Scenarios for long‐term world growth

• In the second of two articles on long‐term world growth, we present a set of stylised scenarios for world growth in the next decade. Our baseline forecast, which sees growth edging down, is compared to scenarios based on ‘lost decades’ in China and India, lower productivity and investment growth and a bigger drag from excess debt. The more likely of these scenarios could cut world growth by around 0.5 percentage points per year, rising to a 1.5 percentage point cut for the most extreme scenario.
• Our baseline forecast assumes productivity and investment grow at a similar pace in the next decade to the past ten years. But there are downside risks to productivity growth, especially in Emerging Markets (EMs). And with investment in China and in commodity exporters slowing, our investment forecast relies on a significant rebound in the major economies.
• Demographic factors are a significant downside risk to our forecast. The negative impact of demographic changes on growth in Japan since the 1990s was not generally foreseen. This risk exists in the US and Europe but also in emerging Asia, a particular concern given that the latter region accounted for over 50% of world growth in 2000–14.
• Growth in commodity‐exporting economies could undershoot our current predictions. Historical evidence suggests a danger that the drop in commodity prices could extend for several more years. Even with zero real growth in commodity prices, aggregate GDP growth in the main commodity exporters might only be around 2.5% per year.
• Another risk area is debt. International evidence suggests debt ratios above certain thresholds slow growth. We find that countries accounting for 44% and 31% of world GDP respectively exceed these estimated public and corporate debt thresholds. But the share is lower, and has dropped, for household debt, thanks to deleveraging in the G7.

     

Quasi‐rational and ex ante price expectations in commodity supply models: an empirical analysis of the US broiler market

A statistically optimal inference about agents' ex ante price expectations within the US broiler market is derived using futures prices of related commodities along with a quasi‐rational forecasting regression equation. The modelling approach, which builds on a Hamilton‐type framework, includes endogenous production and allows expected cash price to be decomposed into anticipated and unanticipated components. We therefore infer the relative importance of various informational sources in expectation formation. Results show that, in addition to the quasi‐rational forecast, the true supply shock, future prices, and ex post commodity price forecast errors have, at times, been influential in broiler producers' price expectations. Copyright © 2003 John Wiley & Sons, Ltd.     

Optimal consumption and investment under partial information

We consider a stock market model where prices satisfy a stochastic differential equation with a stochastic drift process. The investor’s objective is to maximize the expected utility of consumption and terminal wealth under partial information; the latter meaning that investment decisions are based on the knowledge of the stock prices only. We derive explicit representations of optimal consumption and trading strategies using Malliavin calculus. The results apply to both classical models for the drift process, a mean reverting Ornstein-Uhlenbeck process and a continuous time Markov chain. The model can be transformed to a complete market model with full information. This allows to use results on optimization under convex constraints which are used in the numerical part for the implementation of more stable strategies. Supported by the Austrian Science Fund FWF, project P17947-N12. We thank two anonymous referees for their comments which led to a considerable improvement of the paper.     

Solving the incomplete markets model with aggregate uncertainty using the Krusell–Smith algorithm and non-stochastic simulations

This article describes the approach to computing the version of the stochastic growth model with idiosyncratic and aggregate risk that relies on collapsing the aggregate state space down to a small number of moments used to forecast future prices. One innovation relative to most of the literature is the use of a non-stochastic simulation routine.     

Risk shocks and housing supply: A quantitative analysis

This paper analyzes the role of stochastic uncertainty in a multi-sector housing model with financial frictions. We include time varying uncertainty (i.e. risk shocks) in the technology shocks that affect housing production and provide estimates of the time-series properties of risk shocks by using firm level productivity data. The analysis demonstrates that risk shocks to the housing production sector are a quantitatively important impulse mechanism for understanding housing price movements. Specifically, the model can match the volatility of housing prices observed in the data. It is also demonstrated that adjustment costs are important in replicating the contemporaneous correlation of housing prices with GDP and residential investment. Critically, bankruptcy costs act as an endogenous markup factor in housing prices and are an important determinant of house price volatility. However, in comparison to housing demand shocks, risk shocks have low explanatory power for real quantities.     

Simpler Probabilistic Population Forecasts: Making Scenarios Work

The traditional high-low-medium scenario approach to quantifying uncertainty in population forecasts has been criticized as lacking probabilistic meaning and consistency. This paper shows, under certain assumptions, how appropriately calibrated scenarios can be used to approximate the uncertainty intervals on future population size and age structure obtained with fully stochastic forecasts. As many forecasting organizations already produce scenarios and because dealing with them is familiar territory, the methods presented here offer an attractive intermediate position between probabilistically inconsistent scenario analysis and fully stochastic forecasts.     

Dispersion in analysts’ target prices and stock returns

We propose the dispersion in analysts’ target prices as a new measure of disagreement among analysts and a proxy of ex ante stock risk. In contrast to the negative return predictability of analysts’ earnings forecast dispersion but consistent with the risk hypothesis, we document a significant positive relation between the target price dispersion and future stock returns up to 24 months. The next-month return spread between the highest and lowest deciles sorted on the target price dispersion measures can be over 2%. Our findings cannot be explained by the standard risk factors and stock characteristics including the target price revision. Further supporting the risk hypothesis, the target price dispersion is positively related to future stock risk.     

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.     

运输企业燃料油价格风险管理现状与改进对策

随着全球原油价格持续大幅度上涨和运输行业竞争日趋激烈,燃料油价格风险管理能力将日渐成为运输企业核心竞争能力之一。与国外运输企业相比,我国企业明显缺乏有效的燃料油价格风险管理工具与能力, 这使我国多数运输企业的燃料油成本明显高于国外同类企业,导致企业盈利水平波动大,增加了企业经营风险, 对企业的融资与投资产生不利影响。本文分析了我国运输企业的燃料油价格风险管理现状,建议运输企业积极学习和利用期权、互换、期货等衍生金融工具来改进燃料油价格风险管理手段,以降低燃料油成本,增强企业成本竞争能力。     

Are the profitability and investment factors valid ICAPM risk factors? Pre-1963 evidence

We empirically evaluate whether the profitability and investment factors from Novy-Marx (2013) and Fama and French (2015, 2018) are compatible with Merton’s (1973) intertemporal CAPM (ICAPM) framework in the pre-1963 period. We show that: (i) the covariance risk price estimates of the profitability factors are positive and statistically significant, which indicates that they have explanatory power with respect to the cross-section of stock returns; (ii) the investment factors carry insignificant covariance risk prices and are therefore not valid ICAPM risk factors; and (iii) the profitability factors forecast the first moment of the aggregate stock return and economic activity with the correct sign, which is consistent with their positive covariance risk price estimates and satisfies the sign restrictions associated with the ICAPM.     

Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading

With the rapid growth of carbon trading, the development of carbon financial derivatives such as carbon options has become inevitable. This paper established a model based on GARCH and fractional Brownian motion (FBM), hoping to provide reference for China's upcoming carbon option trading through carbon option price forecasting research. The fractal characteristic of carbon option prices indicates that it is reasonable to use FBM to predict option prices. The GARCH model can make up for the lack of fixed FBM volatility. In this paper, the daily closing prices of EUA option contracts on the European Energy Exchange are selected as samples for price prediction. The GARCH model was used to determine the return volatility, and then the FBM was used to calculate the forecast price for the next 60 days. The results showed that the predicted price can better fit the actual price. This paper further compares the price prediction results of this model with the other three models through line graphs and error evaluation indicators such as MAPE, MAE and MSE. It is confirmed that the prediction results of the model in this paper is the closest to the actual price.     

House Prices, Housing Investment and Demography Forecasts from the LBS/Abbey National Model of the UK Housing Market

A new housing sector has been incorporated into the London Business School model. This article outlines the new housing model, summarizes the research which has gone into its construction, and presents a forecast of the UK housing market. Using the new housing model, we forecast a moderate recovery in the housing market in the later part of 1991 and 1992. This recovery is however short-lived and does not result in such high rates of house price increase as previous house price booms (Chart 1).
Cuts in interest rates following entry to the exchange rate mechanism of the EMS prompt a recovery in house prices from the middle of 1991. House price inflation then peaks with an increase in average UK house prices in 1992 of 11 per cent over the previous year. Increases in real personal disposable income are modest, by the standards of the 1980s, and for this reason the recovery does not develop the momentum of previous house price booms. House price inflation moderates again in 1993 falling back to around 7 per cent. Housing starts and housing investment recover only slightly from their present depressed levels.
the recovery in house prices is weaker than that foreseen in our April Forecast Release. This is because real personal disposable income is now forecast to grow more slowly during 1991. Sterling's membership of the ERM is followed by a fall in interest rates, but it is the timing of interest rate cuts rather than their magnitude which differs from the earlier forecast. The changed profile of interest rates has altered the house price forecast only marginally.     

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.     

Corporate hedging and input price risk

Input price variability is an important source of risk for corporations that process raw commodities. Models of optimal input hedging are developed in this paper based on the maximization of managerial expected utility. The relationship between hedging strategies and output decisions is examined to assess the impact of the ability to set output prices on futures market participation. As a firm's ability to set output prices diminishes in the short run, input futures positions increase although the optimal hedge ratio may either increase or decrease. For a perfectly competitive firm, however, shifts in output price caused by input price changes provide a natural cash market hedge of input price risk and reduce the firm's optimal input futures position.     

Fractional cointegration and price discovery in Canadian commodities

Using as a unifying theme commodities important to the Canadian economy, recently developed tools are applied to studying price discovery in the spot and futures markets. For each commodity the fractionally cointegrated vector autoregression (FCVAR) model of Johansen and Neilsen is estimated and tested against the special case of the conventional cointegrated vector autoregression (CVAR). These models characterize the fundamental value of a commodity as the common stochastic trend shared by its cointegrated spot and futures prices, and so price discovery can be analyzed using the permanent-transitory decomposition of Gonzalo and Granger. Model forecasts are evaluated and compared using a distributional result due to Clark and West. The generalization to fractional cointegration is found to be statistically significant. However the economic significance of this generalization—in terms of forecast accuracy and the profitability of mean–variance dynamic trading strategies—is more fragile than may have been appreciated.     

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.     

存货购销中最优期权合约交易量研究

在分析利用期权合约规避价格波动风险的原理的基础上,分别给出存货购销两个环节中可以运用的期权策略,然后利用均值方差模型计算使投资组合达到效用最大化时所对应的最优期权合约交易量及其对经营利润的影响,研究发现：在存货采购环节,企业可以通过购入看涨期权、购入看涨期权同时售出看跌期权两种策略控制采购价格波动的风险,在存货销售环节,企业可以通过购入看跌期权、同时购入看跌期权并售出看涨期权两种策略来稳定销售利润;从最优期权合约交易量及其对企业经营利润的影响来看,期权工具在控制存货采购价格、稳定销售利润中可以发挥良好作用。     

Auditing Stories about Discomfort: Becoming Comfortable with Comfort Theory

We investigate whether the choice of valuation model affects the forecast accuracy of the target prices that investment analysts issue in their equity research reports, controlling for factors that influence this choice. We examine 490 equity research reports from international investment houses for 94 UK-listed firms published over the period July 2002–June 2004. We use four measures of accuracy: (i) whether the target price is met during the 12-month forecast horizon (met_in); (ii) whether the target price is met on the last day of the 12-month forecast horizon (met_end); (iii) the absolute forecast error (abs_err); and (iv) the forecast error of target prices that are not met at the end of the 12-month forecast horizon (miss_err). Based on met_in and abs_err, price-to-earnings (PE) outperform discounted cash flow (DCF) models, while based on met_end and miss_err the difference in valuation model performance is insignificant. However, after controlling for variables that capture the difficulty of the valuation task, the performance of DCF models improves in all specifications and, based on miss_err, they outperform PE models. These findings are robust to standard controls for selection bias.     

非效率投资、审计监督与股价崩盘风险

选取2008年至2013年A股上市公司的数据,研究非效率投资行为可能引发的经济后果,并研究了审计监督对非效率投资行为经济后果的影响。实证研究表明,在其他条件一定的情况下,管理层的非效率投资状况越严重,就越有动机向外界隐瞒其代理动机和利益侵占行为等负面消息,造成上市公司未来的股价崩盘风险越高。独立审计作为公司治理的外部监督主体与保证机制,能够通过缓解代理问题和降低代理成本,显著抑制非效率投资对股价崩盘风险的影响。研究结论表明防范和化解股市的风险需要从上市公司代理问题的源头上做起这一政策性意义。     

A forecast evaluation of capital investment in agriculture

A stochastic coefficients model developed by Swamy and Tinsley is used to forecast agricultural investment. In two sets of out-of-sample forecasts, one for 5 years, the other for 10 years, the Swamy-Tinsley stochastic coefficients model outperforms competing fixed and stochastic coefficients empirical models of agricultural investment for a wide array of risk functions. The Swamy-Tinsley stochastic coefficients investment model forecasts continued declines in net investment for farm machinery, with greater declines toward the end of the forecast period. The Swamy-Tinsley method produced better predictions than both stochastic and fixed-coefficients competitors.