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Hello, welcome back to Papers with Backtaz podcast.
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Today we dive into another Algo trading research paper.
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Okay, let's unpack this fascinating paper titled Time Series Momentum.
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It was published in the Journal of Financial Economics.
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You might have heard whispers about this thing called Time Series Momentum.
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Right.
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Well, we're going beyond the rumors.
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We're diving deep into the research.
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To uncover what it is.
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Whether it really works. Yeah. And most importantly, how you might be able to use it in your own trading. For sure. What's fascinating here is that this paper goes beyond just identifying a pattern.
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It actually puts this time series momentum to the test.
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Turning it into a trading strategy.
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And backtesting it on a massive amount of data.
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Okay, before we get to the exciting backtest result.
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Let's start with the basics.
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What exactly is time series momentum?
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Imagine this.
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You're looking at a chart of an asset's price.
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Let's say a stock or a commodity.
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If that asset has been consistently going up over the past year. Time series momentum suggests it's more likely to keep rising for at least a little while longer.
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It's almost like the asset has built up this momentum.
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Like a snowball rolling downhill. And that momentum has a tendency to persist.
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So it's not about comparing different assets.
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Like saying this stock is doing better than that bond. Right. It's purely about an asset's own past performance.
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As an indicator of its future performance.
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Exactly.
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That's a key distinction.
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Because there's this other concept.
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Called cross-sectional momentum. Right. Which is about comparing different assets.
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Time series momentum is solely focused on an asset's own price history.
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All right. So we have this idea that winners tend to keep winning.
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At least for a while. Yeah. But how did the researchers actually test this?
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They got their hands on data for a whopping 58 different liquid futures contracts. Wow. We're talking commodities, currencies, equities, bonds.
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The whole spectrum of tradable assets.
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And they looked at over 25 years of data.
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25 years.
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That's a lot of data.
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They must have spent ages just compiling it all.
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I'm sure they did.
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But the payoff was worth it.
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They found that every single one of those 58 contracts.
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Exhibited positive time series momentum. Every single. That's not just a coincidence.
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That's a pattern screaming to be understood.
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That's mind blowing.
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But data mining can be a tricky beast. Yeah. How do we know this isn't just a statistical fluke?
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Did they test this in a way that convinces us this is a real phenomenon? They did. They created a very simple trading strategy.
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Based on this time series momentum idea.
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Here's the rule. Okay. If an asset has been up over the past 12 months. You go long. If it's been down, you go short.
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Hold that position for one month.
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Then rinse and repeat.
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So they're essentially betting on the continuation of recent trends.
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Sounds pretty intuitive so far.
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But did it actually work?
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That's where it gets really interesting.
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It's as simple as it sounds.
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Produced significantly positive returns.
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Even after adjusting for all the usual risk factors.
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Like market beta. Value. And even cross-sectional momentum.
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Wow.
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They even calculated the Sharpe ratio for this strategy.
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You know, the measure of risk-adjusted return. Yeah. And it came in at greater than one.
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That's outperforming a basic diversified portfolio.
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By a significant margin.
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Now you have my attention.
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So. But they've shown us that this time series momentum isn't just some theoretical concept. Right. It has the potential to translate into real trading profits.
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But I'm curious, how does this strategy fare when things get a little rocky in the market?
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You know, during those periods of high volatility.
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When everyone's panicking.
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That's where things get even more intriguing.
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This strategy actually performed exceptionally well.
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During some of the most volatile market periods.
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Like the 2008 financial crisis.
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It's almost as if it thrives on chaos.
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Whoa, that's counterintuitive.
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You'd think a momentum strategy would get crushed during a market crash.
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What's going on there?
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That's a great question.
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The researchers dug a bit deeper into this.
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And found that a lot of it has to do with how different types of traders behave.
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During these volatile periods, they looked at data from the CFTC.
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The Commodity Futures Trading Commission.
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Which tracks the positions of speculators and hedgers.
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In the futures market.
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Ah, the CFTC.
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They're like the market detect.
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Always watching who's doing what in the futures markets.
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What did they uncover?
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Well, they found that speculators.
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Those who are often seen as the more aggressive.
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Taking traders. Right. Were consistently positioned to profit from this time series momentum.
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Meanwhile, the hedgers.
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Those who are typically using futures to manage their existing risks.
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Seem to be on the other side of that trade.
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Potentially losing out.
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So it's almost as if the speculators are capitalizing on the predictable behavior of the hedgers.
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That's pretty interesting.
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But it also raises a question.
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Why are hedgers consistently on the wrong side of this trade?
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Shouldn't they be more aware of these market dynamics?
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It's a fascinating question.
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And one that the researchers delve into a bit further.
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They point out that hedgers aren't necessarily trying to time the market.
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Or profit from short term price movements.
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Their primary goal is to mitigate their risk exposure.
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To the underlying asset. Right. They might be willing to accept a slight loss on their futures positions.
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If it means protecting their overall portfolio.
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That makes sense.
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It's like they're paying a premium for insurance.
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And that premium is going into the pockets of the speculators. Right. Who are more adept at exploiting these short term trends.
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But let's zoom in on the trading strategy itself for a moment.
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You mentioned earlier that the researchers used a simple rule.
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Go long if the asset is up over the past 12 months.
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Short if it's down.
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But I imagine there's more to it than just that, right? Uh-huh. What about things like position sizing?
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Risk management.
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What does action cost?
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All the practical considerations that traders need to grapple with.
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You're absolutely right.
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The devil is always in the details.
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The researchers in their back tests scaled positions.
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To maintain a constant volatility. Okay. That means adjusting your position size.
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Based on the volatility of the asset you're trading,
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a more volatile asset would warrant a smaller position size to manage risk.
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So you're not just blindly betting the same amount on every asset. Right. You're taking into account how wildly the price of that asset tends to swing.
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Makes sense.
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What about other practical considerations?
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Did the researchers address things like transaction costs?
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And potential slippage?
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They did.
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They acknowledged that frequent trading can eat into your profits. Thanks.
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Especially when you factor in things like brokerage fees. Right. And the bid-ask spread.
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In their study, they used monthly rebalancing.
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Which means they only adjusted their positions once a month.
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Okay.
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This helps mitigate transaction costs.
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But it's still something you need to consider carefully.
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When implementing this strategy in the real world.
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So monthly rebalancing seems to be the sweet spot.
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At least according to the researchers. Yeah. What about slippage?
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That's the difference between the expected price of a trade and the price at which it's actually executed,
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right? Right. It could be a real headache.
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Especially in fast-moving markets. Absolutely. Slippage is always a factor to consider.
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Especially when you're dealing with futures contracts.
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Which tend to be more susceptible to these price discrepancies.
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The key is to use limit orders.
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Which allow you to specify the maximum price you're willing to pay.
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Or the minimum price you're willing to sell for.
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This helps minimize slippage.
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And ensures you're not getting filled at... at unfavorable prices.
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Limit orders, a classic tool in the trader's arsenal.
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Now let's go back to this fascinating dynamic.
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Between speculators and hedgers.
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We've established that speculators seem to be better positioned.
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To profit from this time series momentum.
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But is there anything else in the research?
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That sheds light on why this might be the case.
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Well, the researchers dug a bit deeper.
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Into the different components of futures returns.
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Okay.
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You see, a futures contracts price can change for two main reasons.
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The first is changes in the spot price of the underlying asset, which is driven by things like news,
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supply and demand,
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and overall market sentiment.
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The second is something called roll returns,
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which are linked to the shape of the futures curve and are often influenced by hedging pressure.
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Okay, so we have these two separate forces driving futures prices.
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Spot price changes and roll returns.
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Right.
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How does this tie into the time series momentum story?
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What they found is that time series momentum shows up in both of these components. However, there's a crucial difference.
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The momentum in spot prices tends to reverse after a while. Okay. Suggesting that maybe there's some overreaction to news.
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Driving that initial momentum.
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But the momentum in roll returns is much more persistent.
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It doesn't exhibit the same reversal pattern.
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So we have two flavors of momentum.
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Spot momentum, which is flashy and tends to fade.
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And roll return momentum.
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Which is more subdued, but hangs around for longer. This seems like a pretty crucial insight.
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For anyone looking to build a trading strategy.
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Based on time series momentum, wouldn't you say? Absolutely. It suggests that maybe there are two different mechanisms at play here.
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The spot momentum could be driven by behavioral factors.
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Like investors overreacting to news.
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And then correcting their positions later on.
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The roll return momentum.
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On the other hand, might be more structural.
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Reflecting underlying supply and demand dynamics in the futures market.
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This is where it starts to feel like we're peeling back the layers of the onion.
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Getting a glimpse into the intricate workings of the market.
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Fascinating stuff.
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But I imagine our listeners are itching to know.
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How do we actually put all this knowledge into practice?
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What are some concrete steps they can take?
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To incorporate time series momentum into their own trading strategies.
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I think the first step.
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As with any trading strategy.
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Is to backtest it thoroughly. Right. We need to see how this time series momentum strategy performs.
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On historical data.
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Before we even think about putting real money at risk.
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Absolutely.
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Backtesting is crucial.
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You want to test different variations of the strategy. Okay. Explore different asset classes.
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And see how it holds up.
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Under various market conditions. Right. Does it work better for certain types of assets?
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Is it more effective during certain periods of the year?
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These are the kinds of questions backtesting can help answer.
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And remember, when you're backtesting,
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don't forget to factor in realistic transaction costs.
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And potential slippage. Yeah. Those can make a big difference in your overall profitability. Right. You want to make sure your backtests are as close to real-world trading as possible. Right. Otherwise, you might get a false sense of confidence.
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And end up with disappointing results when you go live. Another thing to keep in mind is that this research is just a starting point. Yeah.
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It's giving us a framework for understanding time series momentum.
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And how it might be exploited.
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But the real magic happens.
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When you start experimenting and innovating.
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Exactly.
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Don't be afraid to tweak the parameters of this strategy.
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Try different look back periods.
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Or even combine time series momentum.
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With other indicators or trading rules. Uh-huh. This is where your creativity and analytical skills come into play.
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You might even explore incorporating both types of momentum.
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Spot and roll return into your algorithms.
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Perhaps with different weighting schemes? Right. Or trading rules?
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Depending on the characteristics of each component. Exactly. This is the exciting part of algo trading.
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It's not just about blindly following a recipe.
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It's about taking these insights from research.
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And using them as building blocks.
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To create your own unique and hopefully profitable trading system.
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Now, a word of caution.
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Even the most
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Well-researched and back-tested strategies can go haywire in the face of unexpected market events.
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So risk management is paramount.
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Absolutely. We need to have a plan for handling those inevitable periods of drawdown.
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Absolutely.
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Think about things like stop-loss orders,
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position limits,
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and diversification across different asset classes.
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These are all essential tools for protecting your capital and ensuring your trading strategy can weather the storms.
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So we've covered a lot of ground today.
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We've explored the concept of time series momentum.
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Doved deep into the research.
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And even touched on some practical considerations.
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For implementing this strategy in your own trading.
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Before we wrap up.
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Any final thoughts for our listeners who are eager to put this knowledge into action?
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I'd say.
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Approach it with a healthy dose of skepticism.
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And a spirit of experimentation.
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Don't expect to get rich quick.
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This is a journey of continuous learning and adaptation.
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The markets are always changing.
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So you need to be willing to evolve your strategies along with them.
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Well said.
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Time series momentum is a powerful concept.
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But it's not a magic bullet.
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It's one tool among many in the algo traders arsenal.
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Use it wisely. Use it creatively.
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And always be prepared to learn and adapt.
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Thank you for tuning in to Papers with Backtest podcast.
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We hope today's episode gave you useful insights.
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Join us next time as we break down more research.
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And for more papers and backtests.
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Find us at https.paperswithbacktest.com.
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Happy trading!
