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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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.

Session Outline

    – Introduction to ‘Quantpedia & QuantInsti™’
    – Overview of research in a field of quantitative trading
    – Taxonomy of quantitative trading strategies
    – Where to look for unique alpha
    – Examples of lesser-known trading strategies
    – Common issues in quant research
    – Questions and Answers

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We are really happy to see that guys from QuantInsti did a new independent analysis of a strategy we have in our database. An article is written by Milind Paradkar and is focused on 52-Weeks High Effect in Stocks (Strategy #18) using Indian stocks as an investment universe:

https://www.quantinsti.com/blog/trading-strategy-52-weeks-high-effect-in-stocks/

QuantInsti™ is one of the pioneer algorithmic trading research and training institutes across the globe. With its educational initiatives, QuantInsti™ is preparing financial market professionals for the contemporary field of algorithmic and quantitative trading. They offer a really well-prepared professional training course EPAT™ (Executive Programme in Algorithmic Trading) which is Asia's first algorithmic trading education program. This comprehensive course exposes its participants to various strategy paradigms and enables them to build an algorithmic trading system. QuantInsti™ also offers Quantra™ which is an e-learning portal that specializes in short self-paced courses on algorithmic and quantitative Trading. Quantra™ offers an interactive environment which supports 'learning by doing' through guided coding exercises, videos and presentations.

The original academic paper (“Industry Information and the 52-Week High Effect”) has been authored by Xin Hong, Bradford D. Jordan, and Mark H. Liu. They propose a modified rotational momentum strategy which uses a 52-Week High as a predictor of cross-sectional equity performance to select top performing industries.

Milind Paradkar from QuantInsti performed an independent analysis of a resultant strategy during last 3 years (an out of sample period from 2014 until 2017) on Indian stocks. Overall, the performance isn't very stellar and we can say that Indian market hasn't been very generous for this strategy (total performance has been only 17% flat over those 3 years with a Sharpe ratio around 0.4). But we are really glad for this analysis as it offers a valuable look on a strategy on different universe as most trading strategies are usually academically researched only on US equities.

The final OOS equity curve:

Thanks for nice analysis Milind…

An interesting academic paper about the intristic value of Gold:

Authors: Harris, Shen

Title: The Intrinsic Value of Gold: An Exchange Rate-Free Price Index

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2926454

Abstract:

In this paper, we propose a gold price index that enables market participants to separate the change in the ‘intrinsic’ value of gold from changes in global exchange rates. The index is a geometrically weighted average of the price of gold denominated in different currencies, with weights that are proportional to the market power of each country in the global gold market, where market power is defined as the impact that a change in a country’s exchange rate has on the price of gold expressed in other currencies. We use principal components analysis to reduce the set of global exchange rates to four currency ‘blocs’ representing the U.S. dollar, the euro, the commodity currencies and the Asian currencies. We estimate the weight of each currency bloc in the index in an error correction framework using a broad set of variables to control for the unobserved intrinsic value. We show that the resulting index is less volatile than the USD price of gold and, in contrast with the USD price of gold, has a strong negative relationship with global equities and a strong positive relationship with the VIX index, both of which underline the role of gold as a safe haven asset.

Notable quotations from the academic research paper:

"The market for gold is one of the largest and most liquid in the world, surpassed only by the major currency pairs in terms of daily turnover. The price of gold, like that of many commodities, is conventionally quoted in USD. However, gold is not exclusively a US asset and so the return from an investment in gold, when calculated using the quoted USD price, conflates the change in the value of gold with the change in the value of the USD. In this paper, we develop a gold price index, which when used to compute returns, reflects changes in the intrinsic value of gold independently of concurrent changes in global exchange rates.

Various index-based approaches go some way towards removing the exchange rate component of the gold price, they do not properly reflect the intrinsic value of gold because they use arbitrarily defined weights that do not represent the actual impact that changes in individual exchange rates have on the quoted gold price.

The gold price index that we develop can be thought of as a geometric weighted average of the normalized real (i.e. inflation-adjusted) price of gold in different currencies. The weight of each currency is proportional to that country’s market power in the global gold market, as reflected in the impact that a change in the country’s real exchange rate has on the real price of gold quoted in other currencies. We cast the relationship between the price of gold, exchange rates and a broad set of fundamental variables in a cointegration framework, in which we simultaneously model both the long run relationship between the price of gold and its determinants, and its short run dynamics. We use weekly data from 3 January 1995 to 26 October 2015 for 23 exchange rates against the GBP. In view of the very high correlations between many individual exchange rates, we use principal components analysis to extract the significant underlying exchange rate factors.

Over the full sample, we find that there are four ‘significant’ principal components in real exchange rates, which are shown to represent the USD currency bloc, the commodity currency block, the EUR currency block, and the Asian currency bloc, respectively. The cointegration framework allows us to distinguish between the long run and short run elasticities of the price of gold with respect to exchange rates. In particular, the long run elasticity with respect to an exchange rate (or an exchange rate bloc) partially reflects structural changes and, in particular, the impact that a change in the exchange rate has on fundamentals through, for example, changes in production capacity or demand. In contrast, the short run elasticity reflects the change in the gold price arising directly from changes in the exchange rate, i.e. changes in the price of gold that simply reflect ‘translation’ effects. It is these short run elasticities that are relevant for the construction of the gold price index.

We therefore first estimate the long run relationship between the real price of gold, real exchange rates and proxies for the non-exchange rate related fundamentals, including global equity and bond prices, the oil price and the level of the VIX index of implied volatility, and show that these variables are strongly cointegrated. The gold price has a negative long run relation with global equity prices and a positive long run relation with global bond prices, the price of oil and the VIX index. Gold has a positive long run relationship with the USD bloc, the EUR bloc and the commodity currency bloc, but a somewhat weaker relationship with the Asian currency bloc.

We then estimate the short run dynamics of the change in the gold price as a function of (1) changes in exchange rates, (2) changes in fundamentals and (3) the lagged error correction term that captures the deviation from long run equilibrium. The short run elasticities from this regression are then used as weights in the gold price index. Using the full sample to estimate the model, we show that the (normalized) weights on the USD bloc, the commodity bloc and the EUR bloc are about 21%, 48% and 31%, respectively, reflecting the relative importance of these currency blocks for production, consumption and investment in the global gold market. The weight on the Asian currency bloc is not significantly different from zero. We convert the real gold price index into a nominal USD gold price index to enable a comparison with the USD price of gold. We show that the nominal gold price index is less volatile than the USD gold price and, in contrast with the USD gold price, has a strong negative relationship with global equities and a strong positive relationship with the VIX index, both of which underline the role of gold as a safe haven asset."

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