents:

- Cointegration

- What are the correlations?

- What is cointegration?

- Put-Call Parity

- What are Put and call options?

- The Put-Call parity

- What is the relationship between Put and call options?

- Measures of Finance Performance

- Sharpe ratio

- M2 measure

- Treynor ratio

- Monte-Carlo Simulation

- Final Thoughts

1. Cointegration

Before explaining cointegration, we would like to start with correlation, it is a positive or a negative relationship between the different financial sets. A financial set is a time series of financial data put in a successive order. Now, you would ask what is time series? Well, time series is several observed financial values over a certain period. This is a valuable concept to understand and use because cointegration can help you understand the relationship between the financial time series.

 

What are the correlations?

Between any two financial sets of the same period, one can correlate either -1 or +1. -1 says that there is a negative or opposite relationship between the time series of the financial data, while +1 says there is a positive relationship. There can also be 0, which would indicate that there is no relationship between the sets. These financial values are volatile and keep changing and therefore, the correlation would also be unstable, making it an unreliable marker.

 

What is cointegration?

Cointegration on the other hand is a linear combination of the timeline of financial data. This helps us to quantify how closely the time series are moving. One would need to calculate the mean and standard deviation. If both are constant after the linear combination, we can say that the time series of financial data has been cointegrated.

 

2. Put-Call Parity

Put-call parity describes the relationship between the put and call options. Before divulging the details of how to calculate and how it can be useful, we will first learn about what put and call options.

 

What are Put and call options?

We will start talking about put and call options, but first, let us understand what an option is. An option is a choice for the investor to buy an underlying stock. The user can either buy a call option or the put option. A call option gives the investor the right to buy the stock at a predetermined price on expiration. On the other hand, with the put option, the investor can buy the stock at the same strike rate when the contract is exercised. 

 

The Put-Call parity

The aim is to look for arbitrage opportunities through these put and call options. An arbitrage opportunity means that one can get money without investing or buying the stock. Such opportunities are found by finding out the parity or the relationship between them. 

 

What is the relationship between Put and call options?

An option can have 2 prices - bid and offer prices. If there is no overlap between the bid price of the call and the put options, you can find arbitrage opportunities or gaps to earn free money. If both call and put options have the same strike and expiration, such a relationship is called put-call parity. In turn, when you find that the stocks have a put-call parity, it implies there is high volatility. Unfortunately, such gaps are difficult to find as the market is becoming more knowledgeable and efficient. 

 

3. Measures of Finance Performance

Several key concepts can help to measure financial performance. These are quantitative measures of the financial model. They are the Sharpe ratio, M2 measure, Treynor Ratio, Sortino ratio, and Information Ratio. Here, we would like to explain 3 of those measures.

 

Sharpe ratio

This form of measurement analyzes the returns done in excess at a risk-free rate. It calculates the actual return over the risk. In short, the higher the Sharpe ratio, the lower the risk and therefore a better stock to invest in. 

 

M2 measure

Sharpe ratio doesn’t include the volatility and therefore, we have the M2 measure which converts the Sharpe ratio into a linear model. The risk-free returns are also added.  

 

Treynor ratio

It is similar to the Sharpe ratio but instead of standard deviation, we use systematic risks. It is a rather computerized or rather automatic system where the Capital Asset Pricing Model helps to calculate the risk. 

 

4. Monte-Carlo Simulation

When you need hands-on experience and real-life examples, Monte-Carlo simulation is the way to go. This provides real-life situations. One can predict the prices of future financial transactions based on the current one.

 

Unfortunately, there are too many variances and deviations, which makes this calculation difficult. But thanks to the Monte Carlo simulations, the current prices and the random variables are used as a base to generate multiple simulations. It is one of the most used, frequently used concepts in the financial world.

 

Final Thoughts

There you go, those are some of the best concepts in quantitative finance to remember and put to use. As a data scientist, it is quite important to be familiar with the concepts and keep up with the new and upcoming theories. It would be highly recommended if you could create a working model out of the concepts to get some hands-on experience and understand them even better. You might even choose one concept to do some further deep reading.

 

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