A new interesting research paper related to:

#12 – Pairs Trading with Stocks

Authors: Figuerolla-Ferretti, Serrano, Tang, Vaello-Sebastia

Title: Supercointegrated

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3005358

Abstract:

This paper uses S&P100 data to examine the performance of pairs trading portfolios that are sorted by the significance level of cointegration between their constituents. We find that portfolios that are formed with highly cointegrated pairs, named as "supercointegrated", yield the best performance reflecting a positive relationship between the level of cointegration and pairs trading profitability. The supercointegrated portfolio also shows superior out-of-sample performance to the simple buy-and-hold investments on the market portfolio in terms of Sharpe ratio. We link the time-varying risk of the pairs trading strategy to aggregated market volatility. Moreover we report a positive risk-return relationship between the strategy and market volatility, which is enhanced during the bear market. Our results remain valid when applying the strategy to European index data.

Notable quotations from the academic research paper:

"This article uses an out-of-sample analysis of S&P100 equity data to analyze performance of pairs trading portfolios when sorted by the level of cointegration of their constituents. We focus on the supercointegrated portfolio, that is formed by pairs in the top quintile of tradable targets, all of which do not reject standard cointegration tests at the 1% significance level. Indeed, the primary contribution of this article is to show that pairs trading profitability is monotonically associated with the significance level of cointegration. For the supercointegrated portfolio, the average excess return of 6.2% is earned per annum which is 2.3% higher than the portfolio with pairs in the second quintile. A similar finding is documented in terms of Sharpe ratio, which increases from 0.2 to 0.6 (yielded by the bottom and the top quintile portfolios, respectively).

A regression of the returns of the supercointegrated portfolio against the 3-factor Fama and French (1992) model shows that the market is a statistically significant explanatory variable of the pairs trading profitability. Momentum and book-to-market ratio are not significant in this exercise. We analyze the sources of risk underlying the supercointegrated portfolio and show that portfolio risk can be linked to aggregate market volatility. We then account for volatility persistence by providing maximum-likelihood estimates of an AR(1) model in the realized volatility series. We find that pairs trading profitability exhibits volatility persistence which is linked to that reported for the market. This allows us to show that lagged market realized volatility can be used to explain current pairs trading profitability. Moreover the strength of the relationship between pairs trading profitability and realized market volatility is positively associated with the level of cointegration underlying pairs trading portfolios. An extended regression analysis demonstrates that the link between realized market volatility and pairs trading profitability is enhanced under bear market conditions. This stronger connection is exclusive to the supercointegrated portfolio.

The empirical evidence provided in this article does therefore suggest that the significance level of cointegration can be used for portfolio ranking purposes. Moreover we show that this evidence is maintained under its application to European equity data. In parallel to our findings, this article conducts several exercises to assess the robustness of its main results. The additional checks conclude, once again, the strength of the supercointegrated portfolio.

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Out of curiosity, what is benchmark return for each active trader/investor …

Authors: Doeswijk, Lam, Swinkels

Title: Historical Returns of the Market Portfolio

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2978509

Abstract:

Using a newly constructed unique dataset, this study is the first to document returns of the market portfolio for a long period and with a high level of detail. Our market portfolio basically contains all assets in which financial investors have invested. We analyze nominal, real, and excess return and risk characteristics of this global multi-asset market portfolio and the asset categories over the period 1960 to 2015. The global market portfolio realizes a compounded real return of 4.38% with a standard deviation of 11.6% from 1960 until 2015. In the inflationary period from 1960 to 1979, the compounded real return of the GMP is 2.27%, while this is 5.57% in the disinflationary period from 1980 to 2015. The reward for the average investor is a compounded return of 3.24%-points above the saver’s. We also compare the performance of an investor who holds the market portfolio with an investor who uses simple heuristics for the portfolio allocation. Our results suggest that the market portfolio is close to the mean-variance frontier, but our heuristic allocations achieve a significantly higher reward for risk.

Notable quotations from the academic research paper:

"This study aims to fill both a gap in the academic literature as well as a gap in return data availability for finance practitioners by providing returns of the global market portfolio (GMP) over the period 1960 to 2015 from the perspective of an USD investor. A study on returns of the GMP has not been done before for such a long period and with such a level of detail. We document in detail how we collected historical returns data on global asset classes, which is challenging for the period before 1985. Moreover, we make the resulting data publicly available so other researchers can use them in their own applications. Our GMP basically contains all assets in which financial investors have invested.

This paper contains unique features compared to the scarce academic literature on international asset returns. First, our sample period significantly extends the 1960-1980 period of Ibbotson and Siegel (1983), who are the first with a rigorous study on a global multi-asset market portfolio. They find a nominal compounded return of 8.36% for their so-called world market wealth portfolio over the period 1960-1980. Compared to that study, we focus on assets in which financial investors have actually invested. For example, we do not take farmland into account, as it usually belongs to owners that do not hold it as a financial investment and thereby it is not publicly available.

Second, in comparison with Dimson, Marsh, and Staunton (2002), a groundbreaking study that documents annual returns for equities, government bonds, and treasury bills in sixteen countries for the 101-year period 1900-2000, we include returns for more assets like for example corporate bonds and real estate. Also, we use an all-maturity market capitalization weighted government bonds index instead of a GDP-weighted long term government bonds index. The latter is less useful for representing the performance of the asset class global government bonds. Obviously, the length of their sample period remains unmatched.

New data enables an extensive analysis of return and risk characteristics of the GMP and the asset categories over the period 1960 to 2015. We include conditional analyses on recessionary and inflationary periods. We also compare the performance of an investor who holds the GMP with an investor who uses simple heuristics for the portfolio allocation. By comparing the performance of the GMP with alternative portfolio allocation schemes, we can find out whether the GMP has been a good, if not optimal, portfolio during our 56-year sample period. This analysis will also touch upon mean-reversion across asset classes. Moreover, this new data set provides an opportunity to gauge the difference in return and risk for the average investor and saver."

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Related to multiple strategies, mainly to Carry, Volatility Selling and Trend-Following strategies …

Authors: Sepp

Title: Diversifying Cyclicality Risk of Quantitative Investment Strategies (Presentation Slides)

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2980708

Abstract:

What is the most significant contributing factor to the performance of a quantitative fund: its signal generators or its risk allocators? Can we still succeed if we have good signal generators but poor risk management?

We consider the risk of the skewness and the cyclicality of the key quantitative strategies:
1. Carry strategies
2. Volatility strategies
3. Trend-following strategies

We then present the two approaches for diversification of the cyclicality risk for a master portfolio of these strategies using:
1. Top-down allocation
2. Bottom-up allocation

We illustrate a few examples using back-tested data using systematic quantitative strategies with risk-based allocators.

Notable quotations from the academic research paper:

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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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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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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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