Module 57.1: Fintech in Investment Management Flashcards
What is fintech?
refers to development in technology that can be applied to the financial services industry.
What are the 4 primary areas fintech is developing?
1) increasing functionality to handle large sets of data
2) tools and techniques such as artificial intelligence
3) automation of financial functions such as executing trades
4) emerging technologies for financial recordkeeping
What is big data?
widely used expression that refers to all the potentially useful information that is generated in the economy
What is “corporate exhaust”
bank records and retail scanner data created from businesses
What is the internet of things?
sensors in machines in smart phones and smart buildings.
What are the three main characteristics of big data?
1) volume - size of data, way it’s measured
2) velocity - how quickly data is communicated. fast = “low latency”.
3) variety - data refers to the varying degrees of structure in which data may exist.
What does the field of data science concern? What are the 5 methods of processing bid data?
How to extract big data.
1) capture - collecting and transforming
2) curation - assuring data quality by adjusting for bad or missing data
3) storage - archiving and accessing data
4) search - examining stored data
5) transfer - moving data from their source or a storage medium.
What are the three major challenges to big data management?
1) need to assure the data is of high quality
2) accounting for the possibilities of outliers
3) bad or missing data, or sampling biases
What is machine learning? What does a typical process look like?
computer algorithm is given inputs of source data, with no assumptions about their probability distributions, and may be given outputs of target data.
1) a training dataset in which the algorithm looks for relationships
2) a validation dataset is then used to refine these relationships
3) test dataset to analyze their predictive ability
What is supervised learning vs. unsupervised learning for machine learning? What is deep learning?
supervised - input and output data are labelled, then given new data to perform the same outputs.
unsupervised - input data is not labelled and the machine learns to describe the structure of the data.
Deep learning uses layers of neural networks to identify patterns, beginning with simple patterns and advancing to more complex. some applications include image and speech recognition.
What is overfitting and underfitting for machine learning?
overfitting - learns the input and outputs too exactly, treats noise as true parameters (model is too complex)
underfitting - machine fails to identify actual patterns and relationships, treating true parameters as noise.
What are the five applications of fintech to investment management?
1) text analytics
2) natural language processing
3) risk analysis
4) algorithmic trading
5) robo-advisory services
What is text analytics in terms of fintech application to investment management?
analyzing frequency of words and phrases, could help automate specific tasks such as evaluating company regulatory filings.
What is natural language processing in terms of fintech application to investment management?
computers and artificial intelligence to interpret human language. could be helpful to identify change in sentiment, could be helpful for modeling and testing risk.
What is algorithmic trading in terms of fintech application to investment management?
refers to computerized securities trading based on a predetermined set of rules. could be designed to enter the optimal execution instructions for any given trade. “high frequency trading”.
