## Financialization of Crude Oil Market Wednesday, 26 July, 2017

**Financial variables have become the main driving factors explaining the variation in crude oil returns:**

**Authors:** Adams, Kartsakli

**Title: **Has Crude Oil Become a Financial Asset? Evidence from Ten Years of Financialization

**Link:** https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2999717

**Abstract:**

The financialization of crude oil markets over the last decade has changed the behavior of oil prices in fundamental ways. In this paper, we uncover the gradual transformation of crude oil from a physical to a financial asset. Although economic demand and supply factors continue to play an important role, recent indicators associated with financialization have emerged since 2008. We show that financial variables have become the main driving factors explaining the variation in crude oil returns and volatility today. Our findings have important implications for portfolio analysis and for the effectiveness of hedging in crude oil markets.

**Notable quotations from the academic research paper:**

"We decompose the total variation of crude oil returns and volatility into three distinctive parts: One part that can be explained by economic fundamental factors, one part that can be explained by financialization variables, and a third which consists of the unexplained variation. While decomposing the returns provides information concerning the main drivers of crude oil as an asset, the volatility decomposition reveals the main factors of risk transmission. We show that the relative importance of economic and financial variables changes over time. In particular, the relative importance of financial variables has changed in such a way that crude oil is now closer to a financial asset than to a real physical asset.

Panel A of Figure 4 shows the decomposition of the total variation in crude oil returns. The fraction of the total variation that can be explained by movements in economic variables is indicated by green shaded areas, the percentage that can be explained by financial variables is indicated by the red shaded areas. The remaining variation is unexplained. The large share of unexplained variation may be due to omitted factors such as geopolitical changes, synchronized OPEC oil production, and disrupting weather events. At a given point in time, the sum over all green and red shaded areas represents the R-squared from a regression of monthly crude oil returns on our set of explanatory variables. To obtain time variation, the regression is moved forward in a 5-year rolling window (60 monthly observations). Two observations follow from Figure 4:

During the pre-financialization period, the contemporaneous variation in our eight regressors explains only a small percentage of the total variation in crude oil returns. After the default of Lehman Brothers, the situation changes dramatically. The same set of regressors now explain almost 60% of the return variation. Among the fundamental variables, economic activity and the change in the dollar exchange rate explain 8% and 12% respectively.

The main drivers behind the variation in oil returns are however the financial variables. In particular, the change in the VIX and the S&P 500 returns are responsible for 29% of the variation.

To illustrate this point, the average fraction explained by each set of variables is shown in Panel B of Figure 4. Since the beginning of the financialization period, the financial variables dominate the economic fundamental variables by a significant amount. Traditional fundamental variables have become relatively less important for predicting crude oil returns while recent financial variables can now predict a large share of the return variation. From this finding we conclude that the behavior of crude oil has become more similar to that of financial assets like equities rather than traditional economic demand and supply drivers.

Financial variables can only explain 11% of the total variation in crude oil returns in the years prior to financialization, the impact grows to 35%, becoming the main drivers behind oil price movements. We estimate an even stronger effect on crude oil volatility where the impact of financial variables grows from 19% in the pre-financialization period to 53% since the failure of Lehman Brothers. Our empirical results indicate that crude oil markets underwent significant changes over the last years. These changes were sufficiently large to transform the very nature of crude oil, away from a physical real asset towards a variable that shows a behavior that is comparable to stocks, bonds, and other financial assets."

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## How to Improve Shiller's CAPE Ratio Wednesday, 19 July, 2017

**An interesting idea to create a CAPE Ratio with a better predictability:**

**Authors:** Davis, Aliaga-Diaz, Ahluwalia, Tolani

**Title: **Improving U.S. Stock Return Forecasts: A 'Fair-Value' Cape Approach

**Link:** https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2983860

**Abstract:**

The accuracy of U.S. stock return forecasts based on the cyclically-adjusted P/E (CAPE) ratio has deteriorated since 1985. The issue is not the CAPE ratio, but CAPE regressions that assume it reverts mechanically to its long-run average. Our approach conditions mean reversion in the CAPE ratio on real (not nominal) bond yields, reducing out-of-sample forecast errors by as much as 50%. At present, low real bond yields imply low real earnings yields and an above-average “fair-value” CAPE ratio. Nevertheless, with Shiller’s CAPE ratio now well above its fair value, our model predicts muted U.S. stock returns over the next decade. We believe that our framework should be adopted by the investment profession when forecasting stock returns for strategic asset allocation.

**Notable quotations from the academic research paper:**

"Valuation metrics such as price-earnings ratios are widely followed by the investment community because they are believed to predict future long-term stock returns. Arguably the most popular is Robert Shiller’s cyclically-adjusted P/E ratio (or CAPE) which is currently above its long-run average. However, the out-of-sample forecast accuracy of stock forecasts produced by CAPE ratios has become increasingly poor. In this paper we have shown why and offer a solution to offer a more robust approach to produce long-run stock return forecasts.

The problem is not with the CAPE ratio, but with CAPE regressions. We show that a common industry approach of forecasting long-run stock returns can produce large errors in forecasted returns due to both estimation bias and its strict assumption that the CAPE ratio will revert over time to its long-run (and constant) mean. Although far from perfect, our model’s out-of-sample forecasts for ten-year-ahead U.S. stock returns since 1960 are roughly 40-50% more accurate than conventional methods. Real-time forecast differences in 10-year-ahead stock returns are statistically significant, and have grown to exceed three percentage points after 1985 given the secular decline in real bond yields. In our model, lower real bond yields imply higher equilibrium CAPE ratios. This framework would appear to explain both elevated CAPE ratios and robust stock returns over the past two decades.

Figure 8 shows the actual real-time forecast of our two-step model for U.S. stocks. Our fair-value CAPE approach tracks the actual rolling 10-year-ahead U.S. stock returns fairly well, declining throughout the 2000s and anticipating a strong rebound immediately following the global financial crisis in 2009. Traditional CAPE regressions are also highly correlated with future returns, yet they consistently project lower 10-year-ahead stock returns than what has been actually realized by investors over our sample period.

As of June 2017, our model projects a guarded, lower-than-historical return on U.S. stocks of approximately 4.9% over the coming decade."

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## Is Equity Premium Predictable? Friday, 14 July, 2017

**It is very hard to do a successful un-biased out-of-sample prediction of equity premium:**

**Authors:** Bartsch, Dichtl, Drobetz, Neuhierl

**Title: **Data Snooping in Equity Premium Prediction

**Link:** https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2972011

**Abstract:**

We study the performance of a comprehensive set of equity premium forecasting strategies that have been shown to outperform the historical mean out-of-sample when tested in isolation. Using a multiple testing framework, we find that previous evidence on out-of-sample predictability is primarily due to data snooping. We are not able to identify any forecasting strategy that produces robust and statistically significant economic gains after controlling for data snooping biases and transaction costs. By focusing on the application of equity premium prediction, our findings support Harvey’s (2017) more general concern that many of the published results in financial economics will fail to hold up.

**Notable quotations from the academic research paper:**

"Does equity premium prediction pay off? While the in-sample predictability of the equity premium seems largely undisputed, most investors are ultimately interested in whether forecasting strategies can deliver reliable out-of-sample gains. Recognizing the controversial debate regarding the out-of-sample performance of established stock return prediction models, Spiegel (2008) poses a challenging question: “Can our empirical models accurately forecast the equity premium any better than the historical mean?”

One challenge in answering the question of out-of-sample predictability is that almost all forecasting strategies are tested on a single data set. When many models are evaluated individually, some are bound to show superior performance by chance alone, even though they are not. This bias in statistical inference is usually referred to as ‘data snooping’. Without properly adjusting for this bias in a multiple testing set-up, we might commit a type I error, i.e., falsely assessing a forecasting strategy as being superior when it is not. In fact, Harvey, Liu, and Zhu (2016) note that equity premium prediction offers an ideal setting to employ multiple testing methods.

To the best of our knowledge, our study is the first to jointly examine the out-of-sample performance of a comprehensive set of equity premium forecasting strategies relative to the historical mean, while accounting for the data snooping bias. We construct a comprehensive set of 100 forecasting strategies that are based on both univariate predictive regressions and advanced forecasting models, including strategies that adopt diffusion indices or combination forecast approaches, apply economic restrictions on the forecasts, predict disaggregated stock market returns, or model economic regime shifts.

We use these forecasting strategies to predict the monthly U.S. equity premium out-of-sample based on the most recent 180 months and track their out-of-sample perfor-mance for the subsequent month over the evaluation period from January 1966 to December 2015. We aim to answer Spiegel’s (2008) question, i.e., whether there are forecasting strategies that provide a significantly higher performance than the prevailing mean model. As performance measures, we use the mean squared forecast error and absolute as well as risk-adjusted excess returns.

Why is data snooping a concern in our analysis? Suppose these 100 models are mutually independent, and we apply a t-test to each model with the significance level of 5%. The probability of falsely rejecting at least one correct null hypothesis is 1 – (1 – 5%)100 ≈ 0.994. Therefore, it is very likely that an individual test may incorrectly suggest an inferior model to be a significant one. This simple example emphasizes the importance of an appropriate method that can control such data-snooping bias and avoids spurious inference when many models are examined together.

Our results show that many forecasting strategies outperform the historical mean when tested individually. However, once we control for data snooping, we find that no forecasting strategy can outperform the historical mean in terms of mean squared forecast errors. With respect to return-based performance measures, we find marginal evidence for statistically significant economic gains at least on a risk-adjusted excess return basis when using the equity premium forecasts in a traditional mean-variance asset allocation, even after controlling for data snooping bias. In contrast, the benefits for a pure market timing investor are limited. Taken together, our findings strengthen the results of Goyal and Welch (2008) that the out-of-sample predictability of the equity premium is questionable."

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## Invitation to webinar - Classification of Quantitative Trading Strategies Monday, 3 July, 2017

Dear readers,

We at Quantpedia are pleased to invite you to a our new webinar **Classification of Quantitative Trading Strategies** prepared in cooperation with our friends from QuantInsti. Webinar is scheduled on Tuesday 11th July, 9:30 AM EST | 7:00 PM IST | 9:30 PM SGT and will cover a range of topics related to applicability of financial academic research in a real trading.

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- Introduction to ‘Quantpedia & QuantInsti™’

- Overview of research in a field of quantitative trading

- Taxonomy of quantitative trading strategies

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- Examples of lesser-known trading strategies

- Common issues in quant research

- Questions and Answers

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## Are REITs a Distinct Asset Class? Thursday, 29 June, 2017

**Interesting academic paper about REITs:**

**Authors:** Kizer, Grover

**Title: **Are REITs a Distinct Asset Class?

**Link:** https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2965146

**Abstract:**

Real estate investment trusts (REITs) are often considered to be a distinct asset class. But, do REITs deserve this designation? While exact definitions for asset class may vary, a number of statistical methods can provide strong evidence either for or against the suitability of the designation. The authors step back from the established real estate and REITs literature and answer this broader question. Beginning with a set of asset class criteria, the authors then utilize a variety of statistical methods from the literature and factor-based asset pricing to evaluate REITs for their candidacy as a distinct asset class. REITs fail to satisfy almost all of the relevant criteria which leads the authors to conclude that REITs, in fact, are not a distinct asset class but do deserve a market capitalization weighted allocation in a diversified investment portfolio.

**Notable quotations from the academic research paper:**

"Many investors think of real estate investment trusts (REITs) as a distinct asset class because, in aggregate, they have historically had relatively low correlation with both stock and bond markets. However, this is a far too simplistic definition for what defines a distinct asset class. Many individual stocks have low correlation with the overall stock and bond markets, yet no one would (hopefully) consider a single stock, or a small handful of stocks, to be an asset class. For individual equities, a better definition would be a well diversied portfolio of securities which has historically demonstrated statistically significant excess return relative to what is explained by a generally accepted factor model like the Carhart [1997] four-factor model. For example, early research on the size and value premiums argued that these two types of equity securities are distinct equity asset classes because their excess returns are not fully accounted for by CAPM.

On a relative basis, public REIT equities are a young investment vehicle. The REIT Act title law of 1960 allowed the creation of REITs and accordingly, the ability for investors to gain access to diversified real estate portfolios. The first REIT was formed shortly thereafter and the first public REIT debuted in 1965. Early research into public real estate investment, such as Webb and Rubens [1987], tends to use appraisal-based individual property data and suggests that real estate provides diversication benefits for traditional stock and bond portfolios. Following the growth of the industry and accumulation of sufficient returns histories, REIT indexes debuted. Subsequent studies often used REIT indexes, tending to confirm earlier findings concerning diversification benefits and suggesting sizable portfolio allocations.

We establish a pragmatic list of criteria for consideration as an asset class and then use an array of techniques to evaluate REITs as such. While REITs do indeed exhibit relatively low correlation with traditional equity and fixed income, a deeper dive into their returns reveal shortfalls in their qualifications for asset class distinction. Four- and six-factor regression analyses reveal no statistically reliable alpha generation in REIT returns and coefficient estimates point to REITs being well explained by traditional risk factors. Taking direction from the regression results and attempting a long-only replication of REIT returns with small-value and equities and long-term corporate bonds produces a portfolio that comoves well with REIT returns and exhibits historically superior return and risk characteristics. Utilizing tests of mean-variance spanning, we also examine the diversification properties of REITs on a statistically inferred basis. These tests suggest that REITs do not reliably improve the mean-variance frontier when added to a benchmark portfolio of traditional stocks and bonds. These results, and the associated failure to satisfy our asset class criteria, lead us to conclude that REITs are not a distinct asset class.

In conclusion, we want to make clear that we are not suggesting that REITs deserve no allocation in an investment portfolio. Nor are we suggesting that any results previously brought forth in the literature are spurious or incorrect. The results of this study lead us only to suggest that REITs, as an equity security with only marginal diversication benefits, should not receive a weighting in investor portfolios that significantly deviates from market capitalization based weights. The Dow Jones U.S. Select REIT Index represents a non-trivial approximately 2.5 percent of the Russell 3000 Index, as of early 2017, on a market capitalization basis, which we would argue is a valid starting point for a REITs allocation in a diversified portfolio."

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