Alpha is a term that measures the active return of an investment, it is the excess return or abnormal return that an investment yields. Alpha gauges the ability of an investment to outperform the market or beat its edge. It measures the performance of a strategy in beating the return in the market. Alpha is also known as the active return on an investment, it is represented by symbol α (a greek letter).
Alpha can be positive or negative, it refers to an investment’s return relative to the return of the market index or benchmark. Investments such as mutual funds or bonds are ranked using the alpha. Alpha measures the performance of a portfolio in comparison to the benchmark index in the market.
A Little More on What is Alpha
There are many metrics used in the market to evaluate the risk and return of an investment. Alpha is one of the tencinal risk-return metrics used in the modern portfolio theory (MPT), portfolio managers seek to generate alpha in investments, portfolios and assets they manage. Aside from apla, other technical risk ratios are beta, R-squared, Sharpe ratio and standard deviation. Alpha measures the performance of an investment or diversified portfolio relative to the market benchmark.
An alpha of zero indicates that the investment has not made any significant return neither has it made any loss in the market.
Active portfolio managers often aim to generate investment returns that beat that of the benchmark in the market, they track the performance of their investment using the Alpha. However, it is often difficult for these asset managers to beat the market benchmark.
Historical record and past evidence reveal that it is difficult for active mutual funds to have an alpha higher than that of the benchmark in the market. According to historical records, less than 10% of active funds have been able to earn an alpha higher than the benchmark, using a time frame of 10 years.
References for Alpha in Financial Analysis
Academics research on “Alpha”
Statistics notes: Cronbach’s alpha, Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach’s alpha. Bmj, 314(7080), 572. Many quantities of interest in medicine, such as anxiety or degree of handicap, are impossible to measure explicitly. Instead, we ask a series of questions and combine the answers into a single numerical value. Often this is done by simply adding a score from each answer. For example, the mini-HAQ is a measure of impairment developed for patients with cervical myelopathy.1 This has 10 items (table 1)) recording the degree of difficulty experienced in carrying out daily activities. Each item is scored from 1 (no difficulty) to 4 (can’t do). The scores on the 10 items are summed to give the mini-HAQ score.
Coefficient alpha and the internal structure of tests, Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.
What is coefficient alpha? An examination of theory and applications., Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of applied psychology, 78(1), 98. Psychological research involving scale construction has been hindered considerably by a widespread lack of understanding of coefficient alpha and reliability theory in general. A discussion of the assumptions and meaning of coefficient alpha is presented. This discussion is followed by a demonstration of the effects of test length and dimensionality on alpha by calculating the statistic for hypothetical tests with varying numbers of items, numbers of orthogonal dimensions, and average item intercorrelations. Recommendations for the proper use of coefficient alpha are offered. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
Three-dimensional alpha shapes, Edelsbrunner, H., & Mücke, E. P. (1994). Three-dimensional alpha shapes. ACM Transactions on Graphics (TOG), 13(1), 43-72. Frequently, data in scientific computing is in its abstract form a finite point set in space, and it is sometimes useful or required to compute what one might call the “shape” of the set. For that purpose, this article introduces the formal notion of the family of &agr;-shapes of a finite point set in R3. Each shape is a well-defined polytope, derived from the Delaunay triangulation of the point set, with a parameter &agr; &egr; R controlling the desired level of detail. An algorithm is presented that constructs the entire family of shapes for a given set of size n in time 0(n2), worst case. A robust implementation of the algorithm is discussed, and several applications in the area of scientific computing are mentioned.
EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis, Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain research reviews, 29(2-3), 169-195. Evidence is presented that EEG oscillations in the alpha and theta band reflect cognitive and memory performance in particular. Good performance is related to two types of EEG phenomena (i) a tonic increase in alpha but a decrease in theta power, and (ii) a large phasic (event-related) decrease in alpha but increase in theta, depending on the type of memory demands. Because alpha frequency shows large interindividual differences which are related to age and memory performance, this double dissociation between alpha vs. theta and tonic vs. phasic changes can be observed only if fixed frequency bands are abandoned. It is suggested to adjust the frequency windows of alpha and theta for each subject by using individual alpha frequency as an anchor point. Based on this procedure, a consistent interpretation of a variety of findings is made possible. As an example, in a similar way as brain volume does, upper alpha power increases (but theta power decreases) from early childhood to adulthood, whereas the opposite holds true for the late part of the lifespan. Alpha power is lowered and theta power enhanced in subjects with a variety of different neurological disorders. Furthermore, after sustained wakefulness and during the transition from waking to sleeping when the ability to respond to external stimuli ceases, upper alpha power decreases, whereas theta increases. Event-related changes indicate that the extent of upper alpha desynchronization is positively correlated with (semantic) long-term memory performance, whereas theta synchronization is positively correlated with the ability to encode new information. The reviewed findings are interpreted on the basis of brain oscillations. It is suggested that the encoding of new information is reflected by theta oscillations in hippocampo-cortical feedback loops, whereas search and retrieval processes in (semantic) long-term memory are reflected by upper alpha oscillations in thalamo-cortical feedback loops.