Ray Dalio, the founder of Bridgewater Associates L.P. and the creator of the All-Weather investment strategy, recommends having some gold in a contemporary environment. He states, “In a world of ongoing pressure for policymakers across the globe to print and spend, zero interest rates, tectonic shifts in where global power lies, and conflict, gold has a unique role in protecting portfolios. It’s wise to hold some gold.” Therefore, one would ask a question, what is the optimal weight of gold in a portfolio?

Corporate insiders often have insight into a company’s private information, which might help them predict how the shares’ price will move in the coming days. However, laws and regulations are designed to keep them from trading based on this knowledge, as it would be unfair and hurt the company’s other shareholders. This includes the prohibition of insider trading or designing a 10b5-1 plan, which we will discuss in this article. Anyways, knowing about incoming losses or the will to create profits might lead these insiders to different practices that could be questioned. Let’s look at some of the newest research concerning these issues.

There are many ways one can lose money when investing, and exchange rates are one of the potential risk factors. Luckily, there are several ways to minimize this type of loss in your portfolio. Systematic FX hedging that uses currency factor strategies is a way of protecting an existing or anticipated position from an unwanted move in an exchange rate. It does not eliminate the risk of loss completely but helps to manage currency exposure better.

Order book information is crucial for traders, but it can be complex. With the numbers of stocks listed in stock exchanges, it is impossible to track all the available information for the human mind. Therefore, the order flows could be an interesting dataset for machine learning models. The novel research of Kolm, Turiel and Westray (2021) utilizes deep-learning for high-frequency return forecasts for 115 NASDAQ stocks based on order book information at the most granular level.

The paper has several key contributions. Firstly, it does not forecast one single return but rather a whole vector of returns – a term structure consisting of mid-price return forecasts at a specified horizon. The forecasted term structure provides essential information about the most optimal execution algorithms (or a trading strategy). According to the authors, forecasts have an „accuracy peak“ at two price changes, after which the accuracy declines. Secondly, the paper compares several methods: autoregressive model with exogenous inputs, MLP, LSTM, LSTM-MLP, stacked LSTM, and CNN-LSTM. Therefore, the article could also serve as a horse race across several possible forecasting methods. Lastly, using more traditional statistical approaches, the authors have identified a better forecasting performance in more information-rich stocks. As a result, this novel research could benefit many areas such as high-frequency trading (but trading costs must be considered), optimal execution strategy, or market-making.

Hello all,

What have we accomplished in the last month?

– A new Portfolio Clustering Quantpedia Pro report
– 13 new Quantpedia Premium strategies have been added to our database
– 10 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 10 new backtests written in QuantConnect code
– And finally, 5+3 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Five new strategies have been added.

Five new related research papers have been included into existing strategy reviews and four short free blog posts have been published during last few weeks. Plus, five trading strategies have been backtested in QuantConnect in the previous two weeks.