A nice peak into the hedge funds industry kitchen. At the end, it is an additional evidence that a lot of hedge funds are trend-followers. And the main reason is that they are more successful because of it :

Authors: Liang, Zhang

Title: Do Hedge Funds Ride Market Irrationality?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3018483

Abstract:

We document significant evidence that hedge funds temporarily ride rather than attack high market irrationality but neither ride irrationality in the long run nor ride low irrationality. Hedge funds actively ride market irrationality during the formation period of the tech bubble in 2000 but not during the formation period of the housing bubble in 2007. Irrationality-riding funds outperform irrationality-attacking funds by 4.4% per year on a risk-adjusted basis. This outperformance is attributed to irrationality-riding during high irrationality periods-the formation period of the tech-bubble, and the bursting period of the housing bubble. The adoption of irrationality riding strategy is related to manager skill as well as investment styles. Our results are consistent with the behavioral theories that sophisticated investors ride rather than attack unsophisticated investors’ strong misperception. Finally, we do not find that mutual fund managers have the irrationality riding ability.

Notable quotations from the academic research paper:

"The conventional efficient market hypothesis (e.g., Freidman, 1953; Fama, 1965; Fama and French, 1996; Ross, 2001) suggests that rational investors attack market irrationality by conducting arbitrage trades to correct mispricing quickly and profit from their attacking strategy.

In contrast, behavioral studies (e.g., Delong, Shleifer, Summers and Waldman, 1990b; Abreu and Brunnermeier, 2002, 2003; Dumas, Kurshev and Uppal, 2009; Mendel and Shleifer, 2012) claim that rational investors choose to temporarily ride rather than attack noise traders’ high irrationality because the corresponding arbitrage may not be implementable. More interestingly, the behavior theory predicts that riding funds outperforms attacking funds, which is opposite to the conventional efficient market hypothesis theory.

This goal of this study is to distinguish the above two opposing views by empirically testing whether hedge funds, as rational investors, ride noise traders’ high irrationality in short run. Using a large sample of 5,617 equity-oriented hedge funds from the Lipper TASS database over the period from January 1994 to December 2013, we examine whether hedge fund managers ride or attack noise traders’ irrationality, by comparing the percentage of irrationality-riding funds with the portion of irrationality-attacking funds.

Following convention in the noise trading literature, we choose the noise trader sentiment index approximated by the Index of Consumer Sentiment from the University of Michigan as our base proxy for market-wide irrationality.2 We measure irrationality-riding via the timing coefficient in the conventional market timing models. Both the efficient market hypothesis and behavioral theory imply that hedge funds riding market irrationality should have significantly positive coefficients on the interaction term of the market index and the sentiment index, while funds that attack irrationality should have negative coefficients to offset the effect of irrationality on stock prices.

Out of the entire sample, about 20% of hedge fund managers have t-statistics of the riding coefficients equal to or greater than 1.65. The portion of hedge funds with a t-statistic equal to or lower than -1.65 is only 4.6%. These facts suggest that hedge fund managers do not attack, but ride noise traders’ irrationality.

This distribution pattern of the t-statistic significantly varies across investment styles. For example, 62.5% of multi-strategy funds and 35% of global macro funds adopt irrationality-riding strategy but the fraction of irrationality-riding funds among equity market neutral, convertible arbitrage or event driven funds is trivial. Moreover, the fraction of hedge funds with a t-statistic of riding coefficient equal to or greater than 1.65 is 31.4% during the high irrationality periods and is reduced to 17.0% during the lower irrationality periods. This fraction is 32.4% during normal time and 14.4% during the period of two financial crises, including the tech bubble crisis from March 2000 to December 2002 and the subprime crisis from June 2007 to December 2009. Hedge funds actively ride market irrationality during the tech bubble formation period from January 2000 to February 2000, but not during the housing bubble formation period from January 2005 to May 2007. Hedge fund managers do not show meaningful propensity to ride market irrationality in the long run either. The proportion of funds that choose to ride the 12-month leading market irrationality is smaller than the proportion that chooses to attack.

Further, we investigate whether hedge funds’ irrationality-riding choice is attributed to randomness or skill. In sum, our empirical results are consistent with the behavioral theory but not with the efficient market theory. We conclude that hedge fund managers choose to ride high market irrationality in the short run but to attack it in the long run.

Given the fact that market irrationality-riding is generally adopted by hedge funds, we examine whether this strategy is economically significant by comparing the performance of irrationality-riding funds with irrationality-attacking funds in subsequent periods.

The performance difference between the riding and attacking funds in the subsequent periods is consistent with the behavioral predictions but against the predictions of the efficient market hypothesis. The Fung and Hsieh (2004) seven-factor alpha delivered by the riding portfolio is at least 0.31 % per month, or equivalently 3.7% per year, significantly higher than that of the attacking portfolio over the subsequent one to twelve months. The risk-adjusted outperformance of the riding funds relative to the attacking funds in next one month is 0.49% (t-stat=12.02) during the high irrationality periods and -0.03% (t-stat=-0.90) during the low irrationality periods."

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A new financial research paper has been published and is related to:

#31 – Market Seasonality Effect in World Equity Indexes
#41 – Turn of the Month in Equity Indexes
#75 – Federal Open Market Committee Meeting Effect in Stocks

Authors: Hull, Bakosova, Kment

Title: Seasonal Effects and Other Anomalies

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3165669

Abstract:

We revisit a series of popular anomalies: seasonal, announcement and momentum. We comment on statistical significance and persistence of these effects and propose useful investment strategies to incorporate this information. We investigate the creation of a seasonal anomaly and trend model composed of the Sell in May (SIM), Turn of the Month (TOM), Federal Open Market Committee pre-announcement drift (FOMC) and State Dependent Momentum (SDM). Using the total return S&P 500 dataset starting in 1975, we estimate the parameters of each model on a yearly basis based on an expanding window, and then proceed to form, in a walk forward manner, an optimized combination of the four models using a return to risk optimization procedure. We find that an optimized strategy of the aforementioned four market anomalies produced 9.56% annualized returns with 6.28% volatility and a Sharpe ratio of 0.77. This strategy exceeds that Sharpe ratio of Buy-and-Hold in the same period by almost 100%. Furthermore, the strategy also adds value to the previously published market-timing models of Hull and Qiao (2017) and Hull, Qiao, and Bakosova (2017). A simple strategy which combines all three models more than doubles the Sharpe ratio of Buy-and-Hold between 2003-2017. The combined strategy produces a Sharpe ratio of 1.26, with annualized returns of 18.03% and 13.26% volatility. We publish conclusions from our seasonal trend and anomaly model in our Daily Report.

Notable quotations from the academic research paper:

"In this paper we combine seasonal anomalies, Fed announcement and trend in a walk forward way. Numerous papers present compelling evidence on seasonal effects of the market, with Turn of the Month and the Halloween effect being the most convincing. At the same time there appears to be an excess return prior to Fed meetings. We combine these effects with the new trend indicator to create an effectiv emodel that beats Buy-and-Hold. This Seasonal Anomaly and Trend Model is then combined in an ensemble with other market timing models into an even more powerful strategy.

We start with four robust seasonal anomalies, and propose a simple deterministic trading strategy for each. Then we introduce a few different options of combining these four strategies. First, we look at the mean-variance optimization algorithm (Markowitz 1952), and second, we perform a grid search on model weights to look for optimal combination of signals rather than using portfolio optimization. Last, we also consider a simple equal weight portfolio for comparison. The results are as follows. From 1976 to 2017, the equally weighted model produces the highest Sharpe ratio (0.89 compared to 0.77 of the grid search algorithm and 0.80 for the mean-variance algorithm). However, the grid search model produces the highest Sharpe ratio of 0.84 in more recent period (1996-2017), compared to 0.82 and 0.78 for equal weight and mean-variance algorithm respectively. The univariate strategies are restricted to be between 0% and 150% invested in S&P 500, with the exception of the trend model which is capped between -50% and 150%. We maximize the backtested Sharpe ratio in our analysis, since this metric is rather invariant to scaling. Investors wishing to deploy these models in their portfolios can subsequently choose their level of leverage based on their risk preferences.

The seasonal trend model also enhances the market-timing models of Hull and Qiao (2017) and Hull, Qiao, and Bakosova (2017). A simple strategy which combines all three models almost triples the Sharpe ratio of Buy-and-Hold between 2003-2017. The combined strategy produces a Sharpe ratio of 1.26, with annualized returns of 18.03% and 13.26% volatility.

Figure 13 shows the wealth accumulation of the combined strategy relative to Buy-and-Hold. The outperformance is not economically significant in the bull market of the early 2000s but becomes more pronounced in the volatile period during the Global Financial Crisis (GFC) and in the jittery markets of 2011-2012.

"

Quantpedia & Genovest cooperation

We started a very interesting cooperation with a guys from Genovest. They started to analyze some of Quantpedia's suggested strategies. The first article analyzes a well-known Graham's Net Current Asset Value strategy and shows that the strategy has not lost its outperformance during the last few years:

https://genovest.com/blog/putting-quantpedia-to-the-test/

The strategy's rules are really simple. Investor only buy stocks with NCAV (net current asset value, as defined by Graham, is current assets minus all liabilities, divided by the number of shares outstanding) over 1.5, exclude lightly-regulated companies, and exclude companies in the financial sector. The portfolio of stocks is formed annually in July, and held for one year with equal weighting.

We are looking forward to any new strategy's backtest …

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Is Bitcoin a new gold – aka. a hedge or safe heaven asset during equity downturns? Short answer – No. Again, recommended read about cryptocurrencies … :

Authors: Klein, Hien, Walther

Title: Bitcoin Is Not the New Gold: A Comparison of Volatility, Correlation, and Portfolio Performance

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3146845

Abstract:

Cryptocurrencies such as Bitcoin are establishing themselves as an investment asset and are often named the New Gold. This study, however, shows that the two assets could barely be more different. Firstly, we analyze and compare conditional variance properties of Bitcoin and Gold as well as other assets and nd differences in their structure. Secondly, we implement a BEKK-GARCH model to estimate time-varying conditional correlations. Gold plays an important role in financial markets with flight-to-quality in times of market distress. Our results show that Bitcoin behaves as the exact opposite and it positively correlates with downward markets. Lastly, we analyze the properties of Bitcoin as portfolio component and nd no evidence for hedging capabilities. We conclude that Bitcoin and Gold feature fundamentally different properties as assets and linkages to equity markets. Our results hold for the broad cryptocurrency index CRIX. As of now, Bitcoin does not reflect any distinctive properties of Gold other than asymmetric response in variance.

Notable quotations from the academic research paper:

"Cryptocurrencies, in particular Bitcoin, have been labeled the New Gold by some media,banks, and also data providers throughout the last years. While this view might be motivated by fast and high returns in a gold rush like environment, we compare Gold and Bitcoin from an econometric perspective and focus on the economic aspects of cryptocurrencies as an investment asset. We address the question how cryptocurrencies can be classified based on volatility behavior and how they are correlated with already established asset classes.

The analysis in this paper is subdivided into three parts. Firstly, we start by investigating the volatility behavior of cryptocurrencies in comparison to stock indices and commodities.

Secondly, this research explores the hedge and safe haven capabilities of cryptocurrencies in comparison to Gold by means of a dynamic correlation analysis. We apply the definition of hedge, diversifier, and safe haven given in Baur & Lucey (2010). An asset which is uncorrelated or negatively correlated with another asset is defined as a hedge whereas a safe haven asset is uncorrelated or negatively correlated with other assets in distressed markets only. Assets which are a diversifier are positively (on average), but not perfectly correlated to other assets.

What makes the Bitcoin – S&P 500 correlation fundamentally different from the correlationsof Gold and the index is the behavior during market distress. Interestingly, correlations are steeply increasing from negative to a positive relationship while the index is in a downward movement. This indicates that Bitcoin follows the downturn, which is observable in the raw as well as smoothed correlations in Fig. 4. The same behavior holds for the Bitcoin – MSCI World correlations. While Gold prices increase in the flight-to-quality, Bitcoin prices are decreasing with the markets.

To further highlight the differences, Fig. 5 visualizes the smoothed correlations of Gold and Bitcoin with the S&P 500. Interestingly, the movements in correlations appear to be mirrored from 2015 on, while being negative on average. This falls into the time where Bitcoin is becoming more popular and price increases begin to accelerate. From the joint plot, it becomes clear that Bitcoin, viewed as an asset, behaves differently than Gold. Comparing the correlations of Gold and Bitcoin with the MSCI World, plotted in the Appendix, the mirrored movements are more emphasized and span over different signs.

Concluding our correlation analysis, we find Gold to be a hedge rather than a safe haven in recent years. Bitcoin, on the other hand, behaves completely different, especially from 2015 on. The cryptocurrency couples with markets during bearish environments, with correlations rapidly turning to positive values in these times. This holds true for both the S&P 500 and the MSCI World index. We also observe inverse movements of correlations of Gold and Bitcoin with these two indices. While correlations increase for Gold, Bitcoin correlations decrease to the same market and vice versa. This is a clear indication that Bitcoin and Gold have different connectedness to markets.

"

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A new financial research paper related to:

#38 – Accrual Anomaly

Authors: Detzel, Schaberl, Strauss

Title: There are Two Very Different Accruals Anomalies

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3069688

Abstract:

We document that several well known asset-pricing implications of accruals differ for investment and non-investment-related components. Exposure to an investment-accruals factor explains the cross-section of returns better than the accruals themselves, and this factor’s returns are negatively predicted by sentiment. The opposite results hold for non-investment accruals. Further tests show cash profitability only subsumes long-term non-investment accruals in the cross-section of returns and economy-wide investment accruals negatively predict stock-market returns while other accruals do not. These results challenge existing accruals-anomaly theories and help resolve mixed evidence by showing that the anomaly is two separate phenomena: a risk-based investment accruals premium and a mispricing of non-investment accruals.

Notable quotations from the academic research paper:

"To measure current-period performance with earnings, accountants add accruals to free cash flow that adjust for long-term investment expenditures and differences in timing between the earning and receipt of cash flows. The evidence in this paper shows that the asset-pricing implications of investment and non-investment components are fundamentally different. These findings challenge existing theories of the accruals anomaly and demonstrate that there are not one, but two, accruals anomalies to explain: a risk-based premium for accruals that capture real investment, and a short-lived mispricing of accruals that capture transitory adjustments to profitability.

Characteristics-vs-covariances tests show that an investment-accruals factor better explains the cross-section of returns than the investment accruals themselves. This result is evidence against earnings fixation and profitability-related mispricing explanations of the investment-accruals premium, which do not predict a factor structure of returns. In contrast, the opposite pattern holds for non-investment accruals, consistent with mispricing in the form of a violation of the law of one price. These results are corroborated by evidence that investment accruals predict the cross-section of returns for more than two years (consistent with persistent risk) while non-investment accruals only predict returns for one to eight months (consistent with short-lived mispricing).

While the investment-accruals premium is explained by a risk factor and is therefore not an arbitrage opportunity, the underlying factor is at least partially driven by sentiment as opposed to entirely rational demand. The negative investment-accruals premium is most significant in times of high sentiment, which is consistent with firms responding to sentiment-induced overvaluation with high levels of real investment. In contrast, the negative non-investment-accruals premium is significant only when sentiment is in its bottom quartile. This finding challenges existing mispricing explanations of accruals that do not predict that overvaluation of high-accruals firms should be
concentrated in low-sentiment periods. Moreover, the profitability of non-investment accruals in low-sentiment times challenges the theory that anomaly returns should increase with sentiment because of the relative difficulty in arbitraging over-valuation.

Following Lewellen and Resutek (2016), we decompose total accruals into three components: working-capital accruals (
WC), long-term investment accruals (IA), and long-term non-investment or "nontransaction" accruals (NTA). The IA component includes items such as new PP&E that represent expenditures in real investment. The
WC and NTA include items such as accounts payable and receivable as well as depreciation that do not represent new long-term investment expenditures, but only transitory accounting adjustments to cash flows. Hence we refer to
WC and NTA collectively as "non-investment accruals".

The Fama-Macbeth framework can provide additional evidence of risk versus mispricing. Ball et al. (2016) argue that risk should be more persistent than mispricing and investigate whether longer lags of OA and COP continue to predict returns in Fama-Macbeth regressions. Based on the same motivation, Figure 2 presents Fama-Macbeth regression slopes and their corresponding 95% confidence intervals from regressions of monthly stock returns on control variables and lagged values of the three accruals measures (
WC, IA, and NTA). Figure 2 demonstrates NTA and
WC have the least persistent predictive power for returns. In contrast, IA is a more persistent predictor of
returns and remains significant for up to 28 additional months. Overall, the evidence from Figure 2 is consistent with the IA premium arising from risk, whereas
WC and NTA premia appear to be consistent with mispricing that is arbitraged away after several months.

"

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An interesting paper, an analysis of a day of the week effect in the crypto currency market … :

Authors: Caporale, Plastun

Title: The Day of the Week Effect in the Crypto Currency Market

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3082117

Abstract:

This paper examines the day of the week effect in the crypto currency market using a variety of statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal-Wallis test, and regression analysis with dummy variables) as well as a trading simulation approach. Most crypto currencies (LiteCoin, Ripple, Dash) are found not to exhibit this anomaly. The only exception is BitCoin, for which returns on Mondays are significantly higher than those on the other days of the week. In this case the trading simulation analysis shows that there exist exploitable profit opportunities that can be interpreted as evidence against efficiency of the crypto currency market.

Notable quotations from the academic research paper:

"There exists a vast literature analysing calendar anomalies (the Day of the Week Effect , theTurn of the Month Effect, the Month of the Year Effect, the January Effect, the HolidayEffect, the Halloween Effect etc.), and whether or not these can be seen as evidence againstthe Efficient Market Hypothesis. However, with one exception (Kurihara and Fukushima, 2017) to date no study has analysed such issues in the context of the crypto currency market – this being a newly developed market, it might still be relatively inefficient and it might offer more opportunities for making abnormal profits by adopting trading strategies exploiting calendar anomalies. We focus in particular on the day of the week effect, and for robustness purposes apply a variety of statistical methods (average analysis, Student's ttest, ANOVA, the Kruskal-Wallis test, and regression analysis with dummy variables) as well as a trading robot approach that replicates the actions of traders to examine whether or not such an anomaly gives rise to exploitable profit opportunities.

We examine daily data for 4 crypto currencies, choosing those with the highest market capitalisation and the longest data span (2013-2017), namely BitCoin, LiteCoin, Ripple and Dash. The data source is CoinMarketCap (https://coinmarketcap.com/coins/).

The complete set of results can be found in Appendix B. The results of the parametric and non-parametric tests are reported in Appendices C, D, E and F) and summarised in Table 3 and 4. There is clear evidence of an anomaly only in the case of BitCoin.

Since the anomaly occurs on Mondays (when returns are much higher than on the other days of the week) the trading strategy will be the following: open long positions on Monday and close them at the end of this day. The trading simulation results are reported in Table 5. In general this strategy is profitable, both for the full sample and for individual years, but in most cases the results are not statistically different from the random trading case, and therefore they do not represent evidence of market inefficiency.

"

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There are a lot of media articles showing how "expensive" the current stock market (or some equity factor) is. However, these articles can be based on a weak statistical analysis:

Authors: Boudoukh, Israel, Richardson

Title: Long Horizon Predictability: A Cautionary Tale

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3142575

Abstract:

Long-horizon return regressions have effectively small sample sizes. Using overlapping long-horizon returns provides only marginal benefit. Adjustments for overlapping observations have greatly overstated t-statistics. The evidence from regressions at multiple horizons is often misinterpreted. As a result, there is much less statistical evidence of long-horizon return predictability than implied by existing research, casting doubt over claims about forecasts based on stock market valuations and factor timing.

Notable quotations from the academic research paper:

"Pronouncements in the media about how “cheap” or “rich” the stock market or aggregate factor portfolios have become are quite common. These views also creep into the practitioner/academic finance literature.

Empirical support for these types of statements originates from seemingly “impressive” evidence of long-horizon predictability of stock returns based on valuation measures. Further, practitioners often document strong levels of statistical significance using overlapping long-horizon returns based on standard errors that they believe correct for overlapping data.

The issue is there are few independent long-horizon periods in the short samples used to study markets. Using overlapping returns in the hope of increasing the sample size offers little help. Intuitively, no matter how the data is broken down, you can’t get around the issue of short sample sizes. Therefore, findings of long-horizon predictability are illusory and reported statistical significance levels are way off. A quarter-century of statistical theory and analysis of long-horizon return regressions strongly makes this case. The bottom line is that practitioners need to be aware of these issues when performing long-horizon return forecasts and need to appropriately adjust long-horizon statistical metrics.

We show theoretically and demonstrate via simulations that there is only a marginal benefit to overlapping data for the types of return forecasting problems faced in finance. For example, in forecasting 5-year stock returns using 50 years of data, the effective number of observations, from nonoverlapping (10 periods) to monthly overlapping (600 overlapping periods), increases from 10 to just 12 observations. Statistical significance emerges only because reported standard errors (and t-statistics) are both noisy and severely biased. For example, at the 5-year stock return horizon with 50 years of data, the range of possible standard error estimates is so wide to make inference nonsensical, with the expected t-statistics effectively double their “true” value. Applying the appropriate statistics to data on long horizon stock returns and valuation ratios drastically reduces the statistical significance of these tests.

Background for Why Long-Horizon Return Regressions Are Unreliable:

To gain intuition and for illustrative purposes, the left-hand side of Figure 1 shows the scatter plot of the inverse of cyclically adjusted price earnings ratio (1/ ) and subsequent 5-year stock returns post 1968 and 10-year stock returns post 1883. Note that the number of nonoverlapping observations is 8 and 12, respectively. The point estimates of the correlations are quite large and positive, 0.26 and 0.38. However, there is very little data to back up these estimates. For example, suppose one were to take away the most outlier point in the plot; the correlations respectively become 0.04 and 0.28. Of course, this finding should not be a surprise. Under the null of no predictability, and putting aside any bias adjustment, the standard error of the correlation coefficient is 1/SQRT(T), which is 0.35 and 0.29 for 8 and 12 observations, respectively. In other words, it is quite possible the true correlation is zero or negative, especially for 5-year stock returns used in the late subsample.

In an attempt to combat this issue, practitioners will often sample long horizon stock returns more frequently using overlapping observations, believing they are increasing their sample sizes significantly. Consistent with this observation, the overlapping scatter plots on the right-hand side of Figure 1 are instark contrast to those on the left-hand side and appear to show overwhelming evidence of a strong positive relation.

For example, in referring to 1/CAPE’s ability to forecast 10-year returns relative to his previous work, Shiller writes in chapter 11 of the latest edition of his book, Irrational Exuberance, “We now have data from 17 more years, 1987 through 2003 (end-points 1997 through 2013), and so 17 new points have been added to the 106 (from 1883)".  As such, in describing this estimated positive relation between 1/CAPE and future long-term returns, Shiller (2015) writes “…the swarm of points in the scatter shows a definite tilt.”

This is fallacy.

In Shiller’s above example, because 1/CAPE (measured as a 10-year moving average of earnings) is highly persistent, only 2, not 17, nonoverlapping observations have been truly added. To see this, note that standing in January 2003 versus in January 2004, looking ahead 10 years in both cases, the future 10-year returns have 9-years in common. So even if stock returns are serially independent through time, the 10-year return in adjacent years will be 0.90 correlated by construction. Moreover, 1/CAPE itself has barely changed due to its 10-year moving average of earnings and fundamental persistence of stock prices during the period between January 2003 and January 2004. It is these facts that create, by construction, Shiller’s “swarm” effect, visible in the figures. In reality, there is just a smattering of independent data points, 12 to be precise.”"

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