Explain the Relationship Between Risk and Return: Identify Examples

Risk and Return Analysis
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Risk and Return Analysis
In the most fundamental sense, risk can be defined as the chance of financial loss. The definition of risk is less explored generally, and not everyone agrees on how to define it let alone measure it. Nonetheless, there are some attributes of risk that everyone subscribes to. An asset that has a higher chance of financial loss in the future is said to be riskier than that with lesser chances of financial loss. Formally, the word risk can be interchangeably used with uncertainty referring to the variability of return associated with a particular investment. For instance holding a government bond worth $1000 for a month that guarantees $100 bears no risk because of the absence of variability. On the contrary, holding a $1000 common investment stock carries significant risk because the variance in return ranges from $0 to $200. In simpler terms, the more nearly sure the return from an investment is the less variability and, therefore, lesser risk. Alternatively, an investment with a high variability bears greater risk due to its uncertainty in associated returns. It is essential to have knowledge of both sides of the coin as to what pertains return and its measurement. Return is defined as the total gain or loss experienced over an investment asset risk during a given period. In most occasions, return is given as cash distribution during the investment period plus the change in value. All this expressed as a percentage of the beginning investment value.
The relationship between risk and return has been one of the most contentious research questions in finance. Several papers in the literature have investigated the relationship to determine as to whether the two subjects are positively or negatively co-related. This is not entirely surprising as this is one of the most, if not the most significant trade-offs in the field of finance and investment. Unfortunately, many scholars have found it difficult to prove with results ranging from widely insignificant to sometimes inconclusive. For instance, the results of ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.2307/2138928", "ISBN" : "00223808", "ISSN" : "0022-3808", "PMID" : "382751", "abstract" : "This paper uses an equilibrium multifactor model to interpret the cross-sectional pattern of postwar U.S. stock and bond returns. Priced factors include the return on a stock index, revisions in forecasts of future stock returns (to capture intertemporal hedging effects), and revisions in forecasts of future labor income growth (proxies for the return on human capital). Aggregate stock market risk is the main factor determining excess returns; but in the presence of human capital or stock market mean reversion, the coefficient of relative risk aversion is much higher than the price of stock market risk.", "author" : [ { "dropping-particle" : "", "family" : "Campbell", "given" : "John Y", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Political Economy", "id" : "ITEM-1", "issue" : "2", "issued" : { "date-parts" : [ [ "1996" ] ] }, "page" : "298-345", "title" : "Understanding Risk and Return", "type" : "article-journal", "volume" : "104" }, "uris" : [ "/documents/?uuid=01a7b5b1-c80e-41ec-affa-e15f91f3eb47", "/documents/?uuid=d656256c-759f-4668-8889-773ae734b1e5" ] } ], "mendeley" : { "formattedCitation" : "(Campbell, 1996)", "plainTextFormattedCitation" : "(Campbell, 1996)", "previouslyFormattedCitation" : "(Campbell, 1996)" }, "properties" : { "noteIndex" : 0 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Campbell, 1996) showed positive though insignificant relationship while ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.jfineco.2005.12.002", "ISBN" : "0304-405X", "ISSN" : "0304405X", "abstract" : "Existing empirical literature on the risk-return relation uses relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large data sets, to summarize a large amount of economic information by few estimated factors, and find that three new factors-termed \"volatility,\" \"risk premium,\" and \"real\" factors-contain important information about one-quarter-ahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 16-20% of the one-quarter-ahead variation in excess stock market returns, and exhibit stable and statistically significant out-of-sample forecasting power. We also find a positive conditional risk-return correlation. ?? 2006 Elsevier B.V. All rights reserved.", "author" : [ { "dropping-particle" : "", "family" : "Ludvigson", "given" : "Sydney C.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ng", "given" : "Serena", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Financial Economics", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2007" ] ] }, "page" : "171-222", "title" : "The empirical risk-return relation: A factor analysis ...