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The purpose of this paper is to investigate whether stock price predictions and investment decisions improve by exposure to increasing price series.

The authors conducted three laboratory experiments in which undergraduates were asked to role‐play being investors buying and selling stock shares. Their task was to predict an unknown closing price from an opening price and to choose the number of stocks to purchase to the opening price (risk aversion) or the closing price (risk taking). In Experiment 1 stock prices differed in volatility for increasing, decreasing or no price trend. Prices were in different conditions provided numerically for 15 trading days, for the last 10 trading days, or for the last five trading days. In Experiment 2 the price series were also visually displayed as scatter plots. In Experiment 3 the stock prices were presented for the preceding 15 days, only for each third day (five days) of the preceding 15 days, or as five prices, each aggregated for three consecutive days of the preceding 15 days. Only numerical price information was provided.

The results of Experiments 1 and 2 showed that predictions were not markedly worse for shorter than longer price series. Possibly because longer price series increase information processing load, visual information had some influence to reduce prediction errors for the longer price series. The results of Experiment 3 showed that accuracy of predictions increased for less price volatility due to aggregation, whereas again there was no difference between five and 15 trading days. Purchase decisions resulted in better outcomes for the aggregated prices.

Investorś performance in stock markets may not improve by increasing the length of evaluation intervals unless the quality of the information is also increased. The results need to be verified in actual stock markets.

The results have bearings on the design of bonus systems.

The paper shows how stock price predictions and buying and selling decisions depend on amount and quality of information about historical prices.

In line with the ongoing global and domestic reforms in agriculture and allied sectors, the Indian Government is reducing its direct market intervention and encouraging private participation based on market forces. This has led to increased exposure of agricultural produce to price and other market risks, which consequently emphasize the importance of futures markets for price discovery and price risk management. The purpose of this paper is to analyze the efficiency of agricultural commodity markets by assessing the relationships between futures prices and spot market prices of major agricultural commodities in India.

The efficiency of the futures market for 12 agricultural commodities, traded at one of the largest commodity exchanges of India, i.e. National Commodity & Derivatives Exchange Ltd, has been explored by using Johansen's cointegration analysis and Granger causality tests. Unit root test procedures such as Augmented Dickey‐Fuller and non‐parametric Phillips‐Perron were initially applied to examine whether futures and spot prices are stationary or not. The hypothesis, that futures prices are unbiased predictors of spot prices has been tested using econometric software package.

Results show that cointegration exists significantly in futures and spot prices for all the selected agricultural commodities except for wheat and rice. This suggest that there is a long‐term relationship between futures and spot prices for most of the agricultural commodities like maize, chickpea, black lentil, pepper, castor seed, soybean and sugar. The causality test further distinguishes and categorizes the commodities based on direction of relationship between futures and spot prices. The analysis of short‐term relationship by causality test indicates that futures markets have stronger ability to predict subsequent spot prices for chickpea, castor seed, soybean and sugar as compared to maize, black lentil and pepper, where bi‐directional relationships exist in the short run.

The results of this study are useful for various stakeholders active in agricultural commodities markets such as producers, traders, commission agents, commodity exchange participants, regulators and policy makers.

There are very few studies that have explored the efficiency of the commodity futures market in India in a detailed manner, especially at individual commodity level.

Empirical publications on the time series modeling and forecasting of electricity prices vary widely regarding the conditions, and the findings make it difficult to generalize results. Against this background, it is surprising that there is a lack of statistics-based literature reviews on the forecasting performance when comparing different models. The purpose of the present study is to fill this gap.

The authors conduct a comprehensive literature analysis from 2000 to 2015, covering 86 empirical studies on the time series modeling and forecasting of electricity spot prices. Various statistics are presented to characterize the empirical literature on electricity spot price modeling, and the forecasting performance of several model types and modifications is analyzed. The key issue of this study is to offer a comparison between different model types and modeling conditions regarding their forecasting performance, which is referred to as a quasi-meta-analysis, i.e. the analysis of analyses to achieve more general findings independent of the circumstances of single studies.

The authors find evidence that generalized autoregressive conditional heteroscedasticity models outperform their autoregressive–moving-average counterparts and that the consideration of explanatory variables improves forecasts.

To the best knowledge of the authors, this paper is the first to apply the methodology of meta-analyses in a literature review of the empirical forecasting literature on electricity spot markets.

Companies in the B2B service sector often sign a series of successive contracts instead of one long contract with their vendors. Economic researchers have shown how the lengths of stand-alone contracts are influenced by economic factors such as asset specificity and economic volatility, but have not researched into contracts that are part of a continuous series. The purpose of this study was to explore if being a part of a series of contracts influences the length of the focal contract and the rental rate.

The authors use data collected from the oil drilling industry to empirically test their hypotheses. The data set consists of 2,621 contracts involving jack-up rig hiring in the Gulf of Mexico region.

The authors empirically show that the series duration affects both the length and rental rate of each constituent contract, even after considering all other plausible economic factors. Specifically, the duration of a series has a positive effect on the length and a negative effect on the rental rate of the constituent contract.

Although contract length is as vital as the rent in B2B service transactions, it is rather unfortunate that marketing scholars have not researched much into this topic. The findings offer a new insight into the forces that shape the B2B service contracts and thus help the B2B managers make a better decision in service contracts.

This study uses parametric hazard models to investigate duration dependence in US stock market cycles over the January 1929 through December 1992 period. Market cycles are determined using the Beveridge‐Nelson (1981) approach to the decomposition of economic time series. The results show that both real and nominal cycles exhibit positive duration dependence. The implication of this finding is that actual prices revert to their permanent or trend level in a non‐random manner as the cyclical component dissipates over time. This process is consistent with mean reversion in price and suggests that predictable periodicity in market cycles may exist. Only limited evidence is obtained that discrete shifts or trends in mean cycle duration exist. The length of market cycles appears not to have changed over the 1929–92 period.

The purpose of this paper is to analyze how the German, Irish and average EU farm gate milk prices have changed after the common agricultural policy (CAP) reform in 2003. In addition the dynamics of these prices are compared to a US farm gate price.

These milk price time series are divided into two time periods, pre and post the CAP 2003 reform, and decomposed into their trend, seasonal and cyclical components. For the decomposition a state space model is used following the approach of Harvey (1989).

The results show that the dynamics of the EU, German and Irish series converged after the CAP 2003 reform were implemented and that a three-year cycle is underlying the European milk prices which is comparable with the cycle length of the US milk price. In addition it is shown that most of the observed price variation in recent times is attributed to the cyclical component.

The division of the milk price time series into periods pre and post the CAP 2003 reform is somewhat subjective because not all measures were immediately applied after the reform. It is also possible that other factors may have contributed to the changed dynamics which have been observed. In addition this leads to a short data sample.

The results show that policy makers should consider counter cyclical policy measures given the importance of the cyclical component. Also most models used to evaluate policies do not account for cycles which may lead to wrong conclusions. In addition farmer should be aware of the cyclical nature of milk prices as they budget and plan for the future.

No previous decomposition studies of European milk prices exist.

Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance.

The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market.

The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data.

The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.

In this chapter, we examine the relationship between the cyclical components of output, the price level and the inflation rate. During the post-war period, there is a negative correlation between output and the price level and a positive correlation between output and the inflation rate. A phase shift in the cyclical component between output and the price level can account for these two facts. The phase shift is consistent with movements in the price level Granger causes movements in output. In addition, we consider time-varying correlations between the two pairs of series. Spectral analysis suggest the price and output have different wavelengths, but the difference is not statistically significant.

It has long been challenged that the distributions of empirical returns do not follow the log-normal distribution upon which many celebrated results of finance are based including the Black–Scholes Option-Pricing model. Borland (2002) succeeds in obtaining alternate closed form solutions for European options based on Tsallis distribution, which allow for statistical feedback as a model of the underlying stock returns. Motivated by this, we simulate two distinct time series based on initial data from NIFTY daily close values, one based on the Gaussian return distribution and the other on non-Gaussian distribution. Using techniques of non-linear dynamics, we examine the underlying dynamic characteristics of both the simulated time series and compare them with the characteristics of actual data. Our findings give a definite edge to the non-Gaussian model over the Gaussian one.