The Pricing Performance of Market Advisory Services in Corn and Soybeans over 1995-2003

The purpose of this research report is to evaluate the pricing performance of market advisory services for the 1995-2003 corn and soybean crops. Four basic indicators of performance are applied to advisory program prices and revenues over 1995-2003. Test results provide little evidence that advisory programs as a group outperform market benchmarks, particularly after considering risk. The evidence is somewhat more positive with respect to the farmer benchmark, even after taking risk into account. For example, the average advisory return relative to the farmer benchmark is $7 per acre with only a negligible increase in risk. While this return is small it nonetheless represents a non-trivial increase in net farm income per acre for grain farms in Illinois. Test results also suggest that it is difficult to usefully predict the year-to-year pricing performance of advisory programs based on past pricing performance. However, there is some evidence that performance is more predictable over longer time horizons, particularly at the extremes of performance rankings.Marketing,

THE PRICING PERFORMANCE OF MARKET ADVISORY SERVICES IN CORN AND SOYBEANS OVER 1995-2000

The purpose of this research report is to evaluate the pricing performance of market advisory services for the 1995-2000 corn and soybean crops. Certain explicit assumptions are made to produce a consistent and comparable set of results across the different advisory programs. These assumptions are intended to accurately depict "real-world" marketing conditions. Several key assumptions are: i) with a few exceptions, the marketing window for a crop year runs from September before harvest through August after harvest, ii) cash prices and yields refer to a central Illinois farm, iii) storage is assumed to occur at on-farm or commercial sites, and iv) marketing loan recommendations made by advisory programs are followed wherever feasible. Based on these assumptions, the net price received by a subscriber to market advisory programs is calculated for the 1995-2000 corn and soybean crops. Market and farmer benchmarks are developed for the performance evaluations. Two market benchmarks are specified in order to test the fragility of performance results to changing benchmark assumptions. The 24-month market benchmark averages market prices for the entire 24-month marketing window. The 20-month market benchmark is computed in a similar fashion, except the first four months of the marketing window are omitted. The farmer benchmark is based upon the USDA average price received series for corn and soybeans in Illinois. The same assumptions applied to advisory program track records are used when computing the market and farmer benchmarks. Four basic indicators of performance are applied to advisory program prices and revenues over 1995-2000. The results provide limited evidence that advisory programs as a group outperform market benchmarks, particularly after considering risk. In contrast, substantial evidence exists that advisory programs as a group outperform the farmer benchmarks, even after taking risk into account. Whether the superior performance of advisory programs versus the farmer benchmark is attributed to luck or skill depends on one's theoretical perspective. Efficient market theory favors a luck interpretation, while behavioral market theory favors a skill interpretation. Regardless of the theoretical perspective, there is little evidence that advisory programs with superior performance can be usefully selected based on past performance.Marketing,

The Pricing Performance of Market Advisory Services in Corn and Soybeans Over 1995-2004

The purpose of this research report is to evaluate the pricing performance of market advisory services for the 1995-2004 corn and soybean crops. Marketing assumptions applied to advisory program track records are intended to accurately depict “real-world” marketing conditions facing a representative central Illinois corn and soybean farmer. Several key assumptions are: i) with a few exceptions, the marketing window for a crop year runs from September before harvest through August after harvest, ii) on-farm or commercial physical storage costs, as well as interest opportunity costs, are charged to post-harvest sales, iii) brokerage costs are subtracted for all futures and options transactions and iv) Commodity Credit Corporation (CCC) marketing loan recommendations made by advisory programs are followed wherever feasible. Based on these and other assumptions, the net price received by a subscriber to market advisory programs is calculated for the 1995-2004 corn and soybean crops. Market and farmer benchmarks are developed for the performance evaluations. Two market benchmarks are specified in order to test the sensitivity of performance results to changing benchmark assumptions. The 24-month market benchmark averages market prices for the entire 24-month marketing window. The 20-month market benchmark is computed in a similar fashion, except the first four months of the marketing window are omitted. Given the uncertainties involved in measuring the average price received by farmers, two alternative farmer benchmarks for central Illinois are specified. The market and farmer benchmarks are computed using the same assumptions applied to advisory program track records. Five basic indicators of performance are applied to advisory program prices and revenues over 1995-2004. Results show that advisory program prices fall in the top-third of the price range relatively infrequently. There is limited evidence that advisory programs as a group outperform market benchmarks, particularly after considering risk. The evidence is somewhat more positive with respect to farmer benchmarks, even after taking risk into account. For example, the average advisory return relative to the farmer benchmarks is 8 to $12 per acre with only a marginal increase in risk. Even though this return is small and mainly from corn, it nonetheless represents a non-trivial increase in net farm income per acre for grain farms in central Illinois. Test results also suggest that it is difficult to predict the year-to-year pricing performance of advisory programs based on past pricing performance. However, there is some evidence that performance is more predictable over longer time horizons, particularly at the extremes of performance rankings. The results raise the interesting possibility that even though advisory services do not appear to “beat the market,” they nonetheless provide the opportunity for some farmers to improve performance relative to the market. Mirroring debates about stock investing, the relevant issue is whether farmers can most effectively improve marketing performance by pursuing “active” strategies, like those recommended by advisory services, or “passive” strategies, which involve routinely spreading sales across the marketing window.Agricultural Finance, Financial Economics,

The Pricing Performance of Market Advisory Services in Corn and Soybeans Over 1995-2001

The purpose of this research report is to evaluate the pricing performance of market advisory services for the 1995-2001 corn and soybean crops. The results for 1995-2000 were released in earlier AgMAS research reports, while results for the 2001 crop year are new. Certain explicit assumptions are made to produce a consistent and comparable set of results across the different advisory programs. These assumptions are intended to accurately depict “real-world” marketing conditions facing a representative central Illinois corn and soybean farmer. Several key assumptions are: i) with a few exceptions, the marketing window for a crop year runs from September before harvest through August after harvest, ii) on-farm or commercial physical storage costs, as well as interest opportunity costs, are charged to postharvest sales, iii) brokerage costs are subtracted for all futures and options transactions and iv) Commodity Credit Corporation (CCC) marketing loan recommendations made by advisory programs are followed wherever feasible. Based on these and other assumptions, the net price received by a subscriber to market advisory programs is calculated for the 1995-2001 corn and soybean crops. Market and farmer benchmarks are developed for the performance evaluations. Two market benchmarks are specified in order to test the fragility of performance results to changing benchmark assumptions. The 24-month market benchmark averages market prices for the entire 24-month marketing window. The 20-month market benchmark is computed in a similar fashion, except the first four months of the marketing window are omitted. The farmer benchmark is based upon the USDA average price received series for corn and soybeans in Illinois. The same assumptions applied to advisory program track records are used when computing the market and farmer benchmarks. Four basic indicators of performance are applied to advisory program prices and revenues over 1995-2001. The results provide limited evidence that advisory programs as a group outperform market benchmarks, particularly after considering risk. In contrast, more evidence exists that advisory programs as a group outperform the farmer benchmark, even after taking risk into account. Little evidence is found that advisory programs with superior performance can be usefully selected based on past performance. The results raise the intriguing possibility that even though advisory services do not appear to “beat the market,” they nonetheless provide an opportunity for farmers to improve marketing performance because farmers under-perform the market. Mirroring debates about stock investing, the relevant issue is whether farmers can most effectively improve marketing performance by pursuing “active” strategies, like those recommended by advisory services, or “passive” strategies, which involve routinely spreading sales across the marketing window.Agricultural Finance, Financial Economics,

Challenges arising from alternative investment management.

Alternative investment management differs from traditional asset management in a number of respects. First, it is distinct in terms of both its targets – aiming to achieve an absolute performance, regardless of trends in underlying markets – and its strategies, in particular exploiting inefficiencies in the valuation of financial assets via opportunistic and discretionary positions. It also differs in terms of the financial techniques implemented, e.g. the extensive use made of leverage, derivatives and short selling, and the specific investment vehicles used (ad hoc structures such as hedge funds that are not bound by ordinary law in the way traditional investment vehicles are). These particularities, alongside the fact that the alternative investment universe is somewhat opaque, make it difficult to measure a fund’s risks or a fund manager’s performance. Specific measurement tools are therefore required, which differ from those commonly used in traditional asset management. Over the past few years, the alternative investment management, a diverse and rapidly-evolving universe, has enjoyed a spectacular development, which is illustrated by the sharp rise in the amounts under management and the proliferation of investment vehicles offered to an increasingly broad investor base. In view of the specific nature of alternative fund managers’ modus operandi, the flourishing of the alternative investment industry raises questions as to its implications in terms of financial stability. It also raises new issues regarding the division of roles between market participants and supervisory authorities in the organisation and monitoring of this asset management sector.

Understanding Mutual Fund and Hedge Fund Styles Using Return Based Style Analysis

We provide an introduction to the use of return based style analysis of Sharpe (1992) in practice. We demonstrate the importance of selecting the right style benchmarks and how the use of inappropriate style benchmarks may lead to wrong conclusions. When style analysis is applied to sector oriented funds such as healthcare, precious metals, energy, technology, etc., the set of benchmarks should include sector or industry indexes. Following Glosten and Jagannathan (1994), Fung and Hsieh (2001), and Agarwal and Naik (2001), we show how to analyze the investment style of hedge fund managers by including the returns on selected option based strategies as style benchmarks. In the examples we consider, return based style analysis provides insights not available through commonly used 'peer' evaluation alone.

A Reality Check on Technical Trading Rule Profits in US Futures Markets

This paper investigates the profitability of technical trading rules in US futures markets over the 1985-2004 period. To account for data snooping biases, we evaluate statistical significance of performance across technical trading rules using White's Bootstrap Reality Check test and Hansen's Superior Predictive Ability test. These methods directly quantify the effect of data snooping by testing the performance of the best rule in the context of the full universe of technical trading rules. Results show that the best rules generate statistically significant economic profits only for two of 17 futures contracts traded in the US. This evidence indicates that technical trading rules generally have not been profitable in US futures markets after correcting for data snooping biases.Marketing,

Hedge Fund Performance: The Canadian Market Case

With the growth of hedge fund industry, investors are interested in the possibility of replicating hedge funds returns by using market indexes. Most papers on the hedge fund performance are based on data prior the 2007-2008 financial crisis. This study uses monthly returns data for 59 Canadian hedge funds in Bloomberg database from January 2009 to September 2016 to investigate the hedge funds performance and possibility of replication in the post-crisis period. We follow Hzsanhodzic’s (2006) linear factor model to determine the significance of expected returns can be explained by six common risk exposures. We find that “clone” hedge funds returns would be hard to realize under Canadian market conditions by using current post-crisis data