Skewness/Lottery Trading Strategy in Cryptocurrencies


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A recent spring 2022 crisis in the cryptocurrency market emphasized the importance of market-neutral crypto trading strategies. It’s not enough just to HODL crypto market and hope for the everlasting bull market. Therefore, we continue our series of research articles about the cryptocurrency market and offer an analysis of the skewness anomaly. So after our description of the skewness effect in commodities, an article about the multi-asset skewness strategy, and observation of the skewness/lottery effect in ETFs, we have one more asset class, where we can find lottery/skewness anomaly – in cryptocurrencies.

Skewness and investing

Skewness is a distortion or asymmetry in a collection of data that deviates from the symmetrical bell curve, or normal distribution. The curve is considered to be skewed if it is displaced to the left or right. Skewness may be expressed as a measure of how much a particular distribution deviates from a normal distribution. The skew of a normal distribution is zero, but a lognormal distribution, for example, has some right-skew.

When evaluating a return distribution, investors look for skewness, which evaluates the data set’s extremes rather than relying just on the average. Short or medium-term investors, in particular, must consider extremes since they are less likely to keep a position long enough to trust the average to sort itself out. Standard deviation is often used by investors to forecast future returns, however, it presupposes a normal distribution. Because few return distributions are near to normal, skewness is an interesting metric to use for predicting performance.

Source: Wikipedia

Skewness risk refers to the possibility that a model incorrectly assumes a normal distribution of instrument returns when the returns are skewed to the left or right of the mean. A positive skew means that the right-hand tail is longer than the left-hand tail and that the majority of the values are located to the left of the mean. A zero value implies that the values are very evenly distributed on both sides of the mean, reflecting a symmetric distribution in most cases (though not always).

Data and the strategy

Cryptocurrencies are relatively a new type of asset, and their popularity is rapidly growing. Cryptocurrencies opened the door to trading for the general public and especially the younger generation found it as a really easy opportunity to make (or indeed lose) money. Due to the high demand for cryptocurrencies and strategies the literature on this field is rapidly growing, even if it is still not well developed. Blockchain is the underlying technology that allows cryptocurrencies to be created. The operation of such a technological gadget is based on the upkeep of immutable distributed ledgers in thousands of nodes. New cryptocurrencies are emerging in the financial sector as a result of the blockchain network’s transactional reliability.

Bitcoin is the most well-known member of the crypto family and the most valuable cryptocurrency in terms of market capitalization. Despite Bitcoin supremacy, cryptocurrencies are becoming increasingly competitive. Indeed, Bitcoin’s market share has fallen from 80% at the end of May 2016 to 48% at the end of May 2017; by 2020, Bitcoin’s market share was about 38%. However, we will not only look at Bitcoin, which we covered well in our articles about Bitcoin’s overnight seasonality and trend/reversion trading but also at a number of other popular coins. More specifically, for this research, we choose the 45 most popular coins purely based on their trading history. To be more specific, we choose cryptocurrencies that have a trading history at least from 1.1.2018 (or earlier) until this date (see Reference 3 for a complete list).


The dataset contains the last four-year daily exchange rates data (from January 2018 to March 2022) for the 45 cryptocurrencies and was obtained from Coinmetrics. In particular, we have selected the daily exchange rates with US Dollar, since such bilateral exchange rates are the most studied by previous literature due to data availability.

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Paula Tekulová, Junior Quant Analyst, Quantpedia


  3. List of the cryptocurrencies: ada, ant, bat, bch, btc, btg, cvc, dash, dgb, doge, drgn, elf, etc, eth, fun, gas, gno, gnt, knc, lend, loutk, itc, maid, mana, mkr, neo, omg, pay, powr, ppt, qash, rep, snt, usdt, usdt_eth, usdt_omni, vtc, weth, wtc, xlm, xmr, xrp, xvg, zec, zrx
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