Stock return forecasting: Some new evidence

This paper makes three contributions to the literature on forecasting stock returns. First, unlike the extant literature on oil price and stock returns, we focus on out-of-sample forecasting of returns. We show that the ability of the oil price to forecast stock returns depends not only on the data frequency used but also on the estimator. Second, out-of-sample forecasting of returns is sector-dependent, suggesting that oil price is relatively more important for some sectors than others. Third, we examine the determinants of out-of-sample predictability for each sector using industry characteristics and find strong evidence that return predictability has links to certain industry characteristics, such as book-to-market ratio, dividend yield, size, price earnings ratio, and trading volume.     

Post-industrial Canada: An econometric modelling approach

This study brings together the predominantly qualitative approach of social forecasting—as manifested by Daniel Bell's concept of the post-industrial society—and the quantitative analysis of econometric forecasting. The econometric model used here is the candide model of the Canadian national economy. The author seeks to verify the compatibility of some hypotheses of the post-industrial concept by using candide to develop a post-industrial scenario; in doing so, he obtains quantitative estimates of many critical features of the concept within the framework of a structured and reasonably consistent model.     

An empirical examination of jump risk in asset pricing and volatility forecasting in China's equity and bond markets

Understanding jump risk is important in risk management and option pricing. This study examines the characteristics of jump risk and the volatility forecasting power of the jump component in a panel of high-frequency intraday stock returns and four index returns from Shanghai Stock Exchange. Across portfolio indexes, jump returns on average account for 45% to 64% of total returns when jumps occur. Market systematic jump risk is an important pricing factor for daily returns. The average jump beta is 62% of the average continuous beta for individual stocks. However, the contribution of jump risk to total risk is limited, indicating that statistically significant jumps in the stochastic process of asset price are rare events but have tremendous impacts on the prices of common stocks in China. We further document that accounting for jump components improves the performance of volatility forecasting for some equity and bond portfolios in China, which is confirmed by in-the-sample and out-of-sample forecasting performance analysis.     

Forecasting EUR–USD implied volatility: The case of intraday data

This study models and forecasts the evolution of intraday implied volatility on an underlying EUR–USD exchange rate for a number of maturities. To our knowledge we are the first to employ high frequency data in this context. This allows the construction of forecasting models that can attempt to exploit intraday seasonalities such as overnight effects. Results show that implied volatility is predictable at shorter horizons, within a given day and across the term structure. Moreover, at the conventional daily frequency, intraday seasonality effects can be used to augment the forecasting power of models. The type of inefficiency revealed suggests potentially profitable trading models.     

Communicating the future

A lot of time, money, and wisdom is wasted if the strategic planner fails to communicate his message. A substantial investment in a long and detailed forecasting exercise can only pay dividends if the results of that exercise are effectively brought to the attention of the clients, the people who can act upon the forecast. Here the author argues that this final stage of a forecasting exercise is as important as all the other stages, but that it is an area which is largely ignored by planners. So, using the example of a business environment, he gives simple practical guidance as to how planners can improve their performance in this final stage, the presentation of the results to the executives. Basically, this involves a shift away from the objective of transmitting the maximum amount of information, towards the idea of maximising the amount of information assimilated by the audience.     

Prediction and social change: The need for a basis in theory

The author argues that social changes cause personality changes, and that personality changes cause social changes. This mutual feedback effect lies at the heart of the problems of social forecasting. Any valid system of forecasting requires a study of social changes ; but such a study itself necessitates an investigation of personality structures and individual values. The weak links in the causal chain of social change are the source of changes in values and the effect of values upon social goals. The author describes the limited evidence that research into values has provided, and examines critically the methodology employed. He suggests an alternative-but potentially complementary-approach and describes specific problem areas ; finally he reasserts his ultimate goal of a theory of social change.     

Forecasting in an Extended Chain‐Ladder‐Type Model

Reserving in general insurance is often done using chain‐ladder‐type methods. We propose a method aimed at situations where there is a sudden change in the economic environment affecting the policies for all accident years in the reserving triangle. It is shown that methods for forecasting nonstationary time series are helpful. We illustrate the method using data published in Barnett and Zehnwirth (2000, pp. 245–321). These data illustrate features we also found in data from the general insurer RSA during the recent credit crunch.     

Overnight information flow and realized volatility forecasting

This study compares various approaches for incorporating the overnight information flow for forecasting realized volatility of the Australian index ASX 200 and seven very liquid Australian shares from March 2007 to January 2014. The analysis shows that considering overnight information separately rather than adding it to the daily realized volatility estimates leads consistently to better out-of-sample results despite the higher number of involved parameters. A novel, very promising approach is to combine the assets’ own overnight returns with realized volatility estimates of related assets from other markets for which intraday data is available while the Australian exchange is closed.     

Modelling and Forecasting UK Public Finances

In this paper, we present a new model of UK public finances which aims to shed light on recent problems of forecasting the PSBR. The main elements of public spending are treated as endogenous variables which rise in line with GDP over the medium term. Also, the cyclical response of public borrowing to rises in the level of economic activity is more muted when growth is export-led than when it is consumer-led. These two features go a long way towards explaining the rapid deterioration of public finances in the early 1990s and the slow pace of improvement since 1993. JEL classification: C53, E62, H62.     

Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market

This study examines the Chinese implied volatility index (iVIX) to determine whether jump information from the index is useful for volatility forecasting of the Shanghai Stock Exchange 50ETF. Specifically, we consider the jump sizes and intensities of the 50ETF and iVIX as well as cojumps. The findings show that both the jump size and intensity of the 50ETF can improve the forecasting accuracy of the 50ETF volatility. Moreover, we find that the jump size and intensity of the iVIX provide no significant predictive ability in any forecasting horizon. The cojump intensity of the 50ETF and iVIX is a powerful predictor for volatility forecasting of the 50ETF in all forecasting horizons, and the cojump size is helpful for forecasting in short forecasting horizon. In addition, for a one-day forecasting horizon, the iVIX jump size in the cojump is more predictive of future volatility than that of the 50ETF when simultaneous jumps occur. Our empirical results are robust and consistent. This work provides new insights into predicting asset volatility with greater accuracy.     

Almanac of anticipations: The age of affluence—of anticipation—of anxiety—of automation—of discontinuity—of longing—of overkill—of space exploration

With this survey of developments in the futures field since 1945, I.F. Clarke completes his present series for Futures. He makes two major points: that forecasting techniques have become essential tools in the management of change; and that nuclear weapons and ecological problems have added a moral dimension to the study of the future. In the next issue of Futures he will start a new series on the range of future-thinking in the USA, from the expectations of the first settlers to the Star Wars programme.     

Investor and (Value Line) Analyst Underreaction to Information about Future Earnings: The Corrective Role of Non-Earnings-Surprise Information

Prior research suggests that financial analysts' earnings forecasts and stock prices underreact to earnings news. This paper provides evidence that analysts and investors correct this underreaction in response to the next earnings announcement and to other (non-earnings-surprise) information available between earnings announcements. Our evidence also suggests that analysts and investors underreact to information reflected in analysts' earnings forecast revisions and that non-earnings-surprise information helps correct this underreaction as well. Controlling for corrective non-earnings-surprise information significantly increases estimates of the degree to which analysts' forecasting behavior can explain drifts in returns following both earnings announcements and analysts' earnings forecast revisions.     

On the efficiency of the gold market: Results of a real-time forecasting approach

Using a real-time forecasting approach, we study whether publicly available information on a large set of financial and macroeconomic variables help in forecasting out-of-sample monthly excess returns on investing in gold. The real-time forecasting approach accounts for the fact that an investor must reach an investment decision in real time under uncertainty concerning the optimal forecasting model. The real-time forecasting approach also accounts for the possibility that the optimal forecasting model may change over time. We account for transaction costs and show that using forecasts implied by the real-time forecasting approach to set up a simple trading rule does not necessarily lead to a superior performance relative to a buy-and-hold strategy, implying that the gold market is informationally efficient with respect to the predictor variables that we study in this research.     

Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach

Volatility is an important element for various financial instruments owing to its ability to measure the risk and reward value of a given financial asset. Owing to its importance, forecasting volatility has become a critical task in financial forecasting. In this paper, we propose a suite of hybrid models for forecasting volatility of crude oil under different forecasting horizons. Specifically, we combine the parameters of generalized autoregressive conditional heteroscedasticity (GARCH) and Glosten–Jagannathan–Runkle (GJR)-GARCH with long short-term memory (LSTM) to create three new forecasting models named GARCH–LSTM, GJR-LSTM, and GARCH-GJRGARCH LSTM in order to forecast crude oil volatility of West Texas Intermediate on different forecasting horizons and compare their performance with the classical volatility forecasting models. Specifically, we compare the performances against existing methodologies of forecasting volatility such as GARCH and found that the proposed hybrid models improve upon the forecasting accuracy of Crude Oil: West Texas Intermediate under various forecasting horizons and perform better than GARCH and GJR-GARCH, with GG–LSTM performing the best of the three proposed models at 7-, 14-, and 21-day-ahead forecasts in terms of heteroscedasticity-adjusted mean square error and heteroscedasticity-adjusted mean absolute error, with significance testing conducted through the model confidence set showing that GG–LSTM is a strong contender for forecasting crude oil volatility under different forecasting regimes and rolling-window schemes. The contribution of the paper is that it enhances the forecasting ability of crude oil futures volatility, which is essential for trading, hedging, and purposes of arbitrage, and that the proposed model dwells upon existing literature and enhances the forecasting accuracy of crude oil volatility by fusing a neural network model with multiple econometric models.     

Forecasting realised volatility using ARFIMA and HAR models

Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010. We allow for different sectors, changing market conditions, variation in the sampling frequency and forecasting horizons. For the overall sample and using the 300 sec sampling frequency, the forecasting performance of both models is indistinguishable. However, differences arise under different market regimes, forecasting horizons and sampling frequencies. ARFIMA models are superior for the crisis and pre-crisis sub-samples. HAR forecasts are less sensitive to regime change and to longer forecasting horizons. Variations in forecasting performance could also be explained using differences in the levels of persistence underlying each model.     

Do Macroforecasters Herd?

We show that typical tests of whether forecasters herd will falsely indicate herding behavior for a variety of types of behavior and forecasting environments that give rise to disagreement among forecasters. We establish that forecasters will appear to herd if differences between them reflect noise as opposed to private information, or if they arise from informational rigidities. Noise can have a behavioral interpretation and if so will depend on the behavioral model under consideration. An application of the herding tests to U.S. quarterly survey forecasts of inflation and output growth data 1981–2013 does not support herding behavior.     

Using Financial and Macroeconomic Indicators to Forecast Sales of Large Development and Construction Firms

Sales forecasting is a pivotal component of a corporation’s planning and control activities. Despite the panoply of approaches to sales forecasting, relatively few published studies in forecasting address firm-specific sales forecasting model development for the construction industry. While there is evidence that events in the macroeconomy significantly affect the construction market, most published studies on construction sales forecasts using S-curve models are unable to account for the economic climate. This study proposes an approach that employs financial and macroeconomic indicators to forecast sales of large development and construction corporations. First, by using data for 37 large development and construction firms listed on the construction sector of the Taiwan Stock Exchange between 1997 and 2006, hypothesis tests uncover useful relationships between firm sales and financial and macroeconomic indicators. Second, based on these relationships, a two-stage mathematical modeling procedure is used to develop firm-specific sales forecasting models for three of the sample firms. Finally, out-of-sample forecasting accuracy is evaluated using Theil’s U-statistic and mean absolute percentage error (MAPE).     

Processes and Accuracy of Cash Flow Forecasting: A Case Study of a Multinational Corporation

Despite its pivotal importance in enterprise management, cash flow forecasting gets little attention from academics perhaps because few of them have access to internal processes and data. In this article, however, the authors explain how cash flow forecasting is organized at Bayer, a large multinational company headquartered in Germany, and which factors influence the accuracy of its forecasts. The research focuses on cash flow forecasts based on the direct method, prepared three times a yearat Bayer, involving about 62,000 individual forecasting items each time. These forecasts form the basis of the company's liquidity and financial risk management, in particular, its foreign exchange risk hedging. The authors explain how local managers in Bayer's entities across the world derive the forecasts, i.e., what information they use as input, how they validate it, and how they deal with potential bias caused by managerial incentive systems. They also analyze whether forecasting processes are affected by characteristics such as business area, size, region, or specific local conditions, and ultimately whether forecasting practices and entity characteristics affect forecast accuracy. The findings show that cash flow forecasting procedures vary substantially across Bayer. While the central finance department gives general guidance on the required cash flow forecasting output and provides direction on the input to be used, there are no detailed instructions on how forecasts are to be prepared. Instead, local managers are free to determine their own forecasting practices. They use different forecasting inputs and validate forecasting inputs and output with different intensities, and they also differ in how they treat possible biases in input data. These findings document the limits of standardization and central control in large multinational corporations resulting from local managers’ need for flexibility to cope with the heterogeneity and dynamism of their environments. At the same time, however, local differentiation increases complexity and may increase errors. Quantitative analysis of forecasting errors shows that forecasts of receipts from customers (cash inflows) are more accurate than forecasts of payments to suppliers (cash outflows). Moreover, forecasting practices affect forecast accuracy. Outflow forecasts are more accurate if managers intensively validate forecasting input; inflow forecasts, if they eliminate input biases that may result from internal target setting or from other managerial incentives, and if they carefully validate their forecasting output. The study provides several insights.

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