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Retirement planning, in a financial context, refers to the allocation of savings or revenue for retirement. The goal of retirement planning is to achieve financial independence.
The process of retirement planning aims to:[1]
Producers such as a financial plannerorfinancial adviser can help clients develop retirement plans, where compensation is either fee-based or commissioned contingent on product sale; see Professional certification in financial services. Such an arrangement is sometimes viewed[by whom?] as in conflict with a consumer's interest, and that the advice rendered cannot be without bias, or at a cost that justifies its value. Consumers can now elect a do it yourself (DIY) approach. For example, retirement web-tools in the form of a calculator, mathematical modelordecision support system are available online. A web-based tool that allows client to fully plan, without human intervention, might be considered a producer. Key motivations of the DIY trend are many of the same arguments for lean manufacturing, a constructive alteration of the relationship between producer and consumer.
A good retirement plan should consider:[2]
Retirement finances touch upon distinct subject areas or financial domains of client importance, including: investments (i.e., stocks, bonds, mutual funds); real estate; debt; taxes; cash flow (income and expense) analysis; insurance; defined benefits (e.g., social security, traditional pensions).
There is often a complex interaction between the things that the person can control over time (like their investment mix, saving level while working, spending level in retirement and, to an extent, the timing of retirement and part time work undertaken) and things that are outside their control (like market performance, inflation, tax and social security rules and the length of their lifespan). [6]
From an analytic perspective, each domain can be formally characterized and modeled using a different class representation, as defined by a domain's unique set of attributes and behaviors. Domain models require definition only at a level of abstraction necessary for decision analysis. Since planning is about the future, domains need to extend beyond current state description and address uncertainty, volatility, change dynamics (i.e., constancy or determinism is not assumed). Together, these factors raise significant challenges to any current producer claim of model predictability or certainty. Volatility in investment markets raises questions for everyone, participants, and fiduciaries alike. With the growth of 401(k) and other individual account retirement plans, many participants are responsible for investing their retirement savings. [7]
Retirees often face significant financial risk in retirement (unless they have guaranteed products like defined benefit pensions or lifetime annuities). Each individual doesn’t know how long they will live or what sequence of market returns they will experience in retirement.
Ideally, retirement models should calculate the probability of achieving the person’s required living standard for as long as they live, and calculate the probability of achieving various other goals. Quantitative specialists and actuaries will fit a statistical distribution to key random variables that impact results - such as market returns, human lifespans and inflation rates. These can be used to generate probability weighted scenarios of what a retiree could experience over the decades in retirement.
The Monte Carlo method is a common form of a mathematical model that is applied to predict long-term investment behavior for a client's retirement planning.[8] Its use helps to identify adequacy of client's investment to attain retirement readiness and to clarify strategic choices and actions. It's important to note the investment domain is only a financial domain and therefore is incomplete on its own. Depending on client context, the investment domain may have very little importance in relation to a client's other domains—e.g., a client who receives a guaranteed annuity or social security pension.
Modern retirement models are starting to applying similar techniques at household level too – projecting all significant assets, liabilities, and incomes of the household and stress testing a full range of market sequences and lifespan scenarios (for each spouse).[9] The approach can deal with the complex interdependencies between subject areas (domains) mentioned above as well as tax and legislative provisions.
Contemporary retirement planning models have yet to be validated in the sense that the models purport to project a future that has yet to manifest itself. The criticism with contemporary models are some of the same levied against Neoclassical economics. The critic[who?] argues that contemporary models may only have proven validity retrospectively, whereas it is the indeterminate future that needs solution. A more moderate school believes that retirement planning methods must further evolve by adopting a more robust and integrated set of tools from the field of complexity science. Recent research has explored the effects of the elimination of capital income taxes on saving-for-retirement opportunities and its impact on government debt.[10]
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