Survival analysis is known by various names such as event history analysis, hazard modeling, duration analysis, or time to event analysis. It is extensively followed and used in the research field to answer a wide range of questions, including a time and duration dimension in the metrics.
In this article, let us know more about survival analysis, its working and its benefits.
What is Survival Analysis?
The term survival analysis comes from biomedical studies where researchers started learning about mortality. For example, diagnosis of a disease and treatment of disease. Survival analysis is a method to measure the effective time taken before the occurrence of a particular event of interest.
More broadly, survival analysis is now used more frequently to indicate whether someone has experienced an event of interest at a given point in time. For example, retirement after the age of 70 years with a good bank balance, Stock market crash, default loans, etc.
For instance, let us suppose a time when a participant enters a study until the event occurs, the study ends or the participant drops out of the study. It can be used to track a wide range of events such as failure of machines, operations, disease occurrence, loans, bankruptcy, etc.
Also, check What is Data Analytics and its importance?
Survival Analysis Key Takeaways
- Survival analysis is used to analyse the occurrence of an event of interest.
- It is more frequently being used in biomedical and life insurance companies to predict what is the expected data of death for different ages, loan maturity, bonds, disease occurrence, machine failure, or other events.
- Nowadays, survival analysis can also be used to assess financial risks, portfolio optimization, investment insights, and more.
Also, check How to measure Financial Risks in a business?
Survival Analysis in Finance
Survival analysis is used to predict the occurrence and timing of important financial events such as market crashes, stock surges, investment returns, loan defaults, etc. The origin of survival analysis was based on calculating the longevity of the insurance holders and estimating the payout dates.
Survival analysis is applied to predict the time of occurrence of a financial event such as loan defaults, corporate bankruptcies, investment durations, and more.
Censoring in Survival Analysis
Censoring is a key analytical problem in survival analysis. It occurs when we have some information present about the individual survival time but we don’t have the exact time.
It occurs when the event of interest such as bankruptcy, loan repayment, default loans, stock market crash, etc does not take place in the observation period. We can only predict the range of time whether it occurred or not rather than the exact time of the event.
Censoring marks the uncertainties due to incomplete information about the exact time of an event of interest. Censoring accounts for instances where a fixed time of event needs to be studied thoroughly and ensure that the analysis remains accurate and unbiased despite incomplete data available.
The uncertainties possible due to partial information can be handled using popular methods like Kaplan Meier Estimator, Cox Proportional Hazards model, Parametric Survival model, and other advanced techniques.
Applications of Survival Analysis in Finance
Let us know some of the major use cases of survival analysis in finance.
1. Credit Risk Modeling
One of the primary applications of survival analysis in finance is modeling the time until a borrower defaults on a loan. By treating default as the event of interest, financial institutions can estimate the probability of default over time and identify factors that influence default rates.
- Time to Default: Survival models can predict the duration until a borrower defaults, allowing lenders to adjust interest rates or take preventive measures.
- Recovery Rates: Understanding the time until default can also help in estimating recovery rates post-default, which is crucial for loss provisioning.
2. Corporate Bankruptcy Prediction
Survival analysis is employed to estimate the likelihood and timing of corporate bankruptcy. By analyzing financial ratios, market conditions, and other metrics, models can assess the survival probability of firms over time.
It can develop early warning systems, and help investors estimate risks to optimise their portfolio, make adjustments, and balance their potential returns over the losses.
3. Investment Duration and Liquidity Analysis
Survival analysis helps in understanding the duration investors hold assets before selling them. This is particularly important for estimating how easily an asset can be sold in the market.
Also, patterns are analysed to provide insights on market sentiments and other financial aspects.
4. Insurance and Risk Management
Financial institutions offering insurance products use survival analysis to model policyholder behaviours, such as the time until a claim is made or a policy is canceled. It is also used to set some of the reserves for future claims based on the expected time of events.
5. Duration of Economic Cycles
Economists and financial analysts apply survival analysis to study the length of economic cycles, such as expansions and recessions. Understanding the duration can aid in policy formulation and investment strategy adjustments.
6. Market Event Analysis
Survival analysis is used to analyze the timings for a certain market event such as a stock market crash or change in the price of an asset, etc. It is important for traders, investors and risk analysts to understand these timings and make preparations beforehand.
Popular Methods for Survival Analysis
Let us analyse the two most popular models used in the survival analysis below.
Kaplan-Meier Estimator
The Kaplan-Meier estimator approach is used to estimate the survival function (S(t)) from lifetime data. It can also be used to measure the length of time people remain unemployed after a job loss, how long a fleshy fruit remains on plants, patients living for a certain time after treatment, etc.
In finance, it can be used to estimate the probability that a firm remains solvent beyond a certain base time.
Cox Proportional Hazards Model
The Cox model is a regression technique used in finance to identify factors that influence default rates or bankruptcy risks. It is one of the most popular techniques used to relate several risk factors to survival time.
Parametric models
The Parametric model is used to assume that the outcome follows a specific distribution. They are important in survival analysis and are an additional alternative to the Cox Proportional hazards regression model.
Pros and Cons of Survival Analysis
There are certain advantages and disadvantages of using survival analysis, as given below.
Advantages of Survival Analysis
- Analysis based on time: Time is an important factor in survival analysis where the timing of events offers insights on when an event of interest is likely to occur. It helps in better risk management, strategic planning, and predicting loan defaults in finance.
- Censored data: It can easily handle the censored data effectively. It handles the data where the event of interest has not occurred as per the planned period.
- Flexibility in model selection: It allows for the usage of appropriate models based on the needs of financial data and research needs.
- Risk assessment: Financial institutions can make adjustments in their investment strategies, anticipate defaults, and adjust credit policies based on the risk predictions.
- Multiple indicators onboard: Survival analysis allows us to consider various financial indicators such as interest coverage, financial ratios, performances, and more to assess the impact on the event to time basis.
Disadvantages in Survival Analysis
- High quality of time event based data is required to be collected which might only sometimes be available.
- Major models in survival analysis are based on assumptions and failing to meet the assumption can lead to unreliable results or false predictions.
- Survival analysis can be complex while implementing and require specialised knowledge of an expert. It can be a barrier for some institutions.
- Survival analysis is not suitable for types of analysis rather than time to-event-based data analysis.
- There is a frequent risk of overfitting the training data with a large number of covariates.
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Survival Analysis FAQs
Q1. What is Survival analysis?
Ans: Survival analysis is a statistical method used to predict the timing of an event such as loan maturity, bonds, disease occurrence, machine failure, market crash, etc.
Q2. What is the importance of survival analysis in Finance?
Ans: In finance, survival analysis can be used to predict the occurrence and timing of important financial events such as market crash, stock surge, investment returns, loan defaults, etc.
Q3. What are the methods of survival analysis?
Ans: There are three popular methods of survival analysis given below.
Kaplan Meier Estimator
Cox Proportional Hazards Model
Parametric Models
Q4. Is Kaplan Meier a survival analysis technique?
Ans: Kaplan Meier analysis is a survival analysis technique used to summarise survival data. It is frequently used to measure the length of time people remain unemployed after a job loss, how long a fleshy fruit remains on plants, patients living for a certain time after treatment, etc.
