Trading system evaluation based on past performance: Random Signals Test

This paper introduces a new method for evaluating a trading system based on its past performance. The method is a hypothesis test that asks whether the system is making random trades. The test controls for price behavior during the test period and the trade characteristics of the system being tested. A system should be traded only if the null hypothesis of random trading is rejected.system evaluation, hypothesis testing, trading

Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning

A diversified risk-adjusted time-series momentum (TSMOM) portfolio can deliver substantial abnormal returns and offer some degree of tail risk protection during extreme market events. The performance of existing TSMOM strategies, however, relies not only on the quality of the momentum signal but also on the efficacy of the volatility estimator. Yet many of the existing studies have always considered these two factors to be independent. Inspired by recent progress in Multi-Task Learning (MTL), we present a new approach using MTL in a deep neural network architecture that jointly learns portfolio construction and various auxiliary tasks related to volatility, such as forecasting realized volatility as measured by different volatility estimators. Through backtesting from January 2000 to December 2020 on a diversified portfolio of continuous futures contracts, we demonstrate that even after accounting for transaction costs of up to 3 basis points, our approach outperforms existing TSMOM strategies. Moreover, experiments confirm that adding auxiliary tasks indeed boosts the portfolio's performance. These findings demonstrate that MTL can be a powerful tool in finance

Trading at the opening bell: gap flling strategy on the e-mini S&P500

The objective of the report is to develop a bidirectional gap filling strategy that will be used in the "Quantitative Investment Strategy" field lab's common part, which will create a portfolio of three investment strategies. The dataset here used was obtained from Trade station and includes historical 1-minute prices of the cash session of the E-miniS&P500 futures from 01/03/2000 to 10/27/2021. For the validation, an innovative methodology is used, which increases the OOS performance stability. All the outcomes and the back test were carried out by coding an entire engine in Python and accelerating it with Numba

Investment, Trading, Portfolio Management

This report is a collection of the results gathered from using various methods of trading to develop a scientific method to turn a profit in multiple financial markets. The purpose of this project was to use statistics, indicators, and historical data to create a scientific system which mitigates the impact of human error that often plagues investors. Each author independently constructed their own system for trading, integrating a combination of various well-established trading techniques into their strategies. We used these traditional methods in innovative ways to gain an edge over other traders. Through the compilation of these individual systems, the group was able to create a “system of systems” which has provided a consistent return on investment

Trend Following and Momentum Strategies for Global REITs

In this study, we investi- gate whether the risk-Adjusted returns of a global REIT portfolio would be enhanced by adopting a trend following global strategy (which is an abso- lute concept sometimes known as absolute mo- mentum), a momentum-based strategy (which is a relative concept and requires individual country al- locations), or indeed a combination of the two. e 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 improve- ments arise when the broad index is replaced with one of the four trend following strategies. The port- folios deliver similar returns but volatility is re- duced 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 draw- down to under 30% compared to 43% when the broad index was used. We thus find that a com- bined momentum and trend following a global REIT strategy can be beneficial for both a dedicated REIT portfolio and adding REITs to a multi-Asset portfolio

Momentum on commodity futures markets: crowds and crashes

Momentum strategies with commodity futures are simple to implement and have been profitable for the past couple of decades. Nonetheless, they yield large drawdowns every once in a while. One theory that can explain these events is related to the high level of activity (crowdedness) in the strategy, which can be the cause of forced unwinding of positions after negative shocks take place due to the use of excessive leverage. Therefore, a measure of activity is used to test whether there is a relationship between returns and crowdedness. Even though the result of an analysis of momentum strategies with 12-month ranking period does not support this theory, strategies with 1-month of ranking period show that the theory might have real foundations