Trend Followers Research Articles

Tuning Trend Following Strategies with Macro ESG Data

Trend Following (TF) is a well-known and documented strategy which has been employed by practitioners since the 1980s. The focus so far has been on the alpha-generating potential of the strategies and in particular on extending the strategy to a broader range of asset markets. In this paper, we describe how to integrate sovereign Environmental, Social and Governance (ESG) information into a macro TF strategy. Notably, we find that the ESG exposure of the macro portfolios can be substantially increased without any cost in performance.

Trend following in financial time series with multi-objective optimization

Trend following (TF) is an investment strategy based on the technical analysis of market prices. Trend followers do not aim to forecast nor predict specific price levels. They simply jump on the uptrend and ride on it until the end of this uptrend. Most of the trend followers determine the establishment and termination of uptrend based on their own rules. In this paper, we propose a TF algorithm which employs multiple pairs of thresholds to determine the stock market timing. The optimal values of thresholds are obtained by particle swarm optimization (PSO) and simulated annealing (SA). The experimental result on 7 stock market indexes shows that the proposed multi-threshold TF algorithm with multi-objective optimization is superior when it is compared to static, dynamic, and float encoding genetic algorithm based TF.

Intelligent REITs in the information age

This paper examines the emergence of the latest powerful structure of the Intelligent Real Estate Investment Trust (iREIT) model: when real estate investment model is empowered by information technology online platform. iREIT is known as a revolutionary and controversial topic since it was born in terms of breaking the ‘glassing ceiling’ that real estate investment became no longer exclusively reserved for high net worth individuals. The barrier of entry is significantly reduced to $1000 USD for anybody who would like to diversify his/her investment portfolio. The originality and invaluable contributions of this paper are twofold: (a) a Trend Following (TF) trading strategy is applied to test the possibility and sustainability of 13% annual returns as some iREIT companies stated. Two modifications on TF are tested here, one is to extend TF over multiple markets, the other is to combine the use of forecasting tool that would predict a future trend. TF would give the insights on buying or selling decisions based on the moving trend patterns. (b) Assess the trend profitability from Top5 Asian countries, verify whether the best practices of the diversified real estate investment portfolio for iREIT exists, hence to explore the possibility of auto-investment over iREIT in future.

Trend Following and Momentum Strategies for Global REITs

This study investigates whether the risk adjusted returns of a global REIT portfolio would be enhanced by adopting a trend following strategy (which is an absolute concept), a momentum based strategy (which is a relative concept and requires individual country allocations), or indeed a combination of the two. We examine the results in terms of both a dedicated Global REIT exposure, and the impact on a multi-asset portfolio. We find that the main improvements arise when the broad index is replaced with one of the four trend following (TF) strategies. The portfolios deliver similar returns but volatility is reduced by up to a quarter to the 8-9% range, the Sharpe ratios increase by 0.1 to 0.5 with the main benefit being the reduction in the maximum drawdown to under 30% compared to 43% when the broad index was used. We thus find that a combined momentum and trend following Global REIT strategy can be beneficial for both a dedicated REIT portfolio and adding REITs to a multi-asset portfolio.

Optimal allocation of trend following strategies

We consider a portfolio allocation problem for trend following (TF) strategies on multiple correlated assets. Under simplifying assumptions of a Gaussian market and linear TF strategies, we derive analytical formulas for the mean and variance of the portfolio return. We construct then the optimal portfolio that maximizes risk-adjusted return by accounting for inter-asset correlations. The dynamic allocation problem for n assets is shown to be equivalent to the classical static allocation problem for n2 virtual assets that include lead-lag corrections in positions of TF strategies. The respective roles of asset auto-correlations and inter-asset correlations are investigated in depth for the two-asset case and a sector model. In contrast to the principle of diversification suggesting to treat uncorrelated assets, we show that inter-asset correlations allow one to estimate apparent trends more reliably and to adjust the TF positions more efficiently. If properly accounted for, inter-asset correlations are not deteriorative but beneficial for portfolio management that can open new profit opportunities for trend followers. These concepts are illustrated using daily returns of three highly correlated futures markets: the E-mini S&P 500, Euro Stoxx 50 index, and the US 10-year T-note futures.

Stock trading rule discovery with an evolutionary trend following model

Evolutionary learning is one of the most popular techniques for designing quantitative investment (QI) products. Trend following (TF) strategies, owing to their briefness and efficiency, are widely accepted by investors. Surprisingly, to the best of our knowledge, no related research has investigated TF investment strategies within an evolutionary learning model. This paper proposes a hybrid long-term and short-term evolutionary trend following algorithm (eTrend) that combines TF investment strategies with the eXtended Classifier Systems (XCS). The proposed eTrend algorithm has two advantages: (1) the combination of stock investment strategies (i.e., TF) and evolutionary learning (i.e., XCS) can significantly improve computation effectiveness and model practicability, and (2) XCS can automatically adapt to market directions and uncover reasonable and understandable trading rules for further analysis, which can help avoid the irrational trading behaviors of common investors. To evaluate eTrend, experiments are carried out using the daily trading data stream of three famous indexes in the Shanghai Stock Exchange. Experimental results indicate that eTrend outperforms the buy-and-hold strategy with high Sortino ratio after the transaction cost. Its performance is also superior to the decision tree and artificial neural network trading models. Furthermore, as the concept drift phenomenon is common in the stock market, an exploratory concept drift analysis is conducted on the trading rules discovered in bear and bull market phases. The analysis revealed interesting and rational results. In conclusion, this paper presents convincing evidence that the proposed hybrid trend following model can indeed generate effective trading guidance for investors.

Trend Recalling Algorithm for Automated Online Trading in Stock Market

Unlike financial forecasting, a type of mechanical trading technique called Trend Following (TF) doesn't predict any market movement; instead it identifies a trend at early time of the day, and trades automatically afterwards by a pre-defined strategy regardless of the moving market directions during run time. Trend following trading has a long and successful history among speculators. The traditional TF trading method is by human judgment in setting the rules (aka the strategy). Subsequently the TF strategy is executed in pure objective operational manner. Finding the correct strategy at the beginning is crucial in TF. This usually involves human intervention in first identifying a trend, and configuring when to place an order and close it out, when certain conditions are met. In this paper, we presented a new type of TF, namely Trend Recalling algorithm that operates in a totally automated manner. It works by partially matching the current trend with one of the proven successful patterns from the past. Our experiments based on real stock market data show that this algorithm has an edge over the other trend following methods in profitability. The new algorithm is also compared to time-series forecasting type of stock trading, and it can even outperform the best forecasting type in a simulation.

Trend following algorithms in automated derivatives market trading

Trend following (TF) is trading philosophy by which buying/selling decisions are made solely according to the observed market trend. For many years, many manifestations of TF such as a software program called Turtle Trader, for example, emerged in the industry. Surprisingly little has been studied in academic research about its algorithms and applications. Unlike financial forecasting, TF does not predict any market movement; instead it identifies a trend at early time of the day, and trades automatically afterwards by a pre-defined strategy regardless of the moving market directions during run time. Trend following trading has been popular among speculators. However it remains as a trading method where human judgment is applied in setting the rules (aka the strategy) manually. Subsequently the TF strategy is executed in pure objective operational manner. Finding the correct strategy at the beginning is crucial in TF. This usually involves human intervention in first identifying a trend, and configuring when to place an order and close it out, when certain conditions are met. In this paper, we evaluated and compared a collection of TF algorithms that can be programmed in a computer system for automated trading. In particular, a new version of TF called trend recalling model is presented. It works by partially matching the current market trend with one of the proven successful patterns from the past. Our experiments based on real stock market data show that this method has an edge over the other trend following methods in profitability. The results show that TF however is still limited by market fluctuation (volatility), and the ability to identify trend signal.

Trend Following Algorithms for Technical Trading in Stock Market

Trend following (TF) is a rule-based trading mechanism that taps on the movements of long-term market trend instead of relying on any forecast or external information to decide when to buy and when to sell a stock. Its simple operation is in contrast to complicated prediction methods which typically would try to predict a future trend by analyzing the historical data and may be other factors. TF makes no prediction and it is well known for its simplicity. Although TF is a popular strategy in finance and was implemented in some commercial trading system decades ago, there have not been many studies of TF in computer science. Hence the objective of this paper is to develop a computer simulator in which TF is implemented as a variety of algorithms. Through the algorithms, readers will see how different parameters are chosen, and how these TF strategies perform in a simulation with real-life data. The simulation results show that TF algorithm can gain an average profit of 75.63% of return-of-investment monthly. However, we observed that TF degrades in performance in proportion to the amount of fluctuation of the market trend. This finding is important to the design of technical trading systems. It implies that the fluctuation of market trend should be monitored; when it exceeds a certain threshold the TF trading should be paused to prevent loss.