Risk Analysis:

VaR Model

Value at Risk (VaR) is defined as the maximum loss with a given probability, in a set time period, with an assumed distribution and under standard market conditions. It is a statistical measure of the risk of loss for an investment. The VAR is calculated with the following methods: the historical, the variance-covariance and the Monte Carlo Simulation.


Sensitivities Analysis 

Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system can be divided and allocated to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; the process of recalculating outcomes under alternative assumptions to determine the impact of a variable under sensitivity analysis can be useful for a range of purposes; testing the robustness of the results of a model or system in the presence of uncertainty.

In the financial markets the Sensitivity analysis is a  model that determines how target variables are affected based on changes in other variables known as input variables. This model is also referred to as "what-if" or simulation analysis. It is a way to predict the outcome of a decision given a certain range of variables. 
Some indicators are:

Dv01   
Credit01   
Inflation01   
Greeks (delta, gamma etc)   


Machine Learning 

Emulators are data-modelling / machine learning approaches that involve building a relatively simple mathematical function, known as an emulator, that approximates the input/output behaviour of the model itself. In other words, it is the concept of "modelling a model" (hence the name "metamodel"). The idea is that, although computer models may be a very complex series of equations that can take a long time to solve, they can always be regarded as a function of their inputs. By running the model at a number of points in the input space, it may be possible to fit a much simpler emulator η(X), such that η(X) ≈ f(X) to within an acceptable margin of error. Then, sensitivity measures can be calculated from the emulator (either with Monte Carlo or analytically), which will have a negligible additional computational cost. Importantly, the number of model runs required to fit the emulator can be orders of magnitude less than the number of runs required to directly estimate the sensitivity measures from the model.


Scenario Analysis 

Scenario analysis is a method for predicting the possible occurrence of an object or the consequences of a situation, assuming that a phenomenon or a trend will be continued in the future.


BackTesting 

Backtesting measures the accuracy of the value at risk calculations or the probability of success of a strategy. Backtesting is the process of determining how well a strategy would perform using historical data. 


Stress Test 

Stress testing is a computer-simulated technique to analyse how investment portfolios fare in drastic economic scenarios. Stress testing helps gauge investment risk and the adequacy of assets, as well as to help evaluate internal processes and controls.


Liquidity Analysis   

Liquidity Assessment of Portfolios   provides the ability to quantitatively evaluate market liquidity across multiple 
asset classes in normal and stressed market conditions.

Forecasting

Predicting stock prices is an uncertain task which is modelled using machine learning to predict the return on stocks. There are a lot of methods and tools used for the purpose of market predictions. The financial markets are considered to be very dynamic and complex in nature. An accurate prediction of future prices may lead to a higher yield of profit for investors through stock investments. As per the predictions, investors will be able to pick the stocks that may give a higher return. Over the years, various machine learning techniques have been used in stock market prediction, but with the increased amount of data and expectation of more accurate prediction, the deep learning models are being used nowadays which have proven their advantage over traditional machine learning methods in terms of accuracy and speed of prediction. Long-Short-Term Memory Recurrent Neural Network belongs to the family of deep learning algorithms. It is a recurrent network because of the feedback connections in its architecture. It has an advantage over traditional neural networks due to its capability to process the entire sequence of data. Its architecture comprises the cell, input gate, output gate and forget gate. The cell remembers values ​​over arbitrary time intervals, and the three gates regulate the flow of information into and out of the cell. The cell of the model is responsible for keeping track of the dependencies between the elements in the input sequence. The input gate controls the extent to which a new value flows into the cell, the forget gate controls the extent to which a value remains in the cell, and the output gate controls the extent to which the value in the cell is used to compute the output activation of the LSTM unit.

  
Financial Engineering:

Knowledge for pricing and hedging  every kind of structured product. Structuring and origination of complex strategies.


Portfolio Enhancement & Cost Analysis:

Find the optimal portfolios that offer the highest expected return for a defined level of risk. At same time the cost analysis allows to reduce the impact of transaction and management costs.


Sentiment Analysis:

Sentiment analysis (also known as emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. In the financial world it allow PM to understand which is the mood of retail and institutional traders and adopt accordigly appropriate strategies. The main difficult is to link tickers to the news and understand when the news is fake or don't effect the price of related stock.