New strategies:
#433 – Computing Power Factor in Cryptocurrencies
Period of rebalancing: Weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Complex strategy
Bactest period: 2015-2019
Indicative performance: 9.88%
Estimated volatility: 9.30%
Source paper:
Bhambhwani, Siddharth and Delikouras, Stefanos and Korniotis, George M.: Do Fundamentals Drive Cryptocurrency Prices?
https://ssrn.com/abstract=3342842
Abstract:
We posit that cryptocurrency prices are influenced by two fundamentals related to their blockchain. They are computing power expended on creating their blockchains and the adoption levels of their respective blockchains. Using data for the most prominent cryptocurrencies, we find evidence of a significant long-run relationship between prices and these two fundamental blockchain measures. Conducting factor analysis, we document that cryptocurrencies are exposed to cryptocurrency risk factors related to aggregate computing power and aggregate adoption, even after accounting for the returns of Bitcoin and cryptocurrency price momentum. We also find that our risk factors explain return variation across a broad set of cryptocurrencies. Overall, our results suggest that in the long-run cryptocurrencies have intrinsic value that is related to the computing power and the adoption of their respective blockchains.
#434 – Network Size Factor in Cryptocurrencies
Period of rebalancing: Weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Complex strategy
Bactest period: 2015-2019
Indicative performance: 8.32%
Estimated volatility: 6.63%
Source paper:
Bhambhwani, Siddharth and Delikouras, Stefanos and Korniotis, George M.: Do Fundamentals Drive Cryptocurrency Prices?
https://ssrn.com/abstract=3342842
Abstract:
We posit that cryptocurrency prices are influenced by two fundamentals related to their blockchain. They are computing power expended on creating their blockchains and the adoption levels of their respective blockchains. Using data for the most prominent cryptocurrencies, we find evidence of a significant long-run relationship between prices and these two fundamental blockchain measures. Conducting factor analysis, we document that cryptocurrencies are exposed to cryptocurrency risk factors related to aggregate computing power and aggregate adoption, even after accounting for the returns of Bitcoin and cryptocurrency price momentum. We also find that our risk factors explain return variation across a broad set of cryptocurrencies. Overall, our results suggest that in the long-run cryptocurrencies have intrinsic value that is related to the computing power and the adoption of their respective blockchains.
New research papers related to existing strategies:
#224 – Profitability Factor Combined with Value Factor
Penman, Reggiani: Fundamentals of Value vs. Growth Investing and an Explanation for the Value Trap
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2494412
Abstract:
Value stocks earn higher returns than growth stocks on average, but a “value” position can turn against the investor. Fundamental analysis can explain this so-called value trap: the investor may be buying earnings growth that is risky. Both E/P and B/P, come into play: E/P (or P/E) indicates expected earnings growth, but price in that ratio also discounts for the risk to that growth; B/P indicates that risk. A striking finding emerges: for a given E/P, high B/P (“value”) is indicates higher expected earnings growth, but growth that is risky. This contrasts with the standard labeling that nominates low B/P as “growth” with lower risk.
#431 – Intraday Momentum in the Indian Equity Markets
Bhandari, Chakravorty: An Intraday Trend-Following Trading Strategy on Equity Derivatives in India
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3342508
Abstract:
In this article, we will present a trend-following based investment strategy on single-stock futures. Using the price movement of the recent past we are able to achieve a Sharpe Ratio of 1.7 on training data by cascading positions on successive positive signals and closing out positions if we hit a stop-loss. The stop-loss is computed using historical volatility. The universe is defined in an unbiased fashion to eliminate overfitting. We have used the top 75% most active single stock futures contracts and we have further filtered out products with low opening 15-minute volume. This was done to improve the scalability of the strategy. This might also help in avoiding contracts where less volatility is expected. We have trained our strategy on historical data from 2012 to 2016 and we have tested the strategy on the data from 2017 to 2018 data. Given the nature of markets in 2017-18, we have also looked at modifying the strategy to take positions more conservatively to avoid volatile situations. Empirical studies on profitable trading strategies are rare. We endeavor to shed light on the process of development of a profitable intraday trading strategy and we hope that this encourages collaboration in the active trading community.
And two additional related research papers have been included into existing free strategy reviews during last 2 weeks:
Interesting research paper related to all equity momentum strategies …
Trigilia, Wang: Momentum, Echo and Predictability: Evidence from the London Stock Exchange (1820-1930)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3373164
Abstract:
We study momentum and its predictability within equities listed at the London Stock Exchange (1820-1930). At the time, this was the largest and most liquid stock market and it was thinly regulated, making for a good laboratory to perform out-of-sample tests. Cross-sectionally, we find that the size and market factors are highly profitable, while long-term reversals are not. Momentum is the most profitable and volatile factor. Its returns resemble an echo: they are high in long-term formation portfolios, and vanish in short-term ones. We uncover momentum in dividends as well. When controlling for dividend momentum, price momentum loses significance and profitability. In the time-series, despite the presence of a few momentum crashes, dynamically hedged portfolios do not improve the performance of static momentum. We conclude that momentum returns are not predictable in our sample, which casts some doubt on the success of dynamic hedging strategies.
Related to #118 – Time Series Momentum
Babu, Levine, Ooi, Pedersen, Stamelos: Trends Everywhere
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3386035
Abstract:
We provide new out-of-sample evidence on trend-following investing by studying its performance for 82 securities not previously examined and 16 long-short equity factors. Specifically, we study the performance of time series momentum for emerging market equity index futures, fixed income swaps, emerging market currencies, exotic commodity futures, credit default swap indices, volatility futures, and long-short equity factors. We find that time series momentum has worked across these asset classes and across several trend horizons. We examine the co-movement of trends across asset classes and factors, the performance during different market environments, and discuss the implications for investors.
