6 1 reading 43 fintech in investment management pdf

LOS Portfolio Management • • • • • • 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 LOS LOS Describe “fintech” What is fintech? • Technological innovation in the design and delivery of financial services and products • Has much to with the use of technology in areas traditionally dominated by humans • Has led to development of new advanced systems for investment advice, financial planning, business lending, and payments • Encompasses technological innovation and automation in financial literacy, advice and education, wealth management, credit administration, money transfers/payments, investment management, and more LOS LOS Describe Big Data, artificial intelligence, and machine learning 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 LOS LOS Describe Big Data, artificial intelligence, and machine learning Defining Properties of Data – The 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) LOS LOS Describe Big Data, artificial intelligence, and machine learning Data Velocity MB GB TB PB Data Volume Web MB = million bytes GB = billion bytes TB = trillion bytes PB = quadrillion bytes LOS LOS Describe Big Data, artificial intelligence, and machine learning 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 LOS LOS Describe Big Data, artificial intelligence, and machine learning Sources of data • Financial markets: equity, swaps, futures, options, and other derivatives • Businesses: financial statements, credit card purchases, and commercial transactions • 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 LOS LOS Describe Big Data, artificial intelligence, and machine learning Artificial Intelligence vs Machine Learning • Artificial intelligence refers to machines that can perform tasks in ways that are "intelligent.‖  Has much to 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 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 LOS LOS Describe Big Data, artificial intelligence, and machine learning Types of machine learning 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 LOS LOS Describe Big Data, artificial intelligence, and machine learning Supervised learning • Computers are only given input data and are tasked with describing the data, for instance by grouping or clustering of data points • Computers learn from data that has not been labeled or categorized The computers then ―react‖ based on the presence or absence of commonalities • Example: trying to group companies based on their financial characteristics and not on geographical or industrial characteristics LOS LOS Describe fintech applications to investment management Application (1) Analysis of large datasets • • 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 LOS LOS Describe fintech applications to investment management Application (2) Automated trading • • • 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 LOS LOS Describe fintech applications to investment management Application (3) Analytical tools • • Machine learning algorithms built to sort through enormous amounts of financial data — company filings, earning calls, profit warnings, etc These algorithms are then able to unearth trends and identify buy/sell stocks LOS LOS Describe fintech applications to investment management Application (4) Automated advice • • Robo-advisors - internet-based intelligence models that provide investment advice with minimal human intervention Example: Betterment  One of the world’s largest robo-advisors with assets worth over $13.5 billion  Once signed up, the investor completes a short survey about their investment needs and risk tolerance, used to develop an automatic investment plan LOS LOS Describe fintech applications to investment management Application (5) Risk management • • • 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 LOS LOS Describe financial applications of distributed ledger technology 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 Ledger Node Node Node Consensus Ledger Node Node Ledger Ledger Node Ledger Describe financial applications of distributed ledger technology Ledger LOS LOS LOS LOS Describe financial applications of distributed ledger technology What Makes DLT So Good? 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 LOS LOS Describe financial applications of distributed ledger technology Smart contracts • These are computer programs that self-execute on the basis of prespecified terms and conditions agreed to by the parties to a contract • In the derivatives market for instance, the system could be se such that daily settlements are made automatically in line with the day‘s market movements  If a counterparty defaults on a payment, collateral can be transferred to the relevant party instantaneously LOS LOS Describe financial applications of distributed ledger technology 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 LOS LOS Describe financial applications of distributed ledger technology Applications of DLT 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 LOS LOS Describe financial applications of distributed ledger technology 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 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 LOS Portfolio Management • 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 Remember to practice! ... Describe fintech applications to investment management Application (4) Automated advice • • Robo-advisors - internet-based intelligence models that provide investment advice with minimal human intervention... over several financial markets worldwide Major selling point: increased market destinations Investment banking departments have reduced no of employees considerably LOS LOS Describe fintech applications... systems for investment advice, financial planning, business lending, and payments • Encompasses technological innovation and automation in financial literacy, advice and education, wealth management,