Advanced Portfolio Management. Giuseppe A. Paleologo
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plus epsilon"/>. The last term is the “noise” around the stock return; it is also called idiosyncratic return of the stock. The terms specific and residual are also common, and we use all of them interchangeably. We would like to believe that this nuisance term is specific to the company: every commonality among the stocks comes the beta and the market return. The simple model of Equation (3.1), with a single systematic source of return for all the stocks, is called a single-factor model.
Figure 3.2 Flowchart illustrating the relationships between market, idio and asset returns, mediated by betas and offset by alphas. Market return times beta is added to alpha and idiosyncratic return to yield the total return.
The term
There is general agreement that accurately forecasting market returns is very difficult, and it is, at the very least, not in the mandate of a fundamental analyst. A macroeconomic investor may have an edge in forecasting the market; a fundamental one typically does not have a differentiated view. We can sketch the roles of various investment professionals as follows:
To estimate as accurately as possible is the job of the fundamental investor.
To estimate , identify the correct benchmarks , and the returns is the job of the quantitative risk manager.
To estimate the expected value of (especially if the expectation changes over time) is the job of the macroeconomic investor.
But ultimately it is the job of the portfolio manager – who combines knowledge about mispricing with portfolio construction – to make use of relationship (3.1) to her own advantage.
3.3 Where Does Alpha Come From?
In the previous sections we used the example of SYF and WMT. We now analyze in more detail the historical estimates for SYF, using returns for year 2018. Table 3.1 shows the estimates and the 95% confidence interval. The error around the alpha estimate is much larger than the estimate itself, which is not the case for beta. Table 3.2 shows the high degree of variability in the alphas, whereas the betas are relatively stable. This is the case for most stocks: estimates of alphas have large associated confidence intervals, and they seem to vary over time. You can't observe alpha directly, and you cannot estimate it easily from time series of returns. It is the job of the fundamental analyst to predict forward-looking alpha values based on deep fundamental research. The ability to combine these alpha forecasts in non-trivial ways from a variety of sources and to process a large number of unstructured data is a competitive advantage of fundamental investing, and one that will not go away soon. We list some of data sources and processes involved in the alpha generation process.
Table 3.1 Parameter estimates for Synchrony's returns regressed against SP500 returns, 2018. Alphas are expressed in percentage annualized returns.
| Parameter | Estimate | 95% conf.interval |
|---|---|---|
| alpha (%) | −24 | (−88, +8) |
| beta | 1.02 | (0.84, 1.20) |
Table 3.2 Parameter estimates for Synchrony's returns regressed against SP500 returns, 2015–2019. Alphas are expressed in percentage annualized returns.
| Year | Alpha (%) | Beta |
|---|---|---|
| 2016 | 6 | 1.45 |
| 2017 | −24 | 1.79 |
| 2018 | −40 | 1.02 |
| 2019 | 16 | 1.15 |
| 2020 | −19 | 1.69 |
Valuation Analysis. This differentiated view comes from a company business model whose data are coming primarily from Cash Flow Statements, Balance Sheets (Statements of Financial Positions), Income Statements and Stakeholder Equity Statements. It is complemented by macroeconomic data affecting the production function of the company as well as demand for its products and services. This is the domain of fundamental analysis [T. Koller and Wessels, 2015] and affects both short-term forecasts, e.g., earnings or the event of a sell-side analyst downgrading or upgrading a recommendation, and long-term forecast, such as the potential long-term unsuitability of a business.
Alternative Data. Enabled by the availability of transactional data (e.g., credit card transactions) and environmental data (such as satellite imaging), these data complement fundamental analysis and give short-term information about demand, supply, costs, and potential risks. A textbook account of alternative data in finance is [Denev and Amin, 2020]; a reference for data sources, published by J.P. Morgan, is [Kolanovic and Krishnamachari, 2017].
Sentiment Analysis. This can be interpreted as the subset of alternative data that gives information about demand that is a function of the consumer's expectations of the future state of the economy. Common macroeconomic time series are Conference Board's Consumer Confidence Index, the University of Michigan's Survey of Consumers, and the Purchasing Managers' Index. At the company level, it is possible to gain information from unstructured data [Loughran and McDonald, 2016].
Corporate Access. Interactions between analysts and company managers occur on a quarterly basis. Although these communications are public information, fundamental analysts can make the best use of them by comparing to previous quarter guidance and by linking them to the vast information accumulated by the analyst on that company.
Liquidity. A company may enter or exit the composition of an index, may issue a secondary offering, or may go through the expiration of a stock lock-up period.