Institutional Activities

A programme on Predictive Analytics and Financial Modelling for engineering students.

*National Institute of Industrial Engineering is happy to offer a Management Development Programme

Mumbai: National Institute of Industrial Engineering is happy to offer a Management Development Programme titled “Predictive Analytics and Financial Modelling (using python)” to address the increasing demand of skill creation and up gradation in data analytics in the industry.

Session Flow

1. Predictive analysis of investment decisions using Time Series Modelling

Univariate Time series modelling to analyse stock return

Model building and forecasting stock returns

Workout:

Modelling and predicting stock prices and its return using univariate time series

models like Autoregressive Moving average (ARMA) models using python

Developing best models and evaluating its forecasting accuracies

Undertaking post estimation diagnostic checking to ensure robust estimates

2. Introduction to Simulation (Wiener process / Geometric Brownian Motion) & Simulating Stock Price movements using Geometric Brownian Motion.

Workout: Simulating Stock Price Dynamics with real-life data and analysing estimates and its implications

3. Multivariate time series analysis: Predictive Analytics

Workout: Cause and effect analysis between indices

4. Introduction to classification problems and modelling the probability of a discrete outcomes.

Logistic Regression, Ordered Logistic Regression and Multinomial Logistic regression models.

Workout: Determinants of equity performance

In workout sessions, hands on practice with live data using python will be undertaken.

The Programme will be conducted between 2nd & 4th week of March,2022 based on convenience of participants

Mode of conduction: Online mode

Payment for the programme: 8260/- per Participant (All-Inclusive Fee)

Time: 15 Hours

Click here to fill the form: https://forms.office.com/r/ZcLx9rNM6E

Students interested can visit the following link https://www.nitie.ac.in/

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