https://www.nber.org/system/files/working_papers/w26299/w26299.pdf

Business cycles and currency returns

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -- a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time.

Enhancing Time Series Momentum Strategies Using Deep Neural Networks

High frequency momentum trading with cryptocurrencies

We investigate the information contained in foreign exchange (FX) volume using a novel dataset from the over-the-counter market. We find that volume helps predict next day currency returns and is economically valuable for currency investors. Predictability implies a stronger currency return reversal for currency pairs with abnormally low volume today, and is driven by the component of FX volume unrelated to volatility, illiquidity, and order flow. We rationalize these findings via a simple model of exchange rate determination, in which volume helps reveal the degree of asymmetric information in currency markets. Testing this prediction shows that asymmetric information is uniform across currency pairs but varies across instruments.

Foreign Exchange Volume

We contrast the time-series and cross-sectional performance of three popular investment strategies: carry, momentum and value. While considerable research has examined the performance of these strategies in either a directional or cross-asset settings, we offer some insights on the market conditions that favor the application of a particular setting.

Dissecting Investment Strategies in the Cross Section and Time Series

Recently, liquidity-driven economic policies have mounted a challenge to time-series value investing. In this paper, we examine the risk-return performance of simple value indicators.We find that: 1) even though long term equity returns are partially predictable, directional value investing delivers mediocre results; 2) the "Central Bank put" is a fly in the ointment for value investors; 3) relative value has broadly outperformed absolute value. Lastly, we conjecture that the poor performance of directional value investing hides substantial overvaluations in U.S. equities.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2704358_code2481841.pdf?abstractid=2700691&mirid=1&type=2

Equity Trading: The Trouble with Value

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Dissecting Investment Strategies in the Cross Section and Time Series

Equity Trading: The Trouble with Value