The Use of Valuation Models by UK Investment Analysts

This paper examines the use of valuation models by UK investment analysts. The study is based on, first, semi-structured interviews with 35 sell-side analysts from 10 leading investment banks and with 7 buy-side analysts from 3 asset management firms and, second, content analysis based on 98 equity research reports for FTSE-100 companies covered by the sell-side interviewees. We observe that analysts perceive the discounted cash flow (DCF) (and to some extent ‘sophisticated’ models in general) to have become significantly more important than prior survey evidence suggests, although we also find the (somewhat paradoxical) continued importance of ‘unsophisticated’ valuation multiples, notably the price/earnings ratio (PE). We find perceived limitations in the technical applicability of the DCF, which cause analysts to rely in practice upon valuation multiples and subjective judgement of whether the market price ‘feels right’. We also find that contextual factors, notably the analysts' need for their research to be credible to buy-side clients, cause the use of subjective, unsophisticated methods of valuation to be played down. Given the inherent flexibility of the DCF model, coupled with its ostensible credibility, it becomes the natural vehicle for conveying the analyst's research, even though it is very rarely relied upon to determine target prices and investment recommendations. We conclude that, while the literature has focused on the technical merits of alternative valuation models, analysts' actual usage of valuation models also requires an understanding of social and economic context and motivations.     

证券分析师估值模型选择偏好及其对目标价的影响研究——基于企业特征角度

以2017—2020年证券分析师研报为依据,将分析师估值模型选择偏好与分析师目标价结合探讨,并率先提出目标价投资参考价值这一全新概念且对其评估方法进行设计。研究发现,分析师存在特定的估值模型类型偏好,并且在不同行业中偏好不同的估值模型。然而,分析师偏好的估值模型不一定能够生成质量良好的目标价。对于不同特征的企业,分析师存在迥然各异的估值模型选择偏好,该偏好对目标价具有显著影响。结论可帮助分析师优化估值模型选择,提高目标价质量。     

Valuation Accuracy and Infinity Horizon Forecast: Empirical Evidence from Europe

This paper focuses on the assumptions of infinite-horizon forecasting in the field of firm valuation. The estimate of long-run continuing values is based on the hypothesis that companies should have reached the steady state at the end of the period of explicit forecasts. It is argued that the equivalence between cash accounting and accrual accounting is the way of verifying the steady-state assumption, defined as the state when a firm earns exactly its cost of capital, i.e., what we would expect in pure-competition settings. From this definition, we derive that the "ideal" growth rate to use in steady state is equal to the reinvestment rate times Weighted Average Cost of Capital. To validate our approach, we collect a sample of 784 analyst valuations and compare how the implied target prices deviate from what the target prices would have been using the "ideal" steady-state growth rates. Using Logit and Cox regression models, we find that this deviation has predictive value over the probability that actual market price reaches the target price over the following 12-month period—the smaller the deviation the greater is the likelihood that the market price reaches the target price.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.     

A THEORETICAL AND PRACTICAL PERSPECTIVE ON THE EQUITY RISK PREMIUM*

Abstract In historical perspective, equity returns have been higher than interest rates but have also varied a good deal more. However, the average excess return has been larger than what could be expected based on classical equilibrium theory: the equity risk premium (ERP) puzzle. This paper has two objectives. First, the paper presents a comprehensive overview of the vast literature developed aimed at adjusting theory and testing the robustness of the puzzle. Here we will show that the failure of theory to link asset prices to economics is mostly quantitative by nature and not qualitative (anymore). Second, beyond providing a survey of theory, we aim for a relevant practical angle as well. Our main contribution is that we spend time on why returns have been higher than investors reasonably could have expected. We present evidence that forecasts of equity returns can be enhanced by valuation models: low valuation levels (low price‐to‐earnings ratios) portend high subsequent returns. While conventional wisdom (several years ago) was to use historical returns to forecast future returns, a growing consensus now recognizes that the predictive power of valuation ratios is preferred. Finally we provide some practical implications based on this predictability. While the ERP is essentially a long‐term issue, the likelihood of a lower risk premium increases risk for many and means that short‐term volatility might not be neglected.     

Leverage and the price volatility of equity shares in equilibrium

The traditional valuation formulas for corporate debt, which are derived in a complete market setting and are based on the no-arbitrage principle, imply that equity prices become more volatile as leverage increases. If the asset structure is incomplete, the presence of corporate debt affects the linear subspace spanned by the payoffs of the existing assets, and the pricing of corporate debt and shares of levered firms becomes a simultaneous valuation problem. This paper characterizes the relationship between the price of corporate debt and the share price of a levered firm in an equilibrium framework where corporate debt is a non-redundant asset. While, in the absence of bankruptcy, higher leverage always implies riskier equity, it does not necessarily mean more volatile equity prices. In fact, the link between leverage and equity price volatility depends in a particular way on investors’ preferences towards risk.     

Do precious metal prices help in forecasting South African inflation?

In this paper we test whether the key metals prices of gold and platinum significantly improve inflation forecasts for the South African economy. We also test whether controlling for conditional correlations in a dynamic setup, using bivariate Bayesian-Dynamic Conditional Correlation (B-DCC) models, improves inflation forecasts. To achieve this we compare out-of-sample forecast estimates of the B-DCC model to Random Walk, Autoregressive and Bayesian VAR models. We find that for both the BVAR and BDCC models, improving point forecasts of the Autoregressive model of inflation remains an elusive exercise. This, we argue, is of less importance relative to the more informative density forecasts. For this we find improved forecasts of inflation for the B-DCC models at all forecasting horizons tested. We thus conclude that including metals price series as inputs to inflation models leads to improved density forecasts, while controlling for the dynamic relationship between the included price series and inflation similarly leads to significantly improved density forecasts.     

The information content of investment tax credits

This study examines the association between stock prices and tax credits for new investment, which appear in the balance sheet as a tax-free reserve. A number of valuation models were developed for companies listed on the Athens Stock Exchange during the period 1990–4. The empirical findings reveal that retained earnings committed to new investment, i.e. investment tax credits for future investments, are valued differently from both the remaining equity and the remaining earnings. Moreover, the empirical evidence suggests that the investment tax credits in Greece are not always viewed in a positive fashion by the stock market.     

The Relevance of Financial versus Non-Financial Information for the Valuation of Venture Capital-Backed Firms

This study examines the relevance of financial and non-financial information for the valuation of venture capital (VC) investments. Based on a hand-collected data set on venture-backed start-ups in Germany, we investigate the internal due diligence documents of over 200 investment rounds. We document that balance sheet and income statement items capture as much economic content as verifiable non-financial information (e.g. team experience or the number of patents) while controlling for several deal characteristics (e.g. industry, investment round, or yearly VC fund inflows). In addition, we show that valuations based on accounting and non-accounting information yield a level of valuation accuracy that is comparable to that of publicly traded firms. Further analyses show that the industry-specific total asset multiples outperform the popular revenue multiples but lead to significantly less accurate results than those obtained from the more comprehensive valuation models. Overall, our findings might inform researchers and standard-setters of the usefulness of accounting information for investment companies and provide additional evidence to gauge the overall valuation accuracy in VC settings.     

An examination of linear factor models in country equity asset allocation strategies

We examine the out of sample performance of country equity asset allocation strategies between January 1985 and February 2000 that use conditional versions of international asset pricing models to forecast expected returns. We find that strategies that use conditional asset pricing models tend not to outperform a strategy that uses the sample mean to forecast expected returns. We find that this result is fairly robust across different levels of risk aversion, whether riskless lending is available or not, and when we impose upper bound constraints.     

Could we have predicted the recent downturn in the South African housing market?

This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium- and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01–2000:04, the large-scale BVARs, estimated under alternative hyperparameter values specifying the priors, are used to forecast real house price growth rates over a 24-quarter out-of-sample horizon of 2001:01–2006:04. The forecast performance of the large-scale BVARs are then compared with classical and Bayesian versions of univariate and multivariate Vector Autoregressive (VAR) models, merely comprising of the real growth rates of the large-, medium- and small-middle-segment houses, and a large-scale Dynamic Factor Model (DFM), which comprises of the same 268 variables included in the large-scale BVARs. Based on the one- to four-quarters-ahead Root Mean Square Errors (RMSEs) over the out-of-sample horizon, we find the large-scale BVARs to not only outperform all the other alternative models, but to also predict the recent downturn in the real house price growth rates for the three categories of the middle-segment-housing over the period of 2003:01–2008:02.     

Forecasting Bitcoin with technical analysis: A not-so-random forest?

This paper uses data sampled at hourly and daily frequencies to predict Bitcoin returns. We consider various advanced non-linear models based on a multitude of popular technical indicators that represent market trend, momentum, volume, and sentiment. We run a robust empirical exercise to observe the impact of forecast horizon, model type, time period, and the choice of inputs (predictors) on the forecast performance of the competing models. We find that Bitcoin prices are weakly efficient at the hourly frequency. In contrast, technical analysis combined with non-linear forecasting models becomes statistically significantly dominant relative to the random walk model on a daily horizon. Our comparative analysis identifies the random forest model as the most accurate at predicting Bitcoin. The estimated measures of the relative importance of predictors reveal that the nature of investing in the Bitcoin market evolved from trend-following to excessive momentum and sentiment in the most recent time period.     

并购中现金流折现法定价应用研究

我国企业定价不合理,被收购公司价格严重低估。为合理定价被并购公司,用现金流折现法对被并购企业价格估算,详细分析中国石化收购扬子石化并购一案,估算出扬子石化的理论价格,并比较实际价格,提出了并购风险防范方法。     

Forecasting value at risk and expected shortfall with mixed data sampling

I propose applying the Mixed Data Sampling (MIDAS) framework to forecast Value at Risk (VaR) and Expected shortfall (ES). The new methods exploit the serial dependence on short-horizon returns to directly forecast the tail dynamics of the desired horizon. I perform a comprehensive comparison of out-of-sample VaR and ES forecasts with established models for a wide range of financial assets and backtests. The MIDAS-based models significantly outperform traditional GARCH-based forecasts and alternative conditional quantile specifications, especially in terms of multi-day forecast horizons. My analysis advocates models that feature asymmetric conditional quantiles and the use of the Asymmetric Laplace density to jointly estimate VaR and ES.     

The discourse of inflation accounting

The value of a share is given by the dividend discount model as a simple function of future dividends; but the actual determination of the share price is rarely based upon the direct estimation of these future dividends. A ranking of the valuation models used by analysts and fund managers shows a preference for ‘unsophisticated’ valuation using, for example, the dividend yield rather than the dividend discount model. This finding is shown to depend upon the practical difficulty of using currently-available information to forecast future cash flows. This difficulty limits the quantitative basis of valuations to short forecast horizons, while the subjective, qualitative estimation of terminal value assumes great importance. Crucially, both analysts and fund managers use their own assessment of management quality to underpin the estimation of terminal value, on the basis that superior quality causes outperformance and that, whereas management quality can be assessed now, future performance itself is unobservable. Linked with this and with information asymmetry, valuation is a dynamic, company-specific process, focused on personal communication with management and embodying ongoing signalling and implicit contracting, using both dividends and other variables. This method of valuation causes formal valuation models such as the dividend yield to play only a limited role. They offer a benchmark of relative price differences, which serves as a basis from which to conduct subjective, company-specific analysis and to make investment decisions; but valuation models are not used exclusively, in themselves, to value shares.     

Eurozone equities expected to outperform in 2015

• We expect Eurozone equities to somewhat outperform US equities in 2015, so long as downside risks (such as a Greek exit from the Eurozone) do not materialise.
• Absolute valuation measures suggest that Eurozone stocks are fair‐to‐slightly cheap, limiting their upward potential. But relative to US stocks, they appear more attractive. This suggests potential for favourable portfolio reallocations and a possible lift to equity prices in the region.
• That said, earnings growth is likely to provide the main support to Eurozone equity performance in 2015, against a backdrop of converging earnings cycles between the US and the Eurozone.

     

Investment decisions in manufacturing: assessing the effects of real oil prices and their uncertainty

We investigate the effects of real oil prices and their uncertainty on investment decisions. Making use of plant‐level data, we estimate dynamic, discrete‐choice models that allow modeling investment inaction, under different assumptions related to initial conditions and unobserved heterogeneity. We find that increases in real oil price changes and in real oil price uncertainty significantly reduce the likelihood of investment action, in line with the predictions of irreversible investment theory. We also document that investment decisions exhibit strong, pure state dependence and are also significantly affected by initial conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

A scenario-based integrated approach for modeling carbon price risk

Carbon prices are highly dependent on government emission policies and local industrial compositions. When historical data does not exist or limited price data can only be sourced from another country, scenario analysis becomes the only tool for the modelling of future carbon prices. However, various plausible but equally possible scenarios can produce large variations in forecast carbon prices. In a traditional approach of scenario analysis, investment decisions or risk management strategies are proposed and analysed for each given scenario, optimal solutions are determined. However, when the number of scenarios becomes large, it often becomes too complex and intractable to have a clear view on the selection of investment decisions or risk-management strategies because these decisions and strategies are closely linked with each of the many scenarios. In this paper, it is proposed to use a stochastic mean-reversion model to represent future carbon price movements, but this model is calibrated to the forecast carbon prices of all the scenarios. In this approach, a single model is used to capture the underlying uncertainty and expectation of the stochastic carbon prices as projected by all the scenarios, carbon price risk can thus be modeled and analysed without the need for direct references to any specific scenarios. The modelling and management of long-term carbon-price risk are therefore purely dependent on future carbon price levels and volatilities of these scenarios, instead of on the scenarios themselves. Through such an approach, the optimization of investment decisions and risk management solutions can be much simpler because the forecasted carbon prices are the only input data.      

The usefulness of oil price forecasts—Evidence from survey predictions

This paper evaluates survey forecasts for crude oil prices and discusses the implications for decision makers. A novel disaggregated data set incorporating individual forecasts for Brent and Western Texas Intermediate is used. We carry out tests for unbiasedness, sign accuracy, and forecast encompassing, followed by the computation of coefficients for topically oriented trend adjustments and the Theil's U measure. We also control for the forecast horizon finding heterogeneous results. Forecasts are more precise for shorter horizons, but less accurate than the naïve prediction. For longer horizons, topically oriented trend adjustments become more pronounced, but forecasters tend to outperform the naïve predictions.