CP1 Additional Flashcards
Quantity theory of money
The quantity theory of money tells us that there is a direct relationship between the money supply and the level of prices
M x V = P x Y
where
M is the nominal money supply
V is the velocity of circulation
P is the price
Y is the number of transactions
Methods used to value investments
- Market value
- Smoothed market value
- Fair value
- Discounted cashflow
- Stocahstic models
- Arbitrage value
- Historic book value
- Written up and written down book value
Approaches for places a value on equity
- Market value
- Dividend discount model
- mainly for unlisted shares
- Net asset value per share
- For companies with significant tangible assets
- Value added measures
- Economic value added (EVA) looks and one year’s results and deducts the cost of servicing the capital that supports those results
- Measurable key factors
Investment objectives - principles
- Investement objectives should be clearly stated and quantified where possible
- Since it is generally often necessary and appropriate to invest in risky assets the objectives must be framed in such a way as to encompass the permitted degree of risk as well as the required total return and cashflow timing
Multiple investment objectives for funds
- Being able to meet its liabilities as they fall due
- Proving that it will be able to continue to do so on an ongoing basis
- on a realistic basis
- on a statutory basis
- Proving that it could do so on a discontinuance basis
Example of an explicit objective for an investment strategy using a model
- Maximise expected solvency level
- at the end of a three-year period
- subject to the probability of insolvency at any time over that period being < 0.1%
Modelling can be either deterministic or stochastic
Operational issues related to models
- The model being used should be adequately documented
- The workings of the model should be easy to appreciate and communicate. The results should be displayed clearly
- The model should exhibit sensible joint behaviour of model variables
- the output of the model should be capable of independent verification for reasonableness and should be communicable to those to whom advice will be given
- T_he model must not be overly complex_ so that either the results become difficult to interpret and communicate or the model becomes too long or expensive to run. It is important to avoid the impression that everything can be modelled
- The model should be capable of development and refinement - nothing complex can be successfully designed and built in a single attempt
- A range of methods of implementation should be available to facilitate testing, parameterisation and focus on results
- The more frequently the cashflows are calculated the more reliable the output from the model, altheough there is a danger of spurious accuracy. The less frequently the cash flows are calcualted the faster themodel can be run and results obtained.
Personal data must
- be processed fairly and lawfully
- be obtained and processed for specified purposes
- be adequate, relevant and not excessive for the purpose concerned
- be accurate and, where necessary, kept up to date
- not be kept longer than necessary for the purposes concerned
- be processed in accordance with the individual’s rights under the Act
- be processed securely
- not be transferred to a country or territory outside the European Economic Area unless that country or territory ensures an adequate level of protection
Consequences of non-compliance with the relevant data protection laws
- Individuals who commit criminal offences may be prosecuted
- Organisations can be fined for serious breaches
- Breaching data protection rules could lead to adverse publicity which can lead to significant reputational damage for an organisation
For anonymous data the obligations on an organistion are considerably less. In the UK, anonymous data does not constitute personal data and the duties and obligations of the Data Protection Act do not apply.
Big data can be characterised as
- Very large data sets
- Data brought together from different sources
- Data which can be analysed very quickly - such as in real time
Explain “big data analytics”
“Big data analytics” is the process of analysing the large data sets to uncover patterns, trends, correlations and other details that can be used to inform decision-making within the organisation
Issues with big data
- Organisations have to be careful to avoid big data being seen to be excessive or not relevant, and must be transparent when collecting big data
- Anonymisation can be used to avoid the data being classified as personal
Data governance policy
A data governance policy is a documented set of guidelines for ensuring the proper management of an organisation’s data.
Should provide the organisation’s stakeholders with confidence that the organisation is dealing appropriately with the data it holds.
Sets out guidelines with regards to
- Specific roles and responsibilities of individuals in the organisation with regards to data
- How an organisation will capture, analyse and process data
- issues with respect to data security and privacy
- the controls that will be put in place to ensure that the required data standards are applied
- How the adequacy of the controls will be monitored on an ongoing basis with respect to data usability, accessibility, integrity and security
Data governance risks
Organisations that do not have adequate data governance procedures can be exposed to risks relating to
- legal and regulatory non-compliance
- inability to rely on data for decision making
- reputational issues
- incurring additional costs (e.g. fines and legal costs)
Advantages of algorithmic trading
- increased speed and efficiency of trading
- can result in lower dealing costs on trades
- can potentially facilitate the execution of complex trading strategies that would not have previously been possible