Topics 72-79 Flashcards

1
Q

Identify reasons for the failures of funds in the past

A

Following is a concise list of reasons past funds have failed.

  1. Poor investment decisions.
  2. Fraud.
  3. Extreme events.
  4. Excess leverage.
  5. Lack of liquidity.
  6. Poor controls.
  7. Insufficient questioning. Often in a committee-style decision-making process, there may be a dominant member who sways the decision and/or members who are afraid to voice any valid concerns over information they have discovered that would question the merits of the investment manager and/or investment. Ideally, all due diligence team members should be encouraged to play the role of “devil’s advocate” when appropriate and raise reasonable concerns as early as possible, especially before they reach the committee stage.
  8. Insufficient attention to returns. Investment funds attempting to reduce operational risk sometimes overcompensate by implementing excessive controls and may end up bearing too many expenses and not generating enough returns. Ideally, there is a healthy balance between generating strong returns while taking on a reasonable level of risk.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Explain elements of the due diligence process used to assess investment managers

A
  • Prior to investing, an investor performs due diligence on a potential investment manager, which involves assessing the manager, the fund, and the investment strategy. Information such as the investment background, manager’s reputation (e.g., education, employers), and past performance have always been key considerations but are insufficient on their own.
  • An additional element of due diligence involves assessing the investment process and risk controls. The starting point is a review of the fund’s prospectus or offering memorandum.
  • Additionally, an attribution analysis could be performed to determine how the returns were generated. Were they generated through the skill and control of the manager, luck, and/or factors beyond the manager’s control? In addition, was the amount of return in line with the amount of risk taken?
  • Another related element is assessing the fund’s operations and business model. In general, are there internal controls and policies in place to preserve the investors’ funds? Specifically, are the controls in place sufficiently robust to detect and prevent fraudulent activities or are limits imposed on managers to seek higher level approval for transactions exceeding a certain dollar amount or frequency? Is there appropriate segregation of duties between the front office and the back office? What is the process and frequency of asset valuations? What is the fee structure and are there any additional fees after a specific threshold? Are there any limitations or blackout periods on redemptions?
  • In the end, investors should assess potential managers and their investment strategies with an objective and unbiased mind. They should not get caught up with a manager’s past successes.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Identify themes and questions investors can consider when evaluating a manager

A

Manager evaluation is not a task that should be taken lightly by potential investors. This process can be broken down into four areas including strategy, ownership, track record, and investment management.

Strategy

General questions regarding a managers strategy may include:

  • Does the manager follow a particular investment style (e.g., growth, value)?
  • Are there any current “trends” in the fund or specializations in specific securities, industries, or sectors?
  • How has the fund changed its investment style or rebalanced its holdings over the past year? What changes are contemplated in light of anticipated market conditions?
  • What is the extent of turnover and liquidity in the fund? What market signals are used to determine whether to exit or enter a position?
  • What mechanisms are in place to limit any potential losses in the fund?
  • To what extent is quantitative analysis and modeling utilized in the investment process? Have any models been developed or tested to date?
  • Are short sales used to generate excess profits or to hedge? How successful or detrimental have they been so far?
  • Are derivatives used in the portfolio? If so, are they used for hedging or speculative purposes?
  • How does the trade execution process work? Does a central trading desk exist for maximum efficiency?
  • What is the extent of any investment in private company securities and their role in the overall investment strategy?
  • What is the tradeoff between maximizing current returns versus long-term fund growth?
  • Has the fund ever been closed or provided investors with a return of capital?

Ownership

Ownership interests often help align the interests of the investment team and the investors.

They can be useful in attracting and maintaining quality staff, thereby enhancing and/or continuing to generate strong investment returns for investors. Therefore, potential investors should inquire as to whether any members of the investment team (e.g., traders, portfolio managers, research analysts) have ownership interests in the firm.

Track Record

Specific questions about the manager’s and fund’s track records may include:

  • How does the past performance of the manager and/or fund compare to its peers and/or funds that follow the same or similar investment philosophy?
  • Has past performance been audited or verified by a third party?
  • Is there sufficient performance history to perform trend and/or attribution analysis? How did the manager or fund perform during market downturns?
  • What were the investment returns relative to the size of the investment assets?
  • Are most or all of the staff on the investment team that generated those past results still employed by the firm?

Investment Management

Inquiries during manager interviews may include:

  • What is/was the manager’s investment strategy for generating excess returns?
  • How did the manager cope with tough market periods? Reference checks on managers could include the following individuals:
  • Former employers: Was the manager a leader or follower? Proactive or reactive? A team player or individualist?
  • Current and former colleagues, clients, and other independent parties: Ensure consistency but if there are mixed reviews, follow up for explanations and/or obtain clarification from the manager.
  • Current and former investors: What good and bad investment experiences did they have with the manager?

Background checks on managers may include the following questions/activities:

  • Obtaining comprehensive background check reports on the manager.
  • Review the Form ADV filed by the manager with the SEC and state securities authorities. It contains general information about the business as well as more detailed information such as fees, services provided, conflicts of interest, and background of key personnel.
  • Has the manager consistently demonstrated herself to be a person of integrity? This could be verified by examining public databases and the SEC website to look for any past or current instances of litigation or criminal behavior.
  • Has the manager demonstrated strong personal financial responsibility? This could be verified by examining personal credit reports and bankruptcy reports.
  • Are the manager’s stated representations accurate? This could be verified by inquiring with auditors and brokers who are currently working with the manager or have worked with the manager in the past.
  • What is the extent of the manager’s involvement in any related party transactions?
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Describe criteria that can be evaluated in assessing a fund’s risk
management process

A

A proper risk management process should contain an assessment of the following areas: risk, security valuation, portfolio leverage and liquidity, tail risk exposure, risk reports, and consistency of the fund terms with the investment strategy.

Risk

  • Assess the applicable systematic risk factors (i.e., regular market risks common to most or all funds) and unsystematic risk factors (i.e., risks specific to the manager, fund, or strategy).
  • Determine whether written policies and procedures exist regarding measuring and monitoring risk.
  • Determine whether a risk committee exists that would receive such measurements. If so, how often are they reported?
  • Evaluate the extent of the risk management culture among the various types of employees. For example, how actively involved are employees with managing and mitigating the firm’s risks on a day-to-day basis?
  • Assess the information technology resources used to quantify the risks. For example, are they reliable and do they measure items consistently between traders and portfolio managers?
  • Identify the existence and structure of any risk models. What are their inputs and assumptions? Have the models been tested and are they robust?

Security Valuation

  • Identify the proportion of fund assets that are objectively valued through reliable market prices versus those that are more subjectively valued by the broker or through simulation.
  • Examine the independence of valuations. Is valuation performed by the fund administrator (generally more independent) or by the fund manager (generally less independent)?
  • Determine if prices may be overridden for valuation purposes. If so, by whom? Is there documentation or an approval process?

Portfolio Leverage and Liquidity

  • Assess the sources of leverage as well as the current and historical levels of leverage.
  • Calculate the current level of liquidity and observe how it has changed over time. The current level is especially relevant because of the impact on portfolio investment capacity and whether it can take on more investment capital.
  • Within a stated investment strategy, excessive leverage and/or illiquidity could generate actual returns that are significantly different than expected (i.e., no longer comparing apples to apples), thereby requiring an adjustment in expected returns.

Exposure to Tail Risk

  • Analyze information about the fund to conclude whether the fund’s return distribution possesses skewness or kurtosis.
  • Discuss the possibility of tail risk with the manager and determine whether the manager has sufficiently mitigated the risk or whether further action is required by the investor.

Risk Reports

  • Review risk reports prior to investing in the fund. Investors should receive these risk reports on a regular basis (e.g., monthly, quarterly, annually) whether they are prepared in-house or by a third party.
  • Analyze key risk metrics and compare them to other similar funds for benchmarking purposes and for determining if any unusual risks exist in the fund.

Consistency of the Fund Terms with the Investment Strategy

  • Examine the general fee structure of the fund and determine whether it is consistent with similar funds.
  • Identify the existence of any additional fees after a specific threshold (e.g., high-water mark, hurdle rate).
  • Evaluate whether high fees are being paid to managers in search of market alpha (fair) as opposed to beta (unfair).
  • Identify the existence of any limitations or blackout periods on redemptions.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Explain how due diligence can be performed on a funds operational
environment

A

Investors should focus on several key areas when performing operational due diligence on a fund. The focus areas are internal control assessment, documents and disclosure, and service provider evaluation.

Internal Control Assessment

  • A starting point in due diligence is examining the qualifications and attitudes of the personnel.
  • An analyst must also assess whether the internal control staff have sufficient technical and work experience to perform their compliance duties properly.
  • Finally, background checks on critical internal control staff members might be required.
  • Examining the fund’s policies and procedures may also be useful. One drawback is that these documents tend to be general and only demonstrate the intention to have a strong control environment. It is usually a good sign if a fund has been proactive and obtained an audit report and opinion on the effectiveness of its controls. If this report is available, it Should be reviewed.
  • The due diligence process should include an examination of the in-house or outsourced compliance system that is in place.
  • There should be an investigation into how the funds deal with counterparty risk arising from OTC derivatives and other counterparties. Is such risk mitigated by dealing with more than one counterparty? Are the counterparties monitored for risk on a daily basis?
  • Finally, there should be an assessment as to the effectiveness of corporate governance.

Documents and Disclosure

  • As part of the due diligence process, investors must confirm with the fund’s legal counsel its involvement in preparing the original version of the fund documents as well as any subsequent revisions.
  • Conflicts of interest that are disclosed in the offering memorandum should be scrutinized carefully. Lack of clarity in the disclosure may be a red flag and warrant further discussion with the manager and/or require independent information.
  • Similarly, lack of clarity or sufficiency in the disclosure of risks may warrant further investigation. The discussion of very general or irrelevant risk factors may be cause for concern.
  • The focus of any due diligence should be on the manager. As a starting point, the potential investor should determine the extent of the manager’s authority.
  • In analyzing the financial statements, the investor should begin by ensuring the audit opinion is unqualified (i.e., the auditor believes the financial statements contain no material misstatements). The balance sheet and income statement should be examined for consistency with the fund’s investment strategy.
  • In addition, the footnotes (which are also audited) should be examined carefully since they provide more detailed information on key items (e.g., contingent liabilities, related-party transactions) than the corresponding financial statements.
  • Fees paid to the manager by the fund should be scrutinized and recalculated. They should be corroborated with the offering memorandum. Specifically, there should be a check of any incentive fees paid in loss years.
  • Finally, there should be a check for the level of net contributions to the fund by the general partner. .Any fund withdrawals should be questioned.

Service Provider Evaluation

  • Third-party service providers may be hired by a fund for trade execution, information technology, valuation, verification, and asset safeguarding purposes.
  • A starting point for assessing the actual service providers is to examine the internal control letters issued by its auditors and its audited financial statements. Further due diligence could be performed through in-person discussions regarding the service provider’s role.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Explain how a fund’s business model risk and its fraud risk can be
assessed

A

In addition to the previous due diligence, potential investors need to closely examine the fund to ensure that the risks associated with its business model and potential fraud are not excessive.

Business Model Risk

Evaluating business model risk requires assessing whether managers know how to operate the business as well as generate high returns. Typical risks, potentially leading to failure and closure of the fund, include a lack of cash and working capital, a lack of a succession plan, and excessive redemptions in a short period of time.

A fund’s business model risk can be assessed by performing the following tasks:

  • Examining the nature of the revenues and expenses. Calculating the percentage of revenues derived from variable incentive or performance fees (that may not materialize in market downturns).
  • Assessing the significance of the gap between management fees (revenue) and operating expenses.
  • Considering the sufficiency of the amount of working capital (especially cash) in place to cover revenue shortfalls and/or expense overages for a reasonable period of time.
  • Determining how frequently budgets are created and for what period of time.
  • Determining the fund’s breakeven points in terms of assets under management and required performance level. Comparing those amounts to current (actual) and future (projected) amounts.
  • Ascertaining if there is sufficient personnel or capacity to increase the fund’s investment asset base.
  • Ascertaining the existence of key person insurance on relevant individuals and the existence of a succession plan.

Fraud Risk

Fraud risk can always exist even though extensive due diligence has been performed on the manager and fund prior to investing.

Fraud risk may be mitigated by performing the following actions:

  • Check the SEC website for any prior regulatory infractions.
  • Check court records for any prior litigation and bankruptcy records for examples of financial irresponsibility.
  • Inquire with service providers for assurance over their competence and independence from the manager.
  • Perform extensive background checks on the manager.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Describe elements that can be included as part of a due diligence questionnaire

A

The questionnaire should make the following inquiries:

  1. Inquiry into general inform ation on the manager provides a starting point in the due diligence process. Examples of such information include:
  • Confirmation of proper registration with regulatory authorities.
  • Determination of ownership form (e.g., corporation) and structure.
  • Identification of key shareholders.
  • Reference checks.
  • Information on past performance.
  • Business contact information.
  1. Inquiry into general information on the fund also is critical. Examples of general information that should be collected include:
  • Fees.
  • Lockup periods.
  • Redemption policies.
  • Primary broker.
  • Fund director.
  • Administrator.
  • Compliance: auditor and legal advisor.
  • Financial: assets under administration, investment capacity, and historical performance (also see financial statements).
  • Historical drawdown levels.
  1. Inquiry into execution and trading as well as service providers may provide some insight on the speed and accuracy of transaction processing and the existence of related-party service providers, the latter of which may raise red flags with potential investors as discussed earlier.
  2. Inquiry regarding the firm’s third-party research policy may be useful to determine a fund’s sources of research information, thereby allowing the assessment of the extent and quality of the due diligence performed by the fund in its investment process.
  3. Inquiry regarding compliance processes, the existence and degree of involvement of inhouse legal counsel, and the existence of anti-money laundering policy and procedures may help provide comfort that the fund and its managers have a desire to operate in an ethical manner and/or within the boundaries of the law.
  4. Inquiry into the existence of information regarding disaster recovery and business continuity plans as well as insurance coverage and key person provisions may provide some assurance regarding the stability of the firm and, therefore, the safety of any invested funds.
  5. Inquiry into the investment process and portfolio construction provides the potential investor with information required to make an informed decision whether the overall risk and return profile of the fund is consistent with the investor’s investment objectives.
  6. Inquiry into risk controls such as leverage, liquidity, asset concentrations, portfolio diversification, and market risk factors give the investor a more complete picture of the investment risks and how the managers attempt to manage and mitigate them.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Describe the reasons to provision for expected credit losses

A
  • The requirement for banks to set aside funds as capital reserves is unlikely to reduce a banks lending activities during strong economic periods. The result may be excessive lending by banks. Therefore, by provisioning for expected credit losses (ECL), a more accurate cost of lending may be determined (which may ultimately control the amount of lending).
  • The concept of procyclicality refers to being positively correlated with the overall state of the economy. Reducing the procyclicality of bank lending is likely to occur with earlier provisioning for loan losses. Increased (decreased) regulatory requirements pertaining to provisions tend to reduce (increase) the level of bank lending.
  • The use of forward-looking provisions essentially results in the earlier recording of loan losses, which may be beneficial to financial statement users from the perspective of conservatism in a bank’s reporting of earnings.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Compare and contrast the key aspects of the IASB (IFRS 9) and FASB (CECL) standards

A

The IASB and FASB standards are similar in that ECL must be initially recorded at the outset of all loans and updated at the end of each reporting period, taking into account any changes in credit risks of their loan assets. In addition, the standards do not require any specific catalyst to occur in order to report a credit loss. Finally, the standards mandate the use of reliable historical, current, and forecast information (including macroeconomic factors) in computing ECL. For example, both standards measure probability of default (PD) at a point in time (rather than in context of the economic cycle) and measure loss given default (LGD) and exposure at default (EAD) as neutral estimates (rather than downturn estimates).

There are two main differences between the IASB and FASB standards:

  1. FASB requires ECL to be computed over the term of a loan commencing right from the start while IASB requires a series of three stages.
  2. IASB permits the recording of accrued interest income on delinquent loans, regardless of whether loan payments are being received. FASB requires the use of the cash basis (no interest income accrual), cost recovery method (payments applied to principal first, and once principal is repaid, the excess is recorded as interest income), or a combination of both in order to provide a more conservative and reliable method for income recognition on delinquent loans.

International Accounting Standards Board (IASB)

Under IFRS 9, ECL is reported in three stages to represent the deterioration of assets: stage 1 (performing), stage 2 (underperforming), and stage 3 (impaired).

Upon loan purchase or origination, stage 1 begins and the 12-month ECL is recorded (expense on income statement and contra-asset on balance sheet). However, interest revenue is computed on the original loan amount, not the amount net of the ECL. The 12-month ECL is computed as the expected lifetime credit loss on the loan asset multiplied by the probability of default within the upcoming 12-months after the end of the reporting date.

  • Stage 2* for a loan asset occurs upon severe deterioration of credit quality to require classification into a high credit risk category. That would be presumed to occur after the loan is 30 days past due according to IFRS 9. Interest revenue computation in stage 2 remains the same as in stage 1.
  • Stage 3* involves loan assets that are credit-impaired or generating credit losses. The entire lifetime ECL continues to be recorded but the interest revenue is now computed on the original loan amount less the loss allowance.

Financial Accounting Standards Board (FASB)

In contrast to IASB, FASB requires the entire lifetime ECL to be recorded as a provision from the outset instead of dealing with stages. As a result, the FASB standard will result in earlier and larger recognition of losses (whereas there is some delay in IASB for loans classified in stage 1). The two standards are the same when dealing with loans that have considerable credit deterioration (i.e., IASB stages 2 and 3).

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Assess the progress banks have made in the implementation of the
standards

A
  • The IASB standard is effective as of January 1, 2018 (although early adoption is allowed) and the FASB standard as of January 1, 2020 for public companies and January 1, 2021 for all other applicable entities.
  • The Enhanced Disclosure Task Force (EDTF) has recommended specific risk disclosure by banks in the transition period prior to implementation of IFRS 9. The disclosures are qualitative (i.e., differences from current approach, implementation strategy, capital planning impact), but also include quantitative assessments of the impact of using the ECL approach.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Examine the impact on the financial system posed by the standards

A
  • The impact of the IASB standard would cause a dramatic rise in loss provisions at the start of an economic downturn, specifically the increase in amounts between stage 1 (12-month ECL) and stage 2 (lifetime ECL). One argument for a more proactive stance on recording losses is that it restates the balance sheet assets at more conservative levels to make way for possible future recoveries.
  • In one sense, the standards would have no impact for banks that have established sufficiently large capital buffers that could withstand the impact of the increased loan provisions.
  • The provisioning requirements of the standards could end up smoothing the issuance of loans throughout the economic cycle (i.e., slowing the growth of loans in a strong economy while preventing the slowing of growth of loans in a weak economy).
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Describe the issues unique to big datasets

A
  • Structured Query Language (SQL) databases are used for the smaller of the large datasets, but customized systems that expand upon SQL are needed for the largest pools of data.
  • Another potential issue in dealing with a large dataset is known as the overfitting problem. This is encountered when a linear regression captures a solid relationship within the dataset, but has very poor out-of-sample predictive ability. Two common ways to address this problem are to use less complex models and to break the large dataset into small samples to test and validate if overfitting exists.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Explain and assess different tools and techniques for manipulating and analyzing big data.

A
  • Using big data to make predictions is the focus of machine learning. This science may utilize regression if a linear relationship is present. Machine learning might deploy tools, such as classification and regression trees, cross-validation, conditional inference trees, random forests, and penalized regression, if a nonlinear relationship exists.
  • Classification and regression trees can be very useful in explaining complex and nonlinear relationships.
  • One concern with using this process is that trees tend to overfit the data, meaning that out-of-sample predictions are not as reliable as those that are in-sample. One potential solution for overfitting is cross-validation. In a k-fold cross validation, the larger dataset is broken up into “k” number of subsets (also called folds). A large dataset might be broken up into 10 smaller pools of data.
  • This process starts with fold 1 being a testing set and folds 2-10 being training sets. Researchers would look for statistical relationships in all training sets and then use fold 1 to test the output to see if it has predictive use. They would then repeat this process k times such that each fold takes a turn being the testing set. The results are ultimately averaged from all tests to find a common relationship. In this way, researchers can test their predictions on an out-of-sample dataset that is actually a part of the larger dataset.
  • Another step that could be taken is to “prune” the tree by incorporating a tuning parameter λ that reduces the complexity in the data and ultimately minimizes the out-of-sample errors. However, building a conditional inference tree (ctree) is an option that does not require pruning with tuning parameters. The ctree process involves the following steps:
  1. Test if any independent variables are correlated with the dependent (response) variable, and choose the variable with the strongest correlation.
  2. Split the variable (a binary split) into two data subsets.
  3. Repeat this process until you have isolated the variables into enough unique components (each one is called either a “node” or a “leaf” on the ctree) that correlations have fallen below pre-defined levels of statistical significance.
    * The main idea of a ctree is to isolate predictors into the most specific terms possible.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Random forests and penalized regression

A

Constructing random forests is also a way to improve predictions from large datasets. This method uses bootstrapping to grow multiple trees from a large dataset. Using random forests to average many small models produces very good out-of-sample fits even when dealing with nonlinear data. Computers have made this method much more viable as sometimes thousands of trees can be grown in a random forest. There are four steps to creating random forests:

  1. Select a bootstrapped sample (with replacement) out of the full dataset and grow a tree.
  2. At each node on the tree, select a random sample of predictors for decision-making. No pruning is needed in this process.
  3. Repeat this process multiple times to grow a “forest” of trees.
  4. Use each tree to classify a new observation and choose the ultimate classification based on a majority vote from the forest.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Examine the areas for collaboration between econometrics and machine learning

A
  • Most machine learning assumes that data is independently and identically distributed and most datasets are cross-sectional data. In practice, time series analysis may be more useful. Econometrics can use tools like Bayesian Structural Times Series models to forecast time series data.
  • Correlation does not always indicate causation. Traditionally, machine learning has been most concerned with pure prediction, but econometricians have developed numerous tools to reveal cause and effect relationships. Combining these tools with machine learning could prove to be a very meaningful collaboration.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Describe the process of machine learning and compare machine learning approaches

A

In supervised machine learning, a statistical model is built in order to predict outcomes based on specific inputs (e.g., predicting GDP growth based on inputs of various macroeconomic variables). In unsupervised machine learning, data analysis is performed to identify patterns without estimating a dependent variable.

Machine Learning Approaches

Although many approaches exist to analyzing machine learning, it can be applied to three broad classes of statistical problems: regression, classification, and clustering.

  1. Regression problems make predictions on quantitative, continuous variables, including inflation and GDP growth. Regressions can involve both linear (e.g., partial least squares) and nonlinear (e.g., penalized regression in which complexity is penalized to improve predictability) learning methods.
  2. Classification problems make predictions on discrete, dependent variables such as filtering spam email and blood types, where the variable can take on values in a class. Observations may be classified by support vector machines.
  3. Clustering involves observing input variables without including a dependent variable. Examples include anti-money laundering (AML) analysis to detect fraud without knowing which variables are fraudulent. Data can be grouped into clusters, where outputs from unsupervised learning are used as inputs for supervised learning methods.

Other Concepts in Machine Learning

Boosting (or bootstrapping) refers to overweighting scarcer observations to train the model to detect these more easily. For example, overweighting scarcer fraudulent transactions in a dataset can train the model to better detect them. Bagging describes the process of running several hundreds of thousands of models on different subsets of the model to improve its predictive ability. These models may also be combined with other machine learning models, called an ensemble, in order to further improve their out-of-sample predictive capabilities.

Deep Learning

Deep learning approaches move away from the “classic” model approaches we have been discussing until now. Whereas classic models focus on well-defined and structured datasets, deep learning essentially mimics the human brain by applying several layers of algorithms into the learning process and converts raw data to identify complex patterns. Each algorithm focuses on a particular feature of the data (called representations), and the layering of these representations allows the model to incorporate a wide range of inputs, including low quality or unstructured data. Importantly, the layers are not designed by engineers, but instead learned by the model from the various data.

For example, deep learning has been used in face-recognition and natural language learning models. Models have been complex enough to be able to classify not only the discussion topics, but also the emotions of the people involved. However, deep learning models are extremely complex, often requiring several million or hundreds of millions of datasets.

17
Q

Analyze the application of machine learning in three use cases:

  • Credit risk and revenue modeling
  • Fraud
  • Surveillance of conduct and market abuse in trading
A

Credit Risk and Revenue Modeling

Financial institutions recently moved to incorporate machine learning methods with traditional models in order to improve their abilities to predict financial risk. In turn, they have moved away from the less complex traditional linear credit risk model regressions.

However, machine learning models are often unfit to be successfully incorporated into the ongoing risk monitoring of financial institutions. Machine learning models can be overly complex and sensitive to overfitting data. Their (often extreme) complexity makes it difficult to apply jurisdictionally consistent definitions of data, and the models are too complex for regulatory purposes, including internal models in the Basel internal ratingsbased (IRB) approach, because it is very difficult for auditors to understand them.

Fraud

Banks have successfully used machine learning in the detection of credit card fraud. Models are used to detect fraudulent transactions, which can then be blocked in real time. Credit card fraud can incorporate machine learning more usefully than other risk areas because of the very large number of credit card transactions that are needed for the training, backtesting, and validation of models.

Models can also be successfully used in anti-money laundering or combating the financing of terrorism (AML/CFT) activities through unsupervised learning methods, such as clustering.

Clustering identifies outliers that do not have strong connections with the rest of the data. In this way, financial institutions can detect anomalies and reduce the number of false positives.

Surveillance of Conduct and Market Abuse in Trading

Surveillance of trader conduct breaches is another growing area in which machine learning is being increasingly used to detect rogue trading, insider trading, and benchmark rigging activities. Financial institutions find early detection of these violations important because they can cause material financial and reputational damage to the institution.

One of the challenges facing financial institutions in successfully applying machine learning includes the legal complexities of sharing past breach information with developers. Also, systems need to be auditable, but because machine learning models are designed to continuously learn from the data, it can be difficult to explain to a compliance officer why a certain behavior set off an alert. As a remedy to these problems, systems can be designed to combine machine learning with human decisions. By incorporating human decisions with machine learning, systems data can be used to know a comprehensive set of information about a trader, and create a system that is less complex and more suitable for audit and regulatory purposes.

18
Q

Key advantages of central clearing

A

Key advantages of central clearing include:

  • Halting a potential domino effect of defaults in a market downturn.
  • More clarity regarding the need for collateral.
  • Lower operational risk.
  • Better price discovery.
  • More regulatory transparency in OTC markets.
  • Better risk management.

Central clearing has certainly enhanced financial stability and reduced systemic risk, but it has not completely eliminated systemic risk.

19
Q

Describe the transformation of counterparty risk into liquidity risk

A

In the absence of margin requirements for a bilateral OTC trade, the two counterparties would simply mark to market (MTM) their position each day. Such MTM gains/losses are unrealized in nature so they do not have any cash flow impact (i.e., no liquidity impact). However, they do impact asset values and reported income so there is an impact on solvency.

In contrast, the same trade with a CCP has three distinct cash flow impacts:

  • An initial margin from each counterparty must be paid up front.
  • MTM gains/losses between the CCP and clearing members must be settled on a cash basis each day or even more often (i.e., variation margin).
  • Clearing members could be required to contribute to a default/guaranty fund to cover member defaults.

From an overall balance sheet and solvency perspective:

  • The initial margin deposit by the clearing members to the CCP is simply that and is not a transfer of (cash) assets. The clearing member maintains the asset on its balance sheet so there is virtually no impact on solvency.
  • The variation margin deposits (if applicable) would have been previously accounted for as a MTM loss. The actual cash payment to the CCP is treated similarly to the initial margin deposit in that there is no transfer of assets to the CCP. There is simply a transfer from the clearing members liquid to non-liquid assets (i.e., classification change).
  • The default fund contributions are treated similarly to the initial and variation margin defaults. However, the clearing member is subject to a 2% capital charge for the default fund contributions.

From a liquidity perspective:

  • Initial and variation margins must be deposited as liquid assets (i.e., cash) so there is a noted reduction in liquidity.

In summary, the central clearing requirements do not change the clearing member’s overall balance sheet value (assets or equity) so there is no solvency impact. However, there is a reclassification of assets between liquid and non-liquid so there is a liquidity impact. Therefore, the clearing member is giving up counterparty risk and accepting liquidity risk.

20
Q

CCP Loss Sequence

A

A CCP has the following liquidity resources to cover potential losses should a clearing member default (in the following sequence):

  1. Initial margin: The initial margin paid to the CCP by each clearing member is used to cover only the direct losses incurred from the member’s default.
  2. Default contribution of defaulting member: Losses greater than the initial margin may be covered by the defaulting member’s default fund contribution.
  3. Mutualization of large losses: Losses greater than #1 and #2 combined are first covered by a maximum contribution by the CCP (“skin-in-the-game”) to cover the remaining loss. If the skin-in-the-game is not enough, then the remaining losses are covered by other members’ default contributions.
  4. Recovery: Should the entire default fund be insufficient to cover the losses, the CCP could request additional default fund contributions by non-defaulting members, usually limited to the amount of the initial contribution to the default fund. Another source of funds for CCPs is variation margin haircutting (VMGH), which involves CCPs collecting variation margin payments from members with negative balances but keeping a specified percentage to boost its liquidity resources and transferring only the remaining amount to the counterparties.
  5. Failure resolution: May occur if the CCP is unable to recover sufficient funds or if the CCP or its members do not attempt to go through the recovery provisions.

The loss allocation process (or loss waterfall) is illustrated in Figure 2.

21
Q

Margin Requirements and Liquidation Costs

A
  • Initial margins are likely calculated based on market risk measures such as standard deviation (SD), value at risk (VaR), or expected shortfall (ES) at a 99% to 99.73% confidence level. The calculation makes use of either: (1) historical data, (2) scenario analysis, or (3) simulation using specific assumptions on relevant risk factors. The risk horizon can be described as the amount of time needed to liquidate a defaulting member’s positions. That can be anywhere from one day to a few days and is computed based on the asset class being cleared, not the actual portfolio or position.
  • Proper risk management of CCPs would incorporate a liquidity charge in margin calculations to cover the implied extra costs the CCP would be responsible for when liquidating a defaulted position. The charge would increase for larger position sizes and illiquid assets (illiquid assets are being cleared more frequently nowadays).
  • When determining the amount of a CCP’s default fund, liquidation costs should be included. The largest clearing members provide the greatest risk to the CCP given that the former would likely engage in transactions that are more difficult to liquidate. Computing the CCP’s default exposure should be more detailed and consider increased bid-ask spreads and liquidation costs. Liquidation costs are particularly relevant for large clearing members because liquidation costs are proportional to gross, not net positions.
22
Q

Compare and assess methods a CCP can use to help recover capital when a member defaults or when a liquidity crisis occurs

A

There are two methods a CCP can use to help recover capital (in the event a member defaults or a liquidity crisis occurs): default fund assessments and variation margin haircuts (VMGH).

With default fund assessments, the CCP could ask all non-defaulted members for a supplementary contribution that is proportional to their prior contribution and capped at that prior contribution amount. However, assuming that the largest clearing members have defaulted, there is a reasonable risk that some of the non-defaulted members have been subjected to the same losses. As a result, the non-defaulted members may have insufficient liquid resources to cover the assessment. Or if they do have sufficient resources, they may simply choose to avoid the assessment by closing out their positions or moving them to another CCP. Therefore, the shortfall in the default fund demonstrates wrong-way risk, whereby the probability of non-payment is positively related to the default events that would lead to an assessment.

A clearing member may accumulate a large amount of losses over time and ultimately default. Prior to the default, that defaulting member would have already made a corresponding large amount of variation margin payments to other members. With variation margin haircuts (VMGH), the CCP collects the full variation margin payment from the member with the loss, and the CCP discounts the payment (on a pro-rata basis) to the corresponding member with the gain. The difference is held by the CCP to enhance the CCP’s liquidity. The liquidity is financed by the members but if clearing members are already subject to liquidity constraints in weaker market conditions, a haircut on the variation margin payment could exacerbate the liquidity constraints.

23
Q

Describe the links between banks and capital markets

A

The link between banks and capital markets is now global.

24
Q

Explain the effects of forced deleveraging and the failure of covered interest rate parity

A

Effects of Forced Deleveraging

  • Forced deleveraging refers to the reduction in leverage by a borrower following capital market events that necessitate deleveraging. A good gauge of leverage is the haircut in the repurchase agreement (repo) market. The haircut refers to the difference between the value of the collateral pledged and the amount borrowed. The lower the haircut, the higher the leverage implied in the transaction. The 2% haircut implies a leverage factor (or leverage ratio) of 50 for the bank (computed as total assets over equity).

VIX as a Gauge of Leverage

  • Up until the onset of the financial crisis, the volatility index (VIX) represented a good gauge of the appetite for leverage in the markets. The VIX measures implied volatility from stock option (call and put) prices. Prior to the financial crisis, a low VIX implied low “fear” and therefore high leverage. The VIX was able to adequately capture the risk appetite within the financial system. Given that banks typically borrow in order to lend funds, easy conditions for borrowing also created easy conditions for lending, creating a circular series of events that led to ever easier borrowing and liquidity as well as higher leverage.
  • However, the VIX as a reliable gauge of leverage shifted dramatically following the financial crisis. The previous relationships of high VIX-low leverage / low VIX-high leverage ceased to hold and the VIX lost its explanatory power of leverage. While there remains considerable risk appetite for stocks, as witnessed by high stock valuations and low volatility, the banking sector has not fared comparatively well, with low market-to-book value ratios.
  • So what has changed? One explanation is that monetary easing has calmed markets and compressed credit spreads, although this explanation generally holds best when policy rates are positive. Another explanation could be the role of regulation, which impacts bank behavior and may constrain leverage. A counterargument to the role of regulation is that the financial crisis was not brought on by regulatory change (although regulatory change certainly followed in the post-crisis period). Capitalization also plays a role in the financial health of the banking sector. Better capitalized banks weathered the crisis better and have fared well compared to their weaker capitalized counterparts.

Failure of Covered Interest Arbitrage

  • Covered interest parity (CIP) is a parity condition that states that the interest rates implied in foreign exchange markets should be consistent with the money market rate for each currency. In other words, the interest rate implied between the forward and spot rates on a U.S. dollar forward or swap (one side borrows U.S. dollars and lends another currency) should be the same as the money market interest on the dollar. If the relationship does not hold, an arbitrage opportunity would exist for earning a profit on borrowing cheap in one currency, lending out funds at a higher rate in another currency, and concurrently fully hedging currency risk.
  • CIP held up reasonably well before the financial crisis. However, the relationship no longer worked well in the post-crisis period, and a gap between CIP-implied rates and observed rates has persisted. The primary reason for the difference is that CIP is a theoretical concept based on certain simplifying assumptions, including the ability to take on any position in any currency at prevailing market prices. In reality, borrowers and lenders need to transact through banks, which may not have sufficient capital to enter into these transactions or may find the spreads to be uneconomical. Capital may be insufficient partly due to regulation, although banks typically have capital well above regulatory requirements.
25
Q

Discuss the US dollars role as the measure of the appetite for leverage

A
  • In recent years, the U.S. dollar emerged as a viable alternative to the VIX. During periods with a weak dollar, risk appetite tends to be strong. With a strong dollar, risk appetite is weak and market anomalies like the breakdown of CIP occur more frequently.
  • In recent years, interest rates have fallen considerably around many parts of the world. U.S. assets, however, have remained above many advanced economy asset returns. As a result, investors have increased their demand for higher yielding assets denominated in U.S. dollars.
26
Q

Describe the implications of a stronger US dollar on financial stability
and the real economy

A
  • With a change in the dollar, both an institutions asset and liability values will change. A weaker dollar will benefit liabilities (make them smaller), while a stronger dollar will negatively impact liabilities (make them larger).
  • It is important to recognize that the strengthening and weakening of the dollar has opposite impacts in the export and lending markets. A foreign currency appreciation (domestic currency depreciation) is positive for economic activity in the export market, but is negative in the borrowing market as it erodes the strength of the balance sheet.
  • Volatility and changes in the dollar have important implications for the stability of financial markets and for the real economy. As banks reduce their intermediation activities in response to rising volatility, they would inadvertently magnify shocks, rather than absorb them. Furthermore, because the dollar now reflects global risk appetite, a strengthening dollar truly has global implications.
27
Q

Describe how FinTech credit markets are likely to develop and how they will affect the nature of credit provision and the traditional banking sector

A

FinTech lenders may be able to operate in the same way as conventional lenders, yet avoid their large fixed costs (i.e., branch banking system, significant IT infrastructure) as well as regulatory constraints (i.e., capital and liquidity requirements).

Possible Impediments to Development

  • Traditional banks have been in the online banking world for many years and some customers are satisfied with their existing digital banking services and may not be willing to switch to an “unknown” digital lender.
  • Growth may be impeded during an economic downturn. To date, many FinTech lenders have not operated through an entire credit cycle of an upturn and a downturn. Therefore, considerable uncertainty exists as to whether emerging FinTech lenders would survive the downturn.
  • Regulatory requirements may vary widely depending on location and could severely limit the growth of FinTech in jurisdictions where the licensing requirements are overly onerous or where interest rate limits apply. With the ongoing development of FinTech, the related regulations will change and create significant uncertainty for borrowers (i.e., consumer protection) who may feel nervous about online borrowing as a result.
  • There is also the generic concept of reputational risk should some FinTech lenders operate in an unscrupulous manner during a sensitive industry development phase when FinTech lenders are trying to create their presence as an alternative source of funds in the marketplace.
28
Q

Traditional P2P Lending Model

A

There are three basic methods in establishing loan interest rates— in general, borrowers establish the maximum rate and lenders establish the minimum rate. The platform operator uses the information together with the loan amounts to match borrowers and lenders.

  • Potential lenders make interest rate bids on loans within a range (i.e., minimum stated by platform operator based on risk assessment and maximum stated by borrower).
  • The platforms provide the rate consistent with the credit risk assessment for the loan (that may be flexible depending on supply and demand).
  • Borrowers are given a representative rate for an online loan based on a risk assessment and can seek out appropriate lending alternatives based on the rate.

The majority of platforms allow for partial or full prepayment of loans on a penaltyfree basis. On the assumption that payments are made as scheduled, there is no further monitoring of the loan and the borrowers could use the funds for any purpose.

In contrast, should a borrower be potentially delinquent on a loan, they should contact the platform as soon as possible to avoid the platforms contacting debt collection agencies to begin the loan recovery process. At the point of delinquency, the platform may start charging additional fees to the lender. Some platforms have methods to deal with credit losses, which could be in the form of insurance or guarantee/provision funds that provide partial or full coverage of the loan portfolio (i.e., there could be exclusions for higher credit risks). As for the percentage of loss covered, there is a wide range from 2.5% to 70% of the principal amount. An alternative method has the objective to pay out, at a minimum, the expected lifetime default rate for covered loans.

Should lenders wish to exit their loan investments, some jurisdictions allow those creditors to do so by paying fees to the platform and on the condition that other lenders will take over those loans. There also may be no exit guarantee if there are an excessive number of exit requests on the platform at the same time.

29
Q

Notary Model

A
  • The notary model is used frequently in Germany, Korea, and the United States. There is a partnership agreement between a fronting bank and the lending platform because the fronting bank actually originates the loans. The loans are then sold or assigned by the fronting bank directly to interested lenders or through a platform subsidiary (securitization) to institutional investors. The following diagram presents the basic model; some differences exist in its application in some jurisdictions.
  • This is the approach used in Germany because only authorized institutions (i.e., not lending platforms) are permitted to provide loans.
  • In the United States, regulatory restrictions sometimes cause FinTech lenders to work with a lending institution. The lending institution issues the loans to borrowers from the lending platform. The lending institution may either retain the loans or hold them for a very short period of time and then sell them to the platform lender. The platform lender may then either hold the loans or sell them directly to investors.
30
Q

Guaranteed Return Model

A

With the guaranteed return model, the lending platform guarantees the principal and/or interest on the loans.

31
Q

Balance Sheet Model

A

The balance sheet model involves the lending platform operating much the same way as a non-bank lender; it requires capital (i.e., debt, equity, securitization) to originate loans but it also retains the loan receivables as assets.

32
Q

Invoice Trading Model

A
  • Firms often make credit sales and record corresponding receivables (or invoices) on their balance sheets. However, for quicker conversion of those receivables to cash, they will often sell (factor) them at a discount. If the receivables are sold on a non-recourse basis, the discount is larger and the credit risk of the receivables is transferred to the purchaser. On a recourse basis, the discount is smaller and the credit risk of the receivables remains with the seller. Given that non-recourse factoring is riskier, there may be a minimum amount of business activity required. Therefore, recourse factoring seems to be the most common form for start-ups or small businesses.
  • Invoice trading platforms providing recourse factoring have become popular because they include perks such as automatic invoice processing, less delay between invoice processing and cash payment, and a lower level of business activity required.
33
Q

Microfinancial Benefits

A

Lower Financing Costs for Borrowers

  • With FinTechs lower costs through the extensive use of computerization and automation (i.e., loan approval, loan pricing) and the absence of physical “bricks and mortar” operations, the cost savings should theoretically flow through to borrowers in the form of lower interest rates.

Higher Returns for Lenders

  • Following the same logic for passing on cost savings to borrowers, the effect on lenders would be in the form of higher returns. However, quantifying the benefit is problematic because of the difficulty in finding comparable investments with the same risk features (i.e., duration and liquidity) as FinTech loans.

User Convenience

Accessibility

  • Within emerging market economies, surveys indicate that many individuals have never borrowed from a traditional bank. With the user-friendliness of many lending platforms, FinTech is likely to increase accessibility to credit for a substantial number of users who otherwise would not have access.
34
Q

Microfinancial Risks

A

Leverage and Liquidity Risk

  • The majority of lending platforms function as agents to bring investors together with borrowers. Therefore, such platforms have little or no leverage risk. A few platforms take on leverage risk in that they use internal resources to fund loans or provide return guarantees.
  • Most lending platforms also take on little or no liquidity risk (investment and loan durations are usually the same and investors must maintain their loan investments until they mature). At the same time, some platforms are now providing investors with the ability to withdraw amounts early. One example of such a platform allows investors to invest in loans and withdraw amounts at any time and at no charge. Although it is explicitly stated that there is no absolute certainty that the withdrawals will be granted, there is the risk that investors may expect liquidity regardless.

Financial Systems

Operational Risk

  • FinTech platforms face cyber risks given their extensive use of electronic data.

Credit Risk Assessment Quality

  • FinTech platforms make use of big data analytics, which includes some more unusual but relevant data, to supposedly improve credit risk assessment over that of traditional banks. By taking a more focused analytical process and avoiding the pitfalls of outdated IT systems, the credit assessment may be enhanced. To date, it is not possible to conclude with certainty that FinTech platforms have superior credit risk assessment processes.
  • Three key arguments against higher quality credit risk assessment of FinTech platforms include: (1) platforms likely do not have detailed borrower information such as income, assets, and liabilities, (2) some platforms use solely hard data sources and do not consider soft credit risk factors, and (3) loan default data for unchartered borrower segments may be unreliable or unavailable.

Business Model Incentives

  • The use of the agency model where lenders generate fees from creating new loans, but do not bear any credit risks, may promote the wrong incentives and ultimately lead to poorer quality risk assessments. For example, platforms that do not have to absorb any credit losses on defaulted loans would have the incentive to grant as many loans as possible to maximize fees earned. Or if a platform charges fees to borrowers based on risk, there would be the incentive to grant more higher-risk loans to attempt to maximize fees.
  • At the same time, if platforms earn fees based on servicing loans, then the incentive would be to grant loans that perform (and do not default) in order to maximize fees.

Attracting New Business Based on Investor Confidence

  • Reasons for the reduction in investor confidence of platforms could include one or more of the following:
    • Other asset returns have increased relative to those earned by investing in loans on the platform.
    • A greater percentage of FinTech actual loan defaults compared to expected, which could lead to a loss of confidence in the risk analysis and loan granting processes.
    • The inability of investors to withdraw their investments early (even though there is no guarantee that it will be allowed).
    • The platform is subject to legal action for the improper use of data or for the use of improper marketing techniques.
    • Any event that causes a severe disruption to the platforms activities.

Low Barriers to Entry

  • Due to lack of regulation of the FinTech industry in many jurisdictions, the online nature of the services, and the common data sources used, there have been many new entrants into the industry. That reduces the opportunities for any individual platform to be profitable.
  • Additionally, there is always the threat that well-established banks could compete aggressively in the industry by establishing their own platforms. Banks would likely have access to more sophisticated resources pertaining to credit analysis and loan pricing.

Platform Profitability Risk

  • Many large platforms have incurred consistent losses each year, which calls into question whether they can continue to originate new loans into the future. Two arguments to support the continued existence include: (1) the FinTech industry is still in its early stages and requires further expansion to achieve the necessary economies of scale to become profitable, and (2) there is a specific objective to grow and avoid using profits in the shortterm.
35
Q

Examine the implications for financial stability in the event that FinTech credit grows to account for a significant share of overall credit

A

Benefits

  • The growth of FinTech credit could result in greater financial inclusion. There are two key underlying points here: (1) investing in FinTech loans could diversify an investment portfolio, and (2) borrowers such as self-employed individuals or small businesses, who have historically been restricted in the amount of financing obtained from traditional banks, may now have access to sufficient capital to grow their businesses.
  • Assuming that FinTech platforms and traditional banks remain relatively separate in their operations, it should shield the FinTech industry from risks specific to banks. However, some banks are starting to provide operational, loan origination, and referral services to FinTech platforms, which increases their dependence on banks and may make the FinTech industry more vulnerable to the same risks as banks.
  • Unlike traditional banks, the FinTech industry is not exposed to maturity mismatch with its lending so it may serve as a source of credit should the economy otherwise be subject to a major liquidity shock. FinTech platforms tend to lend almost exclusively in the domestic market so compared to banks, they will be far less impacted by international shocks.

Risks

  • With more competition in the lending market with the growth of FinTech, it may significantly cut traditional banks’ revenue and profits, which would lower their access to capital. It may force them to take on more risk to maintain market share or compensate for losses, which could be demonstrated by weaker overall lending standards
  • Some banks may be involved in loan origination for FinTech platforms and then involved in subsequent FinTech loan sales to investors, all of which would be largely unregulated. Should borrowers or investors suffer significant losses due to those transactions, the banks could suffer from reputation risk for being viewed as operating outside of proper credit regulation.
  • The growth of FinTech may also promote procyclical credit provisions. It would manifest itself in more credit being available (i.e., weaker lending standards) when it is needed less in an economic upturn, but less credit being available when it is needed more in a downturn.
  • The nature of FinTech credit would make it more difficult for regulators to properly monitor activities given the likely lack of reporting requirements and supervision. Because FinTech’s activities may largely be unregulated, government policy actions related to strengthening the credit industry during an economic downturn, for example, may be ineffective. FinTech lenders would not be able to take advantage of public safety measures such as emergency liquidity (from the central bank) that would be available to traditional banks.
  • With securitization, the dependency between FinTech and the rest of the financial markets increases, thereby reducing FinTech’s protection from risks faced in the general financial markets (and vice versa). In addition, the repackaging of FinTech loans could make the financial markets even less transparent from both an investing and a regulatory perspective.
36
Q

Explain how different factors can influence the culture of a corporation in both positive and negative ways

A
  • Financial incentives alone are not enough to explain bad behavior.
  • However, just as corporate leaders may encourage bad behavior and promote goals which are immoral, unethical, and in some cases even irrational, they also can encourage employees to be more productive, increasing the competiveness of the firm.
  • Corporate culture may be defined as “a system of shared values that define what is important and the norms that define appropriate attitudes and behaviors for organizational members.” Corporate culture can arise from the top down. In some firms top managers set the tone for excessive risk taking. Culture can also arise from the bottom up. For example, investment banks pride themselves on hiring from Ivy League schools, which comes with its own cultural influences. The environment and the health of the economy also influences culture. Culture can influence a firm in both positive and negative ways.
37
Q

Examine the role of culture in the context of financial risk management

A
  • Firms, including financial firms, often hire “go-getters,” more aggressive people with higher levels of risk tolerance. The thought is that they have the personality and competitive nature to move a firm forward. This personality type is drawn to riskier activities, what sociologist Stephen Lyng refers to as “edgework”.
  • Bad behavior results from incentive problems, and can be corrected once an appropriately designed system of rewards and punishments is constructed. Economists always look to incentives when investigating bad behaviors.
  • Firms often hire aggressive people with higher levels of risk tolerance which affects the culture of the firm. The thought behind hiring these people is that they have the personality and competitive nature to move a firm forward. It may differ across industries, however. Risk management in the insurance industry is quite different than in the banking industry. Revenue in the insurance industry is in large part determined by regulation while bank earnings are more variable and are tied to bank size and the use of leverage. Insurance companies have an incentive to manage risks and protect against the downside and are thus generally more conservative than banks. According to economists, provided an incentive to be good, individuals will be good. Economic self-interest is a learned behavior. However, the environment, not just incentives, shapes behavior and corporate culture.
38
Q

Describe the framework for analyzing culture in the context of financial practices and institutions

A
  • An alternative to the efficient markets hypothesis (EMH) is the adaptive markets hypothesis (AMH) (Lo, 2004, 2013).13,14 In the AMH, individuals compete for scarce resources. These individuals adapt to both their past and current environments. Like people sort themselves based on values into political parties, they also sort themselves into professions.
  • It is possible to examine the role corporate culture plays in firms by considering several firms that have failed, or come to the brink of failure, due to cultural factors. Long-Term Capital Management (LTCM), American International Group (AIG), Societe Generale, and Lehman Brothers all had problems but for different cultural reasons. One can examine corporate culture using these firms as examples. Regulators, such as the SEC’s failure to realize, despite warnings, that Bernie Madoff was running a Ponzi scheme, are also subject to cultural risks.
39
Q

Analyze the importance of culture and a framework that can be used to change or improve a corporate culture

A
  • Andrew Lo suggests the mnemonic SIMON (select, identify, measure, optimize, and notice) as a process for managing the risk of a financial portfolio. SIMON could also be applied to behavioral risk management as in:
  1. Select the behavioral risk that should be managed.
  2. Identify the objective function and constraints to managing the behavior such as the company’s short-run goals or the values of the corporation.
  3. Measure the “statistical laws of motion” for managing behavior (e.g., use something like the dual process theory for moral reasoning or the Office of Personnel Management (OPM) Global Satisfaction Index). This is the weakest link in the analogy to manage risk in a behavioral setting because quantifying human behavior is difficult, if not impossible.
  4. Optimize the objective function subject to the constraints. This step will uncover the best compensation schemes as well as compliance procedures, reporting requirements, and so on to change the culture (i.e., align the culture with the objectives).
  5. Notice (most importantly notice) changes to the system to ensure that the behavioral risk management practices are reaching the desired outcomes. The firm must repeat these five steps as needed to achieve the firm’s objectives.

Corporate culture is vital to the actions and behaviors of financial firm managers and employees.