Capital Optimisation
Optimising debt to equity and share repurchases
This is a paper aims to explore the mechanical effect of a company’s share repurchase on earnings per share (EPS). In particular, while a share repurchase scheme will reduce the overall number of shares, suggesting that the EPS may increase, clearly the expenditure will reduce the net earnings of a company, introducing a trade-off between these competing effects. We first of all review accretive share repurchases, then characterise the increase in EPS as a function of price paid by the company.
Subsequently, we analyse and quantify the estimated difference in earnings growth between a company’s natural growth in the absence of buyback scheme to that with its earnings altered as a result of the buybacks. We conclude with an examination of the effect of share repurchases in two cases studies in the US stock-market.
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Investment Insight
Parameterization of growth and risk
The purpose of this paper is to introduce a new growth adjusted price-earnings measure (GA-P/E) and assess its efficacy as measure of value and predictor of future stock returns. Taking inspiration from the interpretation of the traditional price-earnings ratio as a period of time, the new measure computes the requisite payback period whilst accounting for earnings growth.
Having derived the measure, we outline a number of its properties before conducting an extensive empirical study utilising a sorted portfolio methodology. We find that the returns of the low GA-P/E stocks exceed those of the high GA-P/E stocks, both in an absolute sense and also on a risk-adjusted basis. Furthermore, the returns from the low GA-P/E porfolio was found to exceed those of the value portfolio arising from a P/E sort on the same pool of stocks. Finally, the returns of our GA-P/E sorted porfolios were subjected to analysis by conducting regressions against the standard Fama and French risk factors.
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www.physicalfinance.orgPassive Investing
On Variance and Persistence of Financial Assets
With the rise of passive investment over recent years the following aims to quantify the change in variance as a result of these strategies. We firstly distinguish between momentum and reversal strategies. Secondly, we consider only the ‘sign process’ of the resulting closing prices, i.e. the sequence of +1 and -1 corresponding to the sign of the return. In this light we construct a probabilistic model such that the sign of the next day’s closing returns is equal to the sign of the current day’s closing returns with probability p.
We show that in the long run, the per-day variance of this process is inflated by a factor of p/(1 − p) as a result of the autocorrelations arising from passive investment. In particular we show that reversal strategies contribute to a reduced volatility (p < 12) whereas momentum strategies contribute to an increased volatility (p > 12). Empirical analysis of 2,000 stocks listed on the NASDAQ suggests that the prevailing strategy is that of reversal, suggesting the volatility of this process is reduced as a result of passive trading.
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Investment Option Ranking
Investment Option Ranking by Method of Comparison
This app implements the method of paired-comparisons for the purposes of comparing investment options such as allocation across asset classes, markets and securities. The app employs the method of Saaty & Alexander, [1981].
Consider a set of options n for which we enquire to rank in order of preference. In this case we are able to construct the paired-comparison matrix Aij=wiwj so that Aii=1 and Aij=1Aji. Writing out the matrix we can see that Aw=nw where w=(w1,...,wn)′. The aim of the procedure is therefore to find the values w of relative importance of each action given a paired comparison matrix A
This is achieved through an eigen-decomposition of A even when noisy estimates of A are used, the largest eigenvector is close to the eigenvector we wish to estimate. The elements of the eigenvector represents the relative weighting of the options derived from the series of paired comparison entered into the matrix. Furthermore, we can normalise the weighting in percentage weights and also produce absolute values if the have the correct value of any particular one. Also , λmax−n gives a measure of consistency (the larger the value the less consistent).
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