Tag: quant

Roll’s Serial Covariance Spread Estimator

The book Trading and Exchanges (Amazon,) has a section on Roll’s Serial Covariance Spread Estimator which tackles the problem of estimating the bid/ask spread with only the price series.

The Roll’s serial covariance spread estimator is an econometric model designed to estimate the average bid/ask spread (or effective spread) of a security using only transaction prices, without needing quotation data. It is one of the best-known estimators based on price change serial covariances.

The idea is from the 90’s and we’ve come a long way since then. Now, we have streaming quotes from which the spread can be directly computed. What makes this approach interesting is the decomposition of volatility that was used to estimate the spread can be used to estimate fundamental volatility instead.

Total Volatility = Fundamental Volatility + Transitory Volatility

Fundamental volatility consists of seemingly random price changes that do not revert. These changes often have the properties of a random walk.

Transitory volatility consists of price changes that ultimately revert. This price reversal creates negative serial correlation in the series of price changes.

Using Roll’s model, Fundamental Volatility = Total Volatility – (Effective Spread)2/4

Here’s NIFTY through Roll’s model:

Code on Github.

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Buy Highs/Sell Lows

In equities, buying stocks that hit their All Time Highs is a decent strategy. When combined with a trailing stop loss, it beats the NIFTY 50 index with a Sharpe of around 1.8.

Can a similar long/short strategy work in crypto?

Since everything happens faster in crypto, we need to relax the “All-Time” constraint and consider shorter time-frames. For example, here’s the 200-day Highs stats, for returns of subsequent 1/5/10 & 20 days of L1 and L2 coins:

And here’s the same for 20-day Highs stats:

A similar thing plays out with 200- and 20-day lows.

Theoretically, you can go long coins making 20/50-day highs and go short coins making 20/50-day lows. Apply a reasonable trailing stop loss and you might have a decent strategy.

Code and charts on github.

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Intra-Stock Correlation and Momentum Returns

Vojtko, Radovan and Pauchlyová, Margaréta, How to Improve Commodity Momentum Using Intra-Market Correlation (SSRN) discusses using short-term and long-term correlations between constituents to bet on momentum and reversal.

Since we are always on the lookout for strategies for reducing momentum drawdowns, we did a quick check to find out if a similar strategy can be used for long-only momentum.

The rule is fairly basic. Using the momentum portfolio already formed, if 20-day average correlation between them is greater than 200-day average correlation, then go long, else, go to cash.

Ignoring transaction costs, it looked like it avoided the brutal 2018-2019 drawdown. So, we dived a bit deeper to see if it was materially better than our 50-day SMA idea.

Here, COR_RET represents using only correlations to go long/cash, SMA_RET represents using only SMA, EITHER_RET is correlation or SMA and COMBINED_RET is correlations and SMA.

Going long if either correlations or SMA (EITHER_RET) seemed to be a winning strategy. However, high transaction costs in India can turn any decent strategy into a loser in a heartbeat.

25bps in transaction costs negates most of the advantages of considering the correlation signal. However, the post-COVID data does point towards EITHER_RET outperforming SMA_RET.

The biggest disappointment for us was that there was no improvement in drawdown metrics but 5% of outperformance might be worth the additional complexity.

Code and charts are on github.

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The High Volume Return Premium

Gervais, Simon, Ron Kaniel, and Dan H. Mingelgrin, 2001, “The high‐volume return premium,” The Journal of Finance, unearthed a market anomaly quite similar to momentum where stocks that traded with a higher than average volume went on to give higher returns.

We did a quick test on the top 200 stocks by free-float market cap on the NSE between 2014 and 2024 to check if it makes sense to pursue this further.

A long-only strategy that rebalanced once a month with a 50-day reference period underperformed the NIFTY 100 TR index by a wide margin until the August of 2023. Nine years of underperformance vs. a year of parabolic liftoff doesn’t really speak to the stability of the anomaly.

We’ll look for further publications in this area and report back. In the meantime, you can read the paper and have a look at our code here: github.

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Trend Factor

Han, Yufeng and Zhou, Guofu and Zhu, Yingzi, A Trend Factor: Any Economic Gains from Using Information over Investment Horizons? (SSRN), outlines the construction of a trend factor for equities.

In this paper, we provide a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model. It outperforms substantially the well-known short-term reversal, momentum, and long-term reversal factors, which are based on the three price trends separately, by more than doubling their Sharpe ratios. 

Does the paper’s claim hold true for Indian equities? Not really.

The Long-only Trend Factor underperformed a naïve momentum strategy and its corresponding benchmark. The Long-short Trend factor returns was negative.

Even after “tuning” the look-back periods, the Trend Factor failed to beat momentum.

Constructing a portfolio of stocks using trend following seems to be a dead end. Our previous attempts at this — Dynamic Linear Model v1.0 and Dynamic Equity Trend-following — have yielded similar results as well.

Momentum beats Trend-Following.

Code and charts are on github.

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