Go to Quantitative Methods

Topics

• Introduction
• How Is Fintech used in Quantitative Investment Analysis?
  • Big Data
• Advanced Analytical Tools: Artificial Intelligence and Machine Learning
• Tackling Big Data with Data Science
  • Data Processing Methods
  • Data Visualization
  • Text Analytics and Natural Language Processing

Table of Contents

Introduction

• What is Fintech and how it is used in Investment Analysis?
• A brief explanation of Big Data, Artificial Intelligence, and Machine Learning
• Applications of Big Data and Data Science to Investment Management

How Is Fintech Used in Quantitative Investment Analysis?

Fintech refers to technological innovation in the design and delivery of financial products and services.

Its earlier forms involved data processing and automation of routine tasks. It later advanced into decision-making applications based on complex machine learning logic.

The major drivers of fintech have been:

• Rapid growth in data
• Technological advances

We focus on quantitative analysis in the investment industry:

• Analysis of large datasets
• Analytical tools

Big Data

Big Data refers to vast amount of data generated by industry, governments, individuals, and electronic devices.

Characteristics of big data

• Volume: Amount of data that we are dealing with has grown exponentially.
• Velocity: Speed at which data are communicated.
  • Batch processing → Real time data
• Variety: Structured → Unstructured data.

4. Veracity – Credibility and reliability of different data sources.

Big Data can be structured (can be organized in tables), semi-structured, or unstructured (cannot be represented in a tabular form).

Sources of Big Data

• Traditional data sources include annual reports, regulatory filings, trade price and volume, etc.
• Alternate data include many other sources and types of data.

• The quality of data may be questionable.
• The data may have biases, outliers, etc.
• The volume of data collected may not be sufficient.
• Another concern is the appropriateness of data.
• Usually involves cleansing and organizing the data before we start analyzing it.

Advanced Analytical Tools: Artificial Intelligence and Machine Learning

Artificial Intelligence

• Computers perform tasks that have traditionally required human intelligence.
• AI exhibits cognitive and decision-making ability comparable or superior to that of human beings.
• Neural networks refers to programming based on how the brain learns and processes information.

Machine Learning

Computer-based techniques that extract knowledge from large amounts of data by learning from known examples and then generating structure or predictions without relying on any help from a human.

In ML, the dataset is divided into three distinct subsets:

1. Training dataset: Allows the algorithm to identify relationships between inputs and outputs based on historical patterns in the data.
2. Validation dataset: Used to validate and model tune the relationships.
3. Test dataset: Used to test the model’s ability to predict well on new data.

Broadly speaking there are three main approaches to machine learning:

1. Supervised learning: Both inputs and outputs are labeled. After learning from labeled data, the trained algorithm is used to predict outcomes for new data sets.
2. Unsupervised learning: Input and output variables are not labeled. The ML algorithm has to seek relationships on its own.
3. Deep learning: Neural networks are used by the computers to perform multistage, non-linear data processing to identify patterns.

Deep learning algorithms are used for image, pattern, and speech recognition.

Some challenges associated with machine learning are:

• Over-fitting the data: Sometimes an algorithm may try to be too precise. This leads to over-trained models and data mining bias.
  • Mitigate this by having a good validation dataset.
• Black box: ML techniques can be opaque with predictions that are not very easy to explain.

Tackling Big Data with Data Science

Data science leverages advances in computer science, statistics, and other disciplines for the purpose of extracting information from Big Data.

Data Processing Methods

• Capture: How data is collected from various sources and transformed.
• Curation: Ensuring data quality and accuracy through data cleaning.
• Storage: How data will be recorded and accessed.
  • Refers to the underlying databases design
  • Whether the data is structured, unstructured, or both
  • Need real time access to the data or not.
• Search: How we can find what we want from the vast amount of data.
• Transfer: How data will move from the source to the tools that are being used.

Data Visualization

How the data will ultimately be presented to the analyst/user.

Data Visualization
Graphs, Charts → Heat Maps, Tree Diagrams, and Tag Clouds

Text Analytics and Natural Language Processing

Text Analytics
Use of computer programs to derive meaning from large, unstructured text or voice-based data. Based on this, we can determine if the sentiment is very positive, positive, neutral, or negative.

Natural Language Processing (NLP)
Application of text analytics whereby computers analyze and interpret human language. Such processing is possible because of access to Big Data and processing power.