6 1 reading 43 fintech in investment management pdf

• Big data is a term used to refer to complex, extremely large data that may be analyzed computationally to reveal patterns, trends, and associations, especially those leading to human

• Portfolio Management – An Overview

• Portfolio Risk and Return – Part I

• Portfolio Risk and Return – Part II

• Basics of Portfolio Planning and Construction

• Risk Management – An Introduction

• Fintech in Investment Management

What is fintech?

• Technological innovation in the design and delivery of financial

services and products

• Has much to do with the use of technology in areas traditionally

What’s Big Data?

• Big data is a term used to refer to complex, extremely large data that may be analyzed computationally to reveal patterns, trends,

and associations, especially those leading to human behavior

• It encompasses:

Traditional data sources such as company reports, stock

exchange sources, and data gathered from governments; and

Nontraditional (alternative) data from social media, sensor

networks, and electronic devices

Defining Properties of Data – The 3 Vs

• Volume: the amount of data collected in various forms, including

files, records, tables, etc Quantities of data reach almost

incomprehensible proportions

• Velocity: The speed of data processing can be extremely high In

most cases, investment analysts deal with real-time/near-real-time data

• Variety: the number of types/formats of data The data could be

structured (e.g., SQL tables or CSV files), semi-structured (e.g., HTML code), or unstructured (e.g., video messages)

MB GB TB PB

Web

Data Velocity

Data Volume

MB = 1 million bytes

GB = 1 billion bytes

TB = 1 trillion bytes

PB = 1 quadrillion bytes

Structured vs unstructured data

• Structured data refers to information with a high degree of

organization Items can be organized in tables and are commonly stored in a database where each field represents the same type of information, e.g., the net income of various hedge funds at year end

• Unstructured data refers to information with a high degree of

organization Items are unorganized and cannot be presented in

tabular form, such as text messages, tweets, and emails

• Semi-structured data may have the qualities of both structured and

unstructured data

• Governments: payroll, economic, trade, employment data, etc

• Individuals: product reviews, credit card purchases, social media

posts, etc

• Sensors: shipping cargo information, traffic data, satellite imagery

• The Internet of Things: data generated by ‗smart ‗buildings through

fittings such as CCTV cameras, vehicles, home appliances, etc

Artificial Intelligence vs Machine Learning

• Artificial intelligence refers to machines that can perform tasks in

ways that are "intelligent.‖

Has much to do with the development of computer systems that exhibit cognitive and decision-making abilities comparable or superior to that of humans

Can take the form of ―if-then‖ statements or complex statistical

models that map raw sensory data to symbolic categories

• Machine learning is a current application of AI based on the idea

that machines can do more than merely following coded instructions

It‘s the idea that when exposed to more data, machines can make changes on their own and come up with solutions to problems

without reliance on human expertise, improving their performance over time

Types of machine learning

1 Supervised learning

• Computers learn to model data based on labeled training data that contains both the inputs and the desired outputs

• Each training example has one or more inputs and a desired output

• Example: trying to predict the performance of a stock (up, down, or

level) during the next business day can be modeled through

supervised learning

• Example: trying to group companies based on their financial

characteristics and not on geographical or industrial characteristics

Application (1)

• Crowd-sourced content services analyze large datasets consisting of

security prices, financial statements, economic indicators and qualitative bits of information Results are integrated into a portfolio manager’s

investment decision-making process

• Complex algorithms have been developed to scour social media and

sensor networks in search of consumer sentiments and product performance data

Analysis of large datasets

Application (2)

• We now have systems built to identify systematic investment strategies

and automatically execute multiple trades over several financial markets worldwide

• Major selling point: increased market destinations

• Investment banking departments have reduced no of employees

considerably

Automated trading

Application (3)

• Machine learning algorithms built to sort through enormous amounts of

• These algorithms are then able to unearth trends and identify buy/sell

stocks

Analytical tools

Application (4)

• Robo-advisors - internet-based intelligence models that provide

investment advice with minimal human intervention

• Example: Betterment

billion

 Once signed up, the investor completes a short survey about their

investment needs and risk tolerance, used to develop an automatic investment plan

Automated advice

Application (5)

• In light of recent financial crises, a raft of risk management measures

have been introduced, most of which involve the analysis of enormous amounts of data

• Such data include liquidity information of a company and its competitors

and balance sheet and cash flow positions

• As a result, big data models have been built to aggregate, analyze, and

interpret these data in real time That way, it’s possible to identify weakening positions and adverse trends in advance

Risk management

What is Distributed Ledger Technology all about?

• A distributed ledger is a database held and updated independently

by each participant (or node) in a large network

• Rather than have a central authority, records are independently

constructed and held by every node (computer)

• Each node has the ability to process a transaction and come up with

a conclusion All the nodes then ―vote‖ on the conclusion If the

majority agree with the conclusion, the transaction is completed

successfully, and all nodes maintain their own identical copy of the ledger

• There is no need for a centralized databank as in the case of a

traditional ledger

What Makes DLT So Good?

1 Cryptography

• This refers to algorithmic encryption of data such that it is unusable

in the hands of an unauthorized party

• Before any transaction can be approved, some computers on the network must solve a cryptographic problem

• As a result, DLT has a high level of security and integrity

If a counterparty defaults on a payment, collateral can be

transferred to the relevant party instantaneously

3 Blockchain

• Blockchain is a type of digital ledger in which information, such as changes in ownership of an asset, is recorded sequentially within

blocks that are then linked together and secured using cryptography

• Each block is made up of a group of transactions that are linked to a previous block

• Blockchain enables the distribution of information while at the same time ensuring that it‘s not copied

Applications of DLT

1 Cryptocurrencies

• A cryptocurrency is an electronic currency which enables payments

to be sent between users without passing through a central

authority, such as a bank or payment gateway

• Most Cryptocurrencies use open DLT systems where a

decentralized distributed ledger is used to record and verify all

transactions

2.Tokenization

• Tokenization is the process of converting rights to an asset, say

stocks, bonds, or even a building into a digital token on a

blockchain

• DLT streamlines this process by creating a single, digital record of ownership with which to verify ownership title and authenticity

3 Post-trade clearing and settlement

• Post-trade transactions are known to be complex and time

consuming as they require multiple interactions between

counterparties and financial intermediaries

• DLT has the ability to streamline the entire process by providing

near-real-time trade verification, reconciliation, and settlement

4 Seamless compliance

• In the face of ever-increasing rules and regulations in the field of investment, DLT can help firms ensure compliance by enabling

near-real-time review of transactions

• This would eliminate the need for large post-trade monitoring teams and create operational efficiency

• Portfolio Management – An Overview

• Portfolio Risk and Return – Part I

• Portfolio Risk and Return – Part II

• Basics of Portfolio Planning and Construction

• Risk Management – An Introduction

• Fintech in Investment Management

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