Related to all trendfollowing strategies:

Authors: Sepp

Title: Trend-Following Strategies for Tail-Risk Hedging and Alpha Generation

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3167787

Abstract:

Because of the adaptive nature of position sizing, trend-following strategies can generate the positive skewness of their returns, when infrequent large gains compensate overall for frequent small losses. Further, trend-followers can produce the positive convexity of their returns with respect to stock market indices, when large gains are realized during either very bearish or very bullish markets. The positive convexity along with the overall positive performance make trend-following strategies viable diversifiers and alpha generators for both long-only portfolios and alternatives investments.

I provide a practical analysis of how the skewness and convexity profiles of trend-followers depend on the trend smoothing parameter differentiating between slow-paced and fast-paced trend-followers. I show how the returns measurement frequency affects the realized convexity of the trend-followers. Finally, I discuss an interesting connection between trend-following and stock momentum strategies and illustrate the benefits of allocation to trend-followers within alternatives portfolio.

Notable quotations from the academic research paper:

"Key takeaways:

1. The skewness and the convexity of strategy returns with respect to the benchmark are the key metrics to assess the risk-profile of quant strategies. Strategies with the significant positive skewness and convexity are expected to generate large gains during market stress periods and, as a result, convex strategies can serve as robust diversifiers. Using benchmark indices on major hedge fund strategies, I show the following.
– While long volatility hedge funds produce the positive skewness, they do not produce the positive convexity.
– Tail risk hedge funds can generate significant skewness and convexity, however at the expense of strongly negative overall performance.
– Trend-following CTAs can produce significant positive convexity similar to the tail risk funds and yet trend-followers can produce positive overall performance delivering alpha over long horizons.

2. Trend-following strategies adapt to changing market condition with the speed of changes proportional to the trend smoothing parameter for the signal generation. As result, when we measure the realized performance of a trend-following strategy, the return measurement frequency should be low relative to the expected rebalancing period of the trend-following strategy. Using the data of SG Trend-following CTAs index, I show that trend-followers are expected to produce both the positive skewness and convexity for monthly, quarterly and annual returns. As a result, trend-following strategies should not be seen as diversifiers for short-term risks measured on the scales less than one month. Overall, I recommend applying quarterly returns for the evaluation of the risk-profile of a trend-following strategy.

3. By analyzing quarterly returns on the SG trend-following CTAs index conditional on the quantiles of quarterly returns on the S&P 500 index, I show that trend-following CTAs can serve as diversifiers of the tail risk. On one hand, the trend-followers generate significant positive average returns with positive skewness conditional on negative returns on the S&P 500 index. On the other hand, the trend-followers generate large positive returns, but with insignificant skewness conditional on large positive returns on the S&P 500 index. Conditional on index returns in the middle of the distribution during either range-bound or slow up-drifting markets, the trend-followers generate negative returns yet with significant positive skewness.

4. The nature of trend-followers is to benefit from markets where prices and returns are auto-correlated, which implies the persistence of trends over longer time horizons. I present the evidence that the recent underperformance of trend-followers since 2011 to 2018 could be explained because the lag-1 autocorrelation of monthly and quarterly returns on the S&P 500 index become significantly negative in this sample period. The negative autocorrelation indicates the presence of the mean-reverting regime, even though the overall drift is positive, in which trend-followers are not expected to outperform. I introduce an alternative measure of the autocorrelation that can be applied to test for the presence of autocorrelation in short sample periods. I show that my autocorrelation measure has a strong explanatory power for returns on SG trend-following CTAs index.

5. To quantify the relationship between the trend smoothing parameter, which defines fast-paced and slow-paced trend-followers, and the risk profile of fast-paced and slow-paced trend-followers, I create a quantitative model for a trend-following system parametrized by a parameter of the trend smoothing and by the frequency of portfolio rebalancing. The back-tested performance from my model has a significant correlation with both BTOP50 and SG trend-following CTAs indices from 2000s using the half-life of 4 months for the trend smoothing.

6. Using the trend system parametrized by the half-life of the trend smoothing, I analyze at which frequency of returns measurement the trend-following strategy can generate the positive convexity. The key finding is that the trend-following system can generate the positive convexity when the return measurement period exceeds the half-life of the trend smoothing and the period of portfolio rebalancing. I recommend the following.
– If a trend-following strategy is sought as a tail risk hedge with a short-time horizon of about a quarter, allocators should seek for trend-followers with relatively fast smoothing of signals with the average half-life less than a quarter.
– If a trend-following strategy is sought as an alpha strategy with a longer-time horizon, allocators should seek for trend-followers with medium to low smoothing of signals with the average half-life between a quarter and a year.
An alternative way to interpret the speed of the trend smoothing is to analyze the trend-following strategy beta to the underlying asset. For the slow-moving smoothing, the strategy maintains the long exposure to the up-trending asset with infrequent rebalancing. As a result, the higher is the half-life of the trend smoothing, the higher is the beta exposure to the index. Thus, while fast-paced trend-followers can provide better protection during sharp short-lived reversals, they suffer in periods of choppy markets. There is an interesting article on Bloomberg that some of fast-paced trend-following CTAs fared much better than slower-paced CTAs during the reversal in February 2018.

7. I examine the dependence between returns on the trend-following CTAs and on the market-neutral stock momentum. I show that the trend-followers have a stronger exposure to the autocorrelation factor and a smaller exposure of higher-order eigen portfolios. As a result, the trend-following CTAs produce the positive convexity while stock momentum strategies generate the negative convexity of their returns.

8. The allocation to trend-following CTAs within a portfolio of alternatives can significantly improve the risk-profile of the portfolio. In the example using HFR Risk-parity funds and SG trend-following CTAs index, the 50/50 portfolio equally allocated to Risk-parity funds and trend-following CTAs produces the drawdown twice smaller than the portfolio fully allocated to Risk-parity funds. The 50% reduction in the tail risk is possible because the occurrence of the drawdowns of Risk-parity HFs and Trend-following CTAs are independent. While trend-followers tend to have lower Sharpe ratios than Risk-parity funds, trend-followers serve as robust diversifiers with 50/50 portfolio producing the same Sharpe ratio but with twice smaller drawdown risk.

"

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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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